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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Teknik Informatika (JUTIF) Malcom: Indonesian Journal of Machine Learning and Computer Science
Firmansyah, Hafiz Budi
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Application of Linear Regression Analysis Model on Early Warning System for Inefficiency of Electricity Usage Kesuma, Rahman Indra; Firmansyah, Hafiz Budi; Darmawan, Mahardika Yoga
SENATIK STT Adisutjipto Vol 4 (2018): Transformasi Teknologi untuk Mendukung Ketahanan Nasional [ ISBN 978-602-52742-0-6 ]
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v4i0.258

Abstract

Recently the Indonesian people often get inefficiency of electricity usage. On the other side, in Indonesia, the electricity is mostly produced from steam power plant, which require fuel from non-renewable natural resources. So the highness of demand and the occurrence of inefficiency the electricity usage can increase the consumption of natural resource and the air pollution. Therefore, an early warning system are proposed in this study, become one of the various solution than can increase awareness of the people in efficiency of electricity usage. This system requires the input data of electricity usage in the last 6 months, that will be formed the electricity usage trend from each user using linear regression analysis. Furthermore, this trend will predict the electricity usage for next month, this is used as the limit to give the warning from the system. The outcome from this study is the system that can provide a warning to users if their electricity usage run over the certain limits.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
Comparing BERTBase, DistilBERT and RoBERTa in Sentiment Analysis for Disaster Response Firmansyah, Hafiz Budi; Afriansyah, Aidil; Lorini, Valerio
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4766

Abstract

Social media platforms are vital for real-time communication during disasters, providing insights into public emotions and urgent needs. This study evaluates the performance of three transformer-based models—BERTBase, DistilBERT, and RoBERTa—for sentiment analysis on disaster-related social media data. Using a multilingual dataset sourced from the Social Media for Disaster Risk Management (SMDRM) platform, the models were assessed on classification metrics including accuracy, precision, recall, and weighted F1-score. The results show that RoBERTa consistently outperforms the others in classification performance, while DistilBERT offers superior computational efficiency. The analysis highlights the trade-offs between model accuracy and runtime, emphasizing RoBERTa's suitability for scenarios prioritizing accuracy, and DistilBERT's potential in time-sensitive or resource-constrained applications. These findings support the integration of sentiment analysis into disaster response systems to enhance situational awareness and decision-making.
Comparative Analysis of Machine Learning Algorithms for Software Fault Prediction Iqbal, Amirul; Praseptiawan, Mugi; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Febrianto, Andre
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2659

Abstract

Software fault prediction helps development teams prioritize quality assurance by identifying software components that are likely to contain faults before release. The purpose of this study is to determine whether three recommended optimization strategies: parameter optimization, feature selection, and class-imbalance handling produce consistent performance improvements across different machine learning algorithms. This study compares five machine learning algorithms for software fault prediction, namely Random Forest, K-Nearest Neighbor, AdaBoost, Support Vector Machine (SVM), and a fully connected neural network, using historical change features derived from five open-source software projects. The study evaluates three optimization strategies: parameter optimization, feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and class-imbalance handling through oversampling and undersampling. Model performance is assessed using five-fold stratified cross-validation with Accuracy, Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), and F1-score. The results show that the parameter-optimized baseline models achieved comparable performance across algorithms, with accuracy values above 0.83 and ROC-AUC values modestly above random performance. Feature selection produced limited overall gains and improved all three performance measures only for AdaBoost, while class-imbalance handling generally reduced accuracy although it improved ROC-AUC in several cases. 
Co-Authors Afriansyah, Aidil Algifari, Muhammad Habib Darmawan, Mahardika Yoga David Gunawan, David Eko Dwi Nugroho Febrianto, Andre Iqbal, Amirul Lorini, Valerio Praseptiawan, Mugi Rahman Indra Kesuma Rizkita, Alya Khairunnisa Verdiana, Miranti Winarta, Richard Arya