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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Pengenalan Bahasa Isyarat Indonesia Dengan Algoritma YOLOv8 Berbasis Mobile
Indonesian Sign Language (BISINDO) is the primary means of communication for deaf people in Indonesia, but the general public's understanding of BISINDO is still limited, thus hampering inclusive social interaction. To overcome this obstacle, the development of an artificial intelligence-based BISINDO detection system is a promising solution. One of the latest approaches is the utilization of the YOLOv8 algorithm, which is known to have advantages in real-time object detection with high accuracy and better model efficiency compared to previous versions. The BISINDO detection system using YOLOv8 is trained with image and video datasets of Hand gestures, so that it is able to recognize various BISINDO gestures in various lighting conditions and backgrounds. The main challenge in developing this system is the limited variety of datasets and image quality, so that more diverse data collection and optimization of model parameters are needed. Integration of supporting Augmented Reality (AR) and Transfer Learning technologies also has the potential to improve the learning experience and detection accuracy. Thus, the BISINDO detection system based on YOLOv8 is expected to expand communication access, increase public awareness of BISINDO, and support the realization of a more friendly and inclusive social environment for deaf people in Indonesia
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Analisis Kinerja dan Keamanan Protokol PPTP dan L2TP/IPSec VPN pada Jaringan MikroTik
Virtual Private Network (VPN) has become a critical solution for ensuring secure communication over public networks. This research conducts a comprehensive analysis of the performance and security of two major VPN protocols: Point-to-Point Tunneling Protocol (PPTP) and Layer 2 Tunneling Protocol with Internet Protocol Security (L2TP/IPSec) implemented on MikroTik devices. The research methodology uses an experimental approach by testing throughput, latency, packet loss, and CPU utilization in various network scenarios. The results show that PPTP provides higher throughput with minimal overhead, but has significant weaknesses in security aspects. Conversely, L2TP/IPSec offers superior security levels with strong encryption, although it results in higher latency and lower throughput. These findings provide important insights for network administrators in choosing VPN protocols that suit organizational needs, considering the trade-off between performance and security
Sistem Rekomendasi Resep Masakan Menggunakan Metode Content Based Filtering Berdasarkan Preferensi Pengguna
Food is a fundamental human need that plays a crucial role in supporting health. For housewives, cooking is a daily routine in preparing nutritious meals for their families. In the digital era, technology simplifies recipe searching, but the abundance of available recipes often makes it difficult for users to find ones that match their available ingredients. To address this issue, this study develops a recipe recommendation system based on content-based filtering. The dataset was collected from cookpad.com and includes recipe names, ingredients, cooking duration, and cooking instructions. The system is designed using a content-based filtering approach and cosine similarity to measure the relevance between user input and existing recipe data. Users simply input ingredients, seasonings, or types of dishes they have, and the system provides relevant recommendations. Testing results show that the system can accurately identify and recommend recipes based on keywords, including specific cases such as "Soto Ayam Bening." The recommendations generated are reasonably accurate and assist users in selecting the most suitable recipes according to their needs. This system is expected to offer a practical solution for users in determining appropriate meal choices based on the ingredients available at home
Analisis Protokol Keamanan Jaringan dalam Era Internet of Things (IoT)
This study explores the challenges and solutions related to information system security in the Internet of Things (IoT) era, focusing on smart homes and industrial settings. Using a qualitative approach through a comprehensive literature review, this research analyzes academic sources including journals, technical reports, and research publications. Findings show that cyber threats such as malware, ransomware, and denial of service (DoS) attacks significantly affect IoT system performance and reliability. Key issues include the absence of standardized security measures, limited device interoperability, and low user awareness. The study contributes by mapping critical security concerns in IoT ecosystems and offering strategic recommendations such as enhancing security protocols, developing data protection policies, and promoting user education. This research aims to support the development of secure, adaptive, and sustainable IoT systems in an increasingly connected digital world
Analisis Sentimen Ulasan Pengguna Alikasi Traveloka Pada Google Play Store Menggunakan Algoritma Naive Bayes
The advancement of the digital era has driven increased usage of online reservation applications, including Traveloka. The abundance of user feedback available on the Google Play Store platform has the potential to become a valuable database for development teams in improving service quality. However, the characteristics of unstructured and spontaneous reviews pose challenges in conventional data processing.This research aims to explore sentiment in Traveloka application user comments using the Multinomial Naïve Bayes algorithm. The dataset used consists of 1,500 review samples obtained through web scraping techniques from the Google Play Store. The research methodology includes several data preprocessing stages, including data cleaning, case normalization, word tokenization (tokenizing), stopword removal, and word stemming to their base forms (stemming). Subsequent processes include data categorization, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) approach, and building a classification model with the Multinomial Naïve Bayes algorithm.Test results show that the model is capable of classifying sentiment with an accuracy rate of 79%. The model demonstrates high recall values in identifying negative reviews (0.97), but the recall value for positive reviews remains limited (0.64). This indicates that the model has higher sensitivity to negative expressions. From a total of 1,500 review data, there were 461 positive reviews and 543 negative reviews that were successfully categorized clearly.The findings in this study prove that the implementation of the Multinomial Naïve Bayes algorithm is quite efficient in sentiment classification of user reviews, and is capable of providing strategic insights that can be utilized by development teams to improve application service quality
