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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Sistem Informasi Klasifikasi Atlet Ju-Jitsu Berbasis Web Menggunakan Metode Agile
The development of information technology has increased the need for efficient and accurate athlete data management systems. In Ju-Jitsu, the athlete classification process is still performed manually, making it prone to errors and time-consuming. This study develops a web-based Ju-Jitsu athlete classification system capable of automatically grouping athletes based on weight, gender, and age in accordance with official competition regulations. The system was developed using the Agile method, with React.js for the frontend and Node.js with Express.js for the backend. Classification rules were implemented in a structured manner to ensure consistent calculations and reduce manual errors. System testing using Black Box Testing achieved a 100% success rate, while expert validation produced scores of 3.55 in the first iteration and 3.7 in the second iteration, indicating that the system functions properly and is feasible for use. The system effectively improves the accuracy, consistency, and efficiency of the athlete classification process and supports better data management during competitions
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Analisa Komparatif Klasifikasi Citra Sayuran dengan Algoritma Support Vector Machine dan Convolutional Neural Network
This study presents a comparative analysis between the Support Vector Machine (SVM) algorithm, representing machine learning techniques, and the Convolutional Neural Network (CNN) algorithm, representing deep learning techniques, for vegetable image classification. The research adopts a quantitative approach by conducting multiple experimental schemes involving various SVM feature extraction methods and CNN architectures. The objective of this study is to evaluate and compare the performance, effectiveness, and computational efficiency of SVM and CNN algorithms. The dataset used in this study is a publicly available vegetable image dataset obtained from the Kaggle platform, consisting of 21.000 images categorized into 15 classes. The experimental results indicate that CNN significantly outperforms SVM in terms of accuracy, precision, recall, and F1-score. Moreover, CNN demonstrates superior generalization capability in predicting unseen image data. The best performance of the SVM algorithm was achieved using the Color Histogram feature extraction method, yielding an accuracy of 93%. In contrast, CNN models employing pre-trained architectures achieved higher accuracy, with VGG16 and MobileNetV2 obtaining accuracies of 98% and 100%, respectively. Based on the comparative results, CNN provides higher classification accuracy than SVM; therefore, this study can serve as a scientific reference for the development of image classification systems in digital agriculture and other applications requiring high accuracy and efficient computational performance.
Perancangan Sistem Informasi Retribusi Sampah untuk Meningkatkan Efisiensi dan Transparansi Pengelolaan Keuangan BUMDes
The management of waste collection fees at the village level is still carried out manually, leading to various issues such as delayed billing, irregular record-keeping, and an increased likelihood of administrative errors. These conditions negatively affect the accuracy and effectiveness of financial reporting within the Village-Owned Enterprise (BUMDes). In response to these challenges, this study aims to develop a web-based waste collection fee information system design that is more structured, efficient, and user-friendly to enhance the accountability and transparency of village financial management. Data collection was conducted through interviews, observations, and literature studies to obtain a comprehensive understanding of system requirements. The system design employs the Unified Modeling Language (UML), which includes Use Case Diagrams, Activity Diagrams, Sequence Diagrams, and Class Diagrams, as well as database design using the Relational Database Model. In addition, this study produces user interface (UI) designs as initial representations of the system’s layout and interaction flow to support the operational needs of BUMDes. The findings indicate that the developed UML models, database design, and user interface prototypes can be used to address various limitations of the current manual system, particularly those related to data redundancy, inconsistent documentation, and difficulties in financial report preparation. In conclusion, this study emphasizes that all presented results are still in the design phase and are expected to serve as a foundational reference for the future implementation of a more modern, standardized waste collection fee information system that can be applied in BUMDes operations.
Penerapan Prototyping dalam Perancangan Sistem Informasi Surat Masuk dan Surat Keluar (Studi Kasus Digitalisasi Administrasi)
The rapid growth of information technology encourages educational institutions to improve administrative efficiency, particularly in managing incoming and outgoing letters as part of formal communication. Manual procedures that rely on agenda books and physical document storage often result in delayed dispositions, difficulty retrieving documents, and a higher risk of misplaced or lost records. This study aims to design a web-based correspondence information system as a more structured, accessible, and efficient solution to these issues. A qualitative descriptive approach was applied, using observation, interviews, and literature review, followed by system requirements analysis aligned with operational needs. The design process employed a prototyping method and was documented using UML-based modeling to represent both structure and workflow. The results of this study produce a system design that supports digital recording, electronic archiving, online disposition, and automated notifications. The primary contribution of this research is the development of a relevant and applicable system model that aligns with modern administrative digitalization, offering potential improvements in workflow efficiency, information distribution speed, data accuracy, and secure traceability of administrative records.
