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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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Implementation of the WASPAS Method for Selecting an Optimal Project Leader Erwin Erdiyanto; M. Muharrom Al Haromainy; 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.v8i2.3373

Abstract

Selecting an optimal project leader is a critical organizational process that strongly influences project performance, coordination efficiency, and overall operational outcomes. Poor selection decisions may increase delays, inefficiencies, and reduced team productivity. To address these challenges, this study applies the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate eight project leader candidates using five leadership-related criteria: leadership ability, communication skills, professional experience, technical expertise, and problem-solving capability. All candidate scores were compiled into a decision matrix and normalized to ensure comparability across criteria. WASPAS was implemented through its dual-component structure, combining the additive Weighted Sum Model (WSM) and the multiplicative Weighted Product Model (WPM) to generate comprehensive preference values (Qi). This hybrid mechanism enables the method to capture both absolute and proportional differences in candidate competencies. The results show that WASPAS successfully ranked all candidates and identified the strongest performer, with the highest Qi value recorded at 3.00 and the lowest at 2.09, demonstrating a clear distinction in overall competency levels. The top-ranked candidate, Sintya Dwi Rachmawati, consistently scored high across all criteria, confirming the method’s capability to differentiate performance profiles effectively. These findings highlight the methodological precision of WASPAS in supporting structured leadership selection and underscore its potential to enhance fairness and analytical rigor in organizational decision-making. Overall, the study concludes that WASPAS is a reliable and practical multi-criteria decision-making technique suitable for leadership-oriented evaluations within diverse organizational contexts.
River Water Quality Classification in Special Region of Yogyakarta Using Multi-Layer Perceptron Meily Vira Ellisthia; 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.v8i2.3374

Abstract

River water quality in the Special Region of Yogyakarta has declined significantly in recent years due to rapid urbanization and increasing anthropogenic pressures, causing several physicochemical and biological parameters to exceed environmental quality thresholds. To address the need for a consistent, data-driven assessment framework, this study develops an optimized river water quality classification model using a Multi-Layer Perceptron (MLP) architecture enhanced by Grid Search Cross-Validation. A critical challenge in the dataset is the severe class imbalance, particularly the dominance of the "Heavy Pollution" category. To mitigate this issue, Stratified Sampling was applied across five data-splitting scenarios (90:10, 80:20, 70:30, 60:40, 50:50), ensuring balanced representation of all classes. The optimization process systematically explored combinations of activation functions, learning rates, solvers, and hidden-layer configurations to identify the most efficient and generalizable model. The experimental results show that the optimized MLP model consistently achieves high performance, with an average accuracy above 95% across all scenarios. The 70:30 split was the most effective, yielding an accuracy of 98.66%, an F1-score of 97.99%, and a low Mean Squared Error (MSE) of 0.0285. The optimal architecture used the ReLU activation function, an SGD solver with a learning rate of 0.001, and a compact hidden layer of 30 neurons. The model demonstrates the potential for real-time integration into water quality monitoring systems, providing a scalable decision-support tool for sustainable water resource management and pollution control in Yogyakarta.
Enhancing Sentiment Classification Accuracy Through Pre-Processing In Educational Text Messenger Data Md Abdul Bakir; Suliana Sulaiman; Salem Abdullah Salem Garfan
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.v8i2.3377

Abstract

This paper discusses the critical pre-processing steps for appropriate sentiment analysis (SA) in an educational domain, especially when working on text messenger data from instant messaging applications like WhatsApp and Telegram. As these platforms often generate noisy, unstructured, and multilingual messages that include textisms, emojis, and mixed-language expressions, proper data preparation is essential to ensure reliable analytical outcomes. The primary goal of this work is to discover and validate pre-processing approaches applied for improving the model’s performance when working with such rich data. In order to do so, we performed an SLR to establish best practices on text pre-processing for SA using methods applicable for informal, user-generated content. Characteristics extracted via the SLR, namely, textism normalization, stop word removal, punctuation removal, stemming, translation of mixed-language text and tokenization were next applied to a gathered dataset from educational subject groups. These techniques achieved a great increase of 0.705 to 0.893 based on the BERT model accuracy. These results emphasize the need of well-developed pre-processing pipelines for handling multilingual and unstructured text in educational communication channels. However, the study is limited to text data from WhatsApp and Telegram, focusing only on Malay and English languages. Further studies could explore other languages, platforms and more advanced normalization processes in a way that continues to enhance the predictive capacity of pre-processing strategies for sentiment analysis across an array of educational contexts.
Identification of Dengue Hemorrhagic Fever (DHF) Using the Naïve Bayes Classifier Method Erdy Sutriyatna; Dede Sunandar; Adam Muiz
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.v8i2.3378

