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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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Integrasi Digital Twin Real-Time untuk Kendali Perangkat IoT di Lingkungan Smart Campus Pradana, Reza Putra; Pratama, Afis Asryullah; Rosyady, Ahmad Fahriyannur; Kurniasari, Arvita Agus; Afriansyah, Faisal Lutfi
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.v8i3.3367

Abstract

The increasing demand for intelligent and sustainable energy management within higher education institutions has encouraged the adoption of IoT-based solutions; however, traditional IoT dashboards typically rely on text-based device lists and non-intuitive identifiers that lack spatial context. As a result, users often struggle to understand which physical devices they are controlling, leading to confusion, poor user experience, and a higher risk of operational errors when managing smart campus facilities. This study aims to develop and validate a Digital Twin–based Smart Campus system capable of synchronizing physical electronic devices with an interactive 3D virtual environment in real time, providing a spatially accurate digital representation of the lecturer room that mirrors the real-world layout. The research employs a systematic workflow that includes problem identification, literature analysis, installation of IoT devices such as Zigbee smart switches and ESP32 IR blasters, creation of a web-based Digital Twin interface, and development of optimized 3D room models using Blender. System testing was conducted to evaluate physical-to-digital and digital-to-physical synchronization performance, and FPS benchmarking was performed to assess usability across high-end, mid-range, and entry-level devices. The results show that the Digital Twin maintains 100% synchronization accuracy with millisecond-level response times and runs smoothly on diverse hardware. By enabling users to interact with devices directly through a virtual environment that visually matches the real room, the system reduces operational mistakes, improves user experience, and enhances awareness of energy usage. The study concludes that the proposed Digital Twin approach effectively overcomes key limitations of traditional IoT dashboards and offers a scalable, practical framework for Smart Campus implementations.
Comparative Analysis of LSTM and GRU Algorithms for Inflation Rate Forecasting Ardiyansyah, Moh. Angga; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
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.v8i3.3370

Abstract

Inflation is a critical economic indicator that directly affects price stability, purchasing power, and the formulation of fiscal and monetary policies. In East Java, inflation has demonstrated considerable year-to-year volatility, creating significant challenges for policymakers in maintaining regional economic stability. This situation highlights the need for forecasting models that are both accurate and capable of adapting to complex economic data patterns. This study presents a comparative analysis of two deep learning algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting year-on-year (YoY) inflation in East Java using data from January 2005 to December 2024. The dataset was processed using Min–Max normalization and a 12-month sliding window to capture long-term dependencies and seasonal variations. Model performance was evaluated using RMSE, MAE, and MAPE. The findings demonstrate that no single model performs best across all metrics. The LSTM4 model with a [128,128] architecture achieved the lowest MAE and MAPE values, indicating superior average predictive accuracy and stronger capability in learning complex long-term inflation patterns. In contrast, the GRU1 [64,64] model produced the lowest RMSE and the shortest training time, highlighting its efficiency in minimizing extreme prediction errors and reducing computational cost. These results offer valuable insights for policymakers in East Java: LSTM is more suitable for applications requiring high prediction accuracy, whereas GRU is preferable for real-time or resource-efficient forecasting systems, especially in fast-changing economic environments.
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 Affandi, Ananda Asa Firstha; Rahmat, Basuki; Mumpuni, Retno
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.
A Web-Based Online Reservation System with Personalized Tourism Recommendations Using Content-Based Filtering Amelia, Rizky; Nurlaili, Afina Lina; Aditiawan, Firza Prima
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.v8i3.3382