Penerapan Prototyping dalam Perancangan Sistem Informasi Presensi Kepegawaian (Studi Kasus Pengelolaan Kehadiran Pegawai)
The rapid development of information technology encourages educational institutions to manage employee administration more effectively, particularly in recording staff attendance, which serves as a basis for evaluating discipline and performance. The manual attendance process that relies on physical logbooks and Excel-based recapitulation often leads to delays, input errors, and potential data loss. This study aims to design a web-based employee attendance information system as a more structured, accurate, and accessible solution. The research adopts a Research and Development (R&D) approach, which includes preliminary studies, data collection through observation, interviews, and literature review, as well as needs analysis to formulate a system design that aligns with the school’s operational conditions. The system design was developed using the Prototyping method, while process modeling and functional requirements were documented using UML as the basis for constructing the system workflow. The results of this study produce a system design that supports real-time attendance recording, integrates location validation and photo documentation, and provides automatically generated reports that can be printed or exported. This research contributes by offering a digital attendance system model that is more efficient, accurate, and adaptive to modern administrative needs, with the potential to improve the effectiveness of employee attendance management.
Pengembangan Website Company Profile Menggunakan Codeigniter Untuk Mendukung Branding Perusahaan
A company profile website is a digital medium used by companies to effectively convey information about their profiles, services, and portfolios to the public. In the era of digital transformation, the existence of a website is crucial because it can increase a company's visibility, credibility, and competitiveness. This study aims to design and develop a CodeIgniter 3-based company profile website that adapts to user needs and facilitates content management. Needs analysis was conducted through observation and interviews, while the system design utilizes UML and a responsive interface design that supports various devices. The implementation used PHP with the CodeIgniter 3 framework and a MySQL database. The results show that the website is capable of displaying profile information, services, portfolios, blogs, and contacts, as well as providing a simple CMS for independent content updates. In conclusion, this website can enhance professionalism, strengthen branding, and simplify the management of company information. Further developments could include social media integration, SEO optimization, multi-language support, and visitor analytics features. Additional security features and performance enhancements could also be considered to ensure the system's sustainable quality and reliability in the future.
Penerapan Model LSTM dan CNN Untuk Klasifikasi Sentimen Pada Ulasan Aplikasi Roblox
In the rapidly evolving digital era, online gaming platforms like Roblox have transformed into complex interactive social spaces where users interact, create, and co-build virtual experiences. This study aims to compare the performance of two deep learning architectures Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in classifying user reviews of the Roblox app from the Google Play Store into positive, negative, and neutral sentiment categories. The dataset comprises 5,000 Indonesian-language reviews collected via web scraping using the google_play_scraper library. Preprocessing involved text cleaning, case folding, tokenization, normalization of informal words, stopword removal, stemming, and lexicon-based sentiment labeling. Data were converted to numerical representations using Tokenizer and padding, then split into 80% training and 20% testing subsets. CNN achieved superior performance with 89% accuracy, 0.88 precision, 0.81 recall, and 0.83 F1-score, outperforming LSTM (86.60% accuracy, 0.80 precision, 0.82 recall, 0.81 F1-score). CNN effectively extracts spatial patterns in text, while LSTM captures temporal word dependencies. This research affirms CNN's superiority for short Indonesian text sentiment analysis, provides a deep learning benchmark for gaming app reviews, and offers practical implications for Roblox developers to interpret user feedback for feature enhancements.
Implementasi Sistem Monitoring Suhu dan Kualitas Udara Berbasis Internet of Things (IoT) dengan Kontrol Blower Otomatis
Monitoring temperature and air quality are important factors in creating a comfortable and safe treatment room environment for patients. This study aims to design and implement an Internet of Things (IoT)-based temperature and air quality monitoring system integrated with automatic blower control. The system was developed using the Research and Development (R&D) method by utilizing a DHT22 sensor for temperature and humidity measurement and an MQ135 sensor for air quality detection, controlled by NodeMCU ESP8266. The system was tested by comparing the temperature sensor readings with a thermohygrometer and testing the system's response to changes in environmental conditions. The test results showed that the system was able to monitor temperature and air quality in real-time with a temperature measurement error rate of 0.21% and was able to activate the blower automatically when the temperature and air quality values exceeded the specified thresholds. Thus, the developed system proved to work well and can be used as an IoT-based environmental monitoring solution for treatment rooms.
