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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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Design and Development of a Real-Time Delivery Tracking App Using WebSocket and Haversine Bima Arya Kurniawan; Retno Mumpuni; 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.v8i2.3275

Abstract

The increasing demand for reliable real-time delivery tracking in furniture logistics highlights critical gaps in existing solutions: static geofencing without precise geodesic validation, polling-based protocols causing 1-3 second delays, and non-integrated Proof of Delivery (PoD) lacking automated location verification. These limitations result in inconsistent validation accuracy and limited transparency, directly impacting customer trust and operational costs. This research addresses these gaps by developing a novel tracking system uniquely integrating WebSocket-based bidirectional communication, Location-Based Services (LBS), and the Haversine Formula for geodesic distance validation, a combination not previously implemented for PoD-specific furniture logistics. The system comprises an Android driver application, Laravel-based backend with real-time WebSocket infrastructure, and monitoring interfaces for administrators and customers. Evaluation through 30 latency samples demonstrated stable WebSocket performance with average latency of 44.03 ms (SD=4.38ms; 95% CI=44.03±1.62ms), achieving 23-68 times faster transmission than traditional HTTP polling. Three geofence validation scenarios (50m, 100m, 200m radii) achieved 100% accuracy, with PoD triggering consistently restricted to authorized boundaries. Comprehensive functional testing confirmed reliable operational behavior across mobile and web modules. The practical impact includes automated location verification eliminating manual validation time, enhanced fleet visibility for large delivery trucks, and increased customer transparency through continuous tracking. These findings demonstrate that integrated WebSocket-Haversine-LBS architecture significantly improves delivery transparency, reduces validation errors, and enhances operational decision-making in furniture logistics. Future priorities include offline-first architecture, route optimization for heavy vehicles, and iOS platform support.
StudentSphere: Development of a Digital Gallery Platform for Publishing and Exploring Student Work Muhammad Iqbal Fahrezzi; Khairany Zuhriyyah Jinan Hsb; Augis Dinanti; Debi Yandra Niska
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.v8i2.3276

Abstract

StudentSphere is a digital gallery platform designed to enhance the visibility, accessibility, and academic value of student works in higher education. Recent studies show that universities in Indonesia lack centralized and user-friendly systems for showcasing academic outputs, leading to low discoverability and limited recognition of student achievements. This research aims to develop and evaluate StudentSphere as an institutional-level solution that provides structured publication workflows, efficient search mechanisms, and lecturer–student academic profiling. The platform was built using Laravel 10 and MySQL 8, supported by Tailwind CSS and Alpine.js for lightweight interaction, and evaluated through a multi-phase usability study. The research employed iterative Agile development, involving 57 participants (45 students and 12 lecturers) across five evaluation cycles including surveys, interviews, usability tests, and user acceptance testing. Results show strong usability with a System Usability Scale (SUS) score of 79.8, a task completion rate of 93%, and a satisfaction score of 4.25/5. Load testing also demonstrated stable performance for up to 200 concurrent users. Practical implications include improved academic visibility, streamlined work submission, and enhanced lecturer access to student achievements. However, limitations remain in institutional authentication and real-time engagement features. The study concludes that StudentSphere meets its intended goals, though future development should prioritize SSO integration, analytics, and real-time notifications to strengthen scalability and interoperability.
Responsible AI Practices as a Driver of Human-Centered Innovation and Creative Sustainability Tri Setio Utomo Suharto
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.v8i2.3277

Abstract

This study analyzes how key ethical dimensions of Artificial Intelligence (AI) shape responsible AI usage and subsequently influence human-centered innovation and creative sustainability within Indonesia’s creative industries. Using a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), the research collected responses from 251 practitioners across application development, gaming, graphic design, animation, music, and digital video production. The model incorporates six ethical dimensions Fairness & Non-Discrimination, Transparency, Privacy & Security, Accountability, Human Oversight, and Education & Awareness examined for their effects on Responsible AI Practice (M1) and two outcome variables: Human-Centered Innovation (Y1) and Creative Sustainability (Y2). All constructs demonstrated strong measurement quality, with high reliability and convergent validity. The strongest structural effects were observed in the paths M1 → Y1 (0.874) and Y1 → Y2 (0.846), indicating that responsible AI functions as a central mechanism linking ethical principles to innovation and sustainability outcomes. The overall model demonstrated an excellent fit, confirming the robustness of the proposed framework. Beyond statistical confirmation, the study contributes to existing research by showing that ethical AI is not merely normative but directly actionable through practical mechanisms such as human-in-the-loop supervision, periodic bias audits, transparent documentation, and improved AI literacy. These findings provide clear guidance for stakeholders practitioners, firms, and policymakers on how responsible AI can be operationalized to enhance trust, preserve human creativity, and support sustainable growth in digital creative ecosystems.
Feature Augmentation with XGBoost to Improve 1D CNN Performance in Anemia Recognition Raissa Atha Febrianti; Anggraini Puspita Sari; 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.v8i2.3282

