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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Scientific Journal of Informatics
Isnanto , R Rizal
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementation of K-Nearest Neighbor in Case-Based Reasoning for Mental Health Diagnosis Systems Pamungkas, Ardian; Isnanto , R Rizal; Nugraheni , Dinar Mutiara Kusumo
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.19912

Abstract

Purpose: Assessing a model that employs the K-Nearest Neighbor (KNN) technique within Case-Based Reasoning (CBR) for diagnosing mental health disorders, concentrating on conditions such as anxiety, depression, stress, and normalcy, while enhancing its efficacy through the utilization of historical case data for more accurate and tailored diagnostic suggestions. Methods: This study implements the KNN method in CBR to create a mental health diagnosis system that can provide accurate results without the need for complex models or intensive training. This method effectively addresses various patient needs by utilizing previous case data to provide a personalized and case-based diagnosis. This system is designed to tackle mental health issues like anxiety, depression, and academic stress, utilizing a case study of students from ITBK Bukit Pengharapan. Result: This study developed a KNN-based model for mental health diagnosis, achieving 84.62% accuracy on test data. Data processing techniques like text mining, oversampling, and cosine similarity improved performance. With an optimal K value of 2, the model achieved 88% precision, 85% recall, and an F1-score of 84%. The anxiety label performed perfectly, with 100% precision, recall, and F1-score. Novelty: This study adds innovation by integrating the rarely used CBR and KNN algorithms for mental health diagnosis systems. Innovative techniques like text mining, oversampling to get around data integration, and cosine similarity computations, which greatly enhance model performance, assist this strategy. Because this method improves accuracy and expedites the diagnosis process, both of which support clinical decision-making, it may be able to help mental health professionals.
Spatial Recommendation Model for Internet Infrastructure Needs in Indonesia Using GIS Widjaja, Irena; Isnanto , R Rizal; Somantri , Maman; Nuraya , Sarah Nazly
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2546

Abstract

Indonesia faces a persistent digital divide between urban and rural areas despite significant progress in internet penetration. This disparity, driven by unequal infrastructure and socio-economic differences, highlights the need for a data-driven approach to infrastructure planning. This study proposes an integrated spatial recommendation model to identify and prioritize regions for internet infrastructure development. The methodology integrates multiple spatial analysis techniques, including optimized K-Means and DBSCAN clustering for socio-economic and infrastructure segmentation, Kernel Density Estimation (KDE) for identifying demand hotspots, and logistic regression and spatial regression as proxies for network accessibility and potential analysis. The analytical results are aggregated into a weighted composite score that classifies each region into high, medium, or low priority categories. The evaluation results show that the optimized K-Means model achieved a Silhouette Score of 0.631, DBSCAN scored 0.766, regression achieved R² = 0.68, and network accessibility classification reached an accuracy of 0.733 with an F1-score of 0.692. The final spatial map successfully identifies high-priority areas across provinces, revealing that infrastructure needs are locally distributed rather than regionally uniform. This framework provides an evidence-based decision-making tool for policymakers and telecommunication providers to guide targeted and equitable investments in digital infrastructure across Indonesia.
Co-Authors Nugraheni , Dinar Mutiara Kusumo Nuraya , Sarah Nazly Pamungkas, Ardian Somantri , Maman Widjaja, Irena