Infotek: Jurnal Informatika dan Teknologi
INFOTEK Jurnal Informatika dan Teknologi Fakultas Teknik Universitas Hamzanwadi selanjutnya disebut Jurnal Infotek (e-ISSN: 2614-8773) merupakan Jurnal yang dikelola oleh Fakultas Teknik Universitas Hamzanwadi yang mempublikasikan artikel ilmiah hasil penelitian atau kajian teoritis (invited authors) dalam bidang (1) keilmuan informatika, (2) Rekayasa Perangkat Lunak, (3) Multimedia, (4) Jaringan Komputer, (5) Data Mining, (6) Image Processing, (7) Komputer Vision, (8) Mikrokontroller, (9) Robotik, (10) IOT yang belum pernah dipublikasikan. Sejak Tanggal 09 Februari 2023 Jurnal Infotek telah terakreditasi Peringkat 4 (SINTA 4) berdasarkan Keputusan Mentri Riset dan Teknologi Nomor : 225/E/KPT/2022 Terhitung dari Artikel yang diterbitkan mulai dari Volume 5 No 2 Tahun 2022. Semua artikel pada Jurnal Infotek : Jurnal Informatika dan Teknologi telah memiliki Digital Object Identifier (DOI) dengan DOI Prefix : 10.29408 by Crossref. Jurnal Infotek diterbitkan oleh Fakultas Teknik Universitas Hamzanwadi dua kali setahun yaitu pada bulan Januari dan Juli. Jurnal Infotek Telah Terindeks oleh sejumlah lembaga pengindeks, diantaranya GARUDA, Dimensions, Google Scholar, dan lainnya. Nama Jurnal : Infotek : Jurnal Informatika dan Teknologi Inisial : Infotek Jadwal Terbit : 2 terbitan per tahun | Januari dan Juli DOI : 10.29408 /jit e-ISSN : 2614-8773 Ketua Penyunting : Aris Sudianto Penerbit : Universitas Hamzanwadi Analisa Kutipan : Google Scholar | Garuda | SINTA
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Pola Prestasi Akademik Mahasiswa Informatika Menggunakan K-Means Clustering Studi Kasus Universitas Hamzanwadi NTB
Analyzing students’ academic achievement patterns is important to support data-driven academic decision-making. This study aims to identify academic achievement patterns of Informatics students at Universitas Hamzanwadi using an unsupervised learning approach based on the K-Means algorithm. The main contribution of this research lies in conducting scenario-based experiments using several combinations of academic attributes and applying multi-metric evaluation to determine the most optimal clustering configuration for seventh-semester student data. The dataset includes the Semester Grade Point Average (IPS), Cumulative Grade Point Average (IPK), and the total accumulated credit units (SKS). After data cleaning and preprocessing, 382 student records were obtained for analysis. Data normalization was performed using Min–Max Scaling. The optimal number of clusters was determined using the Elbow method, resulting in three clusters. Cluster quality evaluation produced a Silhouette Score of 0.71, a Davies–Bouldin Index of 0.54, and a Calinski–Harabasz Index of 808.64, indicating good clustering quality. The results revealed three groups of students with high, medium, and low academic achievement characteristics. These findings confirm that the K-Means algorithm is effective in clustering student academic achievement patterns and can be used as a basis for academic evaluation and student development strategies. However, this study is limited to seventh-semester data and can be extended to other datasets or across study programs
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Perbandingan Optimizer dan RMSPROP Menggunakan Arsitektur ANN untuk Klasifikasi Pengenalan Tulisan Tangan
This study discusses the performance comparison of two optimization algorithms, Adam and RMS Prop, in training an Artificial Neural Network (ANN) for handwriting recognition classification tasks using the MOIST dataset. The MOIST dataset contains 70,000 handwritten digit images (0–9) measuring 28×28 pixels. The research stages include dataset input, preprocessing (pixel normalization, training and testing data division, and label conversion to one-hot encoding), weight initialization, and ANN model training with variations of Adam and RMS Prop optimizers. Model performance was tested using accuracy, precision, recall, and F1-score. The test results were carried out using the Google Cola application; the testing scheme was carried out in a structured manner with epoch iterations of 50, 100, 150, 200 and 250 on the Adam and RMS Prop optimizers. The test shows that the Adam optimizer generally provides higher accuracy and faster convergence than RMS Prop on the MOIST dataset, thanks to a combination of momentum and a more stable learning rate adaptation. However, RMS Prop still excels in weight update stability in some cases and tends to be more consistent on datasets with fluctuating gradient distributions.
Implementasi Manajemen Bandwidth pada Jaringan WLAN Hotspot Berbasis MikroTik Menggunakan Metode Network Planning and Analysis (NPA)
This study addresses the importance of stable and equitable internet access in WLAN hotspot networks, particularly in overcoming uncontrolled bandwidth usage that can degrade network service quality. The objective of this research is to implement bandwidth management on a MikroTik-based WLAN hotspot using the Network Planning and Analysis (NPA) method to improve network performance. The NPA method is applied through stages including problem identification, data collection, user needs analysis, network topology design, system implementation, and performance evaluation. The implementation utilizes hotspot features and Simple Queue to allocate bandwidth based on user requirements. Performance evaluation is conducted by comparing Quality of Service (QoS) parameters before and after implementation, including throughput, delay, jitter, and packet loss. The results show a 48% increase in throughput (from 1.25 Mbps to 1.85 Mbps), a 57.14% reduction in delay (from 280 ms to 120 ms), an 83.33% decrease in packet loss (from 6% to 1%), and a 68.42% reduction in jitter (from 95 ms to 30 ms). The main contribution of this study lies in integrating the NPA method with MikroTik-based bandwidth management through a user-based bandwidth planning approach, resulting in a more effective, measurable, and user-oriented network system.
