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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 114 Documents
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Search results for , issue "Vol. 8 No. 1 (2025): bit-Tech" : 114 Documents clear
Optimizing Artificial Intelligence-Based Waste Bank Management Eriana, Emi Sita; Zein, Afrizal
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2526

Abstract

This study examines the implementation of artificial intelligence (AI) technology to optimize waste bank management in West Pamulang, Indonesia. With the national waste volume reaching 68.5 million tons in 2023 and an annual growth rate of 2-4%, sustainable waste management presents critical challenges. West Pamulang accounts for 60% of regional waste, while Indonesia's 8,000 waste banks only reach 1.7% of the contribution to national waste reduction. Using a mixed method approach, the study was conducted in five waste banks in West Pamulang, South Tangerang during January-April 2025, involving 45 participants selected through purposive sampling. Data collection included participatory observations, interviews, questionnaires, and documentation studies. Reliability was assessed using Cronbach's Alpha 0.89, with validity guaranteed through triangulation. Ethical safeguards include informed consent, data anonymization, and institutional ethical approval. The results show significant operational improvements through AI technologies: computer vision-based classification systems, real-time transaction recording, educational chatbots, and volume prediction systems. Quantitative analysis revealed an increase in transaction efficiency by 75%, a 60% decrease in classification errors, and a decrease in data management time from day to minute. The AI predictive model achieves 92% accuracy in volume estimation and 15% fuel savings through route optimization. The classification system shows an accuracy of 89-97%, reducing the sorting time by 70%. Implementation challenges include limited digital literacy, infrastructure gaps, and inadequate policy support. The study recommends training programs, cost-effective platforms, and multi-stakeholder collaboration for a sustainable AI-enhanced waste management system.
Optimizing Internship Registration Process Using a Business Process Reengineering Approach Mubarok, Muhammad Syifa; Nuryasin, Ilyas
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2527

Abstract

The internship registration process at the Malang City Religious Court is still conducted manually, resulting in various administrative problems such as long processing times, risk of data input errors, and inefficient communication flows. These issues conflict with the principles of modern public service, which emphasize efficiency, transparency, and technology-based accessibility. This study aims to optimize the internship registration process by applying the Business Process Reengineering (BPR) approach, which involves fundamentally redesigning business processes to achieve significant improvements in performance. The approach is supported by ESIA (Eliminate, Simplify, Integrate, Automate), a technique focused on eliminating non-value-added activities, simplifying procedures, integrating fragmented processes, and implementing digital automation. This research employs a qualitative case study method involving field observations and in-depth interviews with administrative staff. The current workflow is modeled using Business Process Model and Notation (BPMN), and process performance is measured using throughput efficiency the ratio of value-added activity time to total process duration. The results reveal that the initial manual process, consisting of 38 activities with a total time of 209 minutes, was successfully transformed into a streamlined digital process with only 12 steps and a total duration of 168 seconds. Throughput efficiency increased significantly from 52.15% to 100%. In conclusion, the digitization of the internship registration process using BPR and ESIA has significantly enhanced administrative efficiency. This study contributes a replicable digital system model suitable for non-litigation public services in religious courts and enriches the BPR literature by introducing its application in public sector services rooted in religious legal institutions.
Random Forest – Deep Convolutional Neural Network Ensemble Model for Skin Disease Classification Kurniawan, Ananda Rheza; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2528

Abstract

Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Salsabilah, Andini Fitriyah; Rahmat, Basuki; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Design and Development of Web-Mobile Application for Housing Project Management Using KNN for Prediction Arifa, Salsabila; Akbar, Fawwaz Ali; Putra, Chrystia Aji
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2546

