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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan INTEGER: Journal of Information Technology JUTIM (Jurnal Teknik Informatika Musirawas) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Teknik Informatika (JUTIF) Journal of Information System and Technology (JOINT) International Journal Of Computer, Network Security and Information System (IJCONSIST) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Biner : Jurnal Ilmiah Informatika dan Komputer Ranah Research : Journal of Multidisciplinary Research and Development
Nurlaili, Afina Lina
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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 Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Transportation 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 Nursyahada, Kesya; Rahmat, Basuki; Nurlaili, Afina Lina
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.
Random Forest – Deep Convolutional Neural Network Ensemble Model for Skin Disease Classification Kurniawan, Ananda Rheza; Via, Yisti Vita; Nurlaili, Afina Lina
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.v8i1.2528

Abstract

Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.
Design and Development of a Counseling Service System Using Extreme Programming Methodology Nobrian, Ikhsan; Nurlaili, Afina Lina; Aditiawan, Firza Prima
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.v8i1.2928

Abstract

This study addresses the inefficiency and error-prone nature of manual counseling and student violation point recording processes in schools, which often result in delays and inaccuracies. To overcome these challenges, we propose the development of a digital guidance and counseling service system designed to improve data management and enhance service accessibility for school administrators and counselors. The innovation lies in the creation of an integrated, browser-accessible application built using the MERN (MongoDB, Express.js, React, Node.js) stack, which ensures robust functionality and scalability. By applying modern development and testing methodologies, the system is designed to be both reliable and user-friendly. The core objective of this system is to streamline processes such as counseling appointment scheduling, alumni tracking, certificate submission, and student behavior reporting. It was developed using the Extreme Programming (XP) methodology, which encourages flexibility and iterative planning through close collaboration with end users. White Box Testing techniques, including cyclomatic complexity analysis and independent path testing, were employed to validate the system's internal logic. The system’s usability was assessed using the System Usability Scale (SUS), achieving an excellent score of 93.25, indicating high user satisfaction. Furthermore, the Lighthouse performance test yielded a perfect score of 100, confirming the system's high responsiveness. These results demonstrate that the developed system significantly enhances the efficiency, accuracy, and accessibility of guidance services, reduces administrative burdens, and enables better monitoring of student development, making it ideal for deployment in real-world school environments.
Co-Authors Achmad Junaidi Akbar, Fawwaz Ali Andreas Nugroho Sihananto Ani Dijah Rahajoe Arfiqi, Fawwaz Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Chaurina, Agfanadita Rezkia Dhian Satria Yudha Kartika Eka Prakarsa Mandyartha Fathoni, Fuad Mahrus Fathuddin, Muhammad Abdul Hafizh Firza Prima Aditiawan Gusti Eka Yuliastuti Henni Endah Wahanani Ismiati, Suci Kurniawan, Ananda Rheza Mardhavi, Arif Maulana, Dimas Octa Maulana, Hendra Maulana, Vieri Arief Muhammad Muharrom Al Haromainy Muhsin Muhsin Nobrian, Ikhsan Nursyahada, Kesya Pratama, Novandi Kevin Putra, Chrystia Aji Putri, Della Atika Retno Mumpuni Rifan, Triyono Riski, Agung Mustika Sadewa, Rahma Danu Saharani, Aulia Siregar, Talitha Aurora Nadenggan Waskito, Muhammad Rizal Yana, Baktiar Yudha Yisti Vita Via