Implementasi Automation Testing End-to-End Menggunakan Cypress pada Web E-Conference Parisada serta Evaluasi Efisiensi Pengujian
Manual testing on interactive applications such as web e-conferences often faces constraints in the form of human error and long execution durations, especially in repetitive regression processes. This research aims to implement automated end-to-end (E2E) testing using the Cypress framework and evaluate the level of testing time efficiency on the Parisada platform. The research methodology includes designing core functionality scenarios covering the authentication process, system navigation, meeting room creation, and communication session termination. Testing was conducted through three trials (P1, P2, P3) to simulate various inhibiting variables, such as unstable network latency and user familiarity levels with the application interface. The results showed that all automated testing scenarios successfully achieved a 100% pass rate. In terms of efficiency, the use of Cypress significantly reduced the execution duration from an average of 60.0 seconds in manual testing to only 23.67 seconds in automated testing. Data analysis indicates an average time efficiency increase of 60.55%. Automated testing also proved to be more consistent and reliable in dealing with technical fluctuations compared to manual testing. In conclusion, the transition to automation testing using Cypress has proven highly effective in accelerating the quality assurance (QA) cycle and maintaining continuous software functionality stability on the Parisada application.
Pendekatan Interpretable Machine Learning untuk Analisis Keberhasilan Kampanye Pemasaran Menggunakan CatBoost dan SHAP
Predicting the success of digital marketing campaigns remains a significant challenge due to the complex interactions among various variables, such as budget allocation and advertising channel selection. This study aims to develop a marketing analytics model that achieves high predictive accuracy while also providing clear interpretability of the prediction results. The study uses the SalesMind Marketing Campaigns 2026 dataset, which simulates 3,478 digital marketing campaign records from 2026. The dataset consists of categorical variables such as ad_channel and campaign_type, as well as numerical variables including marketing_spend, impressions, and conversion_rate as the prediction target. The proposed approach applies Interpretable Machine Learning by combining the CatBoost algorithm to predict conversion rates and SHAP (SHapley Additive exPlanations) to analyze the contribution of each variable. Model optimization was performed using GridSearchCV, resulting in excellent performance with an RMSE of 0.0012, an MAE of 0.005, and a coefficient of determination (R²) of 99.12%. The analysis results indicate that budget allocation is the most dominant factor in improving conversion rates without showing indications of diminishing marginal effectiveness. In addition, the use of interactive platforms such as Meta and TikTok significantly contributes to campaign effectiveness. These findings contribute to providing an accurate and informative predictive model that can support strategic decision-making in digital marketing management more effectively.
Rancang Bangun Sistem Rekomendasi Teman Belajar Berbasis Web untuk Kolaborasi Akademik Mahasiswa
The advancement of web-based information technology has encouraged the transformation of collaborative learning processes in higher education environments. However, students still experience difficulties in finding suitable study partners due to the absence of a system capable of integrating learning preferences, available schedules, and academic needs in a structured manner. This study aims to design and develop a web-based study partner recommendation system to support students’ academic collaboration through the EduMate platform. The research method used in this study is Research and Development (R&D) with a software development approach using the Prototype method. The research stages include user requirements identification, system requirements analysis, system design, prototype development, system implementation, and system testing using the Black Box Testing method. The system was developed using Node.js and Express.js on the backend side, HTML, CSS, and JavaScript on the frontend side, and MySQL as the primary database. The system also implements Google OAuth authentication, a scoring matching algorithm, study schedule management, automatic notifications, as well as rating and review features. The results of the study indicate that the EduMate system was successfully implemented and is capable of supporting the process of finding study partners in a more effective and structured manner. Based on the Black Box Testing results, all main system features operated according to the specified requirements with a testing success rate of 100%. The system is also able to provide study partner recommendations based on profile compatibility, learning preferences, and user schedules, thereby improving the effectiveness of collaborative learning among students. Therefore, EduMate has the potential to serve as a digital solution in supporting the collaborative learning ecosystem in higher education institutions.
Pengaruh Sistem Informasi Akuntansi dan Digital Marketing terhadap Kinerja Keuangan UMKM di Eks Karesidenan Surakarta
This study aims to analyze the influence of Accounting Information Systems (AIS) and Digital Marketing on the Financial Performance of Micro, Small, and Medium Enterprises (MSMEs) in the Eks Karesidenan Surakarta region. The study employs a causal associative quantitative approach using a Likert-scale questionnaire distributed to 395 MSME respondents selected through Proportionate Stratified Random Sampling. Data analysis utilizes multiple linear regression. The results indicate that: (1) AIS has a positive and significant effect on MSME Financial Performance (t=5.803; Sig.=0.000); (2) Digital Marketing has a positive and significant effect (t=5.691; Sig.=0.000); and (3) simultaneously both variables have a positive and significant effect (F=47.382; Sig.=0.000) with an Adjusted R² of 52.4%.
Evaluasi Performa Random Forest Dengan Penyesuaian Threshold Klasifikasi Pada Prediksi Penyakit Jantung
Heart disease remains one of the leading causes of death worldwide, necessitating an accurate prediction system to support early detection. This study aims to evaluate the performance of the Random Forest algorithm in predicting heart disease through the application of a threshold adjustment method. This research implements a Youden Index-based threshold adjustment, tested on the Statlog and Cleveland datasets, to assess the consistency of model performance across different data characteristics that share similar features. This method is utilized to determine the optimal threshold to achieve the best balance between recall and specificity. Additionally, the study analyzes the impact of threshold variations on accuracy, recall, specificity, and F1-score metrics to minimize false-negative errors and enhance the clinical relevance of the prediction results. Testing was conducted using the Cleveland Heart Disease Dataset and the Statlog Heart Disease Dataset with stratified sampling techniques. The results demonstrate that threshold adjustment significantly improves classification performance. The Cleveland dataset yielded an accuracy of 0.867, a recall of 0.893, and a Youden Index of 0.737 at an optimal threshold of 4.0, while the Statlog dataset achieved an accuracy of 0.833, a recall of 0.833, and a Youden Index of 0.667 at a threshold of 5.0. Overall, the combination of Random Forest and threshold adjustment proven to be effective in improving the quality of heart disease predictions.
