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Fahmi, Muhammad Aqil
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Computer Science & IT

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Implementasi K-Means sebagai Mekanisme Self-Labeling dalam Arsitektur Ensemble Voting Classifier untuk Prediksi Penjualan Usaha Mikro Kecil dan Menengah (UMKM) pada Data Tanpa Label Fahmi, Muhammad Aqil; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8779

Abstract

Sales forecasting in the Micro, Small, and Medium Enterprises (MSME) sector faces challenges due to the fluctuating (noisy) nature of the data and the absence of class labels (unlabeled) required for training supervised learning models. This study proposes a sequential hybrid architecture in which the K-Means algorithm is employed as a Self-Labeling mechanism to automatically transform raw transaction data into class labels (“Low” and “High”). The resulting synthetic labels are then used to train an Ensemble Voting Classifier model that aggregates predictions from XGBoost, LightGBM, and CatBoost. The experimental evaluation results show that although the single XGBoost model achieves a slightly higher accuracy (96.24%) compared to the Ensemble model (96.07%), the hybrid Ensemble Voting model proves superior in terms of probability calibration, achieving the lowest Loss value of 0.1532. This value outperforms XGBoost (0.1646) and LightGBM (0.1772), indicating more reliable and stable prediction confidence. The model also demonstrates excellent balance with an F1-Score of 0.95 and a Recall of 0.96 for the majority class. This study confirms that the hybrid approach is effective in reducing uncertainty in MSME stock management.
Co-Authors Kurniawan, Defri