Penerapan Metode Naïve Bayes Untuk Mendeteksi Secara Dini Stunting Pada Balita
This study focuses on early detection of stunting in toddlers at integrated health service posts (posyandu) in the Paninggilan Utara Ciledug Tangerang area using the Naive Bayes method. Stunting is a chronic nutritional problem that arises due to prolonged malnutrition. Early detection of stunting in toddlers is very important because it can have an impact on the growth and development of toddlers in the long term. The study used a dataset of 250 toddler records collected randomly from 17 posyandus in the Paninggilan Utara Ciledug Tangerang area. Toddlers were divided into three age groups: Group A (0-11 months), Group B (12-35 months), and Group C (36-59 months). The Naive Bayes method, which is a statistical classification technique, is used to detect early the possibility of stunting in infants under five years old. So that earlier medical action can be given to overcome the stunting condition. The results of the study showed that the Naive Bayes method obtained a higher accuracy of 94.80% compared to the K-Nearest Neighbor (KNN) method which had an accuracy of 85.80%. The high accuracy and speed of the Naive Bayes method make it a suitable tool for screening and early detection of stunting in toddlers
Sistem Rekomendasi Peminjaman Buku Menggunakan Metode Vector Space Model Berbasis Pembobotan TF-IDF dan FastText (Studi Kasus Perpustakaan SMP Negeri 1 Kartasura)
Libraries play an important role in supporting the learning process at school. However, in its utilization, students often find it difficult to find books that suit their needs and interests due to the non-optimal management of the book collection. This problem also occurs at SMP Negeri 1 Kartasura, where there is no system that can generate book recommendations automatically and relevantly. The purpose of this research is to build a Content-Based Filtering-based book recommendation system with the Vector Space Model method which is further developed by combining TF-IDF weights and word vector representation from FastText, resulting in the TF-IDF Weighted with FastText technique. TF-IDF is used to determine the importance of a word in a book description, while FastText is used to capture the semantic meaning of the word in more depth. The system is implemented using book collection data in SMP Negeri 1 Kartasura Library, with input in the form of keywords from users. The document vector representation resulting from the combination of TF-IDF and FastText is then calculated using cosine similarity to determine the most relevant book. Evaluation was then conducted using the Precision@10 metric against ten different keywords, and the system produced an average Precision@10 of 0.93, indicating that 93% of the recommendations provided by this system are relevant to the user's keywords.
Penerapan Metode Weighted Product dalam Sistem Pendukung Keputusan untuk Rekomendasi Tempat Gym Fitness Center Terbaik
This study aims to design and develop a decision support system (DSS) based on the Weighted Product (WP) method to assist the public in selecting the best fitness center in Yogyakarta City. The wide range of available gyms, each offering varying facilities, pricing, services, and programs, presents a challenge for potential users in identifying options that align with their preferences. The proposed system utilizes five key criteria—facilities, membership fees, availability of personal trainers, distance, and user ratings—to generate objective recommendations. An initial trial conducted in Soropadan Village revealed that BlackBox Gym achieved the highest score of 0.040798, followed by The Fit Lab with a score of 0.03887 and Wzone Gym Studio – Jakal with a score of 0.036909. Further testing was performed across 30 sample points, each representing a sub-district within Yogyakarta City. The results indicated that MissFit Studio Gym ranked highest, achieving a preference score of 0.048897. The system demonstrated an accuracy rate of 97.56% when compared to manual calculations. These findings confirm that the application of the WP method is both effective and accurate in producing precise and consistent recommendations, thereby validating its reliability as a decision-making tool in selecting a fitness center.
Pemanfaatan Teknologi Wireless dan Mobile Network Berbasis 5G Untuk Pemerataan Akses Jaringan di Indonesia
This study discusses the utilization of wireless and mobile network technologies based on 5G as a strategic solution to promote equitable access to network connectivity in Indonesia. Given Indonesia's vast and diverse geographical context, 5G offers advantages in terms of high speed, low latency, and spectral efficiency, enabling more inclusive connectivity, particularly in remote and underserved areas. The study also examines the application of supporting technologies such as Wireless Sensor Networks (WSN) and Reconfigurable Intelligent Surfaces (RIS) to expand signal coverage and enhance network quality. The research employs a descriptive-qualitative approach with secondary data analysis derived from various reputable scientific publications, official reports from BAKTI Kominfo, and relevant national regulatory policies. The data were analyzed thematically and evaluatively to identify the potential, challenges, and implementation strategies of 5G in addressing digital access disparity. The results indicate that the integration of 5G technology with WSN and RIS has a positive impact on improving digital access across sectors such as education, healthcare, economy, and governance. Nonetheless, challenges such as limited infrastructure, suboptimal regulations, and digital literacy gaps remain significant barriers. Therefore, multisectoral collaboration is needed in infrastructure development, policy reform, and community empowerment to achieve inclusive and sustainable network accessibility.