Analisis Pengaruh Distance Metric Pada Algoritma K-Medoids Dalam Pengelompokan Kinerja Guru Menggunakan Silhouette Coefficent
This study aims to analyze the effect of distance metrics on the K-Medoids algorithm in clustering teacher performance based on pedagogical, personality, social, and professional indicators. This study was conducted because previous research on teacher performance clustering has generally focused more on the use of clustering algorithms without comparatively evaluating the effect of distance metric selection on the quality of the resulting clusters. The research data were obtained from assessor evaluations of teacher performance at SD Islam Al-Irsyad involving 16 teacher data records, which were first normalized to standardize the scale among attributes before the clustering process was carried out. Clustering was performed using the K-Medoids algorithm with Euclidean, Manhattan, and Cosine distance methods, as well as variations in the number of clusters (K) from 2 to 5 using RapidMiner. The clustering results were evaluated using the Silhouette Coefficient method calculated with Python to determine the cluster quality and the optimal number of clusters. The results showed that the highest Silhouette Coefficient value of 0.290 was obtained at K=4 using the Euclidean and Manhattan methods. Based on these results, the Euclidean method was selected as the best method because it is more widely used and capable of representing the distance between data points more effectively. The clustering results were then analyzed by assigning labels to each cluster based on the average value of the indicators to facilitate interpretation. Thus, this study demonstrates that the selection of distance metrics affects the quality of clustering results in teacher performance grouping.
Prediksi Kualitas Udara Berbasis Citra Menggunakan Pre-Trained Inception V3 dalam Perspektif Keberlanjutan
Advances in artificial intelligence are driving the optimization of deep learning models for image analysis. Air pollution is a significant environmental problem that impacts human health and the balance of ecosystems. Increased emissions from industrial activities, transportation, and the burning of fossil fuels are major factors contributing to deteriorating air quality in various regions. This study aims to analyze the influence of data preprocessing and training strategies on model performance, including the removal of duplicate image data, data splitting, data augmentation, the use of class weights on training data, and learning rate tuning using the Inception V3 transfer learning model. The results show that the combination of these strategies achieved an accuracy of 89.28% with an error rate of only 0.338, demonstrating stability and good model generalization capabilities. The application of appropriate and effective preprocessing and training strategies enhances model performance. Consequently, a more accurate and reliable model can support data-driven decision-making more efficiently and contribute to various sectors such as health and the environment in supporting sustainable development.
Sistem Penjualan E-commerce Berbasis Website Menggunakan Metode Waterfall dengan Model B2C
Research on the development of Laravel-based E-commerce systems using the Waterfall method is generally applied to businesses that have implemented digital technology in some of their business processes. Meanwhile, the implementation of a similar system in newly established garment manufacturing MSMEs that still carry out all operational activities manually has not been widely discussed in previous research. Therefore, this study was conducted to design and implement a website-based E-commerce sales system at PT One Step Factory using the Waterfall method and the Business to Consumer (B2C) model. The developed system integrates automatic stock management, direct sales transactions to customers, and the presentation of real-time sales reports in a single integrated platform. The system was developed using the Laravel framework, the PHP programming language, and the MySQL database. The system provides two access rights: an admin who plays a role in managing products, orders, stock, and reports, and a customer who can search for products, place orders, and monitor transaction history. Testing results using the Black Box Testing method in 12 scenarios indicate that all system functions can run according to predetermined requirements. Furthermore, the time efficiency evaluation results show that the transaction and recording process, which previously required an average of 15 minutes manually, can be completed in an average of 3 minutes after the system is implemented, resulting in an 80% increase in efficiency. The research findings indicate that the developed system is able to support the digital transformation of the sales process at PT One Step Factory, increasing operational efficiency, reducing potential data redundancy, and expanding marketing reach through digital media.
Rancang Bangun dan Evaluasi User Experience Sistem Short Link UNCP Menggunakan Metode System Usability Scale (SUS)
Managing long URLs in higher education environments often poses challenges for efficient academic information dissemination. Universitas Cokroaminoto Palopo (UNCP) faces similar challenges, particularly in distributing links to academic information systems, research portals, and the Learning Management System (LMS). This study aims to design, develop, and evaluate an institution-based short link system deployed under the institutional subdomain short.uncp.ac.id, representing a distinctive institutional advantage over third-party services. The system was developed using the Agile Development methodology, adopting a three-tier architecture consisting of a React 18 and TypeScript-based presentation layer, a business logic layer handling URL validation and base-62 short code generation, and a data layer equipped with security mechanisms including link verification and institutional security badges. Core features include URL shortening, custom alias generation, QR Code generation, and a real-time analytics dashboard that processes click data in near real-time with visualizations of daily trends, user geographic distribution, and per-link engagement metrics. Usability evaluation was conducted using the System Usability Scale (SUS) method on 30 respondents from the UNCP academic community. The SUS instrument comprises 10 statements rated on a 1–5 Likert scale. Results indicate that the system achieved an average SUS score of 84.33, categorized as Excellent and graded B according to Bangor et al. (2009). These findings indicate that the UNCP short link system has a high level of usability and can be widely implemented across the campus environment. This study contributes to the development of an independent, secure, scalable, and institutionally branded academic digital infrastructure.
Pengembangan Web Dashboard Monitoring Kadar Nitrogen, Fosfat, Kalium (NPK), PH Tanah Berbasis Artificial Intelegence (AI) dan Internet Of Things (IoT)
Indonesia is an agrarian country where the agricultural sector plays an important role in supporting the national economy. However, agricultural land management is still largely carried out using conventional methods, resulting in suboptimal monitoring of soil nutrient conditions. Nitrogen (N), Phosphorus (P), Potassium (K), and soil acidity (pH) are critical parameters influencing soil fertility and crop productivity. This study aims to develop a soil monitoring system for NPK and pH levels based on Artificial Intelligence (AI) and the Internet of Things (IoT) using an NPK Modbus sensor, an analog soil pH sensor, and an ESP32 microcontroller. Sensor data are transmitted in real-time to a Firebase database via a Wi-Fi network and displayed on a web dashboard through numerical visualizations, graphs, and ChatGPT-based AI analytic interpretations. The experimental results show that the system successfully acquires and transmits soil condition data consistently. Accuracy testing demonstrated that the system is highly precise, with the pH sensor readings showing an average difference (error rate) of only 0.2% when compared to a standard pH tester. The NPK sensor also exhibited excellent reactivity to changes in soil macronutrients. Furthermore, the integrated AI system successfully provided stable fertility evaluations and fertilization recommendations across multiple repeated tests. This system is expected to facilitate remote field monitoring for farmers and support precise, efficient fertilization decision-making.