Abstract

The rapid growth of digital technologies has transformed the tourism industry and increased the need for personalized recommendation systems to enhance user experience and business competitiveness. However, many small- and medium-scale travel agencies still rely on manual reservation processes and social media–based promotions, which limit service efficiency and personalization. This study designs and implements a web-based reservation and tourism recommendation system for Sumovacation Tour using a Content-Based Filtering approach enhanced with feature weighting and cosine similarity. The main novelty of this study lies in the feature weighting mechanism, which assigns different importance levels to package attributes such as activities, travel duration, package type, and budget, improving recommendation relevance compared to standard content-based methods. Data were collected from Google Maps reviews in 2025, resulting in approximately 300 rating and review entries. The recommendation engine computes weighted relevance scores from user preference tags and package metadata to generate personalized recommendations. System functionality was validated using Black Box Testing, where all core workflows successfully passed, while usability evaluation using the USE Questionnaire showed high user acceptance, with usefulness, satisfaction, and ease of use each scoring 94.4%, and ease of learning reaching 95.2%. During testing, challenges related to data consistency and user input variation were addressed through input validation. The results show that the proposed system improves recommendation relevance while enhancing operational efficiency by reducing manual booking handling and supporting digital reservation management.
Development of Sales Forecasting and Stock Optimization System Using Least Square and Safety Stock Gosal, Andika; Putra, Agung Brastama; Wati, Seftin Fitri Ana
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.v8i3.3388

Abstract

The ongoing digital transformation across business sectors has encouraged micro and small enterprises to adopt information systems that enable accurate data processing and more strategic decision-making. CV. Ragam Jaya is one such business that still depends on manual processes for recording sales transactions and monitoring inventory, resulting in inconsistent stock data, delayed reporting, and limited capability to analyze demand patterns. To address these challenges, this study develops a web-based forecasting and inventory optimization system that integrates Least Square–based demand prediction with Safety Stock calculations. The Rapid Application Development (RAD) framework is utilized to accelerate system construction through iterative prototyping and continuous user involvement. Data were collected through interviews and direct observations to capture operational issues in the existing workflow. The system provides automated forecasting, inventory management, and stock buffer recommendations, enabling users to interpret demand trends more effectively. Experimental evaluation shows that the forecasting module achieves stable trend estimation with an average deviation of less than 8% from historical sales data, indicating strong alignment with actual demand behavior. Blackbox testing was conducted on core modules transactions, forecasting, reporting, and stock optimization and all tests achieved a 100% pass rate, demonstrating consistent system reliability and robustness. The integration of Least Square forecasting and Safety Stock significantly improves inventory planning accuracy by reducing manual discrepancies and supporting timely replenishment decisions. Overall, the developed system is effective in enhancing operational efficiency, minimizing human error, and improving stock control for small distribution businesses seeking to transition toward digitalized management practices.
Predictive Modeling of Student Academic Performance Using Regression Methods Husen, Dede
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.v8i3.3391

Abstract

The advancement of digital technologies has strengthened the use of data driven approaches in understanding the factors that shape students’ academic achievement. This study aims to examine how daily habits, lifestyle patterns, and environmental conditions contribute to exam performance using the Student Habits vs Academic Performance dataset from Kaggle, which contains 1,000 student records covering behavioral, health related, and socioenvironmental attributes. Guided by the CRISPDM framework, the research includes data preparation, exploratory analysis, and predictive modeling using two regression techniques: Linear Regression and Random Forest Regressor. The predictive models were developed to estimate exam scores based on several key variables, including study duration, attendance rate, sleep quality, leisure activities, and parental education level. The results show that Linear Regression achieved the highest accuracy, with an MAE of 4.19, an RMSE of 5.15, and an R² of 0.897, indicating that approximately 89.65% of score variability can be explained by the selected features. Meanwhile, the Random Forest model recorded a slightly lower R² of 0.850, suggesting that the dominant relationships in the dataset follow a largely linear pattern. These findings highlight that consistent study routines, regular attendance, adequate sleep, and supportive home environments are strongly associated with improved academic outcomes. The study emphasizes the importance of interpretable machine learning models in educational analytics and offers insights that may support data informed interventions aimed at enhancing student performance.
Analisis Perbandingan Deteksi Penyakit Daun Jagung Menggunakan YOLO dan CNN Rifqi, Mohammad Habim Hazidan; Haromainy, Muhammad Muharrom Al; 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.v8i3.3392

