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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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Design and Development of a Web and Mobile-Based Project Management System for Software Enterprises Ali, Mushyafa; Sekti Aji, Adam
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3749

Abstract

CV Webpakar, a technology company specializing in software development services, faces significant challenges in internal project management due to its reliance on un-integrated communication tools, leading to miscommunication, undocumented task assignments, and project delays. This study aims to design, develop, and evaluate an integrated project management system utilizing web and mobile platforms, grounded in the DeLone and McLean Information Systems (IS) Success Model as the theoretical lens for assessing system quality and user satisfaction. The system was developed using an Agile methodology with a client-server architecture: a Next.js web application for administrators, a React Native mobile application for team members, and a Django REST Framework backend connected to a MySQL database via RESTful APIs. System evaluation was conducted through a multi-dimensional approach encompassing functional testing (32 test cases, 100% pass rate), API performance benchmarking (average response time of 155.6 ms across 14 endpoints, all below the 300 ms threshold), and usability evaluation using the System Usability Scale (SUS), which yielded an average score of 78.5, falling within the "Good" and "Acceptable" categories. The results demonstrate that the system effectively addresses the operational inefficiencies of manual project management, while providing empirical evidence that high system quality and usability contribute to positive net benefits in SME contexts, consistent with the D&M IS Success Model. This study contributes to the information systems body of knowledge by providing a validated dual-platform project management implementation with quantitative evaluation evidence, an area underexplored in prior literature. Future work includes enhancing dashboard analytics and conducting longitudinal user adoption studies.
EfficientNetB4–Vision Transformer Fusion for Chili Leaf Disease Classification Using Multi-Source Datasets Angga, Reza Putri; Saputra, Wahyu Syaifullah Jauharis; Pratama, Alfan Rizaldy
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3753

Abstract

Chili plants are a commodity susceptible to plant pest organism attacks that can significantly reduce productivity. Visual identification of chili diseases by farmers is often inaccurate due to symptom similarity across disease categories, necessitating a technology-based approach capable of performing classification automatically and accurately. This study proposes a hybrid model combining EfficientNetB4 and Vision Transformer for chili leaf disease classification into four categories healthy, yellowish, curl leaf, and spot leaf. EfficientNetB4 extracts local features through compound scaling and MBConv blocks, while ViT models global relationships among image regions through self-attention, enabling a semantically meaningful integration of local and global feature representations that addresses the individual limitations of CNN and transformer-based architectures. The dataset integrates 4,000 secondary images from GitHub and 800 primary images collected directly from chili cultivation fields in Central Java, with splitting performed separately per source to ensure proportional distribution across subsets. To evaluate generalization capability, the model was assessed across three scenarios: training and testing on secondary data only 98.25%, testing on primary field data without prior field exposure 87.50%, and training and testing on integrated data 99.17%, with a perfect accuracy of 100% on the primary-only test set. These results demonstrate that incorporating field-collected data into training directly bridges the generalization gap caused by domain shift between laboratory and real-world conditions, outperforming both single-architecture and previous hybrid approaches reported in prior studies. The findings provide a methodological foundation for developing robust automated disease detection systems applicable across diverse agricultural crops and real-world farming environments.
IoT-Based Smart Aquaponics System with Firebase and Telegram Integration Wahyu Ardila, Asti; Gunanto, Sigit
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3754

Abstract

Aquaponic systems require stable environmental control to maintain the balance between fish and plant growth. This study proposes a novel Internet of Things (IoT)-based smart aquaponic system that integrates real-time monitoring, adaptive automatic control, and cloud-based data management within a unified sensor–edge–cloud architecture. The key innovation of this work lies in the seamless integration of the Firebase Realtime Database for low-latency synchronization, an interactive web-based dashboard for real-time visualization, and a hysteresis-based adaptive control mechanism that overcomes the limitations of conventional threshold-based systems, particularly rapid actuator switching (chattering). The system employs an ESP32 as the main processing unit, a DHT11 sensor for temperature and humidity measurement, and a TDS sensor for dissolved nutrient monitoring. Data are transmitted every 10 seconds to the cloud and complemented by event-driven Telegram notifications to enable timely user intervention. Experimental results demonstrate stable system performance, achieving a data transmission success rate of 98.47% over 24 hours. The temperature measurement shows a Mean Absolute Error (MAE) of 0.48°C (≈1.6% relative error), while an average latency of 1.4 seconds indicates responsive real-time synchronization. Furthermore, the implementation of hysteresis-based control effectively reduces actuator instability and enhances system reliability. These findings indicate that the proposed integrated architecture not only improves monitoring accuracy and control stability compared to existing IoT-based aquaponic systems, but also enables practical, remotely accessible, and scalable solutions. The system is particularly suitable for small- to medium-scale aquaponic applications, supporting data-driven decision-making and contributing to sustainable agriculture practices.
Effect of CBAM Integration on InceptionV3 for Improved Foot and Mouth Disease Detection Accuracy Andrianto, Mochammad Rifky; Mandyartha, Eka Prakarsa; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3756

