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All Journal Jurnal Rekayasa Sistem Industri Gema Pendidikan International Journal of Artificial Intelligence Research SMARTICS Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Informatics and Communication Technology (JICT) Golden Ratio of Data in Summary Journal of Informatics and Communication Technology (JICT)
Nurapriani, Fitria
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Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Golden Ratio of Data in Summary

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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.
Co-Authors ,, Tukino -, Tukino AA Sudharmawan, AA Ahnaf, Naufal Zubdi April Lia Hananto Arkan Hilman Hakim Hananto, Agustia Hilabi, Shofa Shofiah Huda, Baenil kastiawan, Nurhayadi Kurniawan, Muhamad Dicky Kusumaningrum, Dwi Sulistya Lestari, Renita Lestari, Santi Arum Puspita Novalia, Elfina Nurfauzi, Oka Muhamad Priatna, Bayu Priyatna, Bayu Rahmawati, Adila Ramadan, Mohamad Rizqulloh Sriardi, Mohammad Didit tukino, tukino Tukino, Tukino Yulistina Nur DS