Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Computer Science and Research
Satria Sinurat
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparative Study of Machine Learning Approaches Based on Artificial Neural Network, Regression, and Clustering for Diabetes Prediction Nauval Alfarizi; Adi Putra; Prima Lydia Yosophin Batubara; Satria Sinurat
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents a comparative analysis of three machine learning model and algorithms Artificial Neural Network (ANN), Logistic Regression, and K-Means Clustering using the Pima Indians Diabetes dataset. The main objective is to evaluate the performance of supervised and unsupervised methods in predicting diabetes based on physiological and clinical features. he ANN model was developed using a feedforward and backpropagation approach, Logistic Regression applied the fundamental logit equation, and K-Means Clustering was employed as an unsupervised reference. Model performance was assessed using Accuracy, Precision, Recall, and F1-score for supervised models, and Adjusted Rand Index (ARI) for clustering. Experimental results indicate that Logistic Regression achieved the best accuracy of 0.7573, followed by ANN with 0.7078, while K-Means obtained an ARI of 0.1614. The heatmap comparison shows that supervised models outperform unsupervised approaches, with Logistic Regression offering better interpretability and stability, and ANN demonstrating the ability to model nonlinear relationships. K-Means, though less accurate, provided valuable insight into data structure and natural grouping. Overall, the findings confirm that supervised learning models, particularly Logistic Regression and ANN, are more effective for medical prediction tasks. Future research may explore hybrid or ensemble models that combine the interpretability of Logistic Regression, the adaptability of ANN, and the exploratory capability of clustering to enhance medical diagnostic performance.
ANALISIS KOMPARATIF ALGORITMA K-MEANS DAN K-MEDOIDS DALAM CLUSTERING RASIO DISTRIBUSI ALOKON TERHADAP PUS  DI PROVINSI SUMATERA UTARA TAHUN 2025 Panggabean Siahaan; Muhammad Irfan Sarif; Siti Qomariyah; Satria Sinurat; Norita Tampubolon
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6173

Abstract

Unequal distribution of contraceptive supplies (Alokon) relative to the target population remains a persistent challenge in family planning programs, particularly in regions with high demographic heterogeneity. Evaluation based on absolute distribution values generates proportional bias, as larger-population areas automatically receive higher volumes without accounting for the proportional needs of the Reproductive Age Couples (PUS) population. This study proposes a ratio-based approach — dividing total Alokon distributed by the number of  PUS — as the primary clustering variable to enable proportional comparison and reduce population-scale bias across 33 districts and cities in North Sumatra Province. Two algorithms, K-Means and K-Medoids based on Partitioning Around Medoids (PAM), were comparatively evaluated using Silhouette Score as the evaluation metric. The optimal number of clusters (K = 3) was determined through a combination of the Elbow Method — which identified a 75.12% WCSS reduction from K = 2 to K = 3 — and Silhouette Score validation. Results show that both algorithms produced identical cluster compositions: 16 districts in the low-distribution group (48.5%; = 0.1687), 13 districts in the moderate group (39.4%; = 0.3117), and 4 districts in the high group (12.1%; = 0.6077), with equal average Silhouette Scores of = 0.6998 (reasonable structure). Densely populated areas such as Medan City and Deli Serdang — despite receiving the highest absolute distribution volumes — were classified in the low group when measured proportionally, demonstrating the superiority of the ratio-based approach. To the best of the authors' knowledge, this study is the first to apply comparative clustering on Alokon distribution using a proportional ratio framework in North Sumatra Province, providing empirical evidence on algorithm performance in normalized health service distribution data. 
Co-Authors Adi Putra Muhammad Irfan Sarif Nauval Alfarizi Norita Tampubolon Panggabean Siahaan Prima Lydia Yosophin Batubara Siti Qomariyah