Perbandingan Prediksi Pengunjung Website Menggunakan SARIMA, LSTM dan Holt-Winters TES
Rapid technological developments have changed people's lifestyles, marked by an increase in online activity. APJII states that by 2025, 229 million Indonesians will be internet users, while BPS recorded 3,816,750 digital businesses in 2023. This growth has encouraged the use of websites as the primary medium for business and information. Therefore, understanding visitor trends and seasonal patterns is crucial for effective and efficient management. This study offers a new contribution by predicting the number of visitors to the website of PT. XYZ, a tea company in Indonesia. It uses three time series models: Seasonal Auto Regressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and Holt-Winters Triple Exponential Smoothing (Holt-Winters TES) with a prediction period of 71 days prior and to predict 14 days ahead. The dataset used consists of 32,518 visitor data entries. Model performance is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM achieved the lowest error with an MSE of 8257.23, an RMSE of 90.87, and a MAPE of 12.73%. Therefore, the LSTM model achieved the highest accuracy, while Holt-Winters TES performed better than SARIMA in certain aspects. Visitor predictions can support strategic decision-making and content management
Perancangan Sistem Rekomendasi Adaptif Latihan dan Nutrisi Berbasis Reinforcement Learning
This research aims to design and develop a personalized Adaptive Exercise and Nutrition Recommendation System, using a Reinforcement Learning (RL) approach, to overcome the limitations of conventional fitness programs that are generic and static. Unhealthy lifestyle trends have increased non-communicable diseases, but available wellness solutions fail to adapt in real-time to users' dynamic conditions—such as fatigue levels, weight changes, or nutritional responses—making them a major factor in consistent failure. This research adopts the Design Science Research Methodology (DSRM) methodology. The main problem is solved through designing an intelligent system architecture that is capable of managing and processing dynamic user data. The Deep Q-Network (DQN) algorithm is implemented effectively to produce optimal and personalized training and nutrition program recommendations on an ongoing basis. The system is trained and evaluated using synthetic data to enable simulation of complex feedback scenarios and address sensitive data privacy issues. The research output is a functional prototype (Proof-of-Concept) which will be analyzed and evaluated for its performance comparatively with conventional fitness programs to measure the level of program optimization and user consistency.
Perancangan Desain UI/UX Aplikasi Mobile Diet Anti-Inflamasi Bagi Penderita Lupus Dengan Metode Design Thinking
Lupus or Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that attacks various organs in the body and impacts the quality of life of sufferers. ODAPUS needs to live a healthy lifestyle by maintaining a healthy diet, one of which is by implementing an anti-inflammatory diet that aims to reduce inflammation and prevent the recurrence of symptoms. However, limitations in digital media and access to accurate nutritional information make it difficult for ODAPUS to consistently follow a diet. This study aims to design a UI/UX for an anti-inflammatory diet mobile application using the Design Thinking method, which includes the stages of Empathize, Define, Ideate, Prototype, and Testing. In the Empathize stage, data was collected using a survey of 15 members of the Sahabat Cempluk Yogyakarta Community. This study produced a High-Fidelity Prototype with four main features, namely ODAPUS profile, calorie calculator, diet menu recommendations, and food preparation guidelines. The Testing stage used the online Focus Group Discussion (FGD) method, which was attended by 16 participants. The evaluation covered visual aspects, navigation, diet menu recommendation features, clarity of information, processing guides, colors and text, and suitability of needs. The evaluation results showed a 100% acceptance rate in all test aspects. Thus, the Design Thinking method proved to be effective in producing a simple, empathetic, and ODAPUS-friendly mobile application UI/UX design.
Sistem Prediksi Kualitas Air Konsumsi Machine Learning Menggunakan Algoritma Random Forest
This study aims to design and implement a clean water quality prediction system based on machine learning using the Random Forest algorithm. The background of this research is the limited public access to fast information regarding water feasibility, while laboratory testing requires significant time and cost. The data used is synthetic data constructed based on the value ranges and threshold limits of water quality in SNI 3553:2015, SNI 3553:2023, and the Ministry of Health Regulation No. 2 of 2023, so that it remains representative and aligned with real conditions. This dataset was created because field data is difficult to obtain completely and in a standardized form, but it still imitates real condition variations according to national standards, with feasibility labels determined based on official regulations. The system development uses the Agile method through the stages of dataset creation, preprocessing, training, evaluation, and application implementation. The Random Forest model is used to classify water into suitable, moderately suitable, and unsuitable categories. The test results show that the model with three physical parameters achieved an accuracy of 96.67%, while the model with ten chemical parameters achieved an accuracy of 100%, confirming that adding more parameters can improve prediction accuracy. This system is expected to help the public and environmental officers in conducting an initial assessment of water quality quickly before laboratory testing and can still be further developed to become more applicable in various regions.
