Classification of Production Machine Spare Part Stock Data Request Needs Using The K-Nearest Neighbor Method
Main Article Content
Abstract
Spare parts encompass various items that are offered, owned, utilized, or consumed to fulfill consumer desires and requirements. This research implements the K-Nearest Neighbor algorithm on a test dataset consisting of 100 data objects, resulting in a novel classification perspective. The study includes a comprehensive model evaluation process involving Cross Validation on both training and testing datasets, comprising 1000 records with 36 critical and 64 non-critical outcomes. Performance assessment and testing utilizing the RapidMiner Studio application yield optimal results under various modeled scenarios. The accuracy of this algorithm model stands at 98.00%, with a standard deviation of +/- 4.00%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Arimi, I., Purwaningsih, R. and Rosyada, Z.F., 2022. METODE K-NEAREST NEIGHBOR UNTUK MEMPREDIKSI PENJUALAN PRODUK PADA UMKM PENGOLAHAN IKAN MAJU JAYA. Industrial Engineering Online Journal, 12(1).
Renhoran, B.S., Nurhandayani, N. and Septiana, L., 2018. Penerapan Algoritma C4. 5 Untuk Menentukan Data Stok Dan Target Permintaan Material Yang Paling Dibutuhkan Gudang Logistik Pada PT. PLN (Persero) Area Kebon Jeruk. INTI Nusa Mandiri, 12(2), pp.13-20.
Darmi, Y.D. and Setiawan, A., 2016. Penerapan metode clustering k-means dalam pengelompokan penjualan produk. Jurnal Media Infotama, 12(2). DOI: https://doi.org/10.37676/jmi.v12i2.418.
Adiana, B.E., Soesanti, I. and Permanasari, A.E., 2018. Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering. Jurnal Terapan Teknologi Informasi, 2(1), pp.23-32. DOI: https://doi.org/10.21460/jutei.2018.21.76.
Suyanto, S., Meliana, S., Wahyuningrum, T. and Khomsah, S., 2022. A new nearest neighbor-based framework for diabetes detection. Expert Systems with Applications, 199, p.116857. DOI: https://doi.org/10.1016/j.eswa.2022.116857.
Susena, I.G.N.E., Furqon, M.T. and Wihandika, R.C., 2018. Optimasi Parameter Support Vector Machine (SVM) dengan Particle Swarm Optimization (PSO) Untuk Klasifikasi Pendonor Darah Dengan Dataset RFMTC. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(12), pp.7278-7284.
Retno Tri Vulandari, 2017. Data Mining . Yogyakarta: Gava Media.
Saepudin, A., Aryanti, R., Fitriani, E. and Dahlia, D., 2020. Optimasi Algoritma SVM Dan k-NN Berbasis Particle Swarm Optimization Pada Analisis Sentimen Fenomena Tagar# 2019GantiPresiden. Jurnal Khatulistiwa Informatika, 6(1), pp.95-102.
Ulya, S., Soeleman, M.A. and Budiman, F., 2021. Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa. Techno. Com, 20(1), pp.83-96. DOI: https://doi.org/10.33633/tc.v20i1.4215.
Liklikwatil, R.D., Noersasongko, E. and Supriyanto, C., 2018. Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Memprediksi Harga Komoditi Karet. E-JURNAL JUSITI: Jurnal Sistem Informasi dan Teknologi Informasi, 7(2), pp.172-182. DOI https://doi.org/10.36774/jusiti.v7i2.252.
Miftahuddin, Y., Umaroh, S. and Dwiutama, A.A., 2020. Identifikasi Jenis Font Menggunakan Metode Genetic Modified K-Nearest Neighbor. Rekayasa Hijau: Jurnal Teknologi Ramah Lingkungan, 4(3), pp.157-166. DOI: https://doi.org/10.26760/jrh.v4i3.157-166.
Hermawan, Y.D., Hariadi, V. and Amaliah, B., 2017. Implementasi Algoritma K-Nearest Neighbors dengan Particle Swarm Optimization dalam Klasifikasi Trouble pada Base Transceiver Station (BTS). Jurnal Teknik ITS.
Nofriansyah, D. and Nurcahyo, G.W., 2015. Algoritma data mining dan pengujian. Deepublish.
Fahlevi, R., 2020. PENERAPAN GENETIC MODIFIED K-NEAREST NEIGHBOR DALAM KLASIFIKASI PENERIMA BERAS SEJAHTERA (Thesis, Universitas Islam Negeri Sultan Syarif Kasim Riau).
Iku, M.H., Mustofa, Y.A. and Kumala, I.S., 2019. Metode k-nearest neighbor untuk memprediksi harga eceran beras di pasar tradisional gorontalo. Jurnal Cosphi, 3(2).
Villacampa, O., 2015. Feature selection and classification methods for decision making: a comparative analysis (Thesis, Nova Southeastern University).
Sharif, A., 2019. Data mining untuk memprediksi itemset promosi penjualan barang menggunakan metode market basket analysis (mba)(studi kasus: toko sentra ponsel). Jurnal Mantik Penusa, 3(2, Des).
Angriani, H., 2017. SISTEM MANAJEMEN PERSEDIAAN BARANG PADA RETAILER MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING. JTRISTE, 4(1), pp.60-71.
Hutabarat, S.M. and Sindar, A., 2019. Data Mining Penjualan Suku Cadang Sepeda Motor Menggunakan Algoritma K-Means. Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), 2(2), pp.126-132. DOI: https://doi.org/10.32672/jnkti.v2i2.1555