Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization
Abstract
The demand for brown sugar products often falls below the level of production, resulting in unsold goods when market demand surpasses the production capacity. This paper addresses the challenge faced by many brown sugar businesses in estimating production yields. Another issue, apart from production uncertainty, is the presence of a dataset with a significant nominal range. The study focuses on a specific brown sugar producing company in Indonesia. To address the production estimation problem, this research proposes the use of k-NN supervised learning as a forecasting method. However, instead of relying solely on k-NN, the study suggests employing z-score normalization to handle the dataset's large nominal range. The production data used for analysis spans from March 2019 to February 2022, comprising 144 weekly records. The dataset is divided into training and testing data, employing an 8:2 split validation ratio. The proposed method consists of several steps, including data normalization using z-score, processing k-NN based on the Minkowski distance, and concluding with the de-normalization process. The results demonstrate the successful implementation of the proposed method in predicting production levels. The evaluation indicates an average error margin of 3.34%, which is below the 5% threshold. The evaluation of predictive data for k-NN with z-score normalization proves effective in forecasting brown sugar production uncertainty and addressing the challenge of a large nominal range.
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A. Rifa’i, I. M. Sudarma, and Widhianthini, “Strategi Pengembangan Usaha Industri Gula Merah Tebu di Kabupaten Tulungagung Provinsi Jawa Timur,” Jurnal Agribisnis dan Agrowisata , vol. 8, no. 3, 2019.
H. Gula Pasir Di Jakarta, A. Andri Wiliyana, and M. Yamin Darsyah, “Perbandingan Metode Arima Dan Moving Average Pada Kasus Comparison of The Use Of Arima And Moving Average Methods in the Case of Granulated Sugar Price in Jakarta,” in Prosiding Seminar Nasional Mahasiswa Unimus, 2018, pp. 361–367.
T. K. Belyaeva, E. E. Egorov, T. K. Potapova, T. L. Shabanova, and M. Y. Shlyakhov, “Implementation of the Division Model of Pedagogical Labor in the Teacher Training System of a New Type,” in the 21st Century from the Positions of Modern Science: Intellectual, Digital and Innovative Aspects, E. G. Popkova and B. S. Sergi, Eds., Cham: Springer International Publishing, 2020, pp. 430–438.
P. Gupta et al., “Implementation of demand forecasting – A comparative approach,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jan. 2021. doi: 10.1088/1742-6596/1714/1/012003.
Y. Y. Fan, “Demand Prediction of Production Materials And Simulation Of Production Management,” International Journal of Simulation Modelling, vol. 21, no. 4, pp. 720–731, Dec. 2022, doi: 10.2507/IJSIMM21-4-CO20.
A. Suresh, P. G. Kumar, and M. Ramalatha, “Prediction of major crop yields of Tamilnadu using K-means and Modified KNN,” in 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018, pp. 88–93. doi: 10.1109/CESYS.2018.8723956.
R. G. Hammer, P. C. Sentelhas, and J. C. Q. Mariano, “Sugarcane Yield Prediction Through Data Mining and Crop Simulation Models,” Sugar Tech, vol. 22, no. 2, pp. 216–225, 2020, doi: 10.1007/s12355-019-00776-z.
Z. Deng, X. Zhu, D. Cheng, M. Zong, and S. Zhang, “Efficient kNN classification algorithm for big data,” Neurocomputing, vol. 195, pp. 143–148, 2016, doi: https://doi.org/10.1016/j.neucom.2015.08.112.
S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN classification with different numbers of nearest neighbors,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 5, pp. 1774–1785, May 2018, doi: 10.1109/TNNLS.2017.2673241.
A. Pamuji, “Performance of the K-Nearest Neighbors Method on Analysis of Social Media Sentiment,” JUISI, vol. 07, no. 01, 2021.
M. Suyal and P. Goyal, “A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning,” International Journal of Engineering Trends and Technology, vol. 70, no. 7. Seventh Sense Research Group, pp. 43–48, Jul. 01, 2022. doi: 10.14445/22315381/IJETT-V70I7P205.
R. N. Sukmana, A. Abdurrahman, and Y. Wicaksono, “Implementasi K-Nearest Neighbor Untuk Menentukan Prediksi Penjualan:(Studi Kasus: Pt Maksiplus Utama Indonesia),” Jurnal Teknologi Informasi dan Komunikasi, vol. 9, no. 2, pp. 31–37, 2020.
M. Nanja and P. Purwanto, “Metode K-Nearest Neighbor Berbasis Forward Selection Untuk Prediksi Harga Komoditi Lada,” Pseudocode, vol. 2, no. 1, pp. 53–64, Aug. 2015, doi: 10.33369/pseudocode.2.1.53-64.
D. Srianto and E. Mulyanto, “Perbandingan K-Nearest Neighbor Dan Naive Bayes Untuk Klasifikasi Tanah Layak Tanam Pohon Jati,” Techno. Com, 15(3), pp.241-245 2016.
L. Yang, S. Liu, S. Tsoka, and L. G. Papageorgiou, “A regression tree approach using mathematical programming,” Expert Syst Appl, vol. 78, pp. 347–357, 2017, doi: https://doi.org/10.1016/j.eswa.2017.02.013.
S. Tajmouati, B. El Wahbi, A. Bedoui, A. Abarda, and M. Dakkoun, “Applying k-nearest neighbors to time series forecasting: two new approaches,” Mar. 2021.
M. Mailagaha Kumbure and P. Luukka, “A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance,” Granular Computing, vol. 7, no. 3, pp. 657–671, 2022, doi: 10.1007/s41066-021-00288-w.
S. Sinsomboonthong, “Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification,” Int J Math Math Sci, vol. 2022, p. 3584406, 2022, doi: 10.1155/2022/3584406.
B. Zaman, A. Rifai, and M. Hanif, “Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur”, J. Inf. Syst. Informatics, vol. 3, no. 4, pp. 582-595, Dec. 2021. doi: https://doi.org/10.51519/journalisi.v3i4.213
V. B. S. Prasath et al., “Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review,” Aug. 2017, doi: 10.1089/big.2018.0175.
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