PENERAPAN METODE K-MEANS UNTUK CLUSTERING HASIL PRODUKSI BERDASARKAN BERAT PRODUK
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Published:
Sep 21, 2023
Keywords:
Data Mining,
Clustering,
K-Means
Main Article Content
Abstract
In the industrial world, especially in manufacturing companies that convert raw materials into finished goods. Quality is the main thing that absolutely must be fulfilled in all products. There are several factors or parameters that can be used as a basic benchmark in determining the suitability of a product. As a manufacturing company engaged in the motor vehicle tire manufacturing industry, of course there are many parameters used to determine product suitability. One of them is the weight of the product. This research aims to group products based on their weight, namely Normal, Overweight and Underweight. The data grouping process is carried out using Data Mining rules by utilizing Clustering techniques. The Data Mining process is carried out using Knowledge Discovery in Database steps.
The method used to obtain process results is by using the K-Means algorithm. Data processed using Microsoft Excel and implemented using Rapid Miner V7.4 software. The data used in this research was obtained from the Curing department with a data sample of 4758 records. The results of this research are to find out the percentage value of each product weight condition, how many products meet the product eligibility requirements and vice versa. From this, it is hoped that a conclusion can be drawn about how the production process is generally performed.
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Putro, H. P., Saputra, M., & Najmuddin, N. (2023). PENERAPAN METODE K-MEANS UNTUK CLUSTERING HASIL PRODUKSI BERDASARKAN BERAT PRODUK. DESANTA (Indonesian of Interdisciplinary Journal), 4(1), 189–200. Retrieved from https://jurnal.desantapublisher.com/index.php/desanta/article/view/229
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Articles
References
Darwis, M., Hasibuan, L. H., Firmansyah, M., & Ahady, N. (2021). Implementation of K-Means Clustering Algorithm in Mapping the Groups of Graduated or Dropped-out Students in the Management Department of the National University. JISA (Jurnal Informatika Dan Sains), 04(01), 1–9.
Han, J., Pei, J., & Tong, H. (2023). Data Mining Concepts and Techniques Fourth Edition. Massachusetts:Elsevier.
Haviluddin, H., Patandianan, S. J., Putra, G. M., Puspitasari, N., & Pakpahan, H. S. (2021). Implementasi Metode K-Means Untuk Pengelompokkan Rekomendasi Tugas Akhir. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 16(1). https://doi.org/10.30872/jim.v16i1.5182
Hermawati, F. A. (2013). Data Mining. Penerbit Andi.
Nurjanto, F. D. (2013). Tahap-tahap K-Means Clustering.
Diakses tanggal 10 Januari 2024 dari https://fadlikadn.wordpress.com/2013/06/14/tahap-tahap-k-means-clustering/
Prasojo, R., Utami, Y. R. W., & Vulandari, R. T. (2019). Implementasi K-Means Clustering Pada Pengelompokan Potensi Kerjasama Pelanggan. Jurnal TIKomSIN, 7.
Priyatman, H., Sajid, F., & Haldivany, D. (2019). Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(1). https://doi.org/10.26418/jp.v5i1.29611
Sarumaha, N. I. (2021). Implementasi Algoritma K-Means Clustering Pada Analisa Impor Beras. JUSSI: Jurnal Sains Dan Teknologi Informasi, 1(1), 19–27.
Savaram, R. (2023). RapidMiner Tutorial | What Is RapidMiner - A Complete Guide - 2024.
Diakses tanggal 10 Januari 2024 dari https://mindmajix.com/rapidminer-tutorial
Septiana Ananda, P., Sediono, E., & Sembiring, I. (2023). KMeans Clustering Menggunakan RapidMiner dalam Segmentasi Pelanggan dengan Evaluasi Davies Bouldin Index Untuk Menentukan Jumlah Cluster Paling Optimal. Jurnal BATIRSI, 6(2), 8–13.
Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Kenneth C. Lichtendahl, J. (2018). DATA MINING FOR BUSINESS ANALYTICS Concepts, Techniques, and Applications in R (First Edition). New Jersey: John Wiley & Sons, Inc.