Integrasi REST API Rule-Based Dan WebGIS Untuk Otomasi Diseminasi Gempabumi
The speed of disaster information delivery is often hindered by lengthy technical data bureaucracy. At the BMKG Manado Geophysical Station, the dissemination process post-data acquisition from the SeisComP3 machine still heavily relies on the manual intervention of the Duty Officer. This condition triggers work queues, especially when officers must sort through hundreds of micro-earthquake anomalies before copying reports to digital communication groups. To solve this problem, this research uses the Research and Development (R&D) method to design a Zero-Touch Automation ecosystem. We integrated a REST API backend equipped with a Rule-Based System algorithm to independently select seismic parameters, then seamlessly connected it with a WebGIS frontend and Telegram Bot. Quantitative test results in the field indicate that this architecture successfully performs smart data routing; earthquakes meeting the alert threshold are immediately forwarded as early warnings autonomously with an average latency of less than 3 seconds. As a scientific and practical contribution, the implementation of this system, which is equipped with a dynamic analytic panel, is proven to cut crucial reporting times from minutes to seconds, and effectively minimize human error risks in the observation room.
Sistem Pemantauan Pelacak Panel Surya Berbasis Internet of Things dengan Aplikasi Ponsel Pintar
Solar energy is an environmentally friendly renewable energy source that has gained increasing attention due to its sustainability and widespread availability. The use of a solar tracking system capable of following the sun's movement can significantly improve the performance of solar panel systems. This study aims to develop an Internet of Things (IoT)-based monitoring system utilizing a smartphone application (Telegram on Android) to monitor and control a solar panel tracking system. The research employed a Research and Development (R&D) approach. The hardware components consisted of a tracking motor, Light Dependent Resistor (LDR) sensors, and a DC voltage sensor integrated with a solar panel system. The software component utilized a Telegram Bot as a platform for remote monitoring and control. The results demonstrated that the developed system was capable of performing real-time monitoring and control of the solar panel system through an internet connection. The main contribution of this study lies in the integration of an LDR-based solar tracking mechanism with a Telegram Bot-based monitoring and control system. Experimental results indicated that the proposed solar tracking system achieved a 23.18 % increase in average output voltage compared to a conventional static solar panel system. Furthermore, the output voltage remained more stable throughout the day because the solar panel was able to automatically follow the sun's trajectory. These findings indicate that the proposed system can enhance solar energy harvesting performance while providing a practical solution for remote monitoring and control.
Analisis Perbandingan Kualitas Website Gramedia.com dan BPKGunungMulia.com Menggunakan WebQual 4.0 Berbasis Persepsi Pengguna
This study aims to analyze and compare the quality of the Gramedia.com and BPKGunungMulia.com websites based on user perceptions in the Kupang region using the WebQual 4.0 method. This research was conducted because no comparative study had previously examined both online bookstore websites in the Kupang region. A descriptive quantitative approach was employed using a Likert-scale online questionnaire distributed to 200 respondents, consisting of 100 Gramedia.com users and 100 BPKGunungMulia.com users selected through purposive sampling. Data were analyzed using validity tests, reliability tests, and descriptive statistical analysis with SPSS and Microsoft Excel. The results indicate that both websites are categorized as good. Gramedia.com achieved an average score of 79.31, while BPKGunungMulia.com achieved an average score of 79.23. Gramedia.com performed better in Information Quality (79.63) and Service Interaction Quality (79.07), whereas BPKGunungMulia.com performed better in Usability Quality (79.13) and User Satisfaction (79.87). All research instruments were found to be valid and reliable. These findings provide empirical evidence regarding the quality of online bookstore websites based on user perceptions and may serve as a reference for website managers in improving service quality and user experience.
Rancang Bangun Password Manager Berbasis Web Menggunakan Framework Django Dan Enkripsi Fernet
Most prior password manager studies rely on basic encryption without strong key derivation and have not consistently implemented a zero-knowledge architecture, leaving encrypted user data potentially accessible to service providers or third parties. This research contributes by designing and building a web-based password manager that integrates three security layers within a unified Django ecosystem: PBKDF2 key derivation with 100,000 SHA-256 iterations, hardware-based unique random salt per user (os.urandom), and authenticated Fernet encryption (AES-128-CBC + HMAC-SHA256). The novelty of this research lies in the strict enforcement of the zero-knowledge principle, where the master password is never stored in the database, making credentials inaccessible even to system administrators. The combination of personalized salt and PBKDF2 also ensures that two users sharing an identical master password always produce distinct encryption keys — a feature not found in existing Django-based implementations. Black Box Testing across ten functional and security scenarios yielded a 100% success rate, covering CRUD operations, unauthorized access rejection, ciphertext tampering detection, and cross-user salt uniqueness verification. The results demonstrate that integrating PBKDF2, unique salt, and Fernet within the Django framework produces a transparent, secure credential storage system resilient to brute force attacks, rainbow table attacks, and database-level data manipulation.
Analisis Pengaruh Kualitas Portal Akademik Universitas Malikussaleh terhadap Kepuasan Pengguna Menggunakan System Usability Scale (SUS)
The Malikussaleh University Academic Portal is a web-based information system used to support various academic services, such as filling out the Study Plan Card (KRS), accessing the Study Result Card (KHS), class schedules, and other academic administrative information. However, several obstacles are still encountered in its use, such as less than optimal access speed, an interface that is not fully responsive, and navigation that is considered less intuitive by users. This study aims to evaluate the usability level of the Malikussaleh University Academic Portal using the System Usability Scale (SUS) method. The study used a quantitative approach with data collection techniques in the form of observation, interviews, literature studies, and distribution of SUS questionnaires to 50 active Malikussaleh University students selected using a purposive sampling technique. Data were analyzed based on the SUS calculation procedure which produces a usability score in the range of 0–100. The results showed that the Malikussaleh University Academic Portal obtained an average SUS score of 74.5. Based on the SUS interpretation, this value is included in the Good category, is on the Grade Scale B, and is included in the Acceptability Range Acceptable. These results indicate that the academic portal has a good level of usability and is acceptable to users. However, several aspects still need improvement, particularly system access speed, responsiveness on mobile devices, and simplified interface navigation. This research is expected to provide evaluation material for system administrators in improving the quality of digital-based academic services at Malikussaleh University.