Implementasi Internet Of Things (IOT) Untuk Monitoring Kualitas Tanah Tanaman Herbal Dengan Integrasi QR Code
In everyday life, the utilization of herbal plants is still relatively low, due to ignorance of the types and benefits of herbal plants and many plants that wither or die. The factors of plant fertility such as soil moisture and soil temperature. Therefore, this research aims to create an alternative prototype for monitoring the soil quality of herbal plants by utilizing IoT (Internet of Things) technology with the integration of a QR Code as a marker of herbal plant identity at each monitoring location. The research method used is Research and Development (R&D). This research uses ESP32 as a microcontroller to drive electronic devices and soil PH sensors, soil moisture, soil temperature and air temperature and humidity connected to IoT devices. Soil quality data, such as moisture, temperature, and pH levels are collected in realtime and converted into QR codes. This data is then sent to the server for processing and analysis, providing useful information for plant owners to effectively manage soil quality. The results showed that the soil pH, soil moisture, soil temperature and air temperature and humidity measuring instruments can work well and have an accuracy rate of 90% and the data is successfully displayed to the webserver.
Identifikasi Penyakit Daun Durian Menggunakan Penerapan Algoritma Residual Network (RESNET-50)
Durian is one of Indonesia’s leading horticultural commodities, but its productivity can decline due to leaf diseases that are difficult for farmers to identify visually. This study aims to develop an automated durian leaf disease classification system using a deep learning algorithm based on the ResNet-50 architecture. The dataset consists of 420 durian leaf images classified into four categories: Algal Leaf Spot, Leaf Blight, Leaf Spot, and No Disease, collected from the Roboflow platform. Preprocessing steps included annotation, augmentation, and resizing the images to 240x240 pixels.The model was trained using TensorFlow with pretrained ImageNet weights. Three data split scenarios (70:20:10, 75:15:10, and 80:10:10) were applied using both binary and multiclass classification approaches. Model performance was evaluated using confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The best binary classification result achieved 99.8% accuracy and 99.9% F1-score, while the best multiclass result achieved 99.6% accuracy and 96.9% macro F1-score. These results demonstrate that ResNet-50 is effective in accurately detecting durian leaf diseases and can be implemented in mobile applications to assist farmers in early diagnosis and improving crop productivity.
Komparasi Naive Bayes dan Gradient Boosting Machine untuk Klasifikasi Sentimen Publik terkait Kenaikan Harga Pertalite di Media Sosial Twitter
The increase in the price of pertalite fuel in Indonesia has caused various reactions from the public, which are widely expressed through social media such as twitter. Fuel oil (BBM) is one of the basic needs that is very important for the Indonesian people because it plays a major role in supporting various daily activities. BBM is used not only as fuel for motor vehicles, but also as a source of energy for various industrial equipment. This study aims to classify public sentiment towards the issue using the naive bayes algorithm and Gradient Boosting Machine (GBM) as a classification method. The data used in this study were obtained from the Kaggle platform, which contains a collection of tweets related to the issue of fuel prices, especially pertalite. The analysis process begins with text preprocessing, such as data cleaning, tokenization, stopword removal, and stemming. The data already has a sentiment label (positive, negative, neutral) and is divided for model training and testing. The evaluation results show that the GBM algorithm is able to classify sentiment with an accuracy rate of 60% while the Naive Bayes algorithm has an accuracy rate of 90%. These results prove that naive bayes has a higher level of accuracy than the GBM algorithm, so it can be used in processing text data from social media to understand public opinion on government policies, especially regarding fuel price increases.
Anti - Klon Pendekatan Ringan untuk Mendeteksi Serangan Kloning RFID
This study develops an RFID tag cloning detection system based on edge computing to enhance security in educational environments by integrating three core approaches: Lightweight AES-128 encryption with automatic key rotation every 3 minutes, multidimensional signal fingerprinting analysis (RSSI, phase, and Doppler shift), and ensemble learning-based classification using the k-NN algorithm (k = 5) with dimensionality reduction via PCA. The system is implemented using a Raspberry Pi 4 as the main processor and an Arduino Nano for tag interfacing, supported by an Impinj R420 reader and RTL-SDR v3 for signal acquisition. The dataset consists of 1,200 RFID tag samples (800 genuine and 400 cloned) collected at Universitas Indonesia, with cloned tags generated using Proxmark3 and ChameleonMini. Experimental results show that the system achieves a detection accuracy of 96.7%, validated through 10-fold cross-validation, with a response time of 8.2 ms per tag and power consumption of only 34.6 mW. A Kappa coefficient (κ) of 0.82 indicates high classification consistency. In real-world implementation, the system successfully reduced cloning cases from 15 to just 2 per month. The system outperforms conventional methods such as EPC Gen2, which offers only 78.2% accuracy, and AES-256, which has significantly higher power consumption (62 mW). Its advantages lie in real-time operation with low power consumption, enabled by an edge computing architecture that eliminates the need for an external server. The findings demonstrate the effectiveness of the system as a reliable and practical RFID cloning detection solution suitable for resource-constrained educational environments.
