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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 370 Documents
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Blended Learning in Higher Education for Informatics Engineering Education: A Bibliometric and Systematic Literature Review Permata Saputri, Renny; Jalinus, Nizwardi; Abdullah, Rijal; Ridwan; Fransisca, Monica
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.v8i1.2673

Abstract

The evolution of digital technology has prompted a significant transformation in higher education, particularly within the Informatics Engineering curriculum. In this landscape, blended learning has surfaced as a vital strategic method, combining traditional face-to-face teaching with online learning to boost flexibility, effectiveness, and student engagement. This research utilizes a Systematic Literature Review (SLR) and bibliometric analysis to map the trends and research focus of blended learning. Data was gathered from Google Scholar for publications from 2021 onward, using specific keywords. An initial pool of 1,850 articles was refined through a PRISMA-based screening process, resulting in 33 highly relevant articles for detailed analysis. The findings show that publication activity was highest in 2021, with major contributions from the United Kingdom, Indonesia, and the United States. Key themes identified in the literature include the use of Learning Management Systems (LMS), the flipped classroom model, and project-based learning. The evidence consistently suggests that blended learning improves educational outcomes, increases student motivation, and fosters essential 21st-century skills. However, challenges such as insufficient infrastructure and the need for enhanced educator competency remain. This paper recommends strengthening institutional policies and providing faculty training to support the successful and sustainable implementation of blended learning, especially in technology and vocational education.
Forecasting Crab Raw Material Inventory in Seafood Culinary Business Using SARIMA and Prophet Methods Sanyoko, Carisca Rizky; Rizka Hadiwiyanti; Seftin Fitri Ana Wati
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.v8i1.2676

Abstract

Seafood based culinary businesses face significant challenges in inventory management due to fluctuating and unpredictable customer demand. Inaccurate forecasting can lead to either excess stock, resulting in waste and increased storage costs, or stock shortages, which disrupt production and reduce customer satisfaction. Among the various raw materials used, crab is the most critical, accounting for 80.6% of total raw material demand, making it essential to forecast its demand accurately to maintain operational efficiency and avoid disruptions. This study addresses the inventory problem by applying two time series forecasting methods SARIMA and Prophet to predict weekly crab demand. The performance of both models was evaluated using RMSE and MAE to assess the accuracy and reliability of their predictions over time. The SARIMA model with parameters (2,1,10)(0,1,2)[12] achieved the best forecasting performance, with an RMSE of 1.32752 and MAE of 1.24207, clearly outperforming the Prophet model, which recorded an RMSE of 1.4623 and MAE of 1.3506. These results demonstrate that SARIMA is more effective in capturing seasonal patterns and demand trends in crab usage data. In conclusion, the SARIMA model offers more precise and reliable forecasts, making it a more suitable tool for supporting raw material inventory decision-making in seafood culinary businesses, particularly when dealing with high-demand ingredients such as crab.
Website-Based Sales Transaction Data Monitoring Information System Risandi, Arfika Putri; Hasanah, Herliyani; Oktaviani, Intan
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.v8i1.2679

Abstract

Micro, Small, and Medium Enterprises (MSMEs) contribute significantly to the Indonesian economy, yet many still rely on manual transaction recording using Excel, which often results in errors, delays in reporting, and difficulties in accessing historical sales data. These limitations hinder effective decision-making and reduce operational efficiency. In response to these challenges, this study aims to develop a website-based sales transaction data monitoring information system that enables real-time monitoring and structured data management for various user roles: admin, reseller, and leader. The system was developed using the Rapid Application Development (RAD) method, which emphasizes fast prototyping and user feedback to ensure functionality meets actual needs. Features include automated Excel data uploads, interactive dashboards, and reseller performance visualizations tailored to user roles. Testing was conducted using Black Box Testing and User Acceptance Testing (UAT). The results indicate that all system functions operated according to design specifications and received positive feedback from users regarding usability and effectiveness. The implementation of this system successfully addresses the common problems faced by MSMEs in transaction management by improving accuracy, speeding up reporting, and enhancing monitoring transparency. In conclusion, this system offers a practical solution that is ready to be adopted by MSMEs with similar transaction recording issues, supporting digital transformation and operational efficiency in the MSME sector.
Evaluation of Information Security Management Capability Level with COBIT 5 Dita Ayu; Asif Faroqi; Anita Wulansari
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.v8i1.2682