Ye, N. (2014). Data Mining: Theories, Algorithms, and Examples. Florida: CRC Press.
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Article Sidebar
Clustering,
K-Means
Main Article Content
Abstract
In the industrial world, especially in manufacturing companies that convert raw materials into finished goods. Quality is the main thing that absolutely must be fulfilled in all products. There are several factors or parameters that can be used as a basic benchmark in determining the suitability of a product. As a manufacturing company engaged in the motor vehicle tire manufacturing industry, of course there are many parameters used to determine product suitability. One of them is the weight of the product. This research aims to group products based on their weight, namely Normal, Overweight and Underweight. The data grouping process is carried out using Data Mining rules by utilizing Clustering techniques. The Data Mining process is carried out using Knowledge Discovery in Database steps.
The method used to obtain process results is by using the K-Means algorithm. Data processed using Microsoft Excel and implemented using Rapid Miner V7.4 software. The data used in this research was obtained from the Curing department with a data sample of 4758 records. The results of this research are to find out the percentage value of each product weight condition, how many products meet the product eligibility requirements and vice versa. From this, it is hoped that a conclusion can be drawn about how the production process is generally performed.
Downloads
Article Details
References
Darwis, M., Hasibuan, L. H., Firmansyah, M., & Ahady, N. (2021). Implementation of K-Means Clustering Algorithm in Mapping the Groups of Graduated or Dropped-out Students in the Management Department of the National University. JISA (Jurnal Informatika Dan Sains), 04(01), 1–9.
Han, J., Pei, J., & Tong, H. (2023). Data Mining Concepts and Techniques Fourth Edition. Massachusetts:Elsevier.
Haviluddin, H., Patandianan, S. J., Putra, G. M., Puspitasari, N., & Pakpahan, H. S. (2021). Implementasi Metode K-Means Untuk Pengelompokkan Rekomendasi Tugas Akhir. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 16(1). https://doi.org/10.30872/jim.v16i1.5182
Hermawati, F. A. (2013). Data Mining. Penerbit Andi.
Nurjanto, F. D. (2013). Tahap-tahap K-Means Clustering.
Diakses tanggal 10 Januari 2024 dari https://fadlikadn.wordpress.com/2013/06/14/tahap-tahap-k-means-clustering/
Prasojo, R., Utami, Y. R. W., & Vulandari, R. T. (2019). Implementasi K-Means Clustering Pada Pengelompokan Potensi Kerjasama Pelanggan. Jurnal TIKomSIN, 7.
Priyatman, H., Sajid, F., & Haldivany, D. (2019). Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(1). https://doi.org/10.26418/jp.v5i1.29611
Sarumaha, N. I. (2021). Implementasi Algoritma K-Means Clustering Pada Analisa Impor Beras. JUSSI: Jurnal Sains Dan Teknologi Informasi, 1(1), 19–27.
Savaram, R. (2023). RapidMiner Tutorial | What Is RapidMiner - A Complete Guide - 2024.
Diakses tanggal 10 Januari 2024 dari https://mindmajix.com/rapidminer-tutorial
Septiana Ananda, P., Sediono, E., & Sembiring, I. (2023). KMeans Clustering Menggunakan RapidMiner dalam Segmentasi Pelanggan dengan Evaluasi Davies Bouldin Index Untuk Menentukan Jumlah Cluster Paling Optimal. Jurnal BATIRSI, 6(2), 8–13.
Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Kenneth C. Lichtendahl, J. (2018). DATA MINING FOR BUSINESS ANALYTICS Concepts, Techniques, and Applications in R (First Edition). New Jersey: John Wiley & Sons, Inc.
Ye, N. (2014). Data Mining: Theories, Algorithms, and Examples. Florida: CRC Press.
Most read articles by the same author(s)
- Najmuddin Najmuddin, Gugun Gunawan, Memed Saputra, Adith Aulia Rahman, RANCANG BANGUN SISTEM INFORMASI STUDI KASUS PENDATAAN TABUNG GAS PADA PT. UTAMA GAS MULTIPERKASA , DESANTA (Indonesian of Interdisciplinary Journal): Vol. 3 No. 1 (2022): September 2022