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All Journal JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research
Dia Komalla
Universitas Muhammadiyah Bengkulu, Bengkulu
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Algoritma K-Means Clustering untuk Pengelompokan Pola Penjualan Sembilan Bahan Pokok pada Pusat Distribusi Berbasis Dataset Kaggle Fitriah Fitriah; Dia Komalla; Muhajir Yunus
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1054

Abstract

Sales data management of staple food products (sembako) is an important concern for distribution centers because stock storage decisions are still largely based on the estimation or experience of warehouse staff rather than on adequate historical data analysis. This condition creates the risk of overstocking slow-moving products on one hand, and stockouts of high-demand products on the other, potentially causing operational losses. This study aims to apply the K-Means algorithm to cluster staple food sales data based on sales patterns, using product type and total units sold as variables after being encoded and scaled. The dataset used was obtained from Kaggle, consisting of 1,200 sales transaction records. The research stages include problem identification, data collection, data pre-processing (cleaning, transformation, and standardization), implementation of the K-Means algorithm with k=3, result analysis, and model evaluation using the Silhouette Score. The clustering process was carried out using Python libraries in Google Colaboratory. The results show that all sales data were successfully grouped into three clusters labeled -1, 0, and 1, namely cluster -1 (not in demand), cluster 0 (less in demand), and cluster 1 (in demand). Cluster 1 dominates with 930 data points (77.5%), cluster 0 contains 269 data points (22.4%), while cluster -1 contains only 1 data point (0.1%), indicating an outlier among products with very low sales volume. These findings demonstrate that the K-Means algorithm is effective in identifying sales patterns and can be used as a basis for decision-making in inventory management strategies at distribution centers, particularly in determining storage priorities based on product demand levels.
Analisis Performa Algoritma Naïve Bayes dan SVM Menggunakan Python Pada Ulasan Sentimen Game Roblox Dia Komalla; RG Guntur Alam; Ardi Wijaya
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9396

Abstract

The imbalance of user reviews in the Roblox game creates accuracy challenges in sentiment classification, where the number of positive reviews significantly exceeds negative ones, causing the model to struggle particularly in identifying negative sentiment. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine algorithms in classifying sentiment on imbalanced data. The research was conducted through several stages, including web scraping, pre-processing, automatic labeling using CNN, data splitting, model training, and performance evaluation using a Confusion Matrix. The findings reveal that Naïve Bayes tends to classify most samples as positive, resulting in very high recall for the positive class, reaching 0.995–0.997, but poor performance on the negative class, leading to consistent imbalance across all test ratios. In contrast, SVM achieves higher accuracy and more stable performance, with a Macro-F1 score of 0.740–0.769 and an AUC-PR of 0.936–0.942. The performance differences between the two models are statistically significant, with p-values of 0.001 and 0.0004, indicating that SVM is more effective in identifying both majority and minority classes. However, in terms of computational efficiency, Naïve Bayes is superior, requiring only 0.003–0.016 seconds of training time. Therefore, SVM is considered more reliable and robust for sentiment analysis on imbalanced data such as Roblox game reviews, whereas Naïve Bayes is more suitable when processing speed is the priority.
Co-Authors Ardi Wijaya Fitriah Fitriah Muhajir Yunus RG Guntur Alam