Abstract

Information security is a crucial aspect of information technology management, especially in government institutions such as the Department of Communication and Informatics (DISKOMINFO), which often faces challenges such as cyberattacks, lack of formal documentation, and limited resources in managing risks and securing data. These challenges hinder the organization’s ability to protect sensitive information and maintain public trust. This study evaluates the maturity level of information security governance at DISKOMINFO of Sampang Regency using the COBIT 5 framework, focusing on three domains: APO12 (Manage Risk), APO13 (Manage Security), and DSS05 (Manage Security Services). The method used is a case study with a descriptive qualitative approach through interviews and documentation. The results show that all three processes are at Level 1 (Performed Process), with 40.34% in the Partially Achieved category for APO12, 84.60% in the Largely Achieved category for APO13, and 57.23% in the Largely Achieved category for DSS05, where processes are carried out but not formally documented or standardized. There is a lack of monitoring and continuous improvement, making the governance reactive rather than proactive. Improvements are needed through development of policies, formal procedures, and more organized, sustainable security controls. Increasing employee awareness and allocating resources for information security are also critical. This research provides novelty by evaluating three COBIT 5 domains (APO12, APO13, DSS05) in a local government context, which has rarely been done. The findings offer a comprehensive maturity mapping as a strategic reference for improving information security governance in local government institutions.
Development of a Web-Based Interactive E-Learning Platform for a Vocational High School Alfath, Qothrun Nada; Ruslan Rizki Hidayat; Zainul Hakim; Sri Rahayu
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.v8i1.2683

Abstract

Digital transformation is essential in modern education, particularly at SMK Bina Am Ma’mur, where traditional learning methods, such as printed materials and the WhatsApp application, are still primarily used. These conventional methods limit flexibility, interactivity, and student engagement, which are crucial for effective learning. To address these challenges, this study develops an interactive web-based e-learning platform designed to enhance the learning experience at SMK Bina Am Ma’mur. The research employs a Research & Development (R&D) methodology, guided by the ADDIE model, which includes five phases: Analyze, Design, Development, Implementation, and Evaluation. Data were collected through literature reviews, observations, interviews with teachers, expert validation, and student feedback, followed by descriptive analysis to interpret the results. The validation process revealed that the developed platform is highly feasible for educational use. Media experts rated it 95% suitable, while subject matter experts provided a 91% rating. Additionally, the platform received an 89% approval rating from students, indicating its effectiveness in improving material comprehension and engagement. These findings suggest that the interactive e-learning platform is a highly effective tool for enhancing the learning process at SMK Bina Am Ma’mur, making learning more flexible, accessible, and engaging. This innovative platform has the potential to overcome the limitations of traditional media, fostering greater student motivation, interactivity, and overall learning outcomes, particularly in vocational education.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Dewi, Heni Lusiana; Arifiyanti, Amalia Anjani; Najaf, Abdul Rezha Efrat
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.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
User Satisfaction Analysis of Financial Technology Applications Using the End User Computing Satisfaction Model Ernawati, Ida Ayu; Asif Faroqi; Virdha Rahma Aulia
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.v8i1.2690

Abstract

The rapid evolution of digital technology has significantly transformed many sectors, especially financial services, with the rise of fintech innovations such as Flip. Flip offers interbank transfer services free of administrative fees, simplifying transactions like e-wallet top-ups, bill payments, and international money transfers, attracting a substantial user base. However, despite its popularity, users have raised concerns about transaction delays, slow customer service responses, and design issues, highlighting a gap between user expectations and actual service delivery. This study aims to evaluate user satisfaction with the Flip application using the End User Computing Satisfaction (EUCS) model, which assesses satisfaction across five key dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. Data from 415 respondents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the factors influencing satisfaction. The findings reveal that user satisfaction with Flip is generally positive, with significant impacts from Content, Format, Ease of Use, and Timeliness. However, Accuracy, while positively related, had no statistically significant effect. These results emphasize the need for improving transaction timeliness and refining content and interface formats to enhance the user experience. The study provides essential insights for Flip developers to enhance service quality and offers a data-driven foundation for future research on fintech applications.
Smart Diagnostic Assistant for Peugeot 406: A Web Expert System Based on Production Rules Broto, Gilang; Tampubolon, Lely Priska Dameria
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.v8i1.2696