Sistem Informasi Web Klasterisasi UMKM Kota Gorontalo Menggunakan Algoritma K-Means
Micro, Small, and Medium Enterprises (MSMEs) are strategic pillars of the national economy; however, business owners in Gorontalo City still face challenges such as low digital literacy and limited technology adoption. Current government marketing assistance is considered suboptimal because it still relies on a mass mentoring approach (one-size-fits-all), ignoring the heterogeneous maturity levels of each business unit. This study aims to design and develop a web-based information system that automates MSME clustering using the K-Means algorithm while simultaneously acting as a recommendation engine for personalized digital marketing strategies. The software was developed using the linear sequential Waterfall model. The algorithm's processing successfully mapped a population of 112 data points into three functional clusters: Traditional Micro Enterprises (28 units), Developing Small Enterprises (52 units), and Modern Medium Enterprises (32 units). Functionality testing using the Black Box method showed that the system is 100% valid with no errors. Furthermore, interface usability evaluation using the System Usability Scale (SUS) achieved a score of 84.125, earning an "Excellent" rating. This research contributes by providing an integrated system foundation that shifts the government's coaching paradigm from mass promotion to data-driven precision marketing, thereby accelerating the digital transformation of MSMEs objectively and sustainably.
Perbandingan Algoritma K-Means Dan K-Medoids Untuk Clustering Penduduk Kota Bekasi Berdasarkan Tingkat Pendidikan (2021-2023)
Education is crucial for improving human resource quality. This study compares the K-Means and K-Medoids algorithms to analyze the distribution of education levels in Bekasi City. The dataset used was derived from 56 urban villages during 2021–2023, which were averaged to provide a more stable picture. The clustering process was performed using the RapidMiner application, with the results evaluated based on the Davies Bouldin Index (DBI) to measure the validity of the data groups. The evaluation results showed that the K-Means algorithm produced better performance than K-Medoids, indicated by the lowest DBI value of 0.558 at k = 4, while the K-Medoids algorithm produced the best DBI value of 0.803 at k = 3. The lower DBI value in K-Means indicates better cluster quality, thus K-Means was chosen as the best method for mapping education levels in Bekasi City. In addition, the K-Means results are also consistent with the Elbow method which shows the optimal number of clusters at k = 4, while K-Medoids shows a discrepancy between the results of the Elbow Method (k = 5) and DBI (k = 3). The K-Means clustering results form four clusters with different educational level characteristics. Cluster 0 is dominated by secondary education so that it requires scholarship programs and vocational training. Cluster 1 has a higher level of education so that it needs to focus on developing further education infrastructure and creating quality jobs. Cluster 2 has a low to middle level of education so that it requires increasing access to education and extension programs. Meanwhile, Cluster 3 shows a relatively even distribution of secondary to higher education so that it requires improving the quality of education and professional training to support human resource development in Bekasi City.
Evaluasi Kinerja Algoritma K-Means dan K-Medoids untuk Klasterisasi Destinasi Wisata Bali
This study aims to compare the performance of the K-Means and K-Medoids algorithms in clustering tourist destinations in Bali Province based on multidimensional characteristics, including rating, number of reviews, latitude, longitude, category, and district/city. The dataset used comes from Kaggle with 1,000 tourist destination data that have gone through a preprocessing process in the form of data cleaning, feature selection, normalization, and logarithmic transformation. The optimal number of clusters is determined using the Elbow Method in the range of K = 2 to K = 10, while the quality of the clusters is evaluated using the Silhouette Score, Davies-Bouldin Index (DBI), and Mean Squared Error (MSE). The results show that the optimal number of clusters is K = 10. K-Means produces a Silhouette Score of 0.2078, a DBI of 1.2714, and an MSE of 2.2764, thus showing better performance than K-Medoids. This study contributes in the form of a comparative evaluation of the two algorithms and recommendations for clustering methods that are more suitable for Bali tourist destination data. The research results can also support decision making in the management and development of the tourism sector.
Rancang Bangun Sistem Informasi Digitalisasi UMKM Kuliner Berbasis Web sebagai Media Promosi dan Pengelolaan Usaha
This research aims to design and develop a web-based information system for culinary MSME merchants to support the digitalization of promotion and business information management in a more effective and integrated manner. The existing problem is that data management and culinary information dissemination are still conducted manually, causing information related to shelters, food and beverage menus, and merchant data to be delivered less optimally to the public. The system development method used in this research is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was developed using the Laravel framework and MySQL database, while Unified Modeling Language (UML) was utilized as a system modeling tool. The system provides three access levels, namely admin, seller, and buyer, to support MSME data management, menu management, and centralized culinary information services. This study used 10 shelters as implementation testing data from approximately 120 available shelters. The testing results indicate that the system functions properly and assists users in accessing culinary information more quickly, effectively, and efficiently. The developed web-based system is expected to enhance culinary MSME promotion and support digital transformation in culinary business management.
Implementasi Retrieval-Augmented Generation dan Semantic Search pada Chatbot Artificial Intelligence Berbasis Web untuk Optimalisasi Layanan Akademik
Academic information services in higher education institutions still face various obstacles, such as delays in information delivery, limited access to services, and high administrative burdens due to repetitive student inquiries. This study aims to implement Retrieval-Augmented Generation (RAG) and Semantic Search technology in a web-based Artificial Intelligence chatbot to optimize academic services at the Faculty of Engineering, Hamzanwadi University. The research method used is Design Science Research (DSR), which includes data collection, system requirements analysis, design, implementation, testing, and system evaluation. The chatbot's knowledge base is built from academic documents such as academic guidelines, service SOPs, academic calendars, scholarship information, and other administrative documents. The system was developed using an integration of LangChain, Azure OpenAI Service, Azure AI Search, FastAPI, Next.js, and Supabase. Semantic Search techniques are used to perform vector embedding-based searches, while RAG is utilized to generate contextual answers based on relevant documents. Test results show that all key system features performed well with a 100% success rate in unit testing. A user satisfaction evaluation using the Customer Satisfaction Index (CSI) method with 54 respondents yielded a score of 89.34%, categorized as "Very Satisfied," with a Mean Satisfaction Score above 4.37 on a maximum scale of 5.0. The AI ​​chatbot successfully addressed traditional academic information service issues by providing 24/7 service, reducing the workload of campus staff, and ensuring information consistency through RAG technology.