Abstract

This study compares the performance of two deep learning methods, You Only Look Once version 8 (YOLOv8) and the Convolutional Neural Network (CNN) EfficientNetB0, in detecting and classifying maize leaf diseases. The background of this research stems from the importance of early plant disease identification to support food security, as well as the limitations of manual inspection methods, which are slow, subjective, and inefficient. The study combines primary and secondary data, totaling 2,000 images that underwent undersampling, augmentation, resizing, and bounding box annotation for YOLO training needs. Both models were trained on the same dataset with an 80% training and 20% testing split. YOLOv8n was trained using a transfer learning approach for 30 epochs, while the CNN was trained using EfficientNetB0 with similar training parameters. The results show that YOLOv8 achieved high detection performance with an mAP@0.5 of 0.985 and the highest class accuracy in the Healthy category (0.99). Meanwhile, the CNN demonstrated more stable classification performance, achieving the highest accuracy in the Grey Leaf Spot class (0.99) and a validation accuracy of 0.96. The comparison indicates that YOLO excels in object detection and disease localization in field images, whereas the CNN is more consistent in classifying segmented leaf images. These findings provide practical implications for real world deployment: YOLOv8 is suitable for real time detection in field conditions, including potential integration into mobile based early warning systems for farmers, while EfficientNetB0 is more appropriate for offline or laboratory based classification of static leaf samples.
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.
Optimization of Tea Leaf Disease Detection Based on YOLOv8 Using CBAM and BFP Armijantoro, Gilang Rahmadhan; Nugroho, Budi; Via, Yisti Vita
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.v8i3.3394

Abstract

Early identification of tea leaf diseases is essential for sustaining crop productivity and preventing significant yield losses, making accurate automated detection a critical requirement in modern agricultural management. This study aims to improve the robustness of YOLOv8 for disease detection by integrating two complementary optimization modules chosen for their suitability in addressing common challenges in plant imagery: the Convolutional Block Attention Module (CBAM), which enhances discriminative feature focus under complex visual noise, and the Bidirectional Feature Pyramid Network (BiFPN), which strengthens multi-scale feature fusion to capture small or low-contrast lesions. The target diseases include Algal Leaf Spot, Brown Blight, and Grey Blight, using a combined dataset of primary field images and secondary data from Kaggle. Four models were developed—YOLOv8n (baseline), YOLOv8-CBAM, YOLOv8-BiFPN, and YOLOv8-CBAM-BiFPN. Experimental results demonstrate consistent performance gains across all enhanced variants. The baseline model obtained a precision of 0.760, recall of 0.735, and mAP50 of 0.793. Incorporating CBAM increased precision to 0.824 and recall to 0.780, while BiFPN yielded the highest recall of 0.820 with superior multi-scale generalization. The combined CBAM-BiFPN model achieved the strongest overall results, with a precision of 0.879, recall of 0.814, mAP50 of 0.886, and mAP50–90 of 0.739. These findings indicate that integrating CBAM and BiFPN substantially enhances YOLOv8’s capability in complex leaf-disease scenarios and offers practical potential for deployment in real agricultural settings to support faster decision-making and more effective disease management.
Pengembangan Game Edukasi RPG Petualangan 2D tentang Peristiwa Lautan Api Bandung Menggunakan Alur Cerita Naratif Bercabang Sankalla, Sabda; Putra, Chrystia Aji; 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.v8i3.3402

Abstract

This study evaluates the effectiveness of a 2D educational adventure RPG integrating branching narratives and a Numberlink-based puzzle to enhance students’ understanding of the historical Bandung Lautan Api event. Addressing the persistent issue of low engagement in history classrooms, the research investigates whether interactive storytelling and embedded logic-puzzle tasks can strengthen learning outcomes and user experience. Using a design-based research approach, the game was tested with 23 junior high school students through a pre-test/post-test design and the Game User Experience Satisfaction Scale (GUESS-18). The results show a substantial increase in historical knowledge, with post-test scores significantly higher than pre-test scores, indicating strong knowledge acquisition following gameplay. GUESS-18 responses also reveal consistently positive user experiences, with high ratings for narrative quality, educational value, visual and audio aesthetics, and overall enjoyment. Students reported that branching choices improved immersion and reflective thinking, while the Numberlink puzzle supported active reasoning during missions. These findings demonstrate that the integration of interactive narrative structures and logic-based puzzles can effectively support both cognitive and affective dimensions of history learning. Overall, the study confirms the potential of game-based learning to enhance comprehension, motivation, and engagement, providing evidence that well-designed educational games can significantly improve learning performance and serve as a valuable supplement to conventional history instruction.

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