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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sisforma: Journal of Information Systems Bulletin of Electrical Engineering and Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Technomedia Journal International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Informatika dan Sistem Informasi IJIIS: International Journal of Informatics and Information Systems Teknika Journal of Applied Data Sciences Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Pena Dimas: Jurnal Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Digital Market and Digital Currency International Journal Research on Metaverse
Adi Suryaputra Paramita
Universitas Ciputra Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Comparison of K-Means and DBSCAN Algorithms for Customer Segmentation in E-commerce Paramita, Adi Suryaputra; Hariguna, Taqwa
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.3

Abstract

Customer segmentation is crucial for e-commerce businesses to effectively target and engage specific customer groups. This study compares the effectiveness of two popular clustering algorithms, K-Means and DBSCAN, in segmenting e-commerce customers. The primary objective is to evaluate and contrast these algorithms to determine which provides more meaningful and actionable customer segments. The methodology involves analyzing a comprehensive e-commerce customer dataset, which includes various features such as customer ID, gender, age, city, membership type, total spend, items purchased, average rating, discount applied, days since last purchase, and satisfaction level. Initial data preprocessing steps include handling missing values, encoding categorical variables, and normalizing numerical features. Both K-Means and DBSCAN algorithms are implemented, and their performance is evaluated using metrics such as silhouette score, Davies-Bouldin index, and Calinski-Harabasz score. The results indicate that K-Means achieved a silhouette score of 0.546, a Davies-Bouldin index of 0.655, and a Calinski-Harabasz score of 552.9. In contrast, DBSCAN achieved a higher silhouette score of 0.680, a Davies-Bouldin index of 1.344, and a Calinski-Harabasz score of 1123.9. These findings suggest that while DBSCAN performs better in terms of silhouette score, indicating more distinctly separated clusters, its higher Davies-Bouldin index reflects fewer compact clusters. The discussion highlights that K-Means is suitable for applications requiring clear and well-defined segments of customers, as it produces balanced cluster sizes. DBSCAN, with its strength in identifying clusters of varying densities and handling noise, is more effective in detecting niche markets and unique customer behaviors. This study's findings have significant practical implications for e-commerce businesses looking to enhance their customer segmentation strategies. In conclusion, both K-Means and DBSCAN demonstrate their respective strengths and weaknesses in clustering the e-commerce customer dataset. The choice of algorithm should be based on the specific requirements of the segmentation task. Future research could explore hybrid methods combining the strengths of both algorithms and incorporate additional data sources for a more comprehensive analysis.
Predicting Player Performance in Valorant E-Sports using Random Forest Algorithm: A Data Mining Approach for Analyzing Match and Agent Data in Virtual Environments Paramita, Adi Suryaputra; Jusak, Jusak
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.39

Abstract

This study presents a data-driven approach to predict player performance in Valorant, an increasingly popular e-sport, using a Random Forest machine learning model. As e-sports continue to evolve within the metaverse, the need for strategic optimization and player selection has become critical. By analyzing a dataset containing player statistics from the Valorant Champion Tour (VCT), we aimed to predict player Rating, a key performance indicator. The dataset includes various metrics such as Kills Per Round, Average Combat Score (ACS), Clutch Success Ratio, and Kills:Deaths. After preprocessing the data, which involved handling missing values and feature engineering, the dataset was split into training and testing sets (80% and 20%, respectively). The Random Forest model, with 100 estimators and a maximum depth of 10, was trained on the processed data. The model's performance was evaluated using regression metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The results demonstrated that the model could predict player performance with a high degree of accuracy, with an R² value of 0.8831, meaning it explained 88.31% of the variance in player ratings. Furthermore, Kills Per Round emerged as the most significant feature, followed by Kill, Assist, Trade, Survive Ratio and Average Damage Per Round. These insights suggest that key metrics like kills and damage output are crucial for predicting player success. This study not only provides a comprehensive framework for predicting Valorant player performance but also demonstrates the potential of data mining in optimizing e-sports strategies. The findings contribute to the growing body of research on virtual gaming environments and offer actionable insights for teams in the metaverse, enabling data-driven decision-making to enhance performance and strategic outcomes.
Co-Authors Adrie Oktavio Amanda Teonata Arief Ramadhan Arief Ramadhan christian christian Dana Indra Sensuse EVELYN EVELYN Harjanto Prabowo Harjanto Prabowo Hendry Cipta Husada Indra Maryati Isviandhy, Gwynneth Jusak Jusak Kartika Gianina Tileng Laura Mahendratta Tjahjono Marco Febriadi Kokasih Minny Elisa Yanggah Nicoline Odelia, Lisandra Philbert Lukito Setiawan Pratama, Dimas Kurnia Rinabi Tanamal Taqwa Hariguna Tony Antonio Trianggoro Wiradinata Winata, Shalomeira Valencia Yosua Setyawan Soekamto Yulius Hari