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Informatik : Jurnal Ilmu Komputer
Published by Universitas Pembangunan Nasional Veteran Jakarta
ISSN : 02164221     EISSN : 2655139X     DOI : -
Core Subject : Science,
Computer Science & IT
Informatik menerima artikel ilmiah dengan area penelitian pada area Internet Business & Application, Networking & Cyber Security, Statistics & Computation, Elearning & Multimedia, Robotics & Intelligene.
Arjuna Subject : -
Articles 212 Documents
 18   19   20   21   22 
the Implementasi Metode Simple Additive Weighting dalam Penentuan Penerima Manfaat Program pada Desa Lokus Stunting Fandli Supandi; Abdul Gani Fadhlulrahman S H Lihawa
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.13182

Abstract

Stunting a serious nutritional problem often exacerbated by poor environmental sanitation, makes the Community-Based Sanitation (Sanimas) program vital for expanding sanitation access. Given that the manual selection process for Sanimas beneficiaries is time-consuming and inefficient , this study aims to develop a Decision Support System (DSS) to determine recipient priorities objectively and measurably. The research implements the Simple Additive Weighting (SAW) method, an effective Multi-Criteria Decision Analysis (MCDA) technique for multi-criteria decision problems. The SAW method operates by normalizing the decision matrix and calculating the weighted sum of performance ratings to obtain the final preference value . The criteria used are Low-Income Community (C1), Stunting Risk Family (C2), Sanitation Access (C3), Availability of Water Source (C4), Own House (C5), Priority Condition (C6), and Number of Family Members in the Household (C7) 7, with a total weight of 100. The implementation results on sample data in Molintogupo Village, Bone Bolango, show that alternative “SD” obtained the highest Preference Value of 97 (Rank 1). In conclusion, the SAW method is proven effective in systematically generating a ranking of beneficiaries 10; however, the presence of identical preference values among several candidates indicates that the final decision still requires contextual consideration and policy from village-level stakeholders.
Design of an Internet of Things (IoT) Based Composter Monitoring System Using Arduino Leonardo, ESP-01S, DHT-11, MQ-4 and Blynk Tatik Juwariyah; Achmad Zuchriadi P; Santika Sari
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.13404

Abstract

Conventional methods of manually monitoring compost parameters are often inefficient and susceptible to human error. To address this, this study conducted an Internet of Things (IoT) smart composter system designed to track the fermentation process in real-time. The system architecture integrated Arduino Leonardo microcontroller with DHT-11 sensor for temperature and humidity, and MQ-4 sensor to detect the methane concentrations indicative of anaerobic activity. Data were transmitted via ESP-01S Wi-Fi module to the Blynk cloud platform for visualization. A ten-day performance test, with data recorded at six-hour intervals, demonstrated the system's ability to accurately capture the physical fluctuations inherent in decomposition. The results highlighted a temperature rise over five days peaking at 38.7°C, humidity saturation reaching 95.8% RH, and a maximum methane concentration of 218 ppm on the fourth day. Therefore, this system presented a robust technological intervention for agricultural practitioners, facilitating the precise, efficient, and real-time surveillance of compost bins.
SISTEM DETEKSI JATUH UNTUK MENINGKATKAN KESELAMATAN LANSIA MENGGUNAKAN SENSOR MPU-6500 DAN ESP32 BERBASIS IOT Ravi Bimantara; Agung Kridoyono; Mochamad Sidqon; Anton Breva Yunanda; Istantyo Yuwono
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.11961

Abstract

Falls are one of the major risks threatening the safety of the elderly, potentially causing serious injuries or even death. To mitigate this risk, this study developed an Internet of Things (IoT)-based fall detection system using the MPU-6500 sensor and ESP32 microcontroller. The system is designed to be portable and worn in the user's pocket, detecting abnormal motion patterns through the analysis of body acceleration and orientation data. The research methodology includes hardware and software requirements analysis, the design of a fall detection algorithm based on sum vector values and body orientation angles, and integration with a Telegram bot for real-time notifications. The system provides alerts through a wearable alarm, an external siren, and automated messages sent to caregivers or family members. Implementation results show that the system can detect falls accurately, respond quickly, and operate efficiently on low power, making it a practical and cost-effective solution to enhance elderly safety.
Clustering Social Media Addiction Levels Among Students Using the K-Means Clustering Algorithm Muhamad Sandi; Jordy Lasmana Putra; Tyas Setiyorini
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12605