Abstract

This research presents the development of a web-based expert system for diagnosing engine faults in Peugeot 406 vehicles, addressing the challenge of limited access to model-specific diagnostic tools particularly in remote or underserved regions where authorized service centers are scarce and the resulting difficulty in early identification of mechanical issues. The system employs a forward-chaining inference method coupled with a depth-first search (DFS) algorithm for rule traversal within a structured knowledge base of 25 engine symptoms and 13 fault types, all formulated through expert interviews and literature review. Through IF THEN production rules, users input observable symptoms via an intuitive, non-technical interface to receive preliminary diagnoses. System testing encompassed simulated case studies covering the full spectrum of defined fault types as well as real-world trials with Peugeot 406 owners in workshop settings, demonstrating over 85% consistency with professional mechanic evaluations. These findings underscore the system’s potential to improve diagnostic efficiency, reduce repair costs, and extend vehicle longevity by enabling timely, data-driven interventions. The implementation of this rule-based expert system thus offers an effective solution for empowering vehicle owners in areas with restricted service access, and future enhancements such as integration of real-time OBD and IoT sensor data, multilingual support, and adaptive machine-learning rule updates are expected to further boost accuracy, flexibility, and user reach.
Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit Tegar, Alfiyan Tegar Budi Satria; Hasanah, Herliyani; Oktaviani, Intan
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.v8i1.2715

Abstract

The increasing use of social media among students has raised concerns regarding its impact on mental health, academic performance, and interpersonal relationships. This study introduces a Streamlit-based web application that predicts social media addiction levels using the Random Forest algorithm. The model incorporates variables such as daily usage hours, mental health scores, and conflicts caused by social media. The innovation of this approach lies in combining machine learning with interactive visualizations for real-time addiction prediction, providing a user-friendly, data-driven tool for early screening. Unlike traditional models that primarily rely on self-reported data or simple metrics, this method integrates multiple behavioral and psychological indicators to improve prediction accuracy. The model outperforms linear regression in all key metrics, achieving an R² value of 0.9903, which explains 99.03% of the variation in addiction scores. It also reports a low Mean Absolute Error (MAE) of 0.0370, Mean Squared Error (MSE) of 0.0244, and Root Mean Squared Error (RMSE) of 0.1561, highlighting its accuracy. Black-box testing showed an average error of just 0.354% in predictions and confirmed that the app’s features function effectively across devices. These findings emphasize the potential of this application as an effective tool for identifying students at risk of social media addiction, enabling timely interventions, and offering a foundation for future improvements through real-time data integration and advanced machine learning models.
Digital Transformation of Catfish Ponds with AI-based Monitoring System Rizqullah, Iftikhar; Irawan, Yudie
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.v8i1.2716

Abstract

Digital transformation in the aquaculture sector, particularly in catfish farming, holds significant potential to improve operational efficiency and farm productivity. This study developed an artificial intelligence (AI)-based monitoring system called NusAIra to assist farmers in managing ponds in real-time by monitoring water quality, feed management, and harvest prediction. The system integrates physical sensors with a Decision Tree Regression machine learning algorithm, validated using an 80:20 hold-out split strategy and evaluated through accuracy and Root Mean Square Error (RMSE) metrics. NusAIra was built using Flask and Docker frameworks, employing a POST endpoint with JSON-formatted data for seamless data exchange. Implementation was carried out on three catfish ponds in Jepara Regency from February to April 2025. The predictive model achieved an accuracy of 87% with an RMSE of 0.35. One application example demonstrated that the system reduced the Feed Conversion Ratio (FCR) from 1.9 to 1.6, increased productivity by up to 22%, and lowered average operational costs by 15%. Additionally, NusAIra effectively predicted market prices with stable seasonal patterns, such as the projected catfish price in Boyolali for April reaching IDR 36,442, closely aligning with historical data. These results highlight NusAIra’s role in supporting data-driven decision-making. However, challenges remain, including infrastructure constraints and the low level of digital literacy among traditional fish farmers. Further development will focus on enhancing prediction accuracy, integrating adaptive features, and expanding system reach through cloud computing to support the sustainability and food security of Indonesia’s aquaculture sector.

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