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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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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.
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.

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