Abstract

Social media has become an integral part of students’ lives, yet excessive use often leads to symptoms of addiction that negatively affect mental health and academic performance. This study aims to cluster the levels of social media addiction among students and university students using the K-Means Clustering algorithm as an unsupervised learning approach. The dataset was obtained from the Kaggle platform, containing variables such as daily usage duration, access frequency, sleep disturbance, and psychological impact. The Elbow Method was employed to determine the optimal number of clusters, while Principal Component Analysis (PCA) was used for visualization. The results grouped respondents into three categories: mild addiction (46.8%), moderate addiction (22.6%), and severe addiction (30.6%). A strong correlation was observed between high access frequency and symptoms such as sleep disruption and decreased concentration. These findings highlight the importance of designing data-driven prevention strategies within educational environments and provide a foundation for further institutional interventions to maintain digital balance among youth.
Characterization of Network Traffic Features for Intrusion Detection in IoMT Cybersecurity Bayu Hananto; Ridwan Raafi'udin; Didit Widiyanto
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12606

Abstract

Network security in the Internet of Medical Things (IoMT) requires intrusion detection that is accurate and interpretable, yet IoMT traffic is often imbalanced and heavy-tailed, complicating feature selection and evaluation. This study characterizes the MedSec-25 dataset and identifies influential network-flow features for stage-aware IoMT intrusion detection. Using 10,000 stratified flows (approximately 40 features), we apply robust descriptive statistics and compare linear relevance (ANOVA F-score) with nonlinear relevance (Mutual Information), supported by correlation auditing and non-parametric testing. The data exhibit strong class imbalance (IR about 10.9:1) and predominantly non-Gaussian distributions. The overlap of ANOVA and MI highlights a compact, interpretable core of temporal and rate/volume indicators, while multivariate interactions help explain why many univariate Kruskal–Wallis tests are non-significant. Based on these findings, we provide a practical IDS design guideline: an auditable pre-filter followed by a nonlinear classifier, assessed with MCC and AUPRC to better reflect minority attack stages. The analysis offers a reproducible foundation for feature-driven IDS development in healthcare IoMT.
Data Mining for Analyzing Causes of Student Registration Delays at UNMARIS Using Decision Tree Agustinus Japa Ngara; Friden Elefri Neno; Paulus Mikku Ate
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12725

Abstract

This study aims to analyze the factors that influence student registration delays at Stella Maris University Sumba (UNMARIS) by utilizing data mining methods using the C4.5 Decision Tree algorithm. The problem of registration delays often occurs and has an impact on the academic administration process and the orderliness of the lecture schedule. The C4.5 algorithm was chosen because it has the ability to process categorical and numerical data and produces decision rules that are easy to interpret. The data used is student data that includes attributes such as GPA, payment status, distance from residence, occupation, semester, and type of registration. The analysis process begins with the data pre-processing stage, calculation of entropy and information gain values, decision tree formation, and evaluation of results. The results of the study show that the payment status and GPA attributes have the highest information gain values, making them the dominant factors that influence the timeliness of student registration. The resulting decision tree model provides a good level of accuracy and is able to classify students into fast, medium, or slow registration categories. This study is expected to assist academics in formulating more effective policies.
An Intelligent Real-Time Detection and Classification System for Sustainable Aquatic Ecosystem Monitoring in Tropical Waters Nancy Jeane Tuturoong; Jimmy Reagen Robot; Djuwita Aling
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12806

