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All Journal Jurnal Ilmu Komputer Jurnal Pendidikan Teknologi dan Kejuruan Semantik Syntax Jurnal Informatika Scan : Jurnal Teknologi Informasi dan Komunikasi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Transformasi: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization JIKO (Jurnal Informatika dan Komputer) JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sains dan Informatika SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) WIDYA LAKSANA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Gestalt : Jurnal Desain Komunikasi Visual Jifosi International Journal Of Computer, Network Security and Information System (IJCONSIST) Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Krisnadana Jurnal Ilmiah Teknik Informatika dan Komunikasi Harmoni: Jurnal Ilmu Komunikasi Dan Sosial Jurnal Krisnadana Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Modem : Jurnal Informatika dan Sains Teknologi KOMET
Made Hanindia Prami Swari
Informatika, Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : bit-Tech

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Desain dan Pengembangan Aplikasi Pengelolaan Properti Mode Offline Menggunakan Sinkronisasi Otomatis dan CQRS Event Sourcing Adiputra, Muhammad Ariq Hawari; Swari, Made Hanindia Prami; Nurlaili, Afina Lina
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.v8i3.3332

Abstract

The advancement of information technology has accelerated the digitization of project management, particularly in the supervision and monitoring of construction progress previously handled manually through paper-based documents and Excel spreadsheets. Such manual processes have led to delays in reporting, data duplication, and reduced data accuracy. This study aims to design and implement a web- and mobile-based project management and property sales system featuring Offline-First Synchronization, Command Query Responsibility Segregation (CQRS), and Event Sourcing to maintain the integrity of progress data and empower field supervisors to operate without an internet connection. The research method follows the waterfall model, comprising needs analysis, system design with a clear separation of command and query, and the implementation of event log storage as the single source of truth for every data change, using Laravel as the backend and React Native with MMKV for local storage. Testing results demonstrate that the system ensures data consistency through automatic synchronization once network connectivity is available and can reconstruct project development progress using stored event data. Performance benchmarking showed that CQRS bulk operations reduced processing time to 0.053 seconds, outperforming traditional CRUD bulk operations at 0.073 seconds, while query latency in event sourcing read models averaged 0.101 seconds, only slightly higher than 0.089 seconds in direct database queries. The system also achieves reliable auditability and supports efficient task update and historical recalculation via event replay. The findings confirm that applying CQRS and Event Sourcing within an offline-first architecture improves reliability, auditability, and efficiency in field project monitoring.
Prediksi Harga Emas Menggunakan Model Bi-GRU Dengan Monte Carlo Dropout Berdasarkan Data Makroekonomi Prasetyo, Daniel Bergas; Swari, Made Hanindia Prami; Putra, Chrystia Aji
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.v8i3.3393

Abstract

Gold price prediction plays a vital role in financial decision-making, particularly during periods of heightened market volatility when gold functions as a strategic hedge against inflation and economic uncertainty. This study examines the effectiveness of a Bidirectional Gated Recurrent Unit (BI-GRU) model enhanced with Monte Carlo Dropout for forecasting XAU/USD prices using key macroeconomic indicators, namely CPI, DXY, S&P 500, and crude oil prices, covering the period from May 6, 2015, to May 1, 2025. The research addresses the need for forecasting approaches capable of capturing nonlinear dependencies while simultaneously quantifying predictive uncertainty. The methodological workflow includes constructing a multivariate time-series dataset, performing comprehensive preprocessing, and evaluating multiple temporal window lengths and model configurations. Performance is assessed using MAE, RMSE, and R², with uncertainty estimation derived from repeated stochastic forward passes. Empirical analysis reveals strong correlations between gold prices and the S&P 500 (r ≈ 0.93) and CPI (r ≈ 0.89), affirming the substantial influence of macroeconomic conditions on gold dynamics. The optimal configuration, consisting of a 70:30 data split, a 60-day window, 128 BI-GRU units, and a 0.3 dropout rate, achieved an MAE of 0.0354, an RMSE of 0.044, and an R² of 0.9349, outperforming the baseline BI-GRU without dropout. Multi-step forecasting further shows that the model maintains stable predictive behavior during the initial horizon, with uncertainty increasing gradually in extended forecasts. These findings demonstrate that integrating BI-GRU with Monte Carlo Dropout provides a reliable uncertainty-aware framework that offers both accurate predictions and practical value for financial practitioners requiring risk-sensitive forecasting tools.
Hyperparameter Optimization of Hybrid LSTM-GRU using Genetic Algorithm for Stock Price Prediction Lumangkun, Mordekhai Gerin; Swari, Made Hanindia Prami; Sihananto, Andreas Nugroho
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.v8i3.3656