Abstract

Foot and Mouth Disease (FMD) is a highly contagious livestock disease that causes significant economic losses. Timely detection is essential to prevent rapid transmission. While deep learning has shown promise in image-based disease identification, the impact of integrating lightweight attention mechanisms, such as the Convolutional Block Attention Module (CBAM), into robust multi-scale backbones, such as InceptionV3, for FMD detection on small, imbalanced primary field datasets remains underexplored. This study contributes by providing a systematic evaluation of CBAM integration under varying data-splitting scenarios, highlighting the interaction between attention mechanisms and data distribution. This study evaluates the integration of CBAM into InceptionV3 for the classification of cattle lesion images. It compares its performance with the baseline InceptionV3 model across three train-validation-test splits (70:20:10, 80:10:10, and 70:15:15). The dataset comprises 798 primary images (514 FMD-positive and 284 healthy), indicating a limited size with moderate class imbalance. Images were resized to 299 × 299 pixels and normalized to [-1, 1], with augmentation applied only to the training set. The InceptionV3-CBAM model achieved the best performance under the 70:15:15 split, with 96.69% accuracy, 96.25% precision, 98.72% recall, and 97.48% F1-score. These findings suggest that CBAM can enhance lesion-focused feature representation and detection sensitivity. However, performance gains were inconsistent across splits and appear influenced by both architectural changes and dataset characteristics. The model demonstrates potential for early FMD screening in resource-limited settings, but further validation on larger, more diverse datasets is essential to confirm robustness and generalizability
Data-Driven Customer Loyalty Ranking Using SAW-Based Decision Support Framework Aurelza, Diva; Adi Kurniawan, Turkhamun
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3760

Abstract

In increasingly competitive business environments, maintaining customer loyalty has become a critical factor for sustaining long-term organizational performance. However, in many small and medium-sized enterprises, identifying loyal customers is still conducted subjectively, leading to inconsistent, non-transparent, and potentially biased reward allocation decisions. This study proposes a data-driven framework for customer loyalty ranking by integrating a Simple Additive Weighting (SAW)-based decision support approach. The research adopts a quantitative applied methodology using transactional data from a furniture retail business, covering a period of 12 months and involving 1,000 customer transactions. Customer loyalty is evaluated based on three key criteria: monetary value, purchase frequency, and payment reliability, which represent essential behavioral indicators of customer engagement. The SAW method is employed to normalize criteria values, assign relative weights, and compute preference scores for each customer, resulting in a systematic and objective ranking process. The proposed framework is implemented as a web-based decision support system using PHP with the CodeIgniter framework and a MySQL database to ensure structured data management and operational efficiency. The results demonstrate that the framework effectively produces consistent, transparent, and data-driven customer rankings, thereby reducing subjectivity in managerial decision-making. This study contributes by formalizing a practical decision support framework that enhances the reliability, fairness, and effectiveness of customer loyalty evaluation and reward allocation, offering a novel integration of data-driven decision-making paradigms with the SAW-based decision support system, a feature often underexplored in prior studies.
Fashion E-Commerce Website Design Using the Waterfall Method Rosdiyanto, Roynaldy; Novianti, Deny; Zuhro, Siti Fatimatul
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3765

Abstract

The rapid development of information and communication technology has significantly influenced various sectors, including the trade industry. One sector experiencing notable growth is the fashion industry. The increasing use of the internet and digital devices has changed consumer behavior, with many people preferring online shopping for its convenience, efficiency, and accessibility. This trend has encouraged fashion businesses to adopt digital platforms such as e-commerce websites to expand their market reach and improve customer service. This study aims to design and develop a fashion e-commerce website that facilitates online buying and selling transactions. The system is expected to help fashion business owners promote and market their products more effectively while providing consumers with greater convenience in accessing product information and completing purchases. The system's development process uses the waterfall method, comprising the following stages: requirements analysis, system design, implementation, testing, and deployment. This method is chosen because it provides a structured and systematic approach to software development. The results of this study indicate that the fashion e-commerce website was successfully developed, featuring a product catalog, shopping cart, payment system, and order management. System testing was conducted using the black-box testing method, demonstrating that all features function properly in accordance with the specified system requirements. Therefore, the developed website can support digital marketing and enhance the online shopping experience for consumers.
Web-Based Woven Fabric Recommendation System Integrated Fuzzy AHP and MOORA Based on User Preferences Bhakti, Mulyani Satya; Mumpuni, Retno; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3768

