Prediksi Barang Keluar TB. Wijaya Bangunan Menggunakan Algoritma KNN Regression dengan RStudio
DOI:
https://doi.org/10.14421/jiska.2021.6.2.90-97Abstract
TB. Wijaya Bangunan is a business entity that has weaknesses in managing inventories. This study aims to help TB. Wijaya Bangunan in managing inventory based on existing data reduce the difference between the number of incoming goods and the number of outgoing goods. The methods used are data collection, data preparation, data selection, preprocessing, data transformation, distance calculation, calculation of predictions, evaluation, and display of prediction results using a Shiny framework. This study uses the Time Series KNN Regression algorithm to predict the number of outgoing goods based on time series data with existing data. The most predicted results came out in the 9th week period as much as 22.40%. Based on the process that has been done, it can be concluded that the evaluation value of Root Mean Square Error (RMSE) is at least 3.55, which means it has the best predictive accuracy results.
References
Altunsöğüt, Ö., Uçar, E., & Kılıçaslan, Y. (2018). PREDICTING THE AMOUNT OF WASTAGE OF FINISHED GOODS IN TEXTILE DYEING FACTORIES. International Scientific Conference “UNITECH 2018,” 248–288.
Bode, A. (2017). K-NEAREST NEIGHBOR DENGAN FEATURE SELECTION MENGGUNAKAN BACKWARD ELIMINATION UNTUK PREDIKSI HARGA KOMODITI KOPI ARABIKA. ILKOM Jurnal Ilmiah, 9(2), 188–195. https://doi.org/10.33096/ilkom.v9i2.139.188-195
Fatkhuroji, F., Santosa, S., & Pramunendar, R. A. (2019). PREDIKSI HARGA KEDELAI LOKAL DAN KEDELAI IMPOR DENGAN METODE SUPPORT VECTOR MACHINE BERBASIS FORWARD SELECTION. Jurnal Teknologi Informasi, 15(1), 61–76.
Hamdi, A., Indriani, F., & Muliadi, M. (2019). METODE TIMESERIES K-NEAREST NEIGHBOR REGRESSION DALAM PREDIKSI BARANG KELUAR PADA GUDANG PT PUTRA PRENUER BANJARBARU. Seminar Nasional Ilmu Komputer (SOLITER), 2, 37–45.
Indani, & Suhairi, L. (2018). Pengelolaan Usaha Boga Edisi II (2 ed.). Syiah Kuala University Press.
Lestari, S. I. P., Andriani, M., GS, A. D., Subekti, P., & Kurniawati, R. (2019). Peramalan Stok Spare Part Menggunakan Metode Least Square. SEFA BUMI PERSADA.
Mahena, Y., Rusli, M., & Winarso, E. (2015). Prediksi Harga Emas Dunia Sebagai Pendukung Keputusan Investasi Saham Emas Menggunakan Teknik Data Mining. Kalbiscentia Jurnal Sains dan Teknologi, 2(1), 36–51.
Martínez, F., Frías, María, P., Charte, F., & Rivera, Antonio, J. (2019). Time Series Forecasting with KNN in R: the tsfknn Package. The R Journal, 11(2), 229. https://doi.org/10.32614/RJ-2019-004
Mustakim, M., & Oktaviani, G. (2016). Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa. Jurnal Sains, Teknologi, dan Industri, 13(2), 195–202. https://doi.org/10.24014/sitekin.v13i2.1688
Nanja, M., & Purwanto, P. (2015). METODE K-NEAREST NEIGHBOR BERBASIS FORWARD SELECTION UNTUK PREDIKSI HARGA KOMODITI LADA. Pseudocode, 2(1), 53–64. https://doi.org/10.33369/pseudocode.2.1.53-64
Nofriansyah, D. (2014). Konsep Data Mining vs Sistem Pendukung Keputusan (1 ed.). Deepublish.
Putra, S. H., & Putra, B. T. (2018). Klasifikasi Harga Cell Phone menggunakan Metode K-Nearest Neighbor (KNN). Prosiding Annual Research Seminar, 4(1), 242–245.
Sabilla, W. I., & Putri, T. E. (2017). Prediksi Ketepatan Waktu Lulus Mahasiswa dengan k-Nearest Neighbor dan Naïve Bayes Classifier (Studi Kasus Prodi D3 Sistem Informasi Universitas Airlangga). Jurnal Komputer Terapan, 3(2), 233–240.
Sartika, E. (2019). Analisis Metode K Nearest Neighbor Imputation (KNNI) Untuk Mengatasi Data Hilang Pada Estimasi Data Survey. Jurnal TEDC, 12(3), 219–227.
Wijaya, A., & Ananta, W. P. (2017). Hukum Bisnis Properti Indonesia. Grasindo.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Natcha Kwintarini Suparman, Budi Arif Dermawan, Tesa Nur Padilah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.