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All Journal Jurnal Mandiri IT Golden Ratio of Data in Summary
Lestari, Renita
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Computer Science & IT Economics, Econometrics & Finance Library & Information Science Mathematics Social Sciences

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Classification of Reject Patterns Based on Production Stages Using the K-Means Clustering Method Lestari, Renita; Novalia, Elfina; Tukino; Nurapriani, Fitria
Golden Ratio of Data in Summary Vol. 5 No. 4 (2025): August - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i4.1301

Abstract

This study aims to classify reject patterns in the production process using the K-Means Clustering method. The dataset consists of 870 records collected from the production line, containing information such as product name, reject type, process stage, and production quantity. Through a data mining approach, data preprocessing steps such as cleaning, encoding, and normalization were performed prior to the clustering process. The Elbow Method indicated that the optimal number of clusters is three. Each cluster exhibits distinct characteristics: light rejects with small quantities in early stages, heavy rejects with large quantities, and moderate rejects with random distribution. These findings are expected to assist management in formulating more targeted strategies for process improvement and quality control. By identifying common reject patterns within each cluster, companies can adopt a more proactive approach to minimizing production defects and enhancing overall operational efficiency.
Prioritizing micro, small, and medium enterprises assistance areas in West Java using analytical hierarchy process Lestari, Renita; Huda, Baenil; Novalia, Elfina; Hananto, April Lia
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.527

Abstract

This study aims to develop a Decision Support System (DSS) to prioritize areas for receiving assistance for Micro, Small, and Medium Enterprises (MSMEs) in West Java Province using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The AHP method is used to determine the importance weight of each criterion based on its priority level, while the SAW method is used to carry out the normalization process, calculate preference values, and rank alternative areas. The criteria used include the number of MSMEs, workforce, financial stability ratio, legality ratio, BPP ratio, digital ratio, and innovation ratio. The results of the study indicate that the system built is able to produce an objective and consistent ranking of priority areas for MSME assistance, as evidenced by the agreement between the results of manual calculations using Microsoft Excel and the results of calculations in the system. Thus, this system is expected to assist relevant parties in making decisions regarding the distribution of MSME assistance in a more targeted and structured manner and rank 27 administrative regions in West Java Province. The results show that the highest-ranked region achieved a preference value of 0.8573, indicating its highest priority for MSME assistance, while the lowest-ranked region obtained a value of 0.5129. These results demonstrate the system’s capability to generate consistent and objective rankings. In addition, this study contributes by applying a combined AHP–SAW approach at a regional (macro) level, which is still limited in previous studies, thereby providing a more comprehensive framework for data-driven policy decision-making.
Co-Authors April Lia Hananto Huda, Baenil Novalia, Elfina Nur ‘Azah Nurapriani, Fitria