J-Icon : Jurnal Komputer dan Informatika

CONSTRUCTING A DATASET FOR INFECTIOUS DISEASE PREDICTION AND SPATIAL CLUSTER ANALYSIS

Husni Iskandar Pohan — Tue, 12 Aug 2025 12:24:12 +0000

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, Wiyli Yustanti, Ervin Yohannes — Sun, 31 Aug 2025 12:14:38 +0000

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 — Fri, 26 Sep 2025 12:40:08 +0000

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.