Pengembangan Aplikasi Web Pembelajaran Matematika Terintegrasi Fitur Evaluasi Otomatis
The current development of information technology provides a significant opportunity for digital transformation in the education sector, particularly to address difficulties in understanding concepts in Mathematics at the elementary school level. Based on observations at SDN Loang Tuna, the material delivery process is still dominated by conventional teaching methods utilizing textbooks and whiteboards. The limitations of these teaching aids result in low levels of interactivity and independent learning interest among students. The purpose of this study is to design and develop an information system in the form of a web-based Mathematics e-learning application that comprehensively integrates interactive material modules with an automatic evaluation feature. The software development methodology used in this study adopts the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The stages of this methodology include the identification of system requirements, the design of interface and database architectures, programming with multi-user access rights management, and software functionality testing using the Black-Box Testing method. The results of the study indicate that the application was successfully built according to the specifications. Based on the Black-Box Testing, the system demonstrates a functionality success rate of 88%, which is categorized as highly feasible. The automatic evaluation feature is proven to have high accuracy in processing real-time grade calculations, which significantly reduces the administrative workload of teachers and provides instant feedback for students. Therefore, the interactive interface of this application is effective in increasing user engagement, making it an innovative and measurable technological solution to advance the quality of Mathematics learning.
Rancang Bangun Sistem Penyiraman Tanaman Bibit Cabai Menggunakan Mikrokontroler Arduino
Manual watering of chili seedlings requires considerable time and effort and is often inconsistent in the amount of water provided, which can affect plant growth. This study aims to design and implement a prototype of a chili seedling watering system based on the Arduino Uno microcontroller that can operate automatically and manually. The research method used is a design and development method, which includes observation, system design, device assembly, programming, and system testing. The system uses a soil moisture sensor as the main controller, a DHT11 sensor to monitor environmental temperature, a relay to control the water pump, a toggle switch for manual operation, and a 16x2 LCD to display soil moisture, temperature, and pump status in real time. The test results show that in automatic mode, the pump activates when the soil moisture is below the threshold of 56% and stops when the moisture reaches 60%. In manual mode, the pump can be controlled directly using the toggle switch, while the LCD successfully displays all system parameters properly. The contribution of this study is the development of a simple and easy-to-use chili seedling watering prototype that helps maintain soil moisture stability and reduces dependence on manual watering
Sistem Deteksi Dini Dehidrasi Dengan Analisis Urine Berbasis Iot
Dehydration frequently goes unnoticed due to its gradual onset, posing a risk of serious clinical complications if left unaddressed. This study aims to develop an Internet of Things (IoT)-based prototype for objective and real-time early dehydration detection through multi-parameter urinalysis, including color (RGB), pH, and temperature. The methodology encompasses user and medical needs analysis, instrument system design utilizing the NodeMCU ESP32, and validity testing using 10 real urine samples cross-validated directly against the official diagnosis (gold standard) from the Cibinong Regional Health Office, Bogor Regency. Quantitative results demonstrate that the system achieves an overall classification accuracy of 80% and a sensitivity of 100% in extreme conditions (No Dehydration and Severe Dehydration). A minor deviation of 20% occurred during transition phases due to optical sensor sensitivity toward ambient lighting variations and non-hydration chemical fluctuations in the urine. The novelty of this research lies in the integration of a temperature parameter to mitigate diagnosis bias caused by febrile (fever) conditions, alongside the implementation of an expert system on the web interface capable of transforming raw sensor data into personalized disease risk predictions and nutritional recommendations. This system contributes a practical, non-invasive, and self-managed instrument for preventive health monitoring.
Perbandingan SVM dan Logistic Regression untuk Klasifikasi Teks Konsultasi Daring Kehamilan dan Menstruasi
The high volume of doctors' response texts in online health consultations, particularly on pregnancy and menstruation topics, has increased the need for an accurate and automated medical text classification system. However, these two topics often share similar medical terminology and consultation contexts, making the classification process challenging. This study aims to develop an automatic classification model for Indonesian-language doctors' responses in online health consultations by comparing two algorithms, namely Support Vector Machine (SVM) and Logistic Regression (LR), which were selected because they are widely used in text classification tasks with TF-IDF feature representation and are capable of handling high-dimensional data. The contributions of this study include the development of a classification model using an underexplored Indonesian doctors' response dataset, feature importance analysis to identify dominant medical terms in each category, and a comparative evaluation of SVM and LR performance validated using McNemar's statistical test. The study utilized the public "Doctor's Answer Text Dataset in Indonesian" from Mendeley Data, comprising 17,807 records. The models were developed using the CRISP-DM framework with TF-IDF feature extraction and evaluated across four train-test split ratios using accuracy, precision, recall, and F1-score metrics. The results showed that Logistic Regression (LR) consistently outperformed Support Vector Machine (SVM), achieving the highest accuracy of 0.9359 with an 80:20 train-test split. Feature importance analysis identified "hamil" (pregnant) and "janin" (fetus) as the dominant features for the pregnancy category, and "menstruasi" (menstruation) and "siklus" (cycle) for the menstruation category. These findings demonstrate that TF-IDF-based LR is an effective approach for classifying Indonesian-language online health consultation texts.
Implementasi Metode Rule-Based Scoring Pada Sistem Penilaian Resiko Penyakit Diabetes Dan Hipertensi Pada Pasien Umum Di Puskesmas Berbasis Web
Health screening procedures at primary healthcare facilities play a crucial role in early disease risk detection. However, the screening process at the Jelakombo Community Health Center is still performed manually, resulting in inadequate data recording and risk assessment. This study aims to design and implement a web-based disease risk assessment system for general patients using the Rule-Based Scoring method. The system was developed using the Laravel framework and a MySQL database, utilizing a digital questionnaire as a data collection medium. The Rule-Based Scoring method was used to calculate a total score based on user responses and classify risk levels into low, medium, and high categories. The results showed that the system was successfully implemented and all main functions of the system ran as required based on Black Box Testing. The system is capable of managing patient data, filling out questionnaires, automatically calculating risk scores, and displaying risk classification results digitally. The developed system can be used as an initial screening tool to support the early detection of disease risks in general patients at the Jelakombo Community Health Center.
