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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) INOVTEK Polbeng - Seri Informatika
Muhammad Jazman
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.
Applying KNN, NBC, and C4.5 Algorithms to Identify Eligibility for Non-Cash Food Aid Rizki Pratama Putra Agri; Inggih Permana Permana; Febi Nur Salisah; Muhammad Jazman; Muhammad Afdal
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/89xvxf70

Abstract

The Indonesian government has implemented the Non-Cash Food Assistance (BPNT) program as an effort to improve people's welfare. However, in its implementation, there are still obstacles in the process of determining the right beneficiaries. Determining the right BPNT recipients is important to ensure that the assistance is received by people who really need it and to prevent budget misuse. This research aims to help the government to easily process data using three classification algorithms, namely K-Nearest Neighbour (K-NN), Naïve Bayes Classifier (NBC), and C4.5 in classifying BPNT recipient data in Air Molek Village, Indragiri Hulu Regency. K-NN, NBC, and C4.5 were chosen because they represent different approaches: K-NN is distance-based, NBC is probability-based, and C4.5 uses decision trees. The stages of the methodology used include data collection, data preprocessing, data splitting (Hold-Out), data balancing and model testing. The results showed that the K-NN algorithm got an accuracy of 70.45%, precision 68.34% recall 72.42%, NBC got an accuracy of 60.58%, precision 58.21%, recall 85.42%, and C 4.5 with an accuracy of 62.56%, precision 59.17%, recall 63.33%. The results of this study can help the government in developing a more objective and data-based decision support system for determining BPNT recipients. The limitation of this research is the use of data that is limited to only one of the data sources.
Co-Authors Febi Nur Salisah Inggih Permana Inggih Permana Permana Muhammad Afdal Rahma Aliya Rice Novita Rizki Pratama Putra Agri