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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Practical Computer Science (JPCS) Jurnal Informatika Ekonomi Bisnis VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian Cahaya Pengabdian
Surojudin, Nurhadi
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Journal : Bulletin of Computer Science Research

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Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Muhidin, Asep; Muhtajuddin Danny; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.745

Abstract

Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.
Analisis Klaster Penyebaran Berat Produk Mesin Sachet Menggunakan Metode Algoritma K-Means Ermanto, Ermanto; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.766

Abstract

This study aims to analyze the distribution patterns of sachet machine product weights using the K-Means algorithm as a clustering technique. The dataset consists of 940 entries of primary production records, each containing ten weight measurement samples per production cycle. The data underwent a cleaning process to ensure the absence of missing values, duplicates, and outliers, followed by the selection of relevant attributes (product weight samples) and transformation using Min-Max normalization to scale all variables within the 0–1 range. The clustering process was performed iteratively by updating the centroids until convergence was achieved. The evaluation results indicate that the optimal number of clusters is three (k=3) with a Silhouette Coefficient of 0.55, reflecting a good balance between intra-cluster homogeneity and inter-cluster separation. Cluster 1 represents products with relatively low weights (8.00–8.18 grams), Cluster 2 includes medium-weight products (8.19–8.34 grams), and Cluster 3 consists of high-weight products (8.36–8.98 grams). Overall, the product weights tend to be stable with low variation, although some anomalies were observed in certain machines. These findings demonstrate that the K-Means algorithm can effectively classify product weight data, providing valuable insights for quality control, product variation identification, and minimizing risks of deviation from production standards.
Co-Authors . Ermanto ., Edora Abdul Ghofir Afriantoro, Irfan Andriani Anshori, Fajri Apisca, Dhea Djoko Nugroho Dodit Ardiatma Effendi, M. Makmun Effendi, Muhammad Makmun Elkin Rilvani Ermanto Fachrial Banyu Asmoro Halim Anshor, Abdul Ikhsan Romli Juluw, Sephia Maharani Niki Maulana, Donny muhidin, asep Muhtajuddin Danny Naufal, Luthfi Eka Nugroho, Agung Pradini, Purnama Sakhrial Pratama, Suria Purnama Sakhrial Putra, Fibi Eko Rosyid, Dimas Abdul Saiful Muktiali Siswandi, Arif Suratman Suratman Suryadi Suryadi, Dikky Turmud Zy, Ahmad Turmudi Zy, Ahmad Yoga Pratama, Evan Zy, Ahmad Turmudi