Integrasi Progressive Web App dan Sistem Informasi Geografis untuk Penyediaan Informasi Lokasi dan Harga Penyewaan Gendang Beleq di Lombok
Gendang Beleq is a traditional musical art of the Sasak people that continues to be preserved in Lombok. However, the process of renting Gendang Beleq instruments in East Lombok is still done manually, which makes it difficult for people to access clear and reliable information about rental locations, prices, and provider contacts. This study aims to develop a Progressive Web App (PWA)-based Geographic Information System that can provide accurate, fast, and easy-to-access information related to Gendang Beleq rentals. The research uses a Research and Development (R&D) approach with the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The developed system displays an interactive map showing the locations of rental providers along with complete details such as prices, contacts, and descriptions of the instruments. The system also supports key PWA features, including app installation and offline access. Performance testing using GTMetrix resulted in scores of 98% for the admin application and 97% for the user application. Functional testing showed that the system is highly feasible, with alpha testing results of 94.5% for the admin system and 100% for the user interface, while beta testing achieved a 98% feasibility score. Overall, the PWA-based GIS developed in this study is considered effective, user-friendly, and capable of supporting the availability of Gendang Beleq rental information in East Lombok.
Perbandingan Performa Sensor DHT11, DHT22, dan 808H5V5 terhadap Pengukuran Kelembapan Udara Berbasis Internet Of Things
This study aims to compare the performance of three air humidity sensors—DHT11, DHT22, and 808H5V5—integrated with an ESP8266 microcontroller based on the Internet of Things (IoT). The system was designed to monitor air humidity in real-time through a web server that displays measurement data from the three sensors along with a hygrometer as a reference. Data collection was conducted every hour from 08:00 to 17:00 under stable environmental conditions. The results show that the DHT22 sensor achieved the highest accuracy, with an average deviation of 0.3% compared to the hygrometer. The 808H5V5 sensor demonstrated the fastest response with an average deviation of 0.6%, while the DHT11 sensor had an average deviation of 1.1% and slower response time. All three sensors successfully transmitted real-time data through an accessible web server. Based on these findings, the DHT22 is recommended as the most accurate sensor for IoT-based humidity monitoring systems, while the DHT11 and 808H5V5 remain suitable for simpler applications requiring moderate precision and lower cost.
Inovasi Sistem Informasi Digital Berbasis Web Untuk Optimalisasi Pembayaran Tagihan Air Layanan Desa
This study focuses on the development of a web-based digital information system to optimize the water bill payment process for village water services at Bumdes Saling Sedok, Peresak Village. The manual system that has been used to date has led to various challenges, such as payment delays, recording errors, and a lack of transparency. Therefore, innovations based on information technology are expected to overcome these problems while improving service quality. The research methodology involves user requirements analysis, system design using a prototyping approach, and the implementation of web-based technology. The software used in the design and implementation of the system includes XAMPP as the web server, phpMyAdmin as the database, and Visual Studio Code as the text editor for developing PHP and HTML files. The developed system provides key features such as real-time access to billing information, online payment facilities, automatic notifications to customers, and financial reports for administrators. The expected outcomes include improved efficiency and effectiveness of the payment process, enhanced transparency of financial data, and easier access to services for the community. In addition, this system supports the empowerment of Bumdes through more modern and professional management. The main contribution of this research is the provision of a technological solution that not only improves the operational performance of Bumdes but also empowers the community to adopt technology for everyday needs. These findings can serve as a foundation for the development of similar systems in other regions, supporting digital transformation and enhancing community-based public services.
Penerapan Algoritma K-Means Dalam Mengelompokkan Ruang Pasien BPJS Untuk Menentukan Pola Perawatan Kesehatan Yang Efektif
The National Health Insurance organizer managed by BPJS Kesehatan plays a crucial role in ensuring equitable and quality healthcare services for all Indonesian citizens. The increasing number of BPJS participants encourages healthcare facilities to implement more efficient data-driven management. Fakhira Clinic in East Lombok still faces challenges in managing treatment rooms and formulating service strategies due to suboptimal use of data analysis, causing decisions to often be made reactively.This study aims to apply the K-Means clustering algorithm to group BPJS patient rooms. The K-Means algorithm is used to cluster BPJS patients based on their characteristics and service needs, such as gender, age, type of illness, visit frequency, duration of treatment, and payment method. Analysis of 500 BPJS patient data resulted in three main clusters: 217 patients with light treatment patterns, 158 patients with moderate care needs, and 125 patients with intensive care requirements. Each cluster shows different tendencies regarding age, length of hospital stay, and diagnosis. This information can be utilized as a basis for arranging treatment rooms, accelerating service processes, and formulating more effective and targeted care strategies.
