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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 168 Documents
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Search results for , issue "Vol. 8 No. 2 (2025): bit-Tech" : 168 Documents clear
Comparative Analysis of IndoBERT, IndoBERTweet, and XLM-RoBERTa for Detecting Online Gambling Comments on YouTube Kevin Iansyah; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
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.v8i2.3257

Abstract

The proliferation of online gambling promotions in YouTube comment sections poses significant challenges for content moderation on Indonesian digital platforms. Although transformer models have proven effective for various Indonesian-language NLP tasks, no systematic comparative evaluation exists for detecting online gambling promotions on YouTube, nor has research explored model sensitivity to hyperparameters in this context. This research identifies the optimal transformer model and configuration for detecting Indonesian-language online gambling promotion comments on YouTube. A total of 26,455 YouTube comments were collected from February to July 2025 and stratified into balanced training (18,926 comments) and validation sets (3,340 comments), plus an imbalanced testing set (4,189 comments with 28.05% promotions and 71.95% non-promotions) reflecting realistic platform conditions. Nine fine-tuning experiments were conducted with three transformer models (IndoBERT, IndoBERTweet, XLM-RoBERTa) using three learning rates (1e-5, 2e-5, 3e-5). Evaluation employed accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results show IndoBERT with learning rate 1e-5 achieved best performance (F1-score 99.57%, recall 99.49%), outperforming IndoBERTweet (F1-score 98.58%) and XLM-RoBERTa (F1-score 99.28%). Interestingly, the formal corpus-trained model (IndoBERT) proved more effective than the social media model (IndoBERTweet), indicating that gambling promotion language patterns tend to be structured despite appearing in informal contexts. IndoBERT demonstrated greatest stability to learning rate variations (standard deviation 0.0011), while XLM-RoBERTa offered fastest inference time (2.48 ms) with optimal performance-efficiency balance. These findings provide practical recommendations for automated content moderation systems on Indonesian social media platforms, with IndoBERT for maximum accuracy scenarios and XLM-RoBERTa for large-scale real-time deployment.
Harmony Search Algorithm Optimization of Fuzzy C-Means for a Hybrid Filtering Movie Recommendation System Muftah Hi M Naser; Eka Prakarsa Mandyartha; M. Muharrom Al Haromainy
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.v8i2.3258

Abstract

In the era of increasingly abundant digital content, personalized recommendation systems play a critical role in helping users efficiently identify relevant movies. However, traditional approaches such as Collaborative Filtering (CF) and Content-Based Filtering (CBF) continue to suffer from data sparsity, cold-start limitations, and unstable clustering performance. To address these constraints, this study proposes a novel hybrid recommendation framework that integrates Harmony Search (HS) optimization with Fuzzy C-Means (FCM) clustering inside a Hybrid Filtering (HF) architecture. Using a subset of the MovieLens dataset consisting of 560 users who rated the same 37 movies, 60% of the rating values were randomly removed to simulate sparse conditions. HS is employed to optimize the initialization of FCM centroids, improving clustering stability and reducing susceptibility to local minima. The resulting clusters are then leveraged in a hybrid combination of CF and CBF to generate final predictions. Experimental results indicate that the optimal configuration (num_cluster = 4, m = 1.5, α = 0.7) achieves RMSE = 0.8974, MAE = 0.7011, Precision = 0.7515, and Recall = 0.4628. Compared to baseline models, the proposed HS–FCM–HF framework improves RMSE by 37.3% over CBF-only and maintains 7.4% better Precision than CF-only, demonstrating stronger robustness and balanced performance under high sparsity. These findings highlight the theoretical and practical value of integrating metaheuristic optimization with hybrid filtering to enhance both accuracy and generalization. Future work may incorporate multimodal features or real-time adaptive mechanisms to further strengthen personalization capability.
Mobile Legends Match Outcome Prediction Based on Players Statistics Using CatBoost and XGBoost Ciptaagung Firjat Ardine; Eka Prakarsa Mandyartha; 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.v8i2.3259

