J-Icon : Jurnal Komputer dan Informatika

CONSTRUCTING A DATASET FOR INFECTIOUS DISEASE PREDICTION AND SPATIAL CLUSTER ANALYSIS

Husni Iskandar Pohan — 2025-08-12

This study presents a structured methodology for constructing a custom dataset derived from patient visit records collected over a three-year period (January 1, 2019 – December 31, 2021) at a healthcare facility in Bandung Regency, Indonesia. The raw medical records were systematically transformed into a machine learning–ready dataset, involving feature extraction, labeling, and geospatial enrichment. Key transformations included the removal of personally identifiable information, the standardization of clinical symptoms into structured variables, and the assignment of diagnostic and referral labels in accordance with ICD-10 classification standards.

Additionally, the dataset was enhanced with spatial coordinates—longitude and latitude—to enable geospatial analyses such as transmission radius estimation, proximity clustering, and identification of regional case densities. This structure supports both supervised and unsupervised learning methods, including classification, referral prediction, and spatial cluster detection.

The resulting dataset has been successfully utilized in several advanced experiments: disease classification, referral status prediction, feature importance interpretation using SHAP and LIME, geospatial clustering, and synthetic data generation to mitigate challenges related to privacy and limited data availability. The methodology outlined in this study is expected to support future research in healthcare analytics and contribute to the development of decision support systems and public health policy planning tools.

A COMPARATIVE STUDY OF SUPERVISED FEATURE SELECTION METHODS FOR PREDICTING UANG KULIAH TUNGGAL (UKT) GROUPS

Windy Chikita Cornia Putri — 2025-08-31

The manual classification of Uang Kuliah Tunggal (UKT) groups at Indonesian public universities
is laborious, subjective, and error-prone, especially given the explosion of socio-economic data captured
via online admission portals. In this study, we evaluate five feature selection techniques Chi-Square filter,
Random Forest importance, Recursive Feature Elimination, LASSO embedded selection, and Exploratory
Factor Analysis on a dataset of 9,369 applicants described by 53 socio-economic variables. Six classifiers
(Decision Tree, Random Forest, SVM-RBF, K-Nearest Neighbor, and Naïve Bayes) were tuned via
stratified 5-fold cross-validation within an 80:20 train-test split. Performance was measured by accuracy,
macro-F1, and training time, and differences in weighted-average accuracy across feature-selection
scenarios were assessed using the Friedman test (χ² = 15.06, p = 0.010). Results show that reducing to 13
features via LASSO (weighted-average accuracy 0.730) or Chi-Square (0.678) significantly outperforms
both the full feature baseline (0.624) and the EFA baseline (0.303), while cutting computational costs by
over 40%. We conclude that supervised feature selection particularly LASSO and Chi-Square enables
simpler, faster, and more transparent UKT prediction without sacrificing accuracy. The novelty of this study
lies in comparing five feature-selection methods within a standardized preprocessing pipeline on real UKT
data from UNESA, resulting in a 13-feature subset aligned with the current UKT policy. This finding is
ready to be integrated into an automated UKT verification system to enhance decision accuracy and
efficiency.

HYPERPARAMETER OPTIMIZATION IN MACHINE LEARNING MODELS ON SKY SURVEY DATA CLASSIFICATION

Efraim Kurniawan Dairo Kette — 2025-09-26

Discovering the optimal model in today's popularity of various machine learning applications remains an essential challenge. Besides data dependency, the performance of classification models is also affected by deciding on suitable algorithm with optimal hyperparameter settings. This study conducted a hyperparameter optimization process and compared the accuracy results by applying various classification models to the observation dataset. This study obtains data from the Sloan Digital Sky Survey Data Release 18 (SDSS-DR18) and Sloan Extension for Galactic Understanding and Exploration (SEGUE-IV). The SDSS-DR18 and SEGUE-IV provide observational data of space objects, such as stellar spectra with corresponding positions and magnitudes of galaxies or stars. The SDSS-DR18 dataset contains magnitude and redshift data of celestial objects with target features of stars, Quasi Stellar Objects (QSOs), and galaxies. The SEGUE-IV dataset contains equivalent-width parameters, inline indices, and other features to the radial velocity of the corresponding star spectrum. This study utilized several machine learning models, such as k-Nearest Neighbor (KNN), Gaussian-Naive Bayes, eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). This study utilized Bayesian, Grid, and Random-based approaches to find the optimal hyperparameters to maximize the performance of the classification model. This study proved that some classification models have improved accuracy scores through the Bayesian-based hyperparameter optimization settings. This study discovers the XGBoost model shows the highest classification results after hyperparameters optimization compared to other models for both datasets with an average accuracy of 99.10% and 95.11%, respectively.