Implementasi Teknologi Kecerdasan Buatan Dalam Pengembangan Media Pembelajaran Pengenalan Budaya Sasak Gendang Beleq Berbasis Web
The use of Artificial Intelligence (AI) in developing learning media can enhance the quality of education, make it more engaging, and help preserve cultural heritage such as the Sasak Culture of Gendang Beleq. Based on observations and interviews that were conducted, it was found that 60% of the total 23 students, or about 13 students from class 3B at Sekolah Dasar Negeri 3 Pancor, were not very familiar with Gendang Beleq, such as knowing the names of the musical instruments, the sound characteristics, and the history of Gendang Beleq in the local content subject. Teachers at SDN 3 Pancor still use books as teaching materials for students. The content from the books is usually not interactive, so students can only read the information without interacting, which may make learning feel boring and less engaging for some students. Therefore, this study aimed to address the difficulties students face in recognizing the Sasak Culture of Gendang Beleq at Sekolah Dasar Negeri 3 Pancor by developing web-based interactive learning media that utilizes Artificial Intelligence technology to create videos, images, and audio as tools for teaching and learning activities. This research was carried out through problem analysis, data collection, design phase, development phase, testing, and implementation. The results of the study showed positive feedback, evidenced by the testing of the interactive learning media which did not show any errors and received a success rate of 79%, which falls into the good category. Thus, it can be said that the interactive learning media on the Sasak Culture of Gendang Beleq is suitable for use at SDN 3 Pancor.
Pengembangan Model Deep Learning Long Short-Term Memory untuk Generasi Musik Berbasis Data MIDI
This study develops an automatic music generation model based on Long Short-Term Memory (LSTM) by utilizing MIDI data as a symbolic representation of classical piano music sequences. The approach is computational and experimental, with a workflow that includes extracting and converting MIDI files using music21, constructing note and chord tokens, forming input–output sequences, designing a three-layer LSTM architecture, and generating music in an autoregressive manner. The model is trained for 100 epochs with a batch size of 64 and evaluated using loss, accuracy, top-3 accuracy, and perplexity metrics to assess its predictive capability on unseen data. The experimental results show a consistent decrease in validation loss, with a final value of approximately 2.93 and a validation accuracy of 0.33, while the top-3 accuracy reaches 0.53, indicating that more than half of the correct predictions fall within the top three candidates. A perplexity value around 18 suggests that the model has a reasonably adequate sequence prediction ability for symbolic music data. Qualitatively, the model is able to generate simple melodies whose patterns remain coherent with the note distribution in the dataset, although some parts of the compositions still exhibit repetition and limited variation. An important contribution of this study is the provision of a systematic methodological documentation of the LSTM-based music generation pipeline, which can serve as a practical reference for future development and research in deep learning–based music generation.
Penerapan Metode Waterfall dalam Perancangan Sistem Informasi Administrasi Simpan Pinjam untuk Meningkatkan Efisiensi Layanan BKD Unit Kaligayam
The Kaligayam Village Credit Agency (BKD) plays a vital role in supporting the rural economy through microfinance services, such as fund deposits and loan disbursement. The design of the Savings and Loan Administration Information System at the Kaligayam Village BKD Unit aims to improve efficiency in financial administration, particularly in recording savings and loan transactions. This system is designed to replace the conventional manual process, which is prone to errors and data duplication. The system was developed using the Waterfall model, a sequential and structured method starting from needs analysis, system design, implementation, and system testing. This type of research includes Research and Development (R&D) with system testing activities through functional testing and user acceptance testing to ensure the system's reliability, accuracy, and usability. The results show that the system is capable of recording member data, savings and loan transactions, and generating financial reports automatically and accurately. The system also provides secure and organized data management features, making it easy for users to access information quickly. The main contribution of this research is the creation of an effective and efficient administrative information system, which can be a reference for system development in other microfinance institutions, as well as supporting faster and more accurate decision-making.
Implementasi Sistem Monitoring Perangkat Rectifier dan AC Berbasis Jaringan VPN Menggunakan Mikrotik pada Ruang Server
Server rooms in the telecommunications sector require a reliable monitoring system to maintain service continuity, especially for supporting devices such as rectifiers and air conditioners (AC) which play an important role in power and temperature stability. A problem that often occurs is the limitation of monitoring that has not been integrated and cannot be accessed safely remotely, so it has the potential to cause delays in interference detection. This study aims to implement a monitoring system for rectifier devices and air conditioners based on the Virtual Private Network (VPN) network using MikroTik in the server room. The research methodology includes the stages of problem identification, literature study, system needs analysis, network topology design, VPN implementation and monitoring system, as well as testing and evaluating network performance. The VPN is configured on the MikroTik router to provide a secure communication path between the server room and the network administrator. The test was carried out by comparing the network conditions before and after implementation using connectivity parameters and monitoring access stability. The results of the study show that the proposed monitoring system is able to provide real-time monitoring of the condition of rectifiers and air conditioners with a better level of safety. MikroTik-based VPN implementations are proven to support stable and secure connectivity, thus speeding up the early detection process of interference and improving the operational reliability of server space. This system is expected to be a practical solution in managing server space infrastructure in telecommunication environments.
