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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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MSME Segmentation in Pekanbaru Based on Local E-Catalog Participation Using K-Means Rahma Aliya; Inggih Permana; Febi Nur Salisah; Rice Novita; Muhammad Jazman
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.760

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy; however, their participation in digital government procurement platforms such as the Local E-Catalog in Pekanbaru City remains relatively low. The lack of comprehensive, data-driven mapping of MSME characteristics has resulted in less targeted development and assistance programs. This study aims to segment MSMEs based on revenue, number of employees, and participation status in the Local E-Catalog to generate business groups that can support more effective development strategies. A data mining approach using the K-Means clustering algorithm was applied and implemented through the Orange Data Mining application. The results indicate that a three-cluster configuration is the most optimal, achieving the highest Silhouette Score of 0.444. Cluster 1 represents micro-scale MSMEs with low business capacity and minimal participation in the Local E-Catalog, Cluster 2 consists of growing MSMEs with moderate business capacity, and Cluster 3 comprises established MSMEs with high business capacity and active participation in the Local E-Catalog. These findings provide empirical evidence to support local governments in formulating more targeted and data-driven policies for accelerating MSME digitalization.
Machine Learning Based Prediction of Health Risks in Pregnant Women Rahma Devi; Inggih Permana; Rice Novita; Febi Nur Salisah
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.766

Abstract

Pregnancy is an important phase that requires optimal health monitoring to prevent complications that are risky for both mother and fetus. The high maternal mortality rate in Indonesia emphasizes the importance of early detection of pregnancy risks. The use of machine learning offers an effective predictive approach to quickly and accurately identify pregnancy risks. This study aims to compare the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree C4.5, Random Forest, Support Vector Machine, and Naive Bayes, using the Maternal Health Risk Dataset. The hold-out validation method with data sharing of 80% training data and 20% test data was used in this study. Model evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest had the best performance with an accuracy of 93%, followed by Decision Tree at 93%, SVM at 82%, Logistic Regression at 76%, and Naive Bayes at 72%. Thus, Random Forest is rated as the most optimal algorithm in predicting pregnancy risk and potentially supporting the development of decision support systems for health workers. This research is expected to be the basis for the development of a machine learning-based decision support system to increase the effectiveness of health services for pregnant women.
Co-Authors Febi Nur Salisah Inggih Permana Muhammad Jazman Rahma Aliya Rahma Devi