Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Informatics for Educators and Professional : Journal of Informatics
Darusman Darusman
Nusamandiri University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Prediksi Kualitas Produk Manufaktur Semikonduktor Menggunakan Machine Learning Darusman Darusman; Aries Abbas; Angga Dwi Firmanto
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 1 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i1.3348

Abstract

The semiconductor manufacturing industry faces challenges in efficient and accurate product quality control. Traditional manual inspection methods are still used but have limitations in speed and accuracy. Therefore, the application of machine learning has become a potential solution to improve the prediction of semiconductor production outcomes. This study develops a predictive model using Logistic Regression, Decision Tree, Random Forest, XGBoost, Naïve Bayes, and Support Vector Machine (SVM) with various dataset partitioning techniques such as Normal Data (90:10), Oversampling (70:30), Undersampling (80:20 & 70:30), and Principal Component Analysis (PCA) (90:10). The dataset used is sourced from the Factory Manufacturing Semiconductor Test (FMST), comprising 1,567 samples and 591 features, with product quality test labels (Pass/Fail). The results show that XGBoost and Random Forest achieved the highest accuracy (0.95) on the normal dataset (90:10), while Naïve Bayes had the lowest performance (0.23) due to its limitations in handling datasets with a large number of features. The oversampling technique improved the performance of Decision Tree and Logistic Regression but reduced the accuracy of XGBoost due to the risk of overfitting. Meanwhile, undersampling was more effective for Decision Tree but decreased SVM performance. The application of PCA improved Logistic Regression accuracy to 0.81, proving that dimensionality reduction can enhance model efficiency. Further analysis shows that feature selection using F-score can optimize model performance by eliminating redundant features. This study concludes that XGBoost and Random Forest are the best models for predicting semiconductor manufacturing product outcomes, with broad potential applications in the industry.
Klasifikasi Keparahan Kecelakaan Lalu Lintas Menggunakan Machine Learning Darusman Darusman; Achmad Pahrul Rodji
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 1 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i1.3354

Abstract

Traffic accidents are one of the main causes of injury and death in various countries. Various factors such as the number of vehicles involved, lighting conditions, type of intersection, and the underlying cause of the accident contribute to the severity of the accident. This research aims to classify the severity of traffic accidents using Random Forest, Decision Tree, Naïve Bayes, and Support Vector Machine (SVM) algorithms. The dataset used was obtained from Kaggle with various relevant categorical and numerical features. The research methodology includes data collection and model evaluation. Model evaluation is carried out using accuracy, precision, recall, and F1-score. The research results show that Random Forest provides the highest accuracy of 84%, followed by Naïve Bayes (82%), Decision Tree (77%), and SVM (71%). The SMOTE technique improves the performance of some models by improving data balance, but also increases the risk of overfitting. Meanwhile, applying PCA helps improve model efficiency by reducing dimensions without losing important information. With the model that has been developed, this prediction system has the potential to be used in road safety analysis and to support decision making in mitigating traffic accidents
Cross Project Defect Prediction Menggunakan Random Forest Darusman Darusman; Agus Subekti
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3706

Abstract

This study develops a software defect prediction model using the Random Forest algorithm with a Many-to-One Cross-Project Defect Prediction approach. The model is tested using the AEEEM dataset as the training data source and the PROMISE dataset as the testing target. Both datasets consist of various software projects with important features such as code size, code churn, complexity, and other metrics that play a role in predicting software defects. The model’s performance is evaluated using metrics such as Accuracy, AUC, Recall, Precision, and F1-Score to measure its ability to detect defects across different datasets. The results show that the Random Forest model delivers excellent performance, with accuracy above 94% and AUC greater than 0.92 on most datasets. The model is also able to balance Recall and Precision effectively, resulting in more accurate and reliable predictions. Furthermore, this study applies ensemble techniques such as stacking and voting to combine predictions from multiple models, which significantly improve the stability and accuracy of the predictions. With this approach, the study demonstrates that the use of the Random Forest algorithm in CPDP can enhance the accuracy and efficiency of cross-project software defect prediction.
Robot Pembersih Lantai Otomatis Berbasis Arduino Uno Darusman Darusman; Aries Abbas; Angga Dwi Firmanto
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3731

Abstract

The Arduino-based automatic floor cleaning robot is a system designed to assist in the floor cleaning process efficiently without human intervention. This system uses an ultrasonic sensor to detect obstacles and a dust sensor to measure and display the amount of dirt particles absorbed by the vacuum cleaner. The Arduino Uno is used as the main controller, regulating the robot's movement using DC motors and processing sensor data to be displayed on an LCD as a cleanliness indicator. This study employs a hardware programming method with an experimental approach, where the robot is tested in an environment with obstacles and dust particles as the main objects. Testing is conducted to evaluate navigation effectiveness and dust suction capability. The results show that the robot can operate automatically, avoid obstacles, and clean floors with a fairly high level of efficiency. This research contributes to the development of home automation technology by introducing a more efficient and autonomous floor cleaning robot solution. With this system, it is expected to improve room cleanliness without requiring direct human involvement.
Analisis Perbandingan Evaluasi Deep Learning Untuk Klasifikasi Gaya Arsitektur Darusman Darusman; Zulkarnain Zulkarnain
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 11 No. 1 (2026): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v11i1.3785

Abstract

This study compares the performance of several deep learning models in architectural style classification using architectural image datasets. The models used include InceptionResNetV2, VGG16, MobileNetV2, and ResNet50V2. The data is processed through image augmentation techniques to improve model generalization. Evaluation is carried out using accuracy, precision, recall, F1-score, and Confusion Matrix metrics to measure the effectiveness of the classification. The results show that InceptionResNetV2 and ResNet50V2 have the best performance with an accuracy of 84%, followed by MobileNetV2 (79%) and VGG16 (71%). More complex models show better ability in capturing visual patterns than lighter models. The results indicate that the use of deeper deep learning models can improve the accuracy of architectural classification. This research is expected to contribute to the development of more accurate and efficient architectural classification systems for various applications, including cultural conservation and architectural design.
Co-Authors Achmad Pahrul Rodji Agus Subekti Angga Dwi Firmanto Aries Abbas Zulkarnain Zulkarnain