Abstract

Anemia is one of the most prevalent nutritional and hematological disorders worldwide, characterized by low hemoglobin levels caused by iron deficiency, genetic factors, or chronic diseases. Diagnosis commonly relies on Complete Blood Count (CBC) interpretation, a manual process that is time-consuming and susceptible to human error. This study proposes a novel hybrid framework that integrates Extreme Gradient Boosting (XGBoost) and a One-Dimensional Convolutional Neural Network (1D-CNN) to enhance anemia classification. The methodological novelty lies in employing XGBoost as a feature-augmentation mechanism, where its class-probability outputs are fused with the original CBC features before being processed by the 1D-CNN, enabling richer representation learning compared to conventional single-model approaches. The model was trained and evaluated using a CBC dataset consisting of 364 samples covering four anemia classes (normocytic, microcytic, macrocytic, and normal), with performance assessed through an 80:20 stratified train–test split. Experimental results demonstrate that the proposed XGB–1DCNN model achieves a testing accuracy of 97.26%, precision of 98.68%, recall of 96.46%, and F1-score of 97.48%, outperforming the baseline 1D-CNN model (83.56%). These findings demonstrate that combining ensemble learning and deep learning significantly improves the model’s ability to capture complex nonlinear patterns in CBC data, offering a more reliable solution for AI-based early anemia diagnosis and clinical decision support.
Application of Support Vector Machine Algorithm and Image Processing in Coffee Bean Quality Classification Febi Wulan Dini; Agus Suhendar
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.v8i2.3283

Abstract

This research was conducted to address the problem of the coffee bean sorting process, which is still performed manually in Empat Lawang Regency. The process is time-consuming, requires a large amount of human labor, and often results in inconsistent quality assessment. To overcome this, the study developed an automated classification system based on Support Vector Machine (SVM) utilizing Image Processing. The dataset was obtained directly from local collectors and consists of 740 coffee bean images, encompassing 286 good beans, 240 moldy beans, and 214 damaged beans. Feature extraction was performed based on three main characteristics color, size, and texture. Color features were calculated using the mean of RGB and HSV, while size features were obtained from the calculation of area, perimeter, and roundness. Texture features were extracted using the GLCM method. The SVM model was built using the RBF kernel and optimized with parameters C = 2 and gamma = 0.1. The evaluation results showed an accuracy of 94.37%, precision of 94.41%, recall of 94.37%, and an F1-score of 94.35%. The novelty of this research lies in the integration of color size texture features for the three-class classification of coffee beans using a lightweight model that is easily implementable at the MSME scale. However, the model is still limited to single-object images. Therefore, further research is suggested to include multi-bean datasets and consider deep learning methods that are more adaptive to variations in the number and position of coffee beans, such as CNN with YOLO or R-CNN.
Integrated Application for Worship Scheduling and Church Finance Based on Android and Web Devani Maria Naulu; Anna Dina Kalifia
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.v8i2.3285

Abstract

The development of information technology has driven digital transformation in the management of church administration, including service scheduling, financial reporting, and congregational services. However, most existing systems are still single-platform and do not support real-time data synchronization, resulting in limitations in efficiency and transparency. This research aims to design and implement an integrated application based on Android and Web that is able to manage worship scheduling, seat reservations, and church financial administration synchronously and transparently. The development method used is the Waterfall model, which includes the stages of needs analysis, design, implementation, testing, and maintenance. The system was developed using the Flutter framework with the Dart programming language and Firebase Firestore (NoSQL) as a database that supports real-time data synchronization between platforms. The test is carried out using the Black Box Testing method to assess the functional suitability of the system to the needs of the user. The results of the implementation show that all the main features of worship scheduling, recording financial transactions, and congregational seat reservations work well on both platforms. Real-time data synchronization runs steadily without lag, and the user interface proves to be accessible and responsive. This integrated application has succeeded in improving the efficiency of church administration, transparency of financial management, and the convenience of congregations in participating in worship activities. Thus, this system can be a model for the digitalization of the modern church oriented towards the openness, mobility, and accountability of religious organizations.
Air Quality Prediction using a BiLSTM-Based Approach for Sustainable Environmental Management Mohammad Lucky Kurniawan; Anggraini Puspita Sari; Eva Yulia Puspaningrum
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.v8i2.3286

Abstract

In cities, where particulate matter (PM) levels are particularly high, air pollution has become a major problem that endangers human health and the environment. Accurate PM₁₀ forecasting is essential for effective environmental management and early warning systems. However, conventional LSTM models, which learn temporal patterns in only one direction, often fail to capture complex long-term dependencies. To overcome this limitation, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) model that learns temporal patterns in both forward and backward directions to improve prediction accuracy. Based on data collected from the Satu Data Jakarta platform and the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) over the period January 2010–July 2023, the dataset used herein include daily PM₁₀ concentrations. Three steps were taken to prepare the data: normalizing the Z-score, smoothing the moving average, and linear interpolation. In order to find the best parameters, the BiLSTM model was trained with several configurations of the learning rate. Based on the results of the experiments, the BiLSTM performed best when trained with a learning rate of 0.001. This parameter was associated with a R² value of 0.929, an MAE of 2.283, an RMSE of 3.029, and a MAPE of 5.016%. According to these data, BiLSTM's bidirectional mechanism improves both predictive stability and temporal feature extraction, surpassing the performance of the traditional LSTM model. The outcomes demonstrate that employing a BiLSTM-oriented method yields highly consistent and accurate PM₁₀ predictions, which can strengthen long-term air quality assessment and support environmentally informed policymaking.
Applying NLP and Cosine Similarity in the Preliminary Selection Process of Recruitment Systems Muhammad Wildan Jaffar Rahmatullah; Adi Purnama
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.v8i2.3287

