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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
Optimization of Web-Based Service Management System Using Time and Material Pricing for Tool Maintenance Dhonni, Muhammad Khusnud; Mahendra, Danang; Azizah, Noor
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.v8i1.2496

Abstract

This study develops a web-based Service Management Information System tailored for small-scale tool maintenance businesses that traditionally lack integrated management solutions. These micro enterprises commonly encounter operational inefficiencies such as poor workflow management, inaccurate cost estimation, and manual transaction recording, which hinder service quality and diminish customer trust. The proposed system addresses these challenges by automating critical service functions, including customer queue management, technician diagnostics, spare parts tracking, and invoice generation, thereby improving operational efficiency and transparency. The development process followed the Waterfall model within the Software Development Life Cycle (SDLC), providing a structured framework covering requirement analysis, system design, implementation, testing, and maintenance phases. A key feature of the system is the integration of the Time and Material Pricing (T&M) method, enabling dynamic cost calculation based on actual labor hours and material usage. This method ensures fair and transparent pricing, which is vital for sustaining customer confidence in environments with variable service demands. Functional validation through Black Box Testing confirmed that the system’s core modules met all specified requirements without significant errors. Furthermore, a user satisfaction survey involving 30 respondents indicated substantial improvements in cost transparency, service speed, and ease of status tracking. Overall, the system markedly enhances both operational performance and customer experience in small tool maintenance workshops. The findings suggest that this web-based solution supports digital transformation in a traditionally manual sector, promoting competitiveness and sustainability for micro enterprises. Future enhancements include mobile application development and integration of digital payment systems to further optimize service delivery.
Evaluating the Success of the Mobile JKN Application through the DeLone & McLean Framework Firdaus, Lintang; Pratama, Arista; Wulansari, Anita
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.v8i1.2501

Abstract

The JKN mobile app is a digital platform developed by BPJS Kesehatan to help users access information and manage their membership independently. This research assesses the effectiveness of the application by employing the framework developed by DeLone and McLean, which includes aspects such as the quality of the system, the quality of information, the quality of service, and the intention to use, User Contentment, and Overall Gains. A quantitative approach was used, applying PLS-SEM for data analysis. Based on the Lemeshow formula, the minimum sample size was 384, and 400 active users of the latest app version participated. The analysis was conducted utilizing SmartPLS 4. Results show that the quality of the system and the quality of the information significantly influence user contentment and their inclination to participate. Thus, user contentment is essential in determining the perceived overall advantages. The study indicates that the app's success is more influenced by technical performance and user experience rather than service quality. This highlights the need to improve system reliability, interface usability, and information accuracy. Users tend to value fast access and functionality over support services. Continuous improvement in these areas can help increase satisfaction and long-term usage. The findings suggest that focusing on user-centered design and regular updates will strengthen digital health engagement. These insights can also serve as guidance for similar e-health initiatives in developing countries.
Image Color Correction for Color Vision Deficiency Using ResNet and CycleGAN Adyani, Adelia Putri; Tri Anggraeny, Fetty; Yulia Puspaningrum, Eva
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.v8i1.2506

Abstract

Color blindness is a visual impairment that limits an individual's ability to accurately perceive certain colors, particularly red, green, or blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction system designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5,020 images were used for evaluation, and the proposed system achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The system showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed system offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.
Comparison of Social Media Video Acceptance for Health Knowledge in Generation Z Using TAM Syawalina, Shanti Putri; Pratama, Arista; Safitri, Eristya Maya
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.v8i1.2509

Abstract

The development of information and communication technology has changed the way Generation Z accesses knowledge, with social media such as Instagram and TikTok being the main sources of information. However, challenges arise as health content is often mixed with entertainment content, making the validity of the information delivered difficult to ascertain. Therefore, an evaluation of both platforms is needed to assess their effectiveness and the benefits received by users. This study aims to evaluate the acceptability of videos as a source of health knowledge on Instagram and TikTok using the Technology Acceptance Model (TAM). The main focus was to identify factors that influence the acceptance of health video content and compare the effectiveness of the two platforms. Data was collected from 600 respondents through purposive sampling technique and analyzed using PLS-SEM method. The results showed that the majority of respondents preferred TikTok as a source of health information compared to Instagram. Content Richness affects Users Satisfaction. In addition, Flow State and Personal Innovativeness affect Perceived Ease of Use and Perceived Usefulness. These three variables also influence the acceptability of health video content on Instagram and TikTok. These findings suggest the importance of engaging and informative content to improve Generation Z's health knowledge.
Enhancing Contractor Evaluation Using Fuzzy TOPSIS-Based Decision Support System Barry Nuqoba; Kartono; Adli, Faiz Haidar Satriani; Effendy, Faried; Taufik
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.v8i1.2510

