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All Journal Humanika : Kajian Ilmiah Mata Kuliah Umum LIBRARIA Masyarakat, Kebudayaan dan Politik Martabe : Jurnal Pengabdian Kepada Masyarakat Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JURNAL GOVERNANSI Building of Informatics, Technology and Science MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Journal of Soft Computing Exploration Bidik : Jurnal Pengabdian kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Pustakaloka: Jurnal Kajian Informasi dan Perpustakaan Madania: Jurnal Kajian Keislaman
Margono, Hendro
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Malcom: Indonesian Journal of Machine Learning and Computer Science

 1 
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.
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. 
Analisis Pengelompokan Laporan Panggilan untuk Perencanaan Respons Berbasis Data: Clustering Analysis of Call Reports for Data-Driven Response Planning Cahyani, Retno Tri; Yuadi, Imam; Margono, Hendro
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2168

Abstract

Setiap tahun, Call Center 112 Kabupaten Sidoarjo menerima ribuan laporan dari masyarakat, yang mencakup berbagai kejadian seperti kebakaran, kecelakaan lalu lintas, darurat medis, kabel menjuntai, pohon tumbang, dan masalah PJU. Penelitian ini menganalisis 6.207 laporan berfokus pada koordinat lokasi kejadian dengan tujuan untuk mengelompokkan pola spasial laporan sehingga dapat mendukung tata Kelola pelayanan publik yang lebih responsif. Untuk mencapai tujuan tersebut digunakan dua algoritma pembelajaran yaitu K-Means dan K-Medoids. Metode Elbow digunakan untuk menentukan jumlah klaster (k=3). Metode ini menunjukkan titik optimum ketika nilai inertia mulai menurun secara linier. Analisis menggunakan Google Colab dan ada dukungan pustaka untuk visualisasi seperti scikit-learn, pyclustering, dan matplotlib. Hasil visualisasi menunjukkan bahwa K-Medoids membentuk klaster yang lebih terstruktur secara geografis, sedangkan K-Means menghasilkan klaster yang tumpang tindih. Silhouette Score 0,479, yang lebih tinggi dari K-Means hanya 0,193, K-Medoids terbukti lebih unggul dalam membentuk klaster yang kompak dan konsisten. K-Medoids berhasil mengelompokkan wilayah yang rawan insiden (Waru, Gedangan) dan wilayah infrastruktur dominan (Sidoarjo, Candi) ke dalam klaster yang sesuai secara spasial. Analisis ini mengidentifikasi fitur tiap klaster berdasarkan jenis laporan, mulai dari darurat medis hingga masalah PJU. Penemuan ini berguna untuk mendukung alokasi sumber daya dan layanan publik yang lebih efisien saat membangun kota pintar.
Co-Authors Adriansyah, Muhammad Ali Anastasya, Diva Berta Arief Suyoso, Aldo Lovely Bastian, Devi Cahyani, Retno Tri Dama Putri, Kania Faiza, Nova Auliatul Fauziah, Salsa Fitri Mutia, Fitri Gunarti, Endang Halim, Yunus Abdul Hariani, Nurul Jamila Hary Supriyatno Imam Yuadi Inriani, Sefania Wenny Eka Julian, Antoni Khithobi, Arya Teges Koko Srimulyo Lestari, Santi Dwi Desy Maulidah, Nofiyah Niken Ayu Pratiwi, Bertha Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Putra, Yudistira Ardi Nugraha Setyawan Ragil Tri Atmi, Ragil Tri Rahma, Nadhifia Shabrina Nur Rasyid, Rizka Adristi Rifa, Syafira Dila Rinanda, Tiara Disti Rosyani, Widha Seger Handoyo Setiadi, Yusuf Setiyarini, Melina Dewi Soegiono, Agie Nugroho Soesantari, Tri Sugihartati, Rahma Suharbi, Muhammad Akbar Tondang, Edoardo Tri Atmi, Ragil Vivia Adriyanti, Elvetta Windrasari, Shella Norma Yuwinanto, Helmy Prasetyo