Abstract

The recruitment process plays a crucial role in determining the quality of human resources within an organization. However, many companies still rely on manual screening of Curriculum Vitae (CV), which requires considerable time and introduces a high degree of subjectivity. This study aims to develop an automated preliminary selection system by applying Natural Language Processing (NLP) and the Cosine Similarity method to measure the semantic compatibility between CVs and job descriptions. The research adopts a qualitative approach grounded in observations and interviews with recruiters, while the precision metric is used only as a supplementary measure to check system performance. A total of 92 CVs and six job descriptions were collected, and 20 CVs along with four job descriptions were selected as test data. The text processing stage applies basic normalization, including lowercasing, removal of digits and punctuation, and whitespace cleaning. The normalized text is then converted into dense vector embeddings using a pre-trained multilingual SentenceTransformer model before similarity is computed with the Cosine Similarity function. System performance was measured using precision and achieved an average score of 0.95 across four job positions, indicating consistent retrieval of relevant candidates. Despite its strong performance, the system is constrained by its reliance on text based CVs, the use of a general purpose language model, and the inclusion of precision as the only evaluation metric. These findings highlight the potential of NLP and Cosine Similarity to improve efficiency and objectivity in early stage candidate selection.
Implementation of Continuous Integration and Continuous Deployment for Automated System Deployment Bintang Rahmatullah; Moh. Idris
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.v8i2.3295

Abstract

The increasing demand for software systems that are continuously updated requires deployment processes that are both automated and reliable. Manual deployment often causes delays, configuration inconsistencies, and downtime, especially in dynamic environments where changes occur frequently. To address these challenges, this study focuses on the implementation of a Continuous Integration and Continuous Deployment (CI/CD) pipeline that automates the entire build and release process for the KUPI System Information. The main objective of this research is to design and evaluate a self-hosted CI/CD architecture that integrates Docker, GitHub Actions, and Traefik to achieve a consistent, efficient, and secure deployment workflow. Unlike most previous studies that automate existing systems, this research applies CI/CD from the initial stage of system development, making automation a core architectural principle. The study employs an experimental method combined with iterative validation to test the performance, reliability, and resource utilization of the implemented pipeline. The results show that the automated pipeline successfully reduced the average deployment time to less than ten minutes, eliminated manual configuration errors, and maintained stable operation under continuous deployment cycles. Resource monitoring confirmed that CPU and memory consumption remained within optimal thresholds, ensuring system stability. In conclusion, this research demonstrates that implementing CI/CD from the early stages of system development can significantly enhance deployment speed, reliability, and operational consistency. This novelty provides a practical and reproducible model for small to medium-scale systems adopting DevOps practices with open-source tools while maintaining full control over infrastructure and security.
Siaga Kids: An Educational Game for Enhancing Self-Safety Awareness Among Elementary School Students Husein Abimanyyu; Chanifah Indah Ratnasari; Septiana Widayanti
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.v8i2.3296

Abstract

Safety education is a crucial component of early childhood learning, as elementary school students often have limited ability to identify potential dangers and respond appropriately. Although numerous studies have introduced educational games for safety learning, most focus on a single topic, such as disaster preparedness or traffic safety. There remains a lack of integrated, multi-threat learning media that simultaneously address different types of risks within one child-friendly platform. This study aims to address this gap by developing "Siaga Kids," an Android-based educational game designed to enhance children’s safety awareness through interactive and age-appropriate gameplay. The development process followed the ADDIE model, which consisted of Analysis, Design, Development, Implementation, and Evaluation. The game covers three key safety themes—earthquake preparedness, traffic sign recognition, and awareness of strangers—presented through visual storytelling and simple, engaging interactions suited for young learners. The study involved 20 students from grades 2 and 3 (ages 7–9) at SDII Nurul Musthofa, Klaten, Indonesia. Data collection included a pre-test, post-test, and a usability evaluation using the System Usability Scale (SUS). The findings show an improvement in students’ average scores from 8.10 to 9.75 after playing the game. The SUS score of 80.71 indicates “Acceptable” usability with an “Excellent” adjective rating. These results demonstrate that "Siaga Kids" is easy to use, engaging, and well-received by young users. Overall, the game effectively supports multi-threat safety education and offers a promising digital tool for strengthening personal safety awareness in elementary school children.

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