Penerapan Model CRISP-DM Untuk Klasifikasi Tumor Otak Menggunakan Algoritma CNN
In general, a tumor is an abnormal tissue growth in the body that occurs due to uncontrolled cell division. Tumors can be benign (non-cancerous) or malignant (cancerous). Benign tumors tend to grow locally without spreading to other tissues, while malignant tumors can invade surrounding tissues and spread to other parts of the body through the bloodstream. A brain tumor is a mass or growth of abnormal tissue in or around the brain. Specifically, brain tumors can be primary (originating from the brain cells themselves or surrounding tissues, such as the meninges, pituitary gland, or cranial nerves) or secondary (metastasis from cancer originating from other parts of the body). The main problem today is that brain tumors have many types, therefore research on the classification of brain tumor types is very valuable today. In order to improve accuracy and also speed up the diagnosis process. The results of this study, brain tumor image data classification has been carried out using the CNN Algorithm method with the CRISP-DM data mining technique. The dataset consists of 3,554 datasets, but from the results of testing this algorithm has good performance with an accuracy of 98% with Evaluation and validation using the Confusion Matrix test parameters. and successfully classified human brains affected by tumors and those not affected by tumors, in addition to being able to classify 3 different types of tumors, namely glioma, meningioma, and pituitary.
Sistem Pemantauan Tekanan Air Berbasis IoT Dengan Integrasi Penyimpanan Data Online
Unstable water pressure in the household environment can disrupt daily activities such as bathing, washing, and cooking, and also affect comfort and the availability of clean water. To addres this issue, a system capable of monitoring water pressure in real-time is needed. This research, conducted at PAMSIMAS Dukuh Tawang, aims to design and develop an IoT-based water pressure monitoring system that can present water pressure data online. The system displays real-time water pressure graphs and provides a power ON/OFF feature accessible via an IoT platform. The development process follows the Prototyping method, which includes stages form needs identification to system evaluation. The research results are expected to show that the system performs well in monitoring water pressure in real-time with good accuracy and provides users with informative graphical displays. The implementation of this system is considered at affective in supporting the management of clean water distribution at the household level and contributes to helping the community anticipate water distribution disruptions and improve the quality of access to a more stable and monitored water supply.
Penerapan Algoritma Apriori Dalam Mengidentifikasi Pola Perilaku Belanja Konsumen
The advancement of digital technology has brought significant changes to data management in the commercial sector, especially in retail stores. This study aims to apply the Apriori algorithm to identify patterns of consumer shopping behavior at D&D Mart, Jerowaru District, East Lombok, Indonesia. Data were collected through observation and interviews with customers. Data processing was conducted using Google Colaboratory to obtain visualizations and calculate association rules. The results show that the combination of coffee, detergent, and sugar products has a support value of 31% and a confidence level of 100%, indicating a very strong purchasing pattern for these products to occur together. This finding provides valuable insights for store owners in developing marketing strategies, such as product placement for items frequently purchased together, bundled promotional offers, and more optimal stock management. Overall, this study demonstrates that the Apriori algorithm is effective for analyzing sales transaction data in retail stores and can support data-driven business decision-making to enhance operational effectiveness and customer satisfaction.
Prediksi Kepuasan Pelanggan Berdasarkan Ulasan Produk di Lazada Indonesia Menggunakan Algoritma Decision Tree C4.5
The growth of e-commerce in Indonesia continues to rise with the increasing use of internet and digital technology. Lazada, one of the largest e-commerce platforms in the country, offers a product review system that reflects customer satisfaction. However, the large volume of reviews makes it difficult for sellers to manually evaluate satisfaction levels. This study aims to predict customer satisfaction based on product reviews on Lazada Indonesia using the C4.5 Decision Tree algorithm. The research is quantitative with a data mining approach. Review data was obtained from Kaggle and went through preprocessing, exploration, model building, and testing stages. The attributes used include product name, category, price, number of reviews, and average rating, which were classified into satisfaction levels. The model was evaluated using accuracy, precision, recall, and F1-score. Results show that the C4.5 algorithm can predict customer satisfaction with 77.85% accuracy, 76.74% precision, 83.54% recall, and an F1-score of 80.00%. Product price is the most influential attribute in determining satisfaction. These findings are expected to help sellers understand the key factors influencing customer satisfaction and support improvements in service and product quality on e-commerce platforms.
Klasifikasi Motif Batik Nusantara Menggunakan Vision Transformer (ViT) Berbasis Deep Learning
Batik is a cultural heritage of Indonesia that reflects local philosophies and identities through its diverse motifs. In the digital era, automatic classification of batik patterns plays a crucial role in cultural preservation, education, and commercialization. This study aims to develop a batik motif classification system using Vision Transformer (ViT), a deep learning architecture based on self-attention capable of capturing global spatial relationships in images. The dataset comprises 800 images spanning 20 batik motif classes from various regions, divided into training and testing subsets. The ViT model was fine-tuned using pretrained weights from ImageNet-21k, with standard preprocessing and data augmentation applied to the training set. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and prediction visualization. Results indicate that ViT achieved an overall accuracy of 96%, with most classes recording F1-scores above 0.90. Evaluation on unseen batik images demonstrated excellent generalization capability, achieving 99.94% confidence in prediction. These findings suggest that ViT is an effective and efficient architecture for batik motif classification and offers valuable contributions to cultural preservation through artificial intelligence.