Abstract

Predicting stock prices in the banking sector, particularly for high-capitalisation stocks such as Bank Rakyat Indonesia (BBRI), remains challenging amid market volatility. While Hybrid LSTM-GRU models have demonstrated capability in capturing temporal dependencies in time-series data, prior studies have predominantly focused on manual tuning or optimization of single recurrent architectures, with limited application of Genetic Algorithms for optimizing hybrid recurrent networks in emerging stock markets (R1). This research aims to address this gap by implementing an evolutionary optimization framework using a Genetic Algorithm (GA) to automatically tune the hyperparameters of a Hybrid LSTM-GRU model for enhanced stock price forecasting accuracy. Historical BBRI data from November 2020 to June 2025 were preprocessed through normalization and transformed into supervised time-series sequences before being divided into training, validation, and testing sets. The GA was configured with a population size of 20, 80 generations, and a crossover rate of 0.8 to search for optimal learning rates, batch sizes, and hidden units. The optimized configuration identified 64 units for LSTM and GRU layers, a learning rate of 0.002, and a batch size of 16. The resulting model achieved an RMSE of 82.11 and an MAPE of 1.51%, representing a 20% error reduction compared to baseline hybrid models and outperforming benchmark approaches reported in prior studies (R1). Achieving a 1.51% MAPE indicates reliability for financial forecasting, supporting risk-sensitive investment decision-making (A). Overall, this study demonstrates that evolutionary hyperparameter optimization enhances hybrid deep learning architectures.
Co-Authors Adiputra, Muhammad Ariq Hawari Agung Mustika Rizki, Agung Mustika Aileena Solicitor Costa Rica El Chidtian Akbar, Fawwaz Ali Andreas Nugroho Sihananto Andreas Nugroho Sihananto Andreas Nugroho Sihanto Anggraini Puspita Sari Ani Dijah Rahajoe Arifan, Miftakhul Askara Raditya Aurora Prameswaty, Almira Azhari SN Basuki Rahmat Damayanti, Alfina Diyasa, I Gede Susrama Mas Dwi Wahyuningtyas Eva Yulia Puspaningrum Faisal Muttaqin Faisal Muttaqin Fetty Tri Anggraeny Firmansyah Firdaus Anhar Firza Prima Aditiawan Gilang Gema Ramadhan Handika, I Putu Susila Henni Endah Wahanani Hutagaol, LeonHoss I Gede Winaya I Gusti Ngurah Agung Mahendra I Kadek Susila Satwika I Kadek Susila Satwika I Nyoman Sujana I Putu Susila Handika I Putu Susila Handika I WAYAN SUDIARSA Ika Nur Habibah Jannatul Firdaus Joni Bastian Joni Bastian Julastri, Bregsi Atingsari Kartika Maulida Hindrayani Kevin Santosa, Mochammad Lintang Perdana Rochmat Sugiharto Lumangkun, Mordekhai Gerin Mandyartha, Eka Prakarsa Martoni Martoni Maulana, Hendra Muhammad Farhan Maulana Muhammad Hakam Fardana Muhammad Rifki Bahrul Ulum Muhammad Syafril Hidayat Muttaqin, Faisal Muttaqin, Faisal Nabila Rizky Amali Putri Ngurah Agus Sanjaya ER Nine Alvariqati Varqa Ansori Nugroho Sihananto, Anderas Nurlaili, Afina Lina Permana, Eriko Indra Phitria, Shaum Prasetyo, Daniel Bergas Pratama Wirya Atmaja Pratiwi, Nisa Prismahardi Aji Riyantoko Putra, Chrystia Aji Rabbani, Rafi Rahel Widya Arianti Rahel Widya Arianti Rahmadsyach, Mochammad Taufiq Retno Mumpuni Risnaldy Novendra Irawan Rizki, Agung Mustika Satria, Vinza Hedi Satwika, I Kadek Susila Sugiarto Sukandar, Ivan Christopher Syaifullah Jauharis Saputra, Wahyu Tasya Ardhian Nisaa' Tentra Olivia Tresna Maulana Fahrudin Tresna Maulana Fahrudin Ulummuddin, Ikhya Wahyu Syaifullah Jauharis Saputra Wariyanti Nugroho Putri Wayan Gede Suka Parwita Welda Wirya Atmaja, Pratama Yuniar Purbasari, Intan