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health burden in tropical and subtropical countries, with Indonesia consistently reporting the highest incidence in Southeast Asia since 1968. Early diagnosis traditionally depends on clinical evaluation and laboratory confirmation, processes that may require several days and often delay intervention during the critical plasma leakage phase. Addressing this gap, the present study introduces an intelligent early identification system for DHF based on the Naïve Bayes Classifier, a probabilistic data-mining method recognized for its computational efficiency and strong performance in handling categorical medical attributes. The model was trained using 100 anonymized patient records and DHF screening forms collected from Puskesmas Pasir Buah, Curug, Bitung, spanning 2020–2023, incorporating twelve clinically relevant predictors consisting of symptom-based indicators and basic hematological parameters. Following preprocessing and 10-fold cross-validation, the system achieved an average accuracy of 94.67%, precision of 95.2%, recall of 93.8%, and an F1-score of 94.5%, demonstrating its reliability for preliminary DHF assessment. The resulting web-based prototype allows health workers to input patient symptoms and receive immediate probabilistic classifications (Positive/Negative) accompanied by recommendations for confirmatory laboratory testing. By providing rapid and interpretable diagnostic support, this system has the potential to reduce diagnostic delays at the primary healthcare level and enhance decision-making in resource-limited environments.
Web-Based E-commerce Design Using the Waterfal Method Dede Eko Saputro; Andrian Hidayat; Muhamad Rosdiana
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.v8i2.3400

Abstract

Current advancements in digital technology have transformed transaction and sales activities, yet CV. Mella Vista still relies on manual processes that limit information accessibility and reduce operational efficiency. Responding to this limitation, the study aims to develop a structured web-based e-commerce system that enhances promotional reach, transaction processing, and administrative reporting. The system was designed using the Waterfall method, incorporating sequential phases of analysis, design, implementation, and testing, and implemented using PHP and MySQL to ensure stable data management and functional reliability. Through this approach, the research provides a clear methodological foundation for developing a system aligned with organizational needs. The resulting e-commerce platform offers real-time product visibility, streamlined ordering procedures, and automated reporting features, addressing the reviewers’ emphasis on clarifying methodological contribution and system capabilities. Empirical testing demonstrates that the system operates effectively across key functions—user authentication, product display, cart management, checkout, and payment verification—supporting accuracy and ease of use for both administrators and customers. Quantitative evaluation shows that sales increased from 6,577,500 in November to 8,795,000 in December, representing an approximate 33.7% improvement, thereby providing contextual clarity as requested by reviewers. This measurable gain indicates that the system not only enhances data processing efficiency but also strengthens customer engagement and purchasing activity. Overall, the findings confirm that the implemented e-commerce system significantly improves operational effectiveness, increases informational transparency, and supports stronger competitive positioning, offering a practical digital solution for small enterprises seeking to modernize their sales processes.
A Comparative Study of Naïve Bayes, SVM, and BiLSTM for Indonesian Tweet Gender Classification Purna Aji Wardhana; Chanifah Indah Ratnasari
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.v8i2.3405

Abstract

Indonesian social media platforms, particularly X (formerly Twitter), generate short and highly informal texts that contain linguistic cues useful for demographic inference. Given the scarcity of controlled comparative studies in Indonesian gender prediction, especially with modest datasets, this research evaluates the performance of Multinomial Naïve Bayes, Linear Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BiLSTM) using a balanced corpus of 478 manually labeled Indonesian-language tweets. These three models were selected to represent classical probabilistic learning, margin-based linear classification, and neural sequence modeling, thereby enabling a methodologically coherent comparison across distinct algorithmic paradigms. The study implemented a unified workflow consisting of manual labeling, structured preprocessing with Sastrawi stemming, RandomOverSampler for class balancing, TF-IDF features for classical models, and sequence-based tokenization for BiLSTM. All models were trained and evaluated using a stratified 80:20 split. Experimental results show that Linear SVM achieved the strongest performance, reaching 0.833 accuracy and 0.832 macro-F1, surpassing Naïve Bayes (0.771 accuracy) and BiLSTM (0.740 accuracy). SVM also demonstrated the most stable confusion-matrix distribution and superior AUC characteristics, while BiLSTM exhibited fluctuating validation curves, indicating sensitivity to the limited dataset size. These findings reinforce that classical models—particularly Linear SVM remain highly competitive for Indonesian short-text gender classification in low-resource environments and offer practical advantages where computational constraints and data scarcity are prominent. Although the dataset is topically narrow and limited in scale, the results highlight the need for larger corpora or transformer-based Indonesian models to further enhance generalizability and downstream demographic inference.
AI Integration in E-Learning for Vocational Education Effectiveness Didi Kurnaedi
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.v8i2.3427

