https://ejurnal.undana.ac.id/index.php/jicon/issue/feed J-Icon : Jurnal Komputer dan Informatika 2025-08-12T12:31:50+00:00 Dony Martinus Sihotang jicon@undana.ac.id Open Journal Systems <p style="text-align: right;"><strong>ISSN:&nbsp;<a href="https://issn.brin.go.id/terbit?search=26544091" target="_blank" rel="noopener">2337-7631(Printed)</a></strong></p> <p style="text-align: right;"><strong>ISSN: <a href="https://issn.brin.go.id/terbit?search=26544091" target="_blank" rel="noopener">2654-4091 (Online)</a></strong></p> <p style="text-align: justify; line-height: 2em;">J-Icon : Jurnal Komputer dan Informatika <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="auto" data-phrase-index="0"> is published twice a year (March and October) by the Department of Computer Science, Faculty of Science and Engineering, Undana.</span> <span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="auto" data-phrase-index="1">This journal publishes unpublished research articles in the field of Computer Science.</span> <span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="auto" data-phrase-index="2">Contribution requirements are listed on the inside cover of each issue number.</span></span></p> <p style="text-align: justify; line-height: 2em;">J-Icon : Jurnal Komputer dan Informatika has national accreditation <a title="Peringkat SINTA 4" href="https://sinta.kemdikbud.go.id/journals/profile/5852"><strong>Sinta 4</strong></a> based on the Decree of the Director General of Higher Education, Research and Technology, Ministry of Education and Culture, Research and Technology of Indonesia with Number 225/E/KPT/2022.</p> https://ejurnal.undana.ac.id/index.php/jicon/article/view/23729 CONSTRUCTING A DATASET FOR INFECTIOUS DISEASE PREDICTION AND SPATIAL CLUSTER ANALYSIS 2025-08-12T12:28:51+00:00 Husni Iskandar Pohan husni.pohan@binus.ac.id <p>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.</p> <p>&nbsp;</p> <p>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.</p> <p>&nbsp;</p> <p>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.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> 2025-08-12T12:24:12+00:00 ##submission.copyrightStatement##