AGILE DEVELOPMENT IMPLEMENTATION ON VIDYAMEDIC HEALTHCARE INFORMATION SYSTEM BASED ON SCRUM FRAMEWORK

Isep Lupti Nur — 2025-10-29

Efficient and integrated information systems are essential for managing healthcare services, including patient data, laboratory results, and radiology records. This study presents the development of Vidyamedic, a web-based healthcare information system designed to streamline healthcare data management. The system was developed using HTML, CSS, and JavaScript for the front end and PHP with the Laravel framework for the back end. The development process adopted the Scrum framework, an Agile methodology that supports iterative and adaptive system development. The project was completed over eight weeks, divided into four sprints. Each sprint spanned two weeks or 10 working days. A total of 14 development tasks were completed by a team of four members. Key Scrum activities included product backlog creation, sprint planning, and the use of burndown charts to monitor progress and identify performance trends during development. System validation was conducted through black-box testing to ensure that each feature operated according to user requirements, without evaluating the system's internal structure. The resulting application provided role-based dashboards tailored for physicians, laboratory administrators, radiology administrators, and queue administrators. The findings demonstrate that Agile methodologies, particularly Scrum, can be effectively applied in the healthcare sector to develop reliable and adaptable information systems that improve healthcare data management.

DEVELOPMENT OF A 360° VIRTUAL REALITY-BASED ANDROID APPLICATION FOR CAMPUS INTRODUCTION AT WASTUKANCANA COLLEGE OF TECHNOLOGY USING MDLC METHOD

Rohmat Rohmat — 2025-10-29

This study aims to develop a 360 Virtual Tour application based on Android as an interactive medium to introduce the campus environment of Sekolah Tinggi Teknologi Wastukancana to new students. The application integrates Virtual Reality (VR) technology to provide an immersive exploration experience through 360 panoramic views. Key features include hotspot-based navigation between scenes, automatic audio narration using text-to-speech, and a stereoscopic display mode compatible with Google Cardboard. The development follows the Multimedia Development Life Cycle (MDLC) method, consisting of six stages: concept, design, material collecting, assembly, testing, and distribution. The application was implemented using Unity 2021.3.45 LTS and C# programming language, along with gyroscope sensor support to align the panorama with the user's viewing direction. Functional testing using the blackbox method was conducted with 177 test cases, all of which passed successfully with a 100% success rate. The APK file was distributed through GitHub and Google Sites for direct access by new students. Initial feedback indicated that the application is visually appealing, user-friendly, and capable of delivering a virtual tour experience that closely resembles the real campus environment. These results suggest that the application is effective as a digital campus introduction tool that is informative, practical, and innovative.

EVALUATION OF THE IMPLEMENTATION OF THE INDEPENDENT ACADEMIC INFORMATION SYSTEM (SIAMIR) AT STIKOM UYELINDO USING THE DELONE AND MCLEAN MODEL

Semlinda Juszandri Bulan — 2025-10-31

Academic information systems are a crucial element in supporting the effectiveness and efficiency of academic management at universities. STIKOM Uyelindo uses an academic information system called SiAmir to serve the academic administration needs of students, lecturers, and staff. However, the success of this system's implementation needs to be systematically evaluated to ensure its continued improvement. This study aims to evaluate the implementation of SiAmir using the DeLone and McLean model, which consists of six variables: system quality, information quality, service quality, system usage, user satisfaction, and net benefits. Respondents in this study were 165 students and lecturers at STIKOM Uyelindo. The data analysis method used was Partial Least Squares (PLS) and was run using SmartPLS version 3.0 software. The results showed that six hypotheses were accepted and three were rejected. Information Quality and Service Quality significantly influenced User Usage and Satisfaction, while System Quality had no significant effect. User Satisfaction proved to be a key mediator with the strongest influence on Net Benefits, while Usage had no direct effect. The findings confirm that information system success is determined more by the quality of content and services than by technical aspects, with user satisfaction being the primary mediating factor. Practical implications suggest that organizations need to prioritize investments in information and service quality to maximize the benefits of information systems.