Abstract

The development of digital technology has had a significant impact on the world of education, particularly through the use of e-learning systems. This online learning model provides flexibility, accessibility, and efficiency in the teaching and learning process. However, the main challenges still faced are the lack of interactivity, limited personalization of learning, and the difficulty in adapting materials to student needs in vocational education, which emphasizes practical aspects and skills. The presence of Artificial Intelligence (AI) offers new opportunities to overcome these limitations. With its data analysis capabilities, adaptive learning, and automation, AI can help create more relevant, contextual, and effective learning experiences. This study aims to analyze the integration of artificial intelligence (AI) in e-learning systems to improve learning effectiveness in vocational education. The method used is a sequential explanatory mixed methods design, implemented in two phases. The quantitative phase involved 375 vocational high school student respondents in the Tangerang area through a questionnaire, while the qualitative phase explored the findings through in-depth interviews and observations at 22 selected participating schools. The results show that the use of AI can personalize materials according to student needs, provide automated learning recommendations, create chatbot-based virtual assistants, and support evaluation with an intelligent system. The positive impact of implementing AI in e-learning is increased learning motivation, student engagement, and their readiness to face industry demands. Therefore, integrating AI into e-learning systems can be an effective strategy to strengthen the quality of vocational education. Based on these findings, the study recommends the need to improve digital infrastructure readiness, strengthen teachers' technological competencies, and develop clear regulations regarding the security and ethical use of student data. Implementing supporting policies and ongoing training programs for educators is crucial to maximize the potential of AI and ensure its equitable and sustainable implementation in vocational education environments.
Mobile Intelligent System for VARK-Based Student Learning Style Classification Using KNN Muhammad Saiful Anwar; Sri Wulandari
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.v8i2.3428

Abstract

The growing complexity of learning activities in higher education highlights the need for accurate and scalable mechanisms to identify students’ learning preferences. Conventional VARK-based assessments, which rely on manual self-report questionnaires, remain limited by subjectivity, low practicality, and the absence of real-time feedback. This study addresses these challenges by developing Bandela, a mobile intelligent system that integrates the VARK framework with the K-Nearest Neighbors (KNN) classification algorithm to provide automated learning style identification. Using a Research and Development (R&D) approach, the system was implemented through a three-tier architecture consisting of a Flutter frontend, a Python Flask backend, and a MySQL database. Questionnaire responses collected from students were used as both training and testing datasets for the KNN model, enabling real-time classification across Visual, Auditory, Read/Write, and Kinesthetic categories. Functional evaluation through Blackbox Testing demonstrated that all core features ranging from authentication and questionnaire completion to classification processing, visualization, and community interaction performed reliably and as intended. The findings indicate that Bandela offers an accessible and empirically grounded tool for identifying learning preferences, contributing to more personalized and adaptive learning strategies. This work underscores the practical value of mobile intelligent systems in advancing data-driven personalization within higher education and provides a foundation for future enhancements involving expanded datasets and exploration of additional machine learning techniques.
Evolution of Student School Payment Administration from Excel to Laravel Muhammad Subhana
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.v8i2.3431

Abstract

This study aims to develop a web-based school payment administration system using the Laravel framework to address several challenges commonly found in manual payment processes, including duplicate financial records, delayed verification procedures, and limited access to payment information for parents. The research adopts a qualitative descriptive approach involving direct observation of existing administrative workflows, analysis of user needs, and expert validation conducted with administrative staff and school management. The system developed in this study integrates student profiles, parent information, payment transaction data, and staff audit trails into a centralized and structured platform.  Core features of the system include payment validation based on uploaded proof of transfer, automatic activity logging for administrative staff, generation of daily and monthly financial reports, and role-based access control to improve data security and accountability. Functional evaluation using BlackBox Testing demonstrates that all key features perform according to user expectations and operational needs. In addition, feedback from experts and school personnel indicates that the implemented system contributes to faster administrative processing, more organized financial documentation, and reduced risk of data inconsistencies that typically occur in manual systems. Overall, this study provides a model for an integrated and secure school payment administration system that enhances efficiency and accessibility for parents, administrative staff, and school management. The findings underscore the importance of web transformation in improving the reliability and transparency of school financial operations.
Business Process Modeling and Prototype Development for a Digital Custom Batik Ordering System Mahendi Putri Dinanti; Chanifah Indah Ratnasari
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.v8i2.3439

Abstract

Manual ordering practices pose significant challenges for many craft-based small enterprises in Indonesia, including batik producers, where customer interactions, documentation, and production tracking remain largely informal. These fragmented processes result in inconsistencies, limited data access, and coordination difficulties, particularly in custom-order workflows involving multiple approval stages. This study seeks to redesign Batik Aryu’s ordering system, develop a user-centered prototype, and evaluate its user experience to assess the impact of digitalization on operational efficiency and service quality. The methodology combines Business Process Model and Notation (BPMN) for workflow analysis, evolutionary prototyping for iterative system design, and the User Experience Questionnaire (UEQ) to evaluate perceptions from both customers and administrators. BPMN analysis identified key bottlenecks in the existing manual process, leading to the development of a streamlined digital workflow that improves traceability and communication. The prototype underwent refinement through iterative feedback from the business owner, five administrative staff, and seventeen customers. UEQ results revealed varying perceptions: administrators rated most dimensions as Below Average, while Efficiency was rated as Above Average and Novelty as Good; customers rated Efficiency as Excellent, with Attractiveness and Novelty classified as Good. These findings highlight the effectiveness of the digital system in enhancing customer-facing processes, though administrative usability requires further refinement. This study contributes to the growing body of SME digitalization literature by providing an actionable framework that integrates BPMN, evolutionary prototyping, and UEQ for digital transformation, with recommendations for backend integration and real-world testing.

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