Analisis Penerapan Internet Of Things (IoT) dalam Pertanian Cerdas di Era 4.0
The development of Internet of Things (IoT) technology has had a major impact on various fields, including agriculture. This technology enables monitoring, control, and decision-making processes to be carried out automatically using sensors and systems connected to a network. This study aims to analyze the application of IoT technology in smart agriculture in Indonesia and globally using the Narrative Literature Review method. The method used was to review 17 nationally accredited (SINTA 3-5) and international journals published between 2019 and 2025. The results showed that the application of IoT in agriculture focused on monitoring soil moisture, air temperature, automatic irrigation control, and monitoring the quality of the plant environment. The application of Internet of Things (IoT) technology in agriculture has been proven to increase resource utilization efficiency, crop productivity, and work process effectiveness. In addition, one of the studies reviewed reported that the application of an IoT-based fertilization system was able to reduce fertilizer use by up to 49% for certain commodities. However, there are still several challenges, such as high implementation costs, network infrastructure conditions, data security issues, and farmers' limited technological knowledge. In conclusion, the application of IoT in agriculture has great potential to support digital transformation in Indonesia's agricultural sector towards a more sustainable and efficient agricultural system.
Analisis Komparatif Support Vector Regression dan Decision Tree Regression untuk Peramalan Kasus HIV Jawa Barat
The increasing number of HIV cases in Indonesia, particularly in West Java, which ranks among the regions with the highest infection rates, highlights the need for forecasting models capable of producing accurate estimates to support health policy planning. This situation underscores the importance of analytical approaches that can capture the dynamic progression of cases over time. This study aims to develop a forecasting model for the number of HIV cases in West Java by applying the CRISP-DM framework and utilizing a dataset categorized by age group for the period 2019–2023. Two regression algorithms, Support Vector Regression (SVR) and Decision Tree Regression (DTR), were compared using three evaluation metrics: Coefficient of Determination (R²), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that DTR delivers superior performance, achieving an R² of 83.62, RMSE of 32.29, and MAPE of 33.30. In contrast, SVR produced an R² of 36.33, RMSE of 84.01, and MAPE of 44.87. Based on these results, Decision Tree Regression is identified as the more effective model for forecasting the number of HIV cases in West Java, providing practical support for health policy decision-making, resource allocation, and prioritization of targeted prevention and intervention programs at the regional level.
Implementasi Sistem Informasi Manajemen Aset Inventaris Alat Kerja Karyawan Pustekinfo DPR RI Berbasis Web
Pustekinfo DPR RI plays an important role in supporting information technology operational activities, including the management of employees’ work equipment inventory assets. Asset management that is still carried out using conventional methods has the potential to cause various problems, such as disorganized data, difficulties in monitoring asset transactions, and suboptimal supervision of asset usage. This study aims to design and implement a web-based Information System for Managing Employees’ Work Equipment Inventory Assets at Pustekinfo DPR RI in order to improve the effectiveness of asset management. The system development method used is the Waterfall method, which consists of the stages of requirements analysis, system design, implementation, testing, and maintenance. Data collection techniques include observation, interviews, and literature study. The results indicate that the developed information system is able to support structured asset management, where the Admin can manage users, asset transactions, and monitor employee data, while Users (employees) are able to manage their personal profiles independently. The results of black box testing confirm that all main system functionalities operate properly in accordance with the specified requirements, with no significant functional errors identified. Therefore, the system is considered feasible for implementation and is expected to improve efficiency, accuracy, and transparency in managing work equipment inventory assets within the Pustekinfo DPR RI environment.
Rancang Bangun Sistem Pematangan Buah Pisang Berbasis Internet Of Things (IOT)
The agricultural sector plays an important role in meeting the national food needs, particularly in the production of fruit. However, the storage and marinating of agricultural products still traditionally uses chemicals which may impair crop quality and pose risks to health and the environment. The development of Internet of Things (IoT) technologies offers more secure, efficient and controlled solutions through automated and real-time monitoring systems. The aim of this research is to design and build an Internet of Things-based banana ripening system capable of improving the safety, efficiency and quality of the ripening process. The research method used shall be an experimental method, testing all components of the system. The system is built on a microcontroller ESP32-CAM, a colour sensor for the detection of ripeness based on colour intensity, a DHT22 sensor for monitoring water temperature and humidity, a vibrating alarm for the environmental conditions and a Telegram application for real-time notifications. The test results showed that the colour sensor could distinguish between the condition of immature and mature fruit, indicating the inactive warning when green and active warning when yellow, and the notification sent to the user. The DHT22 sensor registers the average temperature and humidity between 27.50 and 33.20 degrees Celsius and if it exceeds this value, the cooling system is activated and a warning is sent. If network conditions are slow, there are delays in the delivery of notifications of around ±2-5 seconds. In conclusion, the developed maturity system, which is based on the Internet of Things, is capable of working automatically, securely and efficiently to monitor and control the maturity process in real time, with the potential to improve the quality and the value of agricultural products.