Abstract

Sustainable monitoring of aquatic ecosystems in tropical waters requires effective, adaptive, and intelligent technological approaches. This work proposes a design and evaluation of an intelligent real-time system for detecting and classifying freshwater fish species using You Only Look Once version 8 (YOLOv8), a recent deep learning architecture. The dataset used consists of 13,018 images of fishes constituting seven primary species: Catfish, Piranha, Tilapia, Betta, Milkfish, Gourami, and Koi. The research process includes preprocessing images (resizing and augmentation) and training the model using transfer learning techniques to expedite convergence and enhance accuracy. The evaluation findings show that the created system attained a maximum classification accuracy of 100% on the testing dataset. The model was successfully able to recognize species with distinct morphological traits, but a minor decrease in accuracy was reported in classifying fish with inductive body shapes. Overall results substantiate that YOLOv8 has solid potential as an efficient and replicable artificial intelligence-based approach to assisting sustainable aquatic ecosystem monitoring in tropical waters.
Implementation Of Kalman Filter at IoT Animal Weighing Nuryanti; Danu Jaya Saputro; Ismail Rokhim; Hendy Rudiansyah; Sandy Bhawana Mulia; Wahyu Adhie Candra; M Wahyu Firmansyah
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.13604

Abstract

Animal weighing is an important aspect of livestock farming, as it plays a role in feed determination, growth monitoring, and economic evaluation. However, accuracy is often compromised by animal movement, which causes fluctuations in sensor data and unstable readings, making it difficult to determine the actual weight. To address this, this study proposes an IoT-based animal weighing system equipped with a Kalman Filter algorithm to reduce noise and improve measurement stability. The system utilizes four 200 kg load cells, connected via HX711 and controlled by an ESP32 microcontroller, which is supported by an RFID module for automatic animal identification, an RTC for time logging, and Firebase as a cloud storage platform with real-time visualization capabilities through Node-RED. Experimental for moving object weighing results show that the Kalman Filter reduces measurement errors to less than 2% and maintains the coefficient of variation below 2%, demonstrating high precision and stability. Therefore, this system is proven effective for automatic, accurate, and remote-accessible animal weight monitoring, with strong potential for implementation in modern livestock industries.
Design of a New Student Registration Information System Based on a Website Using the Agile Method Zatin Niqotaini; Arafat Febriandirza; Esa Prakasa; Irman Hermadi
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.14007

Abstract

The application of information technology has proven to simplify administrative processes in education, including student enrollment. Currently, TK Islam Perkasa still relies on manual enrollment methods, involving paper-based forms, data verification, and document storage. This process often leads to various issues, such as document duplication, input errors, and inefficiencies in data retrieval and management. To address these challenges, a web-based enrollment information system integrated with a database has been designed. The system is developed using the Agile methodology, which allows for iterative and collaborative software development, ensuring user requirements are better accommodated. The PIECES analysis is employed to identify problems and propose solutions in terms of performance, information, economics, control, efficiency, and service aspects. The system is built using the Laravel framework and PHP programming language, with PostgreSQL as the database to support fast and accurate data management. This system enables online enrollment, saving time and effort for both prospective students and school administrators. Key features include digital form submission, automated data verification, and well-organized data storage and retrieval. The implementation of this system is expected to enhance administrative efficiency, reduce errors, and provide a better user experience. Therefore, this system serves as an innovative solution to support digital transformation in the education sector
Explainable Deep Learning for Multi-Class Plant Disease Classification Using ResNet and EfficientNet with Grad-CAM Analysis Wahyuni Zalmi; Rahmi Putri Kurnia; Dyah Listianing Tyas
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.14396

Abstract

Plant diseases can reduce crop quality and productivity, making early detection an important aspect of modern agriculture. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have shown promising performance in image-based plant disease classification. This study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization. The experiments were conducted using the PlantVillage dataset consisting of 15 classes of healthy and diseased plant leaves.The research process included image preprocessing, data augmentation, transfer learning, model training, performance evaluation, and explainability analysis. The dataset was divided into training and validation sets with a ratio of 80:20. Model performance was evaluated using accuracy, loss, confusion matrix, precision, recall, and f1-score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 92% and a validation loss of 0.19, outperforming EfficientNetB0 which obtained 76% accuracy and 0.82 validation loss. The classification report demonstrated that ResNet50 provided more stable and consistent predictions across most disease classes. Furthermore, Grad-CAM visualization successfully highlighted disease-relevant regions such as lesions, discoloration, and damaged leaf areas, improving the interpretability of the CNN model. The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.

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