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All Journal Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer InComTech: Jurnal Telekomunikasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Applied Technology and Computing Science Journal Jurnal Informatika dan Rekayasa Elektronik bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Nusantara Science and Technology Proceedings Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Informatika Dan Tekonologi Komputer (JITEK) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Basuki Rahmat
Master Of Information Technology, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Other

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Journal : bit-tech

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Optimization of Earthquake B-Value Prediction in Java Using GRU and Particle Swarm Optimization Kesya Nursyahada; Basuki Rahmat; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2521

Abstract

Accurate prediction of earthquake parameters is essential for seismic risk assessment and disaster mitigation, particularly in tectonically active regions such as Java Island, Indonesia. This study presents a novel predictive model for estimating the earthquake b-value a fundamental seismological parameter representing the logarithmic relationship between earthquake frequency and magnitude by integrating a Gated Recurrent Unit (GRU) neural network with Particle Swarm Optimization (PSO). The model is trained using earthquake catalog data from 1962 to 2024, sourced from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The GRU architecture is selected for its effectiveness in modeling temporal dependencies in seismic time series data. PSO is employed to optimize essential hyperparameters, including the number of GRU units, learning rate, and dropout rate. The optimized model achieves notable improvements in predictive performance: Mean Squared Error (MSE) is reduced from 0.00435 to 0.00030, Root Mean Squared Error (RMSE) from 0.0509 to 0.0173, and Mean Absolute Percentage Error (MAPE) from 3.42% to 1.12%. Training time is also reduced from 57 seconds to 33 seconds, indicating greater computational efficiency. The optimal PSO settings include an inertia weight of 0.8, cognitive and social coefficients of 1.0, 40 particles, and 10 iterations. The primary novelty of this study lies in its targeted application of PSO-optimized GRU architecture for b-value prediction in a seismically complex region. These results demonstrate that evolutionary optimization significantly enhances deep learning performance, providing a robust and efficient framework to support earthquake forecasting and risk mitigation efforts in high-risk zones such as Java Island.
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Andini Fitriyah Salsabilah; Basuki Rahmat; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Hyperparameter Optimization with Hyperband for Tuberculosis Classification Yovi Ibnu Nasikhin; Basuki Rahmat; Chrystia Aji Putra
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3273

Abstract

Tuberculosis (TB) is an infectious illness that continues to be a major global health challenge due to its high rates of disease and death. Early detection of TB using chest X-ray images often faces challenges related to subjective interpretation by radiologists and limited sensitivity and specificity. This study develops a Convolutional Neural Network (CNN) model to classify chest X-ray images into Normal and Tuberculosis classes, using a total of 2,198 chest X-ray images consisting of 1,173 Normal and 1,025 Tuberculosis samples. Hyperparameter optimization was carried out using the Hyperband algorithm implemented in the Keras Tuner framework to obtain the best parameter combination that produced optimal model performance. The main hyperparameters tuned included the number of dense layers, the number of units per layer, dropout rate, and learning rate. The optimization process yielded the best configuration consisting of two dense layers with 160 and 64 units, a dropout rate of 0.3, and a learning rate of 0.0011. The optimization process increased the model’s accuracy from 0.84 to 0.88 and reduced the validation loss from 0.44 to 0.34, indicating a more stable and effective learning outcome after optimization using Hyperband. The application of Hyperband successfully enhanced learning stability, accelerated convergence, and improved overall model performance. These results indicate that hyperparameter optimization using Hyperband not only enhances CNN-based TB classification accuracy but also strengthens its potential clinical utility by supporting more consistent, rapid, and objective early diagnosis in real-world healthcare settings.
Stres adalah masalah psikologis umum di kalangan Generasi Z, didorong oleh tekanan akademik, perbandingan sosial, dan paparan digital. Deteksi dini sangat penting untuk mencegah masalah kesehatan mental yang lebih parah seperti gangguan kecemasan, burnout Ananda Asa Firstha Affandi; Basuki Rahmat; Retno Mumpuni
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3375

Abstract

Stress is a common psychological issue among Generation Z, driven by academic pressure, social comparison, and digital exposure. Early detection is essential to prevent more severe mental health problems such as anxiety disorders, burnout, or depression. This study aims to optimize a web-based stress detection system using the Recursive Feature Elimination (RFE) method combined with the Random Forest algorithm. A dataset consisting of 500 psychological assessment records and 12 symptom features (G01 to G12) from A3M Consultant Surabaya was used as the basis for analysis. RFE successfully reduced the number of features to six key indicators, such as G01 (anxiety), G02 (emotional instability), G04 (restlessness), G08 (withdrawal), G09 (confusion), and G12 (suicidal thoughts) while maintaining high model accuracy. The baseline Random Forest using 12 features achieved 0.91 accuracy, while the RFE-optimized model with 6 selected features maintained a comparable accuracy of 0.90. The resulting model achieved an accuracy of approximately 0.90 based on Stratified K-Fold Cross Validation, showing consistent performance across folds. The optimized model was then integrated into a web application called “The Z Space,” which combines data driven predictions from Random Forest with rule- based reasoning using Forward Chaining. This hybrid approach ensures both interpretability and accuracy in determining stress levels. The findings highlight that RFE effectively reduces computational complexity without decreasing model performance, making it suitable for real time web implementation in stress detection systems for Generation Z.
Co-Authors Adhi Prasetya, Guntur ADJIE KURNIAWAN, RAKA Afina Lina Nurlaili Alfi Ramadhaniar Ananda Asa Firstha Affandi Andini Fitriyah Salsabilah Anggraeny, Fetty Anggraini Puspita Sari Avi Oktaviani B. Nugroho Budi Nugroho Budi Nugroho Daniella, Cherry Deri Setiawan Devan Cakra Mudra Wijaya Dody Pintarko Edi Dwi Prasetyo Eva Yulia Puspaningrum Faisal Muttaqin Faisal Muttaqin faisal, Faisal Fahri Ferdiansyah Fetty Tri Anggraeny Fina Amru Millati Handono, Stevanus Frangky Harianto Hariyanto Hariyanto Helmy Widyantara Henni Endah Wahanan Henni Endah Wahanani Hutama, Dhany Satya I Nyoman Sujana Ida Retno Moeljani Ifnu Wisma Dwi Prastya Iqbal Imani Khoirul Akbar Kartini Kartini Kartini Kartini Kartini Kesya Nursyahada Mada Lazuardi Nazilly Minto Waluyo Mohamad Irwan Afandi Muhammad Alfin Jimly Asshiddiqie Naufal Mumtaz, Ahmad Nigel, Mortekiano Nugraha, Novemidu Wilis Prameswari Reksa Agami Putra, Chrystia Aji R MEHINDRA PRASMATIO Raka Adjie Kurniawan Retno Mumpuni Ridho Aji Pangestu Royan Hisyam Rafliansyah Sabrina Laila Sari Singgih Putra Pratama Singgih Putra Pratama Sudiyarto Syaifullah JS, Wahyu TATI NURHAYATI Taufik Nur Firmansyah Tuhu Agung Rachmanto Tuhu Agung Rachmanto Wahyu SJ Saputra Widjajani, Bhakti Wisnu Yovi Ibnu Nasikhin Yuniar Purbasari, Intan Yuniar, Intan