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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 JOISIE (Journal Of Information Systems And Informatics Engineering) bit-Tech 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
Basuki Rahmat
Teknik Informatika Fakultas Ilmu Komputer Universitas Pembangunan Nasional “Veteran” Jawa Timur Jl. Raya Rungkut Madya, Gunung Anyar, Surabaya, Jawa Timur
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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.
DEVELOPMENT OF AN AIoT-BASED COFFEE BEAN CLASSIFICATION AND SORTING SYSTEM USING A VISION TRANSFORMER Dody Pintarko; Basuki Rahmat; Faisal Muttaqin
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 5 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manual coffee bean sorting is highly prone to subjectivity, inconsistency, and low operational efficiency. This study aims to develop an automated classification and sorting system based on the Artificial Intelligence of Things (AIoT). The method integrates a Vision Transformer (ViT) model, TensorFlow Lite, Firebase, and an ESP32 microcontroller within a Mobile–Cloud–Edge Computing architecture. The ViT model was trained on four coffee roast levels to perform real-time inference on Android devices linked to physical sorting actuators. Experimental results showed that the ViT model achieved a 96.87% classification accuracy, while the automated physical sorting mechanism achieved 95.83% accuracy with an average response time of 462 ms. In conclusion, the integration of Vision Transformer and AIoT provides a fast and reliable post-harvest automation solution tailored for smart agricultural applications.
OPTIMASI ALGORITMA XGBOOST UNTUK KLASIFIKASI STATUS GIZI BALITA BB/TB MENGGUNAKAN GRIDSEARCHCV Avi Oktaviani; Basuki Rahmat; Kartini Kartini
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 2 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i2.5601

Abstract

Masalah gizi tetap menjadi perhatian utama kesehatan masyarakat di banyak negara, termasuk Indonesia. Studi ini merancang dan mengevaluasi sistem klasifikasi otomatis untuk status gizi balita berdasarkan indikator berat badan terhadap tinggi badan menggunakan algoritma XGBoost. Dataset terdiri dari 1.763 anak Indonesia berusia 24–60 bulan, dengan status gizi dikategorikan menjadi enam kelas (gizi baik, risiko gizi lebih, gizi kurang, gizi buruk, gizi lebih, obesitas). Variabel prediktor meliputi jenis kelamin, usia dalam bulan, berat badan, tinggi badan, dan skor Z berat badan terhadap tinggi badan. Praproses meliputi pembersihan data, penghapusan duplikat, pengkodean label, standardisasi, dan pembagian data latih-uji 80:20. Model XGBoost dasar dibandingkan dengan model yang disetel menggunakan GridSearch dengan validasi silang 3-fold dan 10-fold. Kinerja dievaluasi menggunakan akurasi, presisi makro, recall makro, skor F1 makro, dan log loss. Model terbaik, XGBoost dengan GridSearch 10-fold, mencapai akurasi 0,8689, skor F1 0,8149, dan log loss 0,3395, lebih baik daripada log loss dasar sebesar 0,4190. Temuan ini menunjukkan bahwa penyetelan hyperparameter XGBoost dengan GridSearch menghasilkan prediksi probabilistik yang terkalibrasi dengan baik dan merupakan alat pendukung keputusan yang menjanjikan untuk deteksi dini kekurangan gizi dan obesitas pada balita di fasilitas perawatan kesehatan primer.
Co-Authors Adhi Prasetya, Guntur ADJIE KURNIAWAN, RAKA Afina Lina Nurlaili Anggraeny, Fetty Avi Oktaviani B. Nugroho Budi Nugroho Budi Nugroho Daniella, Cherry Deri Setiawan Devan Cakra Mudra Wijaya Dody Pintarko Eva Yulia Puspaningrum Faisal Muttaqin faisal, Faisal Fahri Ferdiansyah Fetty Tri Anggraeny Handono, Stevanus Frangky Harianto Hariyanto Hariyanto Helmy Widyantara Hutama, Dhany Satya I Nyoman Sujana Ida Retno Moeljani Ifnu Wisma Dwi Prastya Iqbal Imani Khoirul Akbar Kartini Kartini Kartini Kesya Nursyahada Mada Lazuardi Nazilly Minto Waluyo Mohamad Irwan Afandi Muhammad Alfin Jimly Asshiddiqie Naufal Mumtaz, Ahmad Nugraha, Novemidu Wilis Prameswari Reksa Agami Putra, Chrystia Aji R MEHINDRA PRASMATIO Raka Adjie Kurniawan Ridho Aji Pangestu Royan Hisyam Rafliansyah Singgih Putra Pratama Singgih Putra Pratama Sudiyarto Syaifullah JS, Wahyu TATI NURHAYATI Tuhu Agung Rachmanto Tuhu Agung Rachmanto Wahyu SJ Saputra Widjajani, Bhakti Wisnu Yuniar Purbasari, Intan Yuniar, Intan