Implementasi SIKADES Berbasis Web untuk Mempermudah Layanan Administrasi Desa
Information technology has become a very important tool in improving the efficiency and effectiveness of administrative processes in various organizations, both in the public and private sectors. This study aims to design a web-based population information system and village data administration (SIKADES) using Visual Studio Code and to test the feasibility of the Web. This study uses the Waterfall development method which is one of the methods in the Software Development Live Cycle (SDLC). The development stages include requirement analysis, design, development, and testing. There are 2 instruments used in the study. Observation sheets were given to 2 validators to test the reliability of SIKADES and the feasibility test questionnaire was given to 27 village officials to test user responses to the effectiveness of SIKADES. The results obtained in this study are: (1) The score of all reliability indicators has an average of 4.2 indicating that SIKADES is feasible to use and (2) The score of all effectiveness questionnaire indicators has an average of 4.4 indicating that SIKADES is effective (3) The average user satisfaction score from several indicators is 75% -78% with a category of satisfied to use. Another finding in this study is that SIKADES security still needs to be improved.
Penerapan Metode Content-Based Filtering Pada Sistem Rekomendasi Pemilihan Produk Obat Studi Kasus : Apotek Hero Farma
Selecting the right drug product is an important aspect in pharmaceutical services, especially for customers who do not yet have a deep understanding of the content and function of each drug. The limited number of pharmacists at Hero Farma Pharmacy often causes the service process to be inefficient, especially when still relying on manual recommendation methods that take a long time. This study aims to design a recommendation system that can assist in drug selection by implementing the content-based filtering method. This system is built by processing product attributes such as drug name, category, indication, dosage form, and price to form a profile of each product. The level of similarity between products is then calculated using the TF-IDF and Cosine Similarity methods. The data used in this study were obtained from Hero Farma Pharmacy located in Surakarta, with samples of 10 different types of drugs. The implementation results show that the system can produce recommendations with a good level of accuracy, where Promag drug obtained the highest Cosine Similarity value of 43.03% based on the query entered. This system successfully provides drug recommendations that have similar characteristics based on user needs and can help customers in making decisions correctly and improve the work efficiency of pharmacy staff.
Penerapan Kecerdasan Buatan Untuk Menentukan Gejala Utama Polycystic Ovary Syndrom (PCOS) Berdasarkan Data Klinis Pasien
Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age, often leading to menstrual irregularities, infertility, and metabolic issues. Diagnosing PCOS remains challenging due to the wide range of symptoms and varying patient responses to treatment. Therefore, this study aims to apply artificial intelligence (AI) to identify key symptoms contributing to PCOS based on patients' clinical data.This study employs a machine learning approach, with the Support Vector Machine (SVM) algorithm as the primary method for classifying patients with and without PCOS. The data used was sourced from patient medical records, which included clinical data parameters obtained from the Kaggle website, with a total of 541 patient data samples. The research stages include data collection and preprocessing, selection of main features using feature selection technique, model training with SVM algorithm. The AI model developed produces 10 main features that affect the diagnosis with an accuracy value of 90.74% which shows that the model has the ability to classify PCOS and non-PCOS sufferers. In addition, the matrix shows a balance between the matrix values for precision 87.5%, recall 82.35% and F1 score 84.85%. The results of this study are expected to contribute to the medical field, especially in supporting faster and more accurate early diagnosis and personalization of PCOS treatment.