Penerapan Game Peresean Sebagai Media Untuk Meningkatkan Apresiasi Budaya Sasak Pada Generasi Muda
Peresean is a traditional custom of the Lombok Sasak Tribe, which is armed with rattan (jalin), using a shield (ende) made from cow skin and when the peresean event takes place it will be accompanied by the typical music of the Lombok Sasak Tribe, namely gendang beleq. The clothing worn by peresean participants (pepadu) is a headband (sapuq), sarong (selewoq), cloth belt (bebet) and bare chest. The peresean culture is now starting to be held less frequently and is not very popular with Lombok youth in particular. This is because today's youth prefer digital games that are more varied and do not require a lot of stamina. So from there it became a reference for making a fighting type peresean game using the GDLC method as a means to preserve the Sasak tribe's peresean culture. adjust regulations according to the target audience. The final result of this production is a fighting game that runs on a PC (personal computer), and is targeted at all ages. The features that have been designed have fulfilled what was desired. In this fighting type research game, there is information and moral messages about preserving local culture. It is hoped that this peresean game application can become a medium of introduction and education for the wider community, especially the people of Lombok, about the peresean culture of the Lombok Sasak Tribe.
Aplikasi IoT Untuk Monitoring Kualitas Air Tawar Pada Pemeliharaan Ikan Dengan Metdoe Fuzzy Logic Berbasis Aduino
Freshwater fish farming requires a lot of attention. Water conditions are the main consideration when raising fish. Water conditions, including pH, ammonia, and temperature, will affect fish development. Monitoring water conditions is not always possible visually and on-site. Therefore, a reader connected to the internet is needed so we can read the exact values ​​and monitor anywhere and anytime. This is where the Internet of Things (IoT) plays a crucial role in facilitating freshwater fish farming monitoring. Applications are also used to display data from the existing Internet of Things (IoT). The presence of the Internet of Things (IoT) will increase efficiency in fish farming and facilitate monitoring. The Mamdani Fuzzy Logic method will be implemented and embedded in the ESP32 as a microcontroller. The results of this research are an Internet of Things (IoT) tool along with a website as a data monitoring page. The tool will be tested using Mean Absolute Percentage Error (MAPE) compared to the results of calculations in Matlab. The results showed only a small error between the tool readings and Matlab with an accuracy level of 99%. In conclusion, this tool can help the efficiency of water quality monitoring in fish farming with a reading accuracy and fuzzy logic processing rate of 99%.
Optimasi Mobilenetv2 Dengan Transfer Learning Untuk Klasifikasi Penyakit Daun Cabai
This research effort seeks to establish a robust classification model for chili leaf diseases through optimization of the MobileNetV2 architecture using transfer learning methodology. The presence of diseases in chili plants is often a major barrier to agricultural productivity, necessitating the development of a rapid and accurate early detection system. The dataset used for this investigation includes six different leaf condition categories, specifically: Bacterial Spot, Cercospora Leaf Spot, Leaf Curl Virus, Healthy Leaf, Nutrient Deficiency, and White Spot. The investigation process begins with an image pre-processing phase and the application of data augmentation techniques, which aim to increase the variability of the training data while simultaneously reducing the risk of overfitting. Next, the model is trained using pre-trained weights from ImageNet, which are adjusted to align with the inherent visual characteristics of chili leaves. Model evaluation is conducted rigorously based on accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate outstanding performance, with the model achieving an accuracy rate of 99%, an average F1-score of 0.99, and a validation loss of 0.07. These figures demonstrate the model's highly competent generalization ability when applied to new data. Analysis facilitated by a confusion matrix found a very low error rate, with only 11 images (0.73%) misclassified out of a total of 1,500 test images. These results support the assertion that MobileNetV2 optimization is highly efficient and accurate in identifying chili leaf diseases. This model has significant potential for integration into mobile devices or digital image-based smart farming systems, thereby assisting farmers in making informed decisions in real time.
