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All Journal JURNAL GOVERNANSI Malcom: Indonesian Journal of Machine Learning and Computer Science
Putra, Yudistira Ardi Nugraha Setyawan
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Computer Science & IT Social Sciences

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A Simulation of Student Study Group Formation Design Using K-Means Clustering Putra, Yudistira Ardi Nugraha Setyawan; Margono, Hendro
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1795

Abstract

This research focuses on developing a simulation model for forming student study groups using an enhanced K-Means algorithm, addressing the challenge of optimizing group dynamics to improve learning outcomes. By analyzing the effectiveness of the formed study groups through RMSE (Root Mean Square Error) after dimensionality reduction with various regression models—including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor—we aim to provide educators with a robust tool for assessing group configurations. The study identifies four distinct clusters, revealing that "Previous_Score" and "Attendance" are critical variables, achieving a highest Silhouette Score of 0.64 with five selected features. The ridge regression model also yielded a low RMSE of 0.045, explaining 72.39% of the variance in "Exam_Score." The findings suggest that targeted interventions tailored to each cluster—yellow, purple, blue, and green—can enhance academic outcomes by addressing specific student needs. This data-driven approach optimizes group dynamics and fosters a more inclusive learning environment, enhancing academic performance and cultivating essential social skills. The study underscores the potential of machine learning techniques in education and suggests avenues for future research into alternative clustering methods and their long-term impact on student engagement and success.
DAMPAK NARSISME PEMIMPIN TERHTERDAPATP KINERJA ORGANISASI: TINJAUAN LITERATUR MULTILEVEL PADA TINGKAT INDIVIDU, TIM, DAN ORGANISASI Yuanus, Franki; Choirunnisa, Zuyyinna; Putra, Yudistira Ardi Nugraha Setyawan
Jurnal Governansi Vol 11 No 2 (2025): Jurnal Governansi, Volume 11 Number 2, October 2025
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jgs.v11i2.19243

Abstract

Leader narcissism is a multifaceted personality construct characterized by grandiosity and a need for admiration, which may interact with other traits within the "Dark Triad." In the context of leadership, narcissism exhibits paradoxical characteristics: on one hand, it can foster charisma and ambitious vision, even promoting positive ESG (Environmental, Social, and Governance) behaviors; on the other hand, excessive need for validation can lead to exploitative actions and significant negative outcomes. This literature review examines the multilevel impact of leader narcissism at the individual, team, and organizational levels—drawing upon Trait Activation Theory and Socioanalytic Theory. The findings indicate that leader narcissism can trigger counterproductive subordinate behaviors, increase team turnover, and affect organizational quality. However, political skill may serve as a moderating factor that enhances the functional expression of narcissism. This review identifies conceptual, methodological, and empirical gaps, and proposes future research directions to further elucidate the complexity of this phenomenon.
Predictive Sales Analysis in Coffee Shops Using the Random Forest Algorithm Windrasari, Shella Norma; Margono, Hendro; Putra, Yudistira Ardi Nugraha Setyawan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2023

Abstract

The coffee shop industry has experienced significant growth, evolving into a highly competitive marketplace demanding specialty coffee and personalized experiences. While data-driven strategies are crucial for optimizing operations, many owners still struggle to effectively leverage their sales data to understand dynamic customer behavior and enhance decision-making. Addressing this gap, this study explores the application of machine learning (ML) techniques, specifically the Random Forest Regressor model, to predict sales performance within the coffee shop business environment. By analyzing factors such as transaction timing, store location, product type, and day of the week, this research aims to uncover patterns that can enhance inventory management and customer engagement. The Random Forest model was evaluated through cross-validation, yielding a mean Mean Squared Error (MSE) of 80.97, which indicates moderate predictive accuracy and represents an improvement over traditional forecasting methods commonly employed in the industry. Feature importance analysis revealed that Premium Beans is the most influential predictor, followed by seasonal trends (month), time of day, and weekend sales patterns. These findings underscore the importance of incorporating temporal and contextual factors into forecasting models. 
Co-Authors Margono, Hendro Windrasari, Shella Norma Yuanus, Franki Zuyyinna Choirunnisa