Abstract

This study proposes a web-based decision support system for woven fabric selection by integrating the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). The system addresses limitations in conventional selection processes that rely on subjective judgment and lack structured multicriteria evaluation. The proposed approach combines uncertainty-based weighting using Fuzzy AHP with objective ranking using MOORA, enabling a transparent and systematic decision-making process. Unlike previous hybrid MCDM-based recommender systems, this study integrates user preference modeling within a web-based framework and incorporates consistency validation and sensitivity analysis to ensure reliable results. The experimental results show that fabric type is the most influential criterion, with a weight of 0.33, and that alternative A4 consistently ranks as the best option, with an optimization value of 0.392. Sensitivity analysis shows that the ranking results remain stable across a 20% weight variation, and comparison with the SAW method confirms consistent rankings. In addition, User Acceptance Testing (UAT) involving 20 respondents achieves a score of 86.4%, indicating high usability and user satisfaction. However, the system is evaluated within a limited dataset and does not incorporate adaptive learning mechanisms. Therefore, future work is directed toward expanding the dataset and integrating machine learning-based approaches to enhance adaptability and scalability. Overall, the proposed system provides a structured, transparent, and empirically validated solution for multicriteria decision-making.
Design of Web-Based Decision Support System Using AHP and bcrypt Security Subagio, Moh. Mario; Mumpuni, Retno; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3769

Abstract

This study presents the development of a web-based decision-support system to prioritize village administrative services using a structured, data-driven approach. This research addresses a gap in existing systems, which generally lack integration of systematic decision-making methods and robust security mechanisms, leading to inefficiency, low transparency, and subjective decision-making in village administrative processes. To overcome these limitations, the system integrates the Analytical Hierarchy Process (AHP) to evaluate multiple criteria, including submission time, document completeness, urgency level, service type, and request frequency, thereby enabling qualitative assessments to be transformed into measurable, comparable priority values. In addition, the bcrypt algorithm enhances system security by protecting user authentication data through password hashing, thereby mitigating risks such as unauthorized access, brute-force attacks, and rainbow-table attacks. The system is developed as a web-based application to ensure accessibility, scalability, and centralized data management. Evaluation results indicate that the system produces consistent and reliable priority rankings, as evidenced by a Consistency Ratio (CR) within the acceptable threshold, and demonstrates improved decision accuracy and operational efficiency compared to conventional manual approaches. Document completeness is identified as the most influential criterion in determining service priority. Furthermore, the proposed system offers broader applicability beyond village administration, particularly in other public service domains requiring transparent, efficient, and secure decision-making processes. Overall, this study contributes by integrating AHP and bcrypt within a unified system to enhance both decision quality and data security in digital administrative services.
Development of a DR-ARMA-Based Inventory Forecasting System for Inventory Management Velita, Velita; Tendean, Sandi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3770

Abstract

Inventory management at Micro, Small, and Medium Enterprises in traditional markets remains heavily reliant on intuition, posing high risks of overstocking or stockouts. This study develops a data-driven inventory requirement forecasting system for Toko Mandiri by integrating the Demand Response–Autoregressive Moving Average (DR-ARMA) method into a desktop application using the Rapid Application Development (RAD) approach. One year of historical daily sales data from four fermented products, with sparsity levels ranging from 38% to 51%, was utilized for training and testing. The dataset was partitioned into training (60%), validation (20%), and testing (20%). The DR-ARMA model's performance was evaluated quantitatively, yielding average values of 1.632 units for RMSE, 1.012 units for MAE, and 29.89% for MAPE, demonstrating reliability on fluctuating and sparse data. System usability evaluation involved three respondents across four task scenarios. Results indicate significant improvements in operational efficiency: the time required to determine inventory requirements was reduced from 30 minutes, using manual intuition-based methods involving physical stock checks across different locations, to just 1 minute, based on direct stopwatch measurements for all scenarios. This represents a 96.7% reduction in processing time. Interaction steps were streamlined from an unstructured process to only 6–7 clicks. The system's effectiveness reached an 83.33% task success rate among non-technical users. Integrating DR-ARMA into a practical application effectively transforms inventory decision-making from intuition-based to data-driven, potentially reducing operational risks.
Medical Equipment Inventory Information System in Public Hospitals Dani, Dani
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3771

Abstract

General Hospitals (RSU) require an effective medical equipment inventory management system to support improved healthcare quality. Currently, many hospitals still use manual methods for recording and managing inventory, which often leads to various challenges such as data errors, lost equipment, distribution delays, and difficulties in monitoring the condition and availability of medical equipment. This study aims to design and implement a web-based Medical Equipment Inventory Information System (SIIAM) to improve the efficiency, accuracy, and transparency of hospital inventory management. To address these issues, a web-based Medical Equipment Inventory Information System (SIIAM) was developed, designed to assist hospitals in managing inventory data in an integrated, fast, and accurate manner. The system was developed using the Waterfall method, which includes requirement analysis, system design, implementation, testing, and maintenance, and was evaluated through system testing to measure its effectiveness. SIIAM can record real-time data on the procurement, maintenance, and use of medical equipment. Furthermore, this system can help identify equipment needs, organize periodic maintenance schedules, and generate reports required by hospital management. Based on testing results, implementing SIIAM increased the efficiency of medical equipment inventory management by up to 30%. It reduced the recording error rate by 20%, while providing real-time access to integrated data across hospital units. Therefore, the web-based SIIAM is an effective solution for enhancing efficiency, accuracy, and transparency in medical equipment inventory management, thereby improving healthcare service quality and patient safety in General Hospitals.

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