IMPLEMENTASI DATA MINING DALAM PREDIKSI TARGET PRODUKSI PADA PROSES KERJA MESIN MOLDING MENGGUNAKAN ALGORITMA LINEAR REGRESSION (STUDI KASUS : PT. AIM KARAWANG)
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Production is a process activity from input to produce an output. In the process of working good production will produce good quality products and in large quantities, this is due to a lack of products that are wasted due to production defects (NG products). In the world of industrial production will run simultaneously with a target. A target is a measure of the number of achievements in a certain period of time. The company will make continuous improvements for the smooth running of production and the achievement of production targets. This is because the more products produced, the turnover obtained will also be higher. The purpose of this research is to optimize production targets in the molding machine work process at PT. AIM Karawang. Where the products produced are o rings and rubber products by processing the material in the form of rubber. The method used in this study is the data mining linear regression algorithm. The reason for using this algorithm is because it is the most suitable for the existing conditions, so the daily data report some time ago is used as a reference for determining future target achievement.
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Author Biographies
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia
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References
Total: 15 References- De Cola, T., & Mongelli, M. (2018). Adaptive time window linear regression for outage prediction in Q/V band satellite systems. IEEE Wireless Communications Letters, 7(5), 808-811. DOI: 10.1109/LWC.2018.2826543.
- PRIHANDARI, R. C. (2022). Data Mining: Konsep Dan Apikasi Menggunakan Rapidminer (Series: Supervised Learning Dan Unsupervised Learning).
- Jollyta, D., Ramdhan, W., & Zarlis, M. (2020). Konsep Data Mining Dan Penerapan. Deepublish.
- Koibur, M. E., Murdiyanto, A. W., Munawar, Z., Insany, G. P., Manurung, H. E., Karmana, D., ... & Sastradipraja, C. K. (2023). SAINS DATA: Strategi, Teknik, dan Model Analisis Data. Kaizen Media Publishing.
- Safitri, R., Alfira, N., Tamitiadini, D., Dewi, W. W. A., & Febriani, N. (2021). Analisis Sentimen: Metode Alternatif Penelitian Big Data. Universitas Brawijaya Press.
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