Abstract

Mobile Legends: Bang Bang (MLBB) is a mobile-based Multiplayer Online Battle Arena (MOBA) game with a vast global community and professional ecosystem. Despite the extensive use of machine learning in desktop-based MOBAs such as Dota 2 and League of Legends, predictive modeling for MLBB remains underexplored. This study addresses this research gap by developing and comparing two advanced gradient boosting algorithms CatBoost and XGBoost for predicting match outcomes based on individual player statistics. The dataset, collected through web scraping from the official MPL Malaysia Season 14 website, comprises 1,430 player-level records representing professional-level competitive matches. Both models were trained and evaluated using 5-Fold Cross Validation to ensure stability and robustness. The results indicate that CatBoost achieved the highest predictive accuracy, with an average of 96.15%, outperforming XGBoost, which attained 94.75%. However, XGBoost exhibited exceptional computational efficiency, completing the prediction process 99.62% faster 0.76 seconds compared to CatBoost’s 3 minutes and 21 seconds. These findings highlight the trade-off between accuracy and processing speed in esports predictive modeling. The study demonstrates the potential of gradient boosting approaches for MLBB-specific analytics, providing a novel contribution to the limited body of research on mobile esports prediction. Accordingly, CatBoost is more suitable for analytical or strategic contexts where precision is essential, while XGBoost is better aligned with real-time predictive systems that demand rapid computation and scalability.
Parental Views on Screen Time Intensity and Effects on Preschoolers’ Social Development Nasywa Ariella Nityasa Ricardo; Meiske Yunithree Suparman
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.v8i2.3260

Abstract

The development of digital technology has led to an increase in the use of electronic devices by preschool children. This phenomenon has raised concerns about the impact of screen time intensity on children's social skills, especially in the context of direct interaction and emotional development. Guided by ecological systems theory and social learning theory, this study examines how parental perceptions of screen time intensity relate to the social skills of preschool children in Bantul Regency, Indonesia. Using a quantitative correlational design, data were obtained from 50 parents through validated Screen Time and Social Skills Improvement System (SSIS) scales. Data were collected using questionnaires on screen time intensity and children's social skills, then analyzed using the Pearson Product Moment correlation test with the help of SPSS version 25.0. The results of the analysis showed a significant negative relationship between screen time intensity and preschool children's social skills (r = -0.612; p < 0.000). Sub-dimension analyses reveal that empathy (M = 1.70; SD = 0.63) is the most affected, followed by communication and cooperation. Gender and age analyses show no significant differences, indicating that screen time effects are consistent across demographic subgroups. These findings underscore the critical role of parental supervision in balancing digital engagement and direct social interaction. Practical implications for early childhood education programs and parental guidance strategies are also discussed.
Development of a Mobile Web Information System for Ordering and Managing Local Products Nafis Fausta Zaki; Arief Hermawan
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.v8i2.3263

Abstract

Digital transformation is essential for enhancing operational efficiency, minimizing errors, and increasing transparency, particularly for small and medium-sized enterprises (SMEs). This study investigates the operational challenges faced by Parfum ARX+, a local SME engaged in the perfume industry, which relies on manual ordering and inventory management systems that often lead to service delays, stock inaccuracies, and workflow inefficiencies. To address these issues, this research proposes the design and development of an integrated web-mobile information system aimed at automating order placement, stock control, and sales reporting. The system includes core features such as user registration, dynamic product catalog, real-time inventory updates, digital payments, and administrative dashboards. It was developed using Flutter for mobile applications, React for the web interface, and Firebase for backend services including real-time database and secure user authentication. System evaluation was conducted through black-box testing to assess functional reliability and user experience under operational conditions. The results show that the system successfully met all functional requirements, significantly reducing order processing time by approximately 35%, improving inventory accuracy, and enhancing overall administrative performance. By enabling real-time data access and automated transactions, the system improves customer satisfaction and supports data-driven decision-making for business managers. This research demonstrates how low-cost, scalable digital platforms can transform conventional business operations into efficient, user-friendly, and transparent systems, contributing to improved competitiveness and operational resilience among SMEs. The system serves as a model for similar enterprises seeking to implement digital solutions in order to adapt to evolving market demands and technological developments.
Optimization of Ride Routes in a Tourist Attraction Using Dijkstra’s and Genetic Algorithm Firyal Wishal Nabili; Eva Yulia Puspaningrum; Afina Lina Nurlaili
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.v8i2.3264

