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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
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LSTM with Attention Optimization for IDR-USD Exchange Rate Forecasting Muhammad Abdullah Hafizh; Anggraini Puspita Sari; Henni Endah Wahanani
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.v8i2.3131

Abstract

This study proposes the application of the LSTM-Attention model to forecast the IDR exchange rate against the USD. Exchange rate stability is an important element in national and international economic resilience systems, as currency fluctuations can have a significant impact on trade, investment, banking, and household consumption. In the case of Indonesia, which is highly dependent on imported goods, exchange rate fluctuations cause an increase in import costs, rising inflation, and a decline in the competitiveness of export products in the global market, making accurate forecasting of exchange rate movements essential for economic policy, business strategy, and risk management. Statistical models such as ARIMA have been widely applied in exchange rate forecasting, but they have difficulty capturing the nonlinear of time series data. In recent years, machine learning methods such as Long Short-Term Memory (LSTM) have demonstrated their ability to handle timeseries data. Previous studies have shown that LSTM models generally outperform traditional methods, but they still face limitations in identifying important features across time steps. To overcome this problem, the Attention mechanism allows the model to focus on the most informative parts of the input sequence, thereby improving prediction accuracy. Experimental results show that the LSTM-Attention achieves MAPE of 1.28% and R2 of 0.97 and runtime 45% faster than BiLSTM. While BiLSTM achieved slightly higher accuracy, it’s required nearly twice the training time. Findings indicates that the proposed model offers practical choice for real-time exchange rate forecasting.
Implementation of HMM-GRU for Bitcoin Price Forecasting Rayya Ruwa'im Nafie; Anggraini Puspita Sari; Achmad Junaidi
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.v8i2.3137

Abstract

Bitcoin’s extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov Model–Gated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.
Iterative Enhancement of Academic Information System UI/UX Through Prototype-Based and User Centered Design Methodology Vanessa Priscilia Wijaya; Mychael Maoeretz Engel
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.v8i2.3143

Abstract

The Academic Information System serves as a core platform for managing academic activities, yet despite it being a core platform there are still many institutions face the same issues related to poor user experience (UX) and user interface (UI) design, which affect the operational efficiency and user satisfaction. While many studies have explored usability improvement in academic systems, few have focused on iterative prototyping combined with user-centered design methodologies in fully deployed university platforms. The goal of this study is to address these issues by redesigning the CIS interface through an iterative, prototype-based using Design Thinking and User-Centered Design (UCD) methodologies. It’s focus on improving system clarity, navigation, and user engagement. Online questionnaires and interviews with 50 students were used to gather data for the primary evaluation, which was based on the User Experience Questionnaire (UEQ). The UEQ was used both before and after the redesign to measure six experiential dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. And the results show significant improvements across all categories with rated excellent, indicating it’s successfully enhanced usability, visual appeal, and workflow clarity. These improvements has made an increased to the user satisfaction during academic task completion. Practically, the redesigned CIS enables students to access critical academic information more efficiently, improving daily academic productivity and digital interaction quality. This study highlights the effectiveness of using iterative, and user-centered methodologies in transforming outdated academic systems into intuitive, human-centered platforms that promote both institutional efficiency and user well-being.
Systematic Literature Review: Customer Satisfaction With Mobile Banking Super Apps Rinda Hesti Kusumaningtyas; Aliza Fatha Amanda; Safina Yulianti; Sultan Tedja Permana
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.v8i2.3146

Abstract

The rapid growth of digital banking has transformed customer interaction patterns, leading Islamic financial institutions to develop mobile banking super apps that integrate financial and lifestyle services. However, research comparing customer satisfaction across multiple Islamic super apps remains limited. This study aims to systematically examine and synthesize previous research on user satisfaction factors and analytical approaches used in Islamic mobile banking applications in Indonesia, focusing on BYOND by BSI, Muamalat DIN, and Mobile Maslahah. This research adopts the Systematic Literature Review (SLR) method following the frameworks of Kitchenham (2004) and Petersen et al. (2008). Relevant peer-reviewed articles published between 2021 and 2025 were retrieved from academic databases such as Google Scholar, ResearchGate, and SINTA. The inclusion criteria ensured that only studies explicitly addressing customer satisfaction with Islamic mobile banking were analyzed. Fifteen studies met the inclusion requirements and were synthesized through narrative and thematic analysis. The findings reveal that most studies applied quantitative descriptive approaches, with Technology Acceptance Model (TAM) as the most commonly used framework. Key satisfaction determinants include ease of use, efficiency, trust, service quality, and Sharia compliance. These indicate that both functional performance and Islamic value alignment play crucial roles in shaping customer satisfaction. The review contributes to the literature by highlighting comparative satisfaction patterns among Islamic super apps and identifying research gaps for future empirical validation. Recommendations are offered for banks and developers to enhance user experience while reinforcing Sharia-based trust.
Safety Stock Forecasting using ARMA and DR-ARMA under Different Sparsity Levels Jasmine Putri Halim; Jimmy Tjen; Alvin Lesmana
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.v8i2.3154