Abstract

Contractor evaluation remains a major challenge in safety-critical industries such as oil and gas, where the need to comply with stringent Health, Safety, and Environment (HSE) standards demands a robust and objective assessment mechanism. The existing manual evaluation methods are plagued by subjectivity, inconsistent data handling, and inability to resolve performance ties, leading to unreliable contractor differentiation. To address this problem, this study investigates how can a computational decision support framework minimize subjectivity and enhance ranking precision in contractor evaluations. It proposes a Decision Support System (DSS) based on the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to improve the accuracy, transparency, and efficiency of evaluations within the Contractor Safety Management System (CSMS). The DSS integrates qualitative and quantitative criteria using fuzzy logic and expert-assigned linguistic weights. Developed following the Waterfall software development lifecycle, the system was validated using black box testing and applied to realistic simulated data from ten contractors evaluated across multiple criteria and subcriteria. Results demonstrate that the DSS effectively resolves score ties present in manual evaluations, enabling finer distinctions among contractors, with the highest closeness coefficient of 0.479 achieved by the top-ranked contractor. This value reflects a 47.9% closeness to the ideal performance profile, marking a significant improvement over binary or aggregate-based evaluation methods..User feedback confirmed high satisfaction with system usability and performance. The proposed DSS offers a robust and adaptable framework for contractor evaluation, enhancing decision-making accuracy and operational transparency in high-risk environments. Its novelty lies in the integration of fuzzy linguistic modeling within a CSMS context to operationalize HSE performance evaluations. Future research should focus on incorporating advanced fuzzy logic methods and artificial intelligence to facilitate real-time, dynamic contractor evaluations under uncertainty.
Evaluating ERP System Success Through the DeLone and McLean Model in Financial Organizations Muhammad Darriel Aqmal Aksana; Arista Pratama; Siti Mukaromah
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.v8i1.2513

Abstract

Enterprise Resource Planning (ERP) systems play a crucial role in streamlining business operations and enhancing organizational efficiency. However, their success largely depends on effective user engagement and system utilization. Despite being strategically implemented to support daily operations, many ERP systems face challenges such as technical errors, sluggish performance, inaccurate data, and poor user interfaces—factors that hinder optimal usage and reduce employee productivity. This study evaluates the success of an ERP system implemented in a financial services organization in Indonesia, focusing on employee perspectives to understand critical factors influencing system effectiveness. The DeLone and McLean Information Systems Success Model (ISSM) is employed as the theoretical framework, assessing six core constructs: System Quality, Information Quality, Use, User Satisfaction, Individual Impact, and Organizational Impact. A quantitative survey was conducted with 325 respondents selected through simple random sampling from key operational divisions. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results confirm that Information Quality has a significant effect on both system Use and User Satisfaction, while System Quality strongly affects User Satisfaction. Furthermore, User Satisfaction substantially influences both Use and Individual Impact. Most critically, Individual Impact has a pronounced and statistically significant influence on Organizational Impact (R² = 0.745). These findings emphasize the pivotal roles of information accuracy and user satisfaction in ensuring ERP success. The study provides valuable insights into how employee experience with ERP systems translates into broader organizational outcomes, offering practical implications for future ERP development and implementation strategies.
Application of YOLOv8 Model for Early Detection of Diseases in Bean Leaves Yustiana, Indra; Sujjada, Alun; Tirawati
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.v8i1.2514

Abstract

Bean plant is one of the high economic value horticultural commodities widely cultivated in Indonesia. However, its productivity declines due to pest attacks and leaf diseases. Farmers' limitations in accurately identifying disease types also pose obstacles in early mitigation efforts. Therefore, technology-based solutions capable of quickly and accurately detecting plant diseases are needed. This research aims to develop and evaluate the performance of a leaf disease detection model for bean plants using the You Only Look Once version 8 (YOLOv8) algorithm with a transfer learning approach. The dataset used consists of 1,037 images of bean leaves, classified into three categories: angular leaf spots, leaf rust, and healthy leaves. Data were obtained from two sources, namely field documentation in Sindang Village, Sukabumi Regency, and an open repository on GitHub. The dataset was divided into training data (70%), validation (20%), and testing (10%). The model was trained using the YOLOv8s architecture for 30 epochs and achieved a detection accuracy of 85%. Performance evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The results of this study are expected to be an initial contribution to the application of artificial intelligence in agriculture, particularly in helping farmers efficiently detect leaf diseases in beans to improve productivity and quality of harvest.
Cloud-Based High Availability Architecture Using Least Connection Load Balancer and Integrated Alert System Prinafsika; Junaidi, Achmad; Muharrom Al Haromainy, Muhammad
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.v8i1.2520