Abstract

This research presents a hybrid optimization framework that integrates Dijkstra’s Algorithm, the Genetic Algorithm (GA), and a 2-Opt local search procedure to generate optimal and demographically tailored tourist routes at Wisata Bahari Lamongan (WBL). The methodological novelty lies in the layered design of the hybrid pipeline: Dijkstra is used as a pre-processing stage to reconstruct a complete shortest-path distance matrix from partially measured field data, ensuring that GA operates on accurate inter-attraction distances and avoids unrealistic transitions. The GA then performs route evolution using PMX crossover, swap mutation, and elitism, while 2-Opt refines local segments to prevent suboptimal edge structures. Experiments involved 12 parameter-testing scenarios (CR = 0.7–0.9, MR = 0.05–0.1, population sizes of 50 and 100) across three visitor categories children, adults, and seniors. Benchmark validation on ATSP datasets from TSPLIB (BR17, P43, RY48, FT53) resulted in a mean error rate of 6.189%, confirming the robustness and generalizability of the method. The optimal configuration (CR = 0.7, MR = 0.05, PopSize = 100) produced route distances of 184,750 cm (children), 197,340 cm (adults), and 180,190 cm (seniors), yielding efficiency improvements of 30–50% compared to a pure GA and 3–7% compared to the initial measured paths. These findings demonstrate that the proposed hybrid Dijkstra–GA–2Opt framework offers a conceptually distinct, scalable, and empirically validated approach for real-world tourism route optimization.
Comparative Analysis of Memory and Render Performance: BLoC vs Provider on Low-End Devices Muhammad Albert Nur Agathon; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
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.v8i2.3268

Abstract

Mobile application performance is a critical determinant of user retention, yet optimization strategies for low-end hardware remain underexplored in the Flutter ecosystem. The choice of state management, specifically between Provider and BLoC, is a pivotal architectural decision affecting resource efficiency, particularly when integrated with Clean Architecture. Addressing the scarcity of empirical studies on constrained hardware, this research quantitatively compares the performance of these two libraries on a Vivo 1719 device running Android 7. Two identical Al-Qur'an applications were developed to facilitate a controlled experiment, isolating state management as the single variable. Performance metrics, including Resident Set Size (RSS), Garbage Collection (GC) frequency, and Frame Janks, were measured using Flutter DevTools during intensive scrolling and data loading scenarios. The results demonstrate that BLoC significantly outperforms Provider on low-end specifications. In the heaviest scenario, BLoC recorded lower peak memory usage (201.88 MB) compared to Provider (221.64 MB) and triggered 33% fewer GC events. Furthermore, BLoC reduced frame janks by 40% (15 janks vs. 21 janks). From a software engineering perspective, these findings indicate that BLoC's stream-based, event-driven architecture offers superior resource isolation compared to Provider's listener propagation mechanism, which tends to induce higher garbage collection overhead. Consequently, this study recommends BLoC as the preferred strategy for deployments targeting emerging markets, offering a worthwhile trade-off between development complexity and runtime stability to ensure broader digital inclusivity.
An IoT-ML Based Flood Early Warning Prototype for Disaster Risk Mitigation Doni Prastyo; Imam Halim Mursyidin; Dede Irawan
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.v8i2.3269

