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.