Perbandingan Penggunaan Arsitektur Graph GCN Dan LightGCN Pada Sistem Rekomendasi Hotel Berbasis Rating Pengguna Dengan Dataset Terbatas
Hotel recommendation systems often fail to recommend new hotels due to extreme data sparsity problems (item cold-start) and are vulnerable to the computational over-smoothing phenomenon. This study aims to comprehensively evaluate and compare the ranking architectures of Graph Convolutional Network (GCN) and LightGCN. The method used is a computational experiment using an Ablation Study approach to dissect the effect of propagation depth (1-Layer vs. Multi-Layer) and the injection of external features. The evaluation was conducted on a small-sized dataset from Mendeley Data containing review histories for 16 hotel entities. The main results show that the 1-Layer LightGCN with features is the most superior model for active users (warm-start), achieving an NDCG@10 score of 0.8702. However, in the extreme new hotel scenario (0-shot cold-start), this shallow architecture failed, and the best solution was actually won by the pure Multi-Layer model without features (No-Feature), which achieved a Hit Ratio (HR@10) of 71.43%. In conclusion, there is no single perfect model for all conditions; system implementation is recommended to adopt a dual-framework that integrates the speed of 1-Layer LightGCN and the propagation robustness of the Multi-Layer model.
Evaluasi Fungsionalitas Sistem Informasi Kerja Sama Politeknik Negeri Sriwijaya Menggunakan Black Box Testing
Collaboration management in higher education institutions requires an information system capable of supporting administrative processes, documentation, monitoring, and reporting in an integrated manner. Politeknik Negeri Sriwijaya’s Collaboration Information System was developed to manage proposal submission, verification, approval, document management, collaboration activity recording, and report preparation. This study aims to evaluate the system’s functionality using the Black Box Testing method by assessing the conformity between inputs and outputs without examining the program’s internal structure. Testing was conducted through 10 scenarios covering user login, collaboration proposal submission, verification, data revision, proposal approval and rejection, document uploading, activity input, monitoring, and reporting. Each scenario was evaluated by comparing the system’s actual output with the expected result. The results showed that all test scenarios produced outputs consistent with the expected results, achieving a success rate of 100%. These findings indicate that the system meets its primary functional requirements and is feasible for supporting more structured, centralized, documented, and integrated collaboration management at Politeknik Negeri Sriwijaya.
Analisis Sentimen Komentar Masyarakat di Twitter Menggunakan Algoritma Support Vector Machine
Social media has become the primary means for the public to express their opinions on a wide range of issues, one of which is smartphone products. The social media platforms used to express opinions include Facebook, Instagram, Twitter, WhatsApp and YouTube. Twitter is one of the most widely used platforms for expressing opinions in the form of comments, which may be positive, negative or neutral. This study aims to classify the sentiment of Twitter users’ comments regarding smartphones using the Support Vector Machine (SVM) algorithm. The research employs a quantitative methodology. The data used consists of comments or text data in CSV format, which is processed through a series of pre-processing steps, including data cleaning, tokenisation, stop-word removal and stemming. This will be followed by sentiment modelling and an evaluation of the model’s performance. The results of the study show that the SVM method achieved an accuracy of 82,28%, a recall of 82,26%, a precision of 82,25%, and an F1-score of 82,25%. Based on these results, it can be concluded that the SVM algorithm performs well in sentiment analysis of social media data, particularly Twitter.
Pengembangan Sistem Keamanan dan Pelacakan Sepeda Motor Pintar Berbasis Internet of Things (IoT) Menggunakan RFID dan GPS
The high rate of motorcycle theft has become a significant issue that requires an effective and integrated technology-based solution. This study aims to develop a smart motorcycle security and tracking system based on the Internet of Things (IoT) by utilizing Radio Frequency Identification (RFID) technology as a user authentication system and the Global Positioning System (GPS) as a real-time vehicle tracking system. The methodology employed includes hardware and software design, integration of the RFID RC522 and GPS Neo-6M modules with an ESP32 IoT-based microcontroller, and system testing under eight usage scenarios covering RFID authentication, remote control and location requests via Telegram, GPS testing in open and covered areas, a power-supply test from the 12 V motorcycle battery, and a one-hour endurance test. The results indicate that the developed system is capable of enhancing vehicle security through an RFID-based authentication mechanism that only allows registered users to access the motorcycle. In addition, the GPS-based tracking feature enables accurate and real-time monitoring of the vehicle's location through a web-based or mobile application. The system performance evaluation shows a GPS tracking accuracy of ±3 meters in open areas, an RFID authentication response time of less than 1 second, and an average remote-control response time of 1–2 seconds via Telegram, although coordinate delivery was delayed in covered areas; the system also remained stable during a one-hour endurance test powered by the 12 V motorcycle battery. Therefore, the proposed system has the potential to serve as an innovative solution for improving motorcycle security and monitoring, as well as supporting the development of intelligent transportation technologies based on IoT.
Klasterisasi Status Stunting pada Balita Menggunakan Algoritma K-Means
Stunting is a growth and development disorder in children due to chronic malnutrition and repeated infections characterized by below-standard height. Cadres conduct monthly Posyandu data collection by weighing and measuring toddlers. However, the Posyandu data results only become piles of paper in the cadres' homes. Based on these conditions, it is necessary to process data for clustering that can help the village government in grouping toddler data so that the government knows the number of stunted and normal toddlers, which can later be used as evaluation material in the future in taking action to reduce, prevent and overcome stunting cases. This study aims to group stunted and normal toddlers based on age, weight, and height variables. The method applied is data clustering using the K-Means Algorithm. The results of this study are clusters divided into 2 clusters, in cluster 1 there are 406 toddlers with normal status as many as 119 toddlers and toddlers with stunting status as many as 287 toddlers, while in cluster 2 there are 326 toddlers with normal status as many as 52 toddlers and toddlers with stunting status as many as 274 toddlers. A total of 171 toddlers with normal status, representing 23.4%, and 561 toddlers with stunting, representing 76.6%. These results demonstrate the critical need for support from integrated health posts (Posyandu) and relevant community health centers (Puskesmas) to parents of toddlers to reduce or even eliminate stunting in the following period.