Abstract

Indonesia frequently faces hydrometeorological disasters, with flooding being one of the most common and damaging. This study examines recurrent inundation in the Ciledug Indah Housing complex, Tangerang, an area highly vulnerable to overflow from the Kali Angke river. To address this persistent issue, the research proposes and evaluates an autonomous, field-ready Flood Early Warning System (FEWS) integrating Internet of Things (IoT) sensing, machine learning, and real-time alert delivery. The system deploys JSN-SR04T ultrasonic and tipping bucket sensors, supported by solar power and dual connectivity (Wi-Fi and GSM), enabling continuous operation despite outages or unstable networks conditions frequently experienced during flood events. Its primary scientific contribution is a practical two-stage hybrid machine learning framework: a Long Short-Term Memory (LSTM) model forecasts short-term river water levels, while a Random Forest (RF) classifier translates those predictions into actionable risk categories—Safe, Alert, or Warning. Separating numerical forecasting from categorical decision-making enhances accuracy, interpretability, and usability compared with single-model approaches. Automated community notification is enabled through Firebase Cloud Messaging (FCM), ensuring rapid dissemination of warnings. Experimental evaluation using 49 days of continuous river level and rainfall data (September 27–November 15) demonstrates strong predictive performance (LSTM RMSE 0.4276 m). The RF classifier achieved 99.0% accuracy; however, this figure must be interpreted cautiously due to dataset imbalance dominated by non-critical conditions and the absence of actual flood events. Overall, the proposed FEWS offers a resilient, scalable, and field-validated solution for flood detection, prediction, and public warning, contributing to more proactive urban disaster mitigation.
Hyperparameter Optimization with Hyperband for Tuberculosis Classification Yovi Ibnu Nasikhin; Basuki Rahmat; Chrystia Aji Putra
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.v8i2.3273

Abstract

Tuberculosis (TB) is an infectious illness that continues to be a major global health challenge due to its high rates of disease and death. Early detection of TB using chest X-ray images often faces challenges related to subjective interpretation by radiologists and limited sensitivity and specificity. This study develops a Convolutional Neural Network (CNN) model to classify chest X-ray images into Normal and Tuberculosis classes, using a total of 2,198 chest X-ray images consisting of 1,173 Normal and 1,025 Tuberculosis samples. Hyperparameter optimization was carried out using the Hyperband algorithm implemented in the Keras Tuner framework to obtain the best parameter combination that produced optimal model performance. The main hyperparameters tuned included the number of dense layers, the number of units per layer, dropout rate, and learning rate. The optimization process yielded the best configuration consisting of two dense layers with 160 and 64 units, a dropout rate of 0.3, and a learning rate of 0.0011. The optimization process increased the model’s accuracy from 0.84 to 0.88 and reduced the validation loss from 0.44 to 0.34, indicating a more stable and effective learning outcome after optimization using Hyperband. The application of Hyperband successfully enhanced learning stability, accelerated convergence, and improved overall model performance. These results indicate that hyperparameter optimization using Hyperband not only enhances CNN-based TB classification accuracy but also strengthens its potential clinical utility by supporting more consistent, rapid, and objective early diagnosis in real-world healthcare settings.
Implementation of The Haversine Algorithm and Point of Interest Feature for Patrol Route Optimization Aryan Fitrah Adillah; Moh. Ali Romli
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.v8i2.3274

Abstract

This study presents the design and implementation of Poliscope, a mobile-based patrol monitoring application developed to address inefficiencies in traditional paper-based security systems. Conventional patrol methods often suffer from fragmented data collection, limited real-time visibility, and poor accountability, reducing operational reliability. The system adopts a client-server architecture, utilizing Flutter for the frontend and Node.js with Express.js and MySQL for the backend to ensure performance and scalability. At the core of the system are two computational features: the Haversine algorithm, applied dynamically to calculate real-time geodesic distances between officers and patrol points with an average spatial accuracy of ±10 meters and the Point of Interest (POI) feature, which enables officers to tag, record, and manage field locations directly during patrols. To assess reliability, Black Box Testing was conducted across critical modules such as authentication, patrol session management, and POI handling. All functional scenarios executed successfully, with an average response time under 1.5 seconds, confirming the system’s responsiveness and operational robustness. The findings indicate that Poliscope significantly enhances patrol oversight through improved spatial accuracy, structured documentation, and real-time monitoring capabilities. Its integration of continuous geospatial tracking and dynamic task verification addresses key gaps in legacy systems. As a result, the application supports faster response times, more accountable field reporting, and enhanced supervisory control, offering a practical and scalable solution for modernizing patrol management in both public and private sector security operations.

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