Abstract

Accurate demand forecasting is vital in supply chain management, particularly in the fast-moving consumer goods (FMCG) industry that experiences rapid stock turnover and fluctuating demand. The Auto-Regressive Moving Average (ARMA) has been the standard approach for time series forecasting, however it often underperforms under sparse and fluctuating data. This study contributes to the literature by applying Demand Response-ARMA (DR-ARMA) that was initially developed to address data sparsity and fluctuations under more complete and lower-sparsity data conditions. Using three primary datasets with varying sparsity levels from an FMCG distributor of bottled water products in West Borneo, DR-ARMA was benchmarked against classical ARMA. The results show that DR-ARMA consistently outperforms the classical ARMA model even under more complete, lower sparse data conditions. In lower sparsity datasets, DR-ARMA achieved average Mean of Percentage Error (MAPE) values of 22.64% and 6.41% respectively compared to the baseline ARMA model (235.60% and 180.86%). However, its best performance was observed in higher sparse condition (70.45%), achieving an average MAPE value of 1.79% across all datasets, suggesting the model remains most effective when applied to sparse data as originally intended. These improvement enables more precise safety stock planning, lower holding costs, and position DR-ARMA as a practical forecasting tool that connects analytical performance with real operational impact.
YOLOv11n-Based Deep Learning Approach for Detecting Fractures in Pediatric X-Rays Ahmed Mohammed Mohammed Nasser Alghaili; Izzati Muhimmah
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.v8i2.3155

Abstract

Fracture detection in pediatric wrist radiographs is challenging due to incomplete skeletal ossification, small bone structures, and subtle hairline fractures that are frequently missed in clinical practice, while growth plate radiolucency often mimics fracture appearance. This study evaluates YOLOv11n, a lightweight deep learning architecture with Spatial Pyramid Feature Fusion (SPFF) modules optimized for small-object detection, for automated pediatric wrist fracture identification. The model was trained and validated on the GRAZPEDWRI-DX benchmark dataset comprising 20,327 pediatric wrist radiographs (14,269 training, 4,048 validation, 2,010 test images) using transfer learning and conservative augmentation strategies. YOLOv11n achieved mAP@50 of 0.936 on validation and 0.945 on test sets, with precision of 0.905–0.918 and recall of 0.869–0.871, demonstrating improved accuracy compared to previous YOLOv8 implementations (mAP@50 ≈ 0.92) with 40–60% faster inference. End-to-end processing averaged 3.8 ms per image on NVIDIA Tesla T4 hardware, supporting real-time clinical applications. The mAP@50-95 of approximately 0.56 indicates reduced localization accuracy under stricter IoU criteria, primarily for hairline fractures. Error analysis revealed that 62% of false negatives were non-displaced hairline fractures, while 58% of false positives occurred near growth plate regions. YOLOv11n provides favorable balance between diagnostic accuracy and computational efficiency for pediatric fracture detection. However, prospective multi-institutional validation, integration of multi-view fusion strategies, and incorporation of age-specific anatomical priors are necessary before clinical deployment to enhance detection of subtle fracture presentations and reduce growth plate misclassifications.
Digital Transformation in Human Resource and Operational Management of Snake Fruit Plantations Kadek Dimas Ganes Grahista; Sulistyo Dwi Sancoko
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.v8i2.3160

Abstract

Digital transformation is crucial for modernizing the agricultural sector, improving operational efficiency, record accuracy, and resource management transparency. This study aims to design and develop an integrated information system using both web and mobile platforms to address managerial challenges at I Nyoman Suryadiputra Garden, specifically related to the manual record-keeping of harvest data and employee attendance, as well as the lack of a monitoring system that hinders worker discipline and causes operational inefficiencies. The research is conducted in three phases: first, the initial phase with manual management and disorganized records, second, the development of the digital system, and third, the implementation phase, where digital records enhance efficiency and transparency. Unlike existing digital frameworks in sectors such as oil palm or livestock farming, this system provides a comprehensive solution by integrating harvest records, employee attendance, payroll, monthly reports, monthly performance report, guidelines & procedures, and leave requests into a single platform, support the implementation of the initial steps of Agriculture 4.0. Key features include digital attendance via QR codes, activity reporting, harvest record-keeping, and leave requests, all stored in a centralized MySQL database. Flutter, React, and Laravel were selected due to their scalability and efficiency in handling large datasets. Flutter enables efficient cross-platform mobile development, React offers a dynamic web interface, and Laravel provides an optimal backend framework for large-scale data management. Preliminary analysis predicts a 30% reduction in time spent on attendance tracking, a 40% reduction in harvest data errors, and a 50% reduction in employee absenteeism, indicating enhanced worker discipline. This research follows a design science methodology, employing iterative design, testing, and refinement. The developed system is adaptable to other agribusiness sectors.
Comparative Analysis of RISC and CISC Architectures in Modern Embedded System Development Roynaldy Rosdiyanto; Siti Fatimatul Zuhro; Refi Nisfuwadi
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.v8i2.3161