Penerapan Metode SWARA–COPRAS untuk Rekomendasi Pemilihan Jurusan Mahasiswa Baru di Universitas Semarang
Choosing a college major is an important decision for prospective students as it affects academic performance and future career planning. However, in practice, many prospective students still experience difficulties in selecting a major that matches their interests, abilities, and preferences. Therefore, this study aims to apply the Simple Weighting Assessment Ratio Analysis (SWARA) and Complex Proportional Assessment (COPRAS) methods in developing a decision support system for college major selection. The SWARA method is used to determine criterion weights based on their level of importance, while the COPRAS method is applied to calculate utility values and rank alternative majors. The criteria used include academic ability, interests and experience, profile suitability, cost, and accreditation, with alternative majors consisting of Informatics Engineering, Information Systems, Civil Engineering, and Electrical Engineering. Data were collected from several respondents and processed according to the stages of the applied methods. The results show that each respondent receives different major recommendations based on the COPRAS ranking results. For respondent 1, alternatives A1 and A2 ranked first with a utility value of 100%, while other respondents obtained different top alternatives according to their respective preferences. Validation using Spearman’s Rank Correlation indicates strong to very strong relationships between system recommendations and respondents’ preferences, with one example correlation coefficient of 0.95. Based on these results, the developed decision support system is expected to assist prospective students in selecting college majors in a more structured and objective manner.
Analisis Sentimen Restoran Ulasan Pelanggan Dengan Menggunakan Algoritme Naive Bayes Untuk Meningkatkan Kualitas Pelayanan Restoran “Warung Pedes Gemes”
In the digital era, customer reviews play an important role in shaping a restaurant’s reputation and assessing the quality of its services. Feedback provided by customers through reviews can be utilized as a valuable source of information for service improvement. This study aims to analyze customer sentiment toward Warung Pedes Gemes restaurant using a sentiment analysis approach based on the Naive Bayes algorithm. Review data were collected through questionnaires and processed through several stages, including data preprocessing, weighting using Term Frequency–Inverse Document Frequency (TF-IDF), sentiment classification, and model evaluation using a confusion matrix. The results indicate that the majority of customer reviews were classified as positive, with 543 positive reviews and 22 negative reviews identified. The performance evaluation of the Naive Bayes model shows an accuracy, precision, recall, and F1-score of 100%. These findings demonstrate the effectiveness of the Naive Bayes algorithm in accurately classifying customer sentiment. Therefore, this study contributes to the application of data-driven sentiment analysis as a supporting tool for improving service quality in the culinary industry.
Sistem Rekomendasi Beasiswa Prestasi Akademik Berdasarkan Nilai Rata-Rata Dan Indeks Prestasi Kumulatif Menggunakan Logika Fuzz
Scholarships play a crucial role in enhancing student motivation and academic achievement. However, the selection process often faces challenges in objectively determining eligibility. This study aims to develop an Artificial Intelligence-based scholarship recommendation system using a fuzzy logic approach to address uncertainty and variation in academic grades. The main contribution of this research is improving the objectivity and adaptability of the scholarship selection system through a fuzzy logic approach capable of assessing eligibility in a gradual and proportional manner. Data were collected from various publicly published scholarship agencies, including GPA, grade point average, and other academic requirements. The method used involves fuzzification to convert numerical data into fuzzy sets, applying fuzzy rules in the inference process, and defuzzification to generate consistently interpretable recommendation scores. The system was built using Python on the Google Colab platform and evaluated through white-box testing to ensure all internal logic flows work as designed. The results show that the fuzzy approach can provide more adaptive and objective recommendations compared to fixed threshold-based selection methods. The practical benefit of this system is to provide scholarship institutions with a transparent, efficient decision support tool that reduces subjectivity in the selection process.
Analisis Tingkat Kesadaran Keamanan dan Privasi Data di Kalangan Civitas Akademika Menggunakan Model Knowledge-Attitude-Practice
The rapid digital transformation in higher education increases the risk of data breaches and information security threats. This study analyzes the level of knowledge, attitude, and practice (KAP) regarding data security and privacy among academic community members at Universitas XYZ. A quantitative survey design was employed using a 30-item KAP instrument on a Likert scale, administered in a pilot study to 112 respondents consisting of students, lecturers, and administrative staff. Descriptive analysis shows that knowledge is at a good level (mean = 34.98/50), attitude is very good (mean = 37.39/50), while practice is only at a fair level (mean = 31.30/50). These results indicate that most respondents understand basic concepts of data security and privacy, and have a positive attitude toward protection of personal data, but have not consistently implemented secure digital behaviors in daily activities, such as password rotation, activation of two-factor authentication, and verification of digital signatures. The main finding reveals a significant gap between high attitudes and relatively low security practices, reflecting a privacy paradox in the campus context. The pilot study provides a strong basis to refine the KAP instrument and design a full-scale study to further explore factors influencing digital security behavior in higher education environments