Abstract

Project management in housing development is essential to ensure timely completion, budget efficiency, and market alignment. However, many small to medium sized property developers still use manual systems, causing inefficiencies in monitoring, documentation, and sales planning. PT Bakti Luhur Abadi is one such company that still relies on Microsoft Excel for recording project progress and housing unit sales. This study aims to develop an integrated project management system equipped with a sales prediction feature using the K-Nearest Neighbors (KNN) algorithm. The goal is to improve operational efficiency, streamline decision making, and support strategic sales forecasting. The system was developed using the Waterfall method, comprising requirement analysis, system design, implementation, and testing. A key novelty of this research is the dual platform implementation web for administrators and mobile for directors and field teams enabling real time access, structured documentation, and effective communication. The KNN algorithm was tested with 30 test data and 114 training data using K values of 3, 5, and 7. The best result was achieved at K = 7 with an accuracy of 86.7%. Functional validation using black-box testing confirmed all web and mobile features operated as expected. In conclusion, the proposed application effectively automates project management and enables accurate sales prediction. It provides practical benefits for small and medium-scale property developers by increasing efficiency, improving internal coordination, and supporting data driven planning through an accessible and intelligent solution.
Educational Game Design to Raise Awareness of Social Anxiety and Cognitive Behavioral Therapy Az-Zahro', Syaikhhanun Nabila; Atmaja, Pratama Wirya; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2547

Abstract

Social Anxiety Disorder (SAD) is a common yet often misunderstood mental health condition that significantly impacts the lives of adolescents and university students. Despite its prevalence, awareness and understanding of SAD and its evidence based treatment, Cognitive Behavioral Therapy (CBT), remain limited among young adults. This study aims to design and develop Social Survival, a 2D educational game intended to raise awareness of SAD and introduce CBT techniques through an interactive and engaging medium. The game is developed using the Unity engine and employs the Interactive Digital Narrative (IDN) framework to deliver a singleplayer narrative experience. It presents scenarios simulating SAD symptoms and embeds CBT strategi such as relaxation, cognitive restructuring, and exposure into gameplay via minigames. The development process included a needs analysis, general and detailed design phases, and implementation of mechanics aligned with CBT principles. To evaluate learning effectiveness, a pre-test and post-test were administered and analyzed using the N-Gain formula. Player satisfaction was assessed using the Game User Experience Satisfaction Scale (GUESS-18), which measures dimensions such as enjoyment, engagement, and educational value. The results indicate a positive improvement in player understanding of SAD and CBT, along with favorable user experience ratings. The study concludes that serious games can serve as effective tools for mental health education, although clinical treatment should still be guided by professionals.
Pengembangan Platform Berbasis Web untuk Edukasi Gizi dan Deteksi Dini Stunting Aldhyno Yoghatama; Endang Wahyu Pamungkas
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2559

Abstract

Stunting remains a major public health issue in Indonesia with a prevalence of 21.5%, negatively affecting children’s growth and development. Limited access to interactive and integrated nutrition education poses a barrier to early stunting prevention efforts. This study aims to develop an interactive web-based platform called GiziMu Mijen that integrates evidence-based nutrition education, a nutritional status calculator for children aged 0–60 months based on WHO standards, and an online nutrition consultation service via WhatsApp to support family-level stunting prevention. The system was developed using the Agile Software Development methodology and the Laravel 11 PHP framework, with system design modeled through Unified Modeling Language (UML) for use case, activity, and data structure visualization. Testing was conducted using black-box and acceptance testing involving nutritionists to validate the nutritional calculator and consultation features. Results indicate that the platform successfully provides nutrition education content, complementary feeding recommendations, automated nutritional status calculations, and functional interactive consultation services. The system facilitates parents’ independent monitoring of their children’s nutritional status and quick access to professional advice. In conclusion, GiziMu Mijen offers a comprehensive and effective digital solution to improve nutrition literacy and early stunting detection at the family level. The Agile and Laravel-based development approach ensures flexibility and usability, positioning the platform as a strategic tool in national stunting reduction programs, with recommendations for broader testing and integration into national health information systems for data validation.
Website Security Testing Using PTES Method and OWASP Top 10 Approach Firnanda, Mochammad Yoga; Henni Endah Wahanani; Achmad Junaidi
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2564