Penentuan Mahasiswa Terbaik Dengan Menggunakan Metode Topsis (Teknik Urutan Preferensi Berdasarkan Kesamaan dengan Solusi Ideal)
Determining the best students is a crucial process in higher education to provide recognition and motivation to outstanding students. However, manual selection processes are often subjective and time-consuming. To address this issue, this study proposes the application of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method in identifying the best students. TOPSIS was chosen due to its ability to handle multi-criteria decision-making problems by comparing alternatives based on their proximity to the positive ideal solution and distance from the negative ideal solution. This research was conducted on Communication Science students in the Digital Communication Visual class at Bina Sarana Informatika University, utilizing evaluation criteria such as Cumulative Achievement Index (IPK), number of scientific papers, non-academic achievements, and TOEFL scores. The collected data were analyzed using the steps in the TOPSIS method, including normalization of the decision matrix, weighting, calculation of distances to the positive and negative ideal solutions, and computation of preference values for each alternative. The benefit of using TOPSIS is that it considers multiple criteria simultaneously (such as GPA, research, achievements, TOEFL, attendance, etc.), ensuring that assessments are not solely based on a single dominant factor like GPA. The results indicate that Student E achieved the highest preference value of 0.462, ranking them as the top-performing student. The application of the TOPSIS method in this study proved effective in providing objective, efficient, and transparent results in the selection process of outstanding students. Therefore, this method is recommended for implementation in the selection process of outstanding students in higher education institutions
Sistem Rekomendasi Pemilihan Laptop Menggunakan Metode Content-Based Filtering Berbasis Spesifikasi Produk
The advancement of information technology has led to an increasing demand for computing devices, particularly laptops. The wide variety of options based on specifications, brands, and prices often makes it difficult for consumers to choose the device that best suits their needs. Therefore, this study aims to design a laptop recommendation system using the Content-Based Filtering (CBF) approach to provide relevant suggestions based on user preferences. The developed system applies Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity methods to measure the similarity between key laptop features such as processor type, RAM capacity, storage, and graphics card. Laptop data was obtained through a web scraping process from trusted e-commerce websites and integrated into a web-based platform. Testing results show that the system can automatically generate personalized and highly relevant recommendations. The main contribution of this study is the development of an efficient content-based laptop recommendation system utilizing a combination of TF-IDF and Cosine Similarity techniques. In addition to facilitating faster decision-making in selecting a laptop, the system also demonstrates the practical application of machine learning-based recommendation technologies in the e-commerce sector. This research further provides a foundation for developing similar recommendation systems in other contexts that require content-based personalization approaches.
Pengembangan Model AI Menggunakan Algoritma Intensity Of Character (IoC) dan Reduced Support Vector Machine (RSVM) untuk Transliterasi Citra Aksara Sasak
The limited human resources who are able to read Sasak script palm leaves are the main motivation for developing a transliteration tool for Sasak script images on palm leaves. By using the Reduced Support Vector Machine or RSVM algorithm as one of the classification methods, transliteration efforts can be facilitated with maximum results. The principle of the RSVM method in classifying objects by separating two different classes using a hyperplane has been proven to be able to produce maximum accuracy performance in this study. The research data in the form of Sasak script images resulting from the palm leaf image segmentation process that has been divided into 18 classes. The feature extraction algorithm used is Intensity of Character (IoC) with window sizes of 3x3, 4x4, and 5x5 and 3-Fold, 5-Fold, 7-Fold data Imbalance. The test results at the RSVM classification stage using the Linear Kernel, Polynomial Kernel, Radial Basic Function (RBF) and One against One modeling on the 18 classes tested, where each class contains 20 handwritten Jejawen Sasak script image data on palm leaves, were recorded to produce the highest accuracy, which was 93.6%.
Integrasi Sistem Informasi Geografis Pada Sistem Pengaduan Masyarakat Berbasis Web
Ngabenrejo Village, located in Grobogan Subdistrict, Grobogan Regency, Central Java Province, faces various public issues such as damaged roads, garbage accumulation, flooding, and more. The management of public complaints is still conducted manually, where residents must submit written reports to the village officials. This method often results in unrecorded complaints, difficulty in follow-up, and the risk of reports being overlooked due to the absence of a structured monitoring system. This research aims to design and develop a web-based public complaint system integrated with a Geographic Information System (GIS), referred to in Indonesian as Sistem Informasi Geografis (SIG). The system development method used is prototyping, supported by observation, interviews, and literature studies. The outcome of this research is a web-based complaint system that allows residents to submit complaints online along with the geographical location of the reported issue. Key features include complaint location mapping, email notifications to the complainants, and a dashboard for village officials to manage reports. This system is expected to improve the efficiency of public services and enhance transparency in handling community complaints, as each report is properly recorded, easily monitored, and can be systematically followed up.
Sistem Peminjaman Alat Laboratorium Berbasis RFID dan IoT di Fakultas Kedokteran Gigi UMS
Laboratories play a crucial role in supporting academic activities at health education institutions. However, inventory management at the Laboratory of the Faculty of Dentistry, Universitas Muhammadiyah Surakarta (UMS), is still conducted manually, leading to recording errors, service delays, and difficulties in real-time equipment monitoring. These issues hinder students from accessing information about the availability and location of equipment needed during practical sessions. This study aims to develop a laboratory equipment loan administration system using Radio Frequency Identification (RFID) technology integrated with the Internet of Things (IoT). The system was developed using the prototyping method, utilizing the ESP8266 and RFID RC522 sensor as the main components to support automated, contactless identification of equipment. The developed system is capable of digitally recording and monitoring the borrowing and returning of equipment in a structured and real-time manner. Testing results show that the system functions properly and meets user needs, although it has a limitation in RFID reading distance, with a maximum range of only 3 cm. This constraint presents a challenge for field implementation. Overall, the system has proven to improve efficiency, accuracy, and transparency in laboratory inventory management and supports the digitalization of technology-based equipment loan processes.