Implementasi Algoritma K-Nearest Neighbor pada Sistem Deteksi Tingkat Dehidrasi Berdasarkan Warna Urin Berbasis IoT dan Android
Dehydration is a condition in which the body loses more fluids than it receives, potentially causing health problems and disrupting normal body functions. One indicator of dehydration can be observed through urine color, where darker urine indicates a higher level of dehydration. This study aims to implement the K-Nearest Neighbor (KNN) algorithm in an Internet of Things (IoT)- and Android-based dehydration detection system using urine color analysis. The system utilizes a TCS3200 color sensor to detect urine color, an ESP32 microcontroller to process and transmit data, and an Android application to display detection results in real time. The acquired color data are processed using the KNN algorithm to classify dehydration levels into several categories. The classification results are then displayed on the Android application along with hydration reminder notifications. System testing was conducted using 50 artificial urine color samples with varying color intensities. The results showed that the proposed system was able to classify dehydration levels with an accuracy of 92% and successfully display real-time detection results through the Android application. The developed system is expected to assist users in monitoring their hydration status easily and effectively.
Pengembangan Sistem Informasi Inventaris dan Peminjaman Barang Berbasis Web untuk Meningkatkan Efisiensi dan Transparansi Pengelolaan Aset Desa
Inventory and item borrowing are important parts of asset management in schools, organizations, companies, and government institutions. The Gelanggang Village Office still uses a manual inventory and borrowing system, making it highly vulnerable to errors and inefficient processes. Inventory management is used to record and monitor all owned items so that their condition, quantity, and usage can be clearly identified. Meanwhile, the item borrowing system functions to regulate the use of goods by the village government for a certain period, ensuring that asset utilization is more organized and controlled. This study aims to develop a web-based inventory and item borrowing information system to minimize item damage and improve the efficiency, effectiveness, and transparency of village asset management. The method used in this research is the SDLC Waterfall model. The web-based system includes features for data management, transaction recording, approval processing, and automatic report generation. Testing using the Customer Satisfaction Index (CSI) with 30 respondents resulted in a score of 79.46% (categorized as “satisfied”), indicating that the system is capable of replacing manual recording with a faster and more accurate process. The system has proven feasible for use in Gelanggang Village and has the potential to become a model for the digitalization of inventory management in other villages, supporting technology-based governance.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Diabetes Menggunakan Feature Selection dan Hyperparameter Tuning
Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, requiring early detection to support faster and more accurate disease management. This study aims to compare the performance of several machine learning algorithms for diabetes classification using feature selection and hyperparameter tuning. The dataset used was the Pima Indians Diabetes Dataset obtained from the Kaggle platform. The research consisted of data preprocessing, feature selection using SelectKBest, training and testing data splitting, hyperparameter tuning using GridSearchCV, and model evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and cross validation. The evaluated algorithms included Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes. The results showed that the KNN algorithm achieved the best performance with an accuracy of 74.02%, precision of 63.46%, recall of 61.11%, F1-score of 62.26%, and ROC-AUC of 79.60%. The findings indicate that integrating data preprocessing, feature selection, hyperparameter tuning, and cross validation provides a more comprehensive evaluation process for machine learning models in diabetes classification.
Perancangan dan Implementasi Sistem Informasi Manajemen Operasional dan Penggajian Berbasis Web Menggunakan Model Waterfall
Micro, Small, and Medium Enterprises such as E-Donuts face challenges in daily operational management, sales recording, packaging and donut stock management, and employee payroll because they still rely on manual processes. This study aims to design and implement EdoBest, a web-based operational management and payroll application developed using the Waterfall model. The development process includes planning, requirement analysis through interviews and observation, UML-based system design and responsive interface design, implementation using Laravel 12 and MySQL, black-box testing, and maintenance. The novelty of EdoBest lies in the integration of culinary MSME operational modules into a single system, including delivery batch management, multi-outlet management, packaging and donut stock management, employee activity recording, cash advance management, report validation, and automatic payroll calculation based on daily operational data. The quantitative testing results show that 16 functional test scenarios, consisting of 10 Admin scenarios and 6 Employee scenarios, were successfully executed with a 100% success rate. This research contributes a functionally tested design and implementation of an integrated information system that improves recording accuracy, reporting efficiency, real-time monitoring, and decision-making support for multi-outlet culinary MSMEs.
Pengembangan Sistem Informasi Manajemen Terintegrasi Berbasis Web untuk Optimalisasi Operasional dan Pengambilan Keputusan Strategis pada UMKM
In the digital era, the utilization of information technology—including Management Information Systems (MIS)—is crucial for operational efficiency, data management, and strategic decision-making, particularly for MSMEs. Pesona Cafe Lenek, an MSME in East Lombok, continuously strives to enhance its customer service; however, its reliance on manual transaction processes has proven to be neither effective nor efficient. This study aims to develop a web-based management information system to improve the cafe's operational management and serve as a basis for strategic decisions regarding sales and employee payroll. The system also incorporates features for employee attendance tracking and accounting functions, specifically the general ledger and profit/loss statements. Implementing this information system is expected to streamline transactions, simplify the process of summarizing sales data, and enable faster access to financial and purchasing reports—whether on a monthly or on-demand basis. The results demonstrate that the platform enhances efficiency in promotions and ordering while strengthening the MSME's image through a user-friendly, informative interface. Furthermore, it provides an interactive user experience, expands market reach, and facilitates the sustainable management of sales data and digital marketing strategies. Ultimately, this boosts business competitiveness, fosters customer loyalty, and opens up new collaboration opportunities in the era of digital transformation.
Deteksi Hoaks Berita Berbahasa Indonesia Menggunakan IndoBERT dengan Penanganan Ketidakseimbangan Kelas Berbasis Gabungan Class Weighting dan Focal Loss
The spread of hoaxes in Indonesian-language digital media has risen sharply in the past five years with wide societal harm. Most prior work on Indonesian hoax detection emphasizes model architecture, while the class-imbalance problem in field data receives less attention. This study presents a data-centric approach combining the IndoBERT pretrained language model with a hybrid class-imbalance objective of class weighting and focal loss. The dataset is the indonesiafalsenews corpus of 4231 labelled articles with an approximate 4.5 to 1 hoax-to-fact ratio. Evaluation uses five-seed runs with McNemar and paired-bootstrap significance testing. The proposed configuration achieves an F1-macro of 0.7240, accuracy of 0.8392, ROC-AUC of 0.8083, and a Matthews correlation coefficient of 0.4510, significantly outperforming every TF-IDF baseline at the 0.05 level. Text augmentation does not provide consistent gains, which implies that imbalance handling is the more effective lever for Indonesian hoax detection.