Abstract

Rapid technological advancements have greatly benefited the industrial sector, making technology essential for business operations. However, this reliance also introduces vulnerabilities, particularly in Enterprise Resource Planning (ERP) systems, which are critical for managing business processes and sensitive data. Due to their complexity and integration, ERP systems are prime targets for cyberattacks, emphasizing the need for robust security testing. This research aims to identify, evaluate, and exploit vulnerabilities in the ERP website of PT. XYZ, specifically targeting pages accessible by users with the SPV Marketing role. The Penetration Testing Execution Standard (PTES) methodology was used to guide the process from intelligence gathering to exploitation and reporting. PTES also ensures that testing is conducted legally during the pre-engagement phase. Tools such as Google Dorking, Netcraft, Wappalyzer, and Nmap were employed for intelligence gathering. For threat modeling, ISO 27005 was employed to identify vulnerabilities, while ISO 25010 served as a standard for security quality. A ZAP scan revealed 23 security vulnerabilities, including 18 that fall under the OWASP Top 10, such as Broken Access Control and Injection. Simulated attacks successfully identified Cross-Site Scripting (XSS), Session Hijacking, and Cross-Site Request Forgery (CSRF). Based on the findings, the recommendations focus on enhancing ERP system security according to the OWASP Top 10 guidelines, ensuring clarity for the development team. This study highlights the need for improved ERP security and offers a structured PTES-OWASP framework applicable across sectors. Future research may integrate multiple tools to enhance vulnerability detection.
Optimasi Hiperparameter LSTM Menggunakan PSO untuk Peramalan Bawang Merah dan Bawang Putih Tanjung, Mutiq Anisa; Sari, Anggraini Puspita; Junaidi, Achmad
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2569

Abstract

This research develops a shallot and garlic price prediction model using a Long Short-Term Memory (LSTM) network optimized through the Particle Swarm Optimization (PSO) method. Indonesia experiences an annual increase in demand for these two commodities. This research focuses on optimizing LSTM parameters, such as the number of units in each layer, learning rate, batch size, time step, and number of training epochs using PSO. Various trials were conducted with different PSO parameter settings and data partitioning scenarios to find the best configuration in predicting prices. The results show that the LSTM model optimized with PSO produces an RMSE value of 436,969 for shallots and 173,866 for garlic. In addition to RMSE, the Mean Absolute Percentage Error (MAPE) and R² metrics also show high prediction accuracy. The 90:10 data partitioning scenario showed the best evaluation results, indicating that more data improves the accuracy of the LSTM in learning price patterns. Scatter plots comparing predicted prices with actual prices show a good match, although there is some variation in certain price ranges. This study also highlights the effect of data partitioning on model performance. The LSTM-PSO approach proved effective in improving the accuracy of price predictions and has practical implications for farmers and policy makers in decision making. The model has the potential to be a decision support tool in the agribusiness sector, with the possibility of further development with external factors.
Optimizing Public Information Dispute Resolution Through Digital Business Process Reengineering Strategies Satria, Muhammad Rafid Mursyaday Putra; Nuryasin, Ilyas
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2572

Abstract

This study applies Business Process Reengineering (BPR) combined with digital document management to improve the efficiency of public information dispute resolution at the Ministry of Religious Affairs in Batu City, Indonesia. It identifies key inefficiencies in manual workflows, such as document sorting, verification, and archiving, which slow service delivery and reduce accuracy. By redesigning processes to eliminate non-value-adding steps and integrating automation via a web-based dashboard for real-time monitoring, the study achieves significant operational improvements. Using ASME-standard process mapping and throughput efficiency testing—a rigorous quantitative approach rarely employed in public sector BPR evaluations—the research measures process performance objectively. Results show throughput efficiency increased from 37.32% to 69.43%, with substantial reductions in processing time, enhanced document accessibility, and strengthened information security. Automated workflows reduce human error and improve traceability in dispute handling. The novelty of this study lies in its methodological integration of engineering standards with public administration reform and its practical application of digital BPR in a complex, bureaucratic Indonesian government context. This provides robust empirical evidence on how digital transformation synergized with BPR can enhance transparency, responsiveness, and service quality in public dispute management. The findings offer valuable insights for governmental institutions aiming to modernize administrative procedures, improve accountability, and align operational performance with citizen expectations in the digital era.

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