Implementasi Logika Fuzzy Mamdani Dalam Klasifikasi Kategori Berat Badan Berbasis IMT
Body Mass Index (BMI) is a common method used to classify body weight based on the ratio of weight to height. However, its accuracy is often questioned because it does not account for age and gender, which also influence body composition. This study implements the Mamdani fuzzy logic approach to classify body weight based on BMI while considering age and gender. The system utilizes fuzzy membership functions to dynamically determine categories such as Underweight, Normal, Overweight, and Obese, and is developed using the Python programming language with interactive visualizations. Testing results show that the system can provide more adaptive and personalized classifications. Defuzzification values, such as 59.48 for a BMI of 24.22, indicate a classification consistent with WHO standards—namely, the Normal category. The system also demonstrates that classification results may vary for the same BMI when age or gender differs, as illustrated in multi-demographic visualizations. The centroid defuzzification method produces stable and representative outputs. Evaluation results show high accuracy, consistency in rule base, and an ability to handle data uncertainty. Thus, this system serves as a more flexible alternative to conventional methods in body weight classification.
Evaluasi Kebergunaan Aplikasi Soeselo Online Menggunakan Metode System Usability Scale (SUS), Sauro-Lewis CGS, dan Adjective Rating
The development of information technology in the health sector encourages hospitals to adopt application-based registration systems to improve service efficiency. RSUD dr. Soeselo Tegal Regency developed the Soeselo Online application as a solution to reduce physical queues. However, the low rating and many user complaints on the Play Store indicate potential usability problems. This study aims to evaluate the usability of the Soeselo Online application from the user's perspective. The evaluation was conducted using the Usability Testing approach using System Usability Scale (SUS), Completion Rate, Sauro-Lewis Curved Grading Scale (CGS), and Adjective Rating methods. A total of 25 respondents participated in this study. The results of the analysis show that the application has an effectiveness rate of 97.6% which falls into the “Good” category, as well as an average SUS score of 70.3 which falls into the “B” category on the CGS and “Ok” on the Adjective Rating. This finding shows that although the application is quite effective to use, there are aspects of usability that need to be improved to achieve an optimal level of user satisfaction. The results of this study are expected to be the basis of recommendations for further development of the Soeselo Online application in order to improve the quality and user satisfaction of the application, as well as for the hospital in optimizing digital-based services.
Optimalisasi Prediksi Dalam Kelulusan Berbasis Deep Learning: Perbandingan Kinerja Multi-Layer Perceptron dan Deep Neural Network
Predicting on-time graduation is one of the significant challenges in education, aiming to model the factors influencing academic success. This study aims to compare the performance of two Deep Learning algorithms, namely Deep Neural Networks (DNN) and Multi-Layer Perceptron (MLP), in predicting on-time graduation. The methodology used involves evaluating both algorithms with various performance metrics, including Recall, Accuracy, Precision, AUC, MCC, and Cohen Kappa. The results show that DNN performs better in terms of Recall (0.9766), indicating its ability to capture most of the students who graduate on time, although its AUC (0.8625) and Precision (0.8803) are lower compared to MLP. On the other hand, MLP excels in Accuracy (0.8812) and Precision (0.9037), providing more stable results for MCC and Cohen Kappa, demonstrating a better balance in predicting students who graduate on time and those who do not. Overall, while DNN is more sensitive in capturing students who graduate on time, MLP performs better in terms of balance between accuracy and minimizing prediction errors. This study suggests using MLP if the primary priority is accuracy and prediction stability, while DNN is more suitable when the main focus is capturing as many students as possible who graduate on time.
Pengembangan Media Pembelajaran Android Pada Mata Pelajaran Informatika Untuk Siswa SMA Kelas X
The learning media used in schools are still classified as conventional and have not utilised technology optimally. One solution that can be offered is the development of Android-based learning media as an innovative alternative in supporting the learning process. This research aims to develop Android-based informatics learning media, measure its feasibility level based on the assessment of media experts and material experts, and evaluate user responses. The method used is Research and Development (R&D) with the ADDIE development model, which includes the stages of Analysis, Design, Development, Implementation, and Evaluation. The subjects of this study consisted of 27 class X students at SMAN 1 Sukamulia. The results showed that the learning media developed obtained a feasibility score of 90.7% from media experts and 90% from material experts, both of which were included in the very feasible category. Meanwhile, the user response to the application reached 94% with a very high category. These findings indicate that the developed Android-based learning media is not only feasible to use, but also receives positive acceptance from students. The academic contribution of this research lies in the development of digital media that is contextual and relevant to 21st century learning needs. Therefore, this media is recommended to be implemented more widely as well as being the basis for further development in the study of technology-based learning.
Perancangan Sistem Rekomendasi Pemilihan Objek Wisata Karanganyar Dengan Metode Knowledge Base
Karanganyar Regency has significant potential in the tourism sector, especially nature tourism. However, the abundance of destination choices often makes it difficult for tourists to determine places that match their preferences. Currently available information is general and cannot be tailored to the specific needs of each user, making the decision-making process less efficient. This research proposes a knowledge-base recommendation system designed to assist users in selecting tourist destinations in Karanganyar according to their preferences. This system utilizes five main attributes: category, location, ticket price, rating, and facilities. System development was carried out using the Waterfall method, which includes requirements analysis and system design stages. User input is processed by matching it against the tourism data knowledge base using specific rules, and similarity calculations are performed to measure the degree of resemblance between user preferences and available tourism data. Based on similarity calculation results for 10 nature tourism data points, the system successfully recommended a destination with the highest similarity value of 0.991, namely Bukit Mongkrang, which best matched the user's selected criteria. Bukit Mongkrang met the user's desired criteria, being located in Tawangmangu, classified as nature tourism, having a mid-range ticket price, a rating 0.2 higher than the user's preference, and providing key facilities sought by the user. The results of this knowledge-base recommendation system modeling can serve as a reference for developing similar systems in the tourism field.