Abstract

Ensuring optimal service continuity remains a critical challenge in cloud computing, especially when dealing with high traffic loads and system failure potential that can cause losses. To address this, this research presents the implementation of a high availability (HA) cloud system using the Least Connection load balancing algorithm implemented with Nginx, integrated with early anomaly detection and alert mechanisms. The HA architecture is implemented across two geographically distributed cloud service providers, Alibaba Cloud and Google Cloud, to analyze latency and performance differences under high load conditions. The system's resilience and scalability were evaluated through load testing using K6, simulating workloads ranging from 100 to 1000 Virtual Users (VUs) for single server configurations and 200 to 2000 VUs for HA configurations. The experiment results showed a significant improvement in service availability, reaching 100% uptime with the HA configuration compared to a peak of 98.79% in the single server environment. The Least Connection strategy effectively balanced traffic by monitoring active connections, resulting in a 29.73% increase in processed requests and a 42% reduction in system load at 1000 VUs. Additionally, the alert system successfully sent real-time Telegram notifications for delays or failures, enabling proactive mitigation. These results confirm that combining dynamic load balancing with proactive alerts can significantly improve service reliability, resource efficiency, and resilience to failures in distributed cloud infrastructure providing a viable model for robust and scalable cloud service architectures.
Optimization of Earthquake B-Value Prediction in Java Using GRU and Particle Swarm Optimization Nursyahada, Kesya; Rahmat, Basuki; 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.v8i1.2521

Abstract

Accurate prediction of earthquake parameters is essential for seismic risk assessment and disaster mitigation, particularly in tectonically active regions such as Java Island, Indonesia. This study presents a novel predictive model for estimating the earthquake b-value a fundamental seismological parameter representing the logarithmic relationship between earthquake frequency and magnitude by integrating a Gated Recurrent Unit (GRU) neural network with Particle Swarm Optimization (PSO). The model is trained using earthquake catalog data from 1962 to 2024, sourced from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The GRU architecture is selected for its effectiveness in modeling temporal dependencies in seismic time series data. PSO is employed to optimize essential hyperparameters, including the number of GRU units, learning rate, and dropout rate. The optimized model achieves notable improvements in predictive performance: Mean Squared Error (MSE) is reduced from 0.00435 to 0.00030, Root Mean Squared Error (RMSE) from 0.0509 to 0.0173, and Mean Absolute Percentage Error (MAPE) from 3.42% to 1.12%. Training time is also reduced from 57 seconds to 33 seconds, indicating greater computational efficiency. The optimal PSO settings include an inertia weight of 0.8, cognitive and social coefficients of 1.0, 40 particles, and 10 iterations. The primary novelty of this study lies in its targeted application of PSO-optimized GRU architecture for b-value prediction in a seismically complex region. These results demonstrate that evolutionary optimization significantly enhances deep learning performance, providing a robust and efficient framework to support earthquake forecasting and risk mitigation efforts in high-risk zones such as Java Island.
Measuring User Satisfaction of iPusnas Through the End-User Computing Satisfaction Model Jannatuzzahra, Khoirunisa; Pratama, Arista; Faroqi, Asif
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.v8i1.2523

Abstract

iPusnas serves as Indonesia’s national digital library platform, offering free access to electronic books for the general public. This research aims to evaluate user satisfaction with the iPusnas application by employing the End-User Computing Satifactions (EUCS) model, which comprises five main constructs: content, acuracy, format, ease of use, and timelines, along with two additional dimensions—system speed and system reliability. The study involved 450 participants selected through purposive sampling. Data analysis was conducted using the Partial Least Squares Structural Equations Modeling (PLS-SEM) technique with the assistance of SmartPLS 4 software. The findings indicate that six variables—content, ease of use, format, accuracy, system speed, and system reliability—have a statistically significant and positive impact on user satisfaction. This suggests that a higher level of perceived quality in these six areas corresponds to greater satisfaction among users. On the other hand, timeliness was found to have a significant yet negative influence. These results suggest that delays in delivering content or in system responsiveness remain key issues that negatively affect user experience. Accordingly, this study recommends enhancing system performance, particularly in terms of timeliness to improve user satisfaction and the overall experience. Strengthening these areas is also anticipated to contribute to increased user engagement and further the national objective of expanding digital literacy and equitable knowledge access across the country.

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