Abstract

Computer architecture plays a crucial role in the development of embedded systems, particularly in the era of the Internet of Things (IoT), edge computing, and intelligent devices with low battery power. The two main approaches in computer architecture, namely RISC (Reduced Instruction Set Computing) and CISC (Complex Instruction Set Computing), each have their own advantages and disadvantages in terms of efficiency, speed, and energy consumption. This study aims to compare and analyze the performance of both architectures in the development of modern embedded systems, focusing on several parameters such as instruction efficiency, memory usage, interrupt response time, and support from toolchains and developer ecosystems. This research employs both qualitative and quantitative methods based on a literature review of 20 recent international journals (2020–2025), as well as case studies of implementations on ARM Cortex (RISC), RISC-V, and embedded x86 processors (CISC). The analysis results show that RISC architectures, particularly RISC-V, offer better flexibility, modularity, and energy efficiency, making them well-suited for low-power and real-time applications. However, CISC architectures remain superior in terms of backward compatibility, structured instruction complexity, and broad industry support for traditional applications. The study concludes that the selection of an architecture should consider several factors, including energy efficiency, design complexity, and ecosystem support, according to the specific requirements of the embedded system being developed
Prediction of Air Pollution Standard Index Using CEEMDAN-LSTM Rafie Ishaq Maulana; Muhammad Muharrom Al Haromainy; Achmad Junaidi
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.v8i2.3164

Abstract

Air pollution has become a critical environmental issue, particularly in urban areas such as DKI Jakarta, where pollutant concentrations frequently reach the highest levels in Indonesia. Accurate prediction of the Air Pollution Standard Index (ISPU) is essential for mitigating the adverse health and environmental impacts of poor air quality. However, ISPU data exhibit nonlinear, volatile, and non-stationary characteristics, posing challenges for conventional prediction models. To overcome these challenges, this study proposes a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Long Short-Term Memory (CEEMDAN–LSTM) model, applied to daily ISPU data from 2010 to 2025 comprising 5,686 records. CEEMDAN was selected over conventional decomposition methods such as EEMD and VMD due to its ability to suppress mode-mixing and extract more stable Intrinsic Mode Functions (IMFs) through adaptive noise addition, thereby enhancing signal interpretability and learning efficiency. The ISPU time series was decomposed into multiple IMFs, and the resulting components were reconstructed and modeled using an optimized LSTM architecture obtained through Bayesian hyperparameter tuning. The optimal configuration batch size of 54, dropout rate of 0.37, and hidden units of 6, 33, and 34 achieved an RMSE of 14.0, reflecting a substantial improvement over the baseline LSTM model. The results demonstrate that integrating CEEMDAN with LSTM effectively reduces signal complexity, stabilizes convergence, and improves forecasting accuracy for non-stationary air quality data in DKI Jakarta. This modeling framework provides a robust foundation for developing predictive early-warning systems, supporting evidence-based environmental policy, and enhancing public health preparedness in rapidly urbanizing regions.
Data-Driven Recommendation System Using Google Trends and Marketplace for MSMEs Susana Dwi Yulianti; Asep Kurniawan; Shierra Intan Anggari; Okta Gabriel Sinsaku
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.v8i2.3167

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia’s economic growth but often struggle to remain competitive in the rapidly evolving digital marketplace. A key challenge lies in promoting products that align with dynamic consumer preferences and online search trends. This study aims to design and develop a web-based recommendation system intended for MSME business owners and marketers, integrating product data collected through marketplace web scraping with search trend data from Google Trends. Using a Research and Development (R&D) approach with a prototyping model, the research involves stages of data collection, preprocessing, system modeling, implementation, and evaluation. The system utilizes a dataset of 1,028 marketplace products and applies a Hybrid Filtering approach that combines content-based filtering using TF-IDF and collaborative filtering, enhanced by Google Trends as an external weighting factor to improve contextual relevance. Developed using FastAPI and MySQL, the system achieved strong performance with a precision of 0.87, recall of 0.84, and an F1-score of 0.85. In practice, the system assists MSMEs such as local snack producers in identifying and promoting products aligned with trending consumer interests, thereby enhancing visibility and market competitiveness. This research contributes to advancing data-driven decision-making for MSMEs by offering a practical, adaptive, and trend-aware recommendation framework that supports more effective digital marketing strategies in real time.

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