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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Nurhadi Surojudin
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Asep Muhidin; Muhtajuddin Danny; Nurhadi Surojudin
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; Nurhadi Surojudin
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.
Perbandingan Kinerja Naïve Bayes dengan dan Tanpa SMOTE untuk Klasifikasi Gangguan Kecemasan Mahasiswa pada Data Tidak Seimbang Nurhadi Surojudin; Sufajar Butsianto; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1021

Abstract

Anxiety disorders are one of the most common mental health problems experienced by university students and may affect learning concentration and academic performance. The analysis of psychological survey data using machine learning techniques can support early detection of student anxiety conditions. However, one of the main challenges in mental health data analysis is the presence of class imbalance within the dataset. This study aims to analyze the effect of applying the Synthetic Minority Oversampling Technique (SMOTE) on the performance of the Naïve Bayes algorithm for multi-class classification of student anxiety levels, which are categorized into three classes: No Stress, Eustress, and Distress. The dataset used in this research was obtained from student questionnaire data and underwent several preprocessing steps including data cleaning, feature transformation, and dataset splitting using the hold-out method with a ratio of 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix with evaluation metrics including accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes model without SMOTE achieved an accuracy of 0.84, precision 0.78, recall 0.41, and F1-score 0.54. After applying SMOTE, the model achieved an accuracy of 0.82, precision 0.74, recall 0.69, and F1-score 0.71. These results indicate that SMOTE improves the model's ability to detect minority classes in multi-class classification problems, although a slight decrease in overall accuracy is observed.
Prediksi Risiko Penyakit Jantung Menggunakan Support Vector Machine dengan Seleksi Fitur dan Optimasi Hyperparamete Nurhadi Surojudin; Sufajar Butsianto; Rizal Ainun Yaqin
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10518

Abstract

Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accurate prediction models to support early diagnosis and clinical decision-making. This study proposes a heart disease risk prediction model based on Support Vector Machine (SVM) integrated with Recursive Feature Elimination (RFE) for feature selection and GridSearchCV for hyperparameter optimization. The study utilized the Cleveland Heart Disease Dataset, consisting of 303 patient records, 13 predictive attributes, and one target variable. The research workflow included dataset collection, data preprocessing, feature selection, hyperparameter optimization, model development, and performance evaluation using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Experimental results demonstrate that the proposed model achieved an Accuracy of 86.89%, Precision of 85.71%, Recall of 85.71%, F1-score of 85.71%, and ROC-AUC of 93.18%. Feature selection successfully reduced irrelevant attributes, improving model efficiency, while hyperparameter optimization produced a more effective parameter configuration than the default settings. These findings indicate that integrating RFE with GridSearchCV enhances the predictive performance of SVM and provides a promising approach for supporting heart disease diagnosis using machine learning techniques.
Co-Authors Andri Firmansyah Asep Muhidin Ermanto Ermanto Muhtajuddin Danny Rizal Ainun Yaqin Sufajar Butsianto