Pengembangan Aplikasi Mobile Sistem Pengendalian Oven Tembakau Berbasis Internet of Things
Tobacco leaf drying is an important process in determining the final quality of tobacco as a raw material for the cigarette industry. This process is generally carried out using a tobacco oven which is influenced by two main factors, namely temperature control and oven ventilation. If the temperature and ventilation settings are wrong, it can reduce the quality of the tobacco leaves produced. To overcome this, this study developed a tobacco oven control system based on the Internet of Things (IoT) which is integrated with a mobile application. This system is designed to monitor and regulate the temperature and open and close the oven ventilation automatically. The DHT11 temperature sensor is used to read the temperature in the oven, while the DC Servo motor is used to control the ventilation. All sensor data is displayed in real-time via an Android application connected to the esp8266 Ethernet module to the Arduino Uno microcontroller. The test results show that the system can run well, where the oven temperature and ventilation control work according to the specified threshold values. In addition, the oven owner can easily monitor the condition of the oven via a mobile application, so that the tobacco leaf drying process becomes more efficient and of higher quality.
Analisis Komparatif Feture Selection dan Metode Klasifikasi Intrution Detection System (IDS)
An Intrusion Detection System (IDS) is a computer system that analyzes user traffic and data on a network. IDS will provide a warning signal to network users that are attacked by intruders. However, IDS still needs to be developed due to the ever-evolving attack pattern. Feature Selection and Classification Network data are important components of IDS development to identify attack patterns based on network traffic data. To identify attack patterns, many researchers have proposed Feature Selection methods such as Chi-square, Gini Index, Information Gain, Relief, Gain Ratio, and Uncertainty. Researchers have also proposed Machine Learning Classification methods such as Random Forest, SVM, Naive Bayes, AdaBoost, and Neural Network. However, among the methods they propose, the Selection and Classification of Features method, the best, has not been studied. Therefore, we propose to conduct a comparative study of this method to find a better method of selection and classification of features. This study compares Feature Selection and Classification methods to identify those suitable for IDS by comparing Recall, Precision, F1, Accuracy, and Area Under Curve (AUC) for each method. The results showed that the Gain Ratio Feature Selection method was better than other Feature Selection methods. In addition, this study shows that the AdaBoost classification method is more accurate than other classification methods.
Pengaruh Pelanggaran Privasi Data Terhadap Keputusan Pembelian Pengguna Tokopedia Menggunakan Theory Of Planned Behavior (TPB)
The rapid growth of e-commerce in Indonesia, led by platforms like Tokopedia, has transformed consumer behavior while also raising serious data privacy concerns. The 2020 Tokopedia data breach, involving 91 million user records leaked on the dark web, highlighted the vulnerability of personal data and risks such as identity theft and online fraud. This study investigates the impact of privacy breaches on Tokopedia users’ purchasing decisions in Jambi City using a quantitative approach grounded in the Theory of Planned Behavior (TPB). The study examines the influence of perceived data security, risk perception, attitude, subjective norms, and perceived behavioral control on purchase intention and actual buying behavior. Data were collected from 240 respondents through a questionnaire and analyzed using PLS-SEM with SmartPLS 4.0. Findings reveal that perceived data security has a significant positive effect on purchase intention, while risk perception has a significant negative effect. However, attitude and subjective norms show no significant impact on purchase intention, and behavioral intention does not significantly affect actual purchasing behavior. These results suggest that after a privacy breach, consumers prioritize data security over social or personal factors. E-commerce platforms must enhance data protection and maintain transparent communication to rebuild and retain consumer trust.
Sistem Pendukung Keputusan Penerima Bantuan Langsung Tunai Dana Desa (BLT-DD) Menggunakan Algoritma Preference Selection Index (PSI)
The Village Fund Direct Cash Assistance (BLT-DD) program is one of the government's efforts to help poor families in the village. However, the recipient selection process is still done manually through deliberations of village officials, making it prone to mistargeting and lack of transparency. This research aims to build a Decision Support System (SPK) that is able to determine prospective BLT-DD recipients objectively and efficiently. The method used is the Preference Selection Index (PSI), which can solve multicriteria decision-making problems without the need to assign weights between criteria. The five main criteria used include welfare decile, health status, program participation, marital status, and gender of the family head. Testing was conducted on 10 alternative data. The calculation results show that alternative A3 has the highest score of 1.66. This system is able to improve accuracy, efficiency, and transparency in BLT-DD distribution.