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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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Klasifikasi Penyakit Mata Menggunakan ResNet-50 Berdasarkan Citra Fundus Kurniawan, Muh. Irsyad Dwi; Sari, Anggraini Puspita; Junaidi, Achmad
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.v8i3.3306

Abstract

Visual impairment from diabetic retinopathy, glaucoma, and cataracts remains a critical global health issue, emphasizing the need for early and accurate diagnosis to prevent permanent vision loss. This research presents an automated detection system utilizing ResNet-50, a deep learning architecture, to classify fundus images into multiple retinal disease categories. Unlike conventional convolutional neural networks used in prior studies, this approach leverages ResNet-50's residual learning mechanism to better identify complex retinal patterns. The study employed 4,184 fundus photographs from Kaggle, divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. Images were preprocessed through resizing to 224×224 pixels, normalized with ImageNet parameters, and augmented using random rotation and flipping techniques to enhance model generalization. Dataset splitting followed stratified sampling with an 80-20 train-test ratio, maintaining balanced class representation. Model training spanned 20 epochs using the Adam optimizer across three learning rates: 0.1, 0.01, and 0.001. The 0.001 learning rate produced optimal results with 90.35% accuracy, 90.28% precision, 90.18% recall, and 90.21% F1-score. The confusion matrix indicated strong performance in detecting diabetic retinopathy (219 correct predictions) and normal cases (189 correct predictions), though minor misclassifications occurred between glaucoma and normal categories. These findings validate ResNet-50's residual architecture as an effective tool for extracting discriminative retinal features, offering a computationally efficient solution for automated eye disease screening. Future work should incorporate explainability methods like Grad-CAM to enhance clinical interpretability and build trust among healthcare professionals in AI-assisted diagnostic systems.
Optimizing Gaussian Mixture Model Using Principal Component Analysis for Welfare Clustering Wahyu Gunawan, Rafif Ilafi; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
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.v8i3.3310

Abstract

Welfare inequality among regions remains a fundamental challenge in achieving balanced development across East Java Province. The complexity of social, economic, and development indicators often obscures the true patterns of regional welfare. To address this issue, this study proposes a more efficient analytical approach by integrating Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) to cluster regions based on welfare levels. The dataset, obtained from the Central Bureau of Statistics (BPS) of East Java for the 2010–2024 period, includes diverse social and economic indicators. PCA was employed to reduce dimensionality and eliminate variable correlations, preserving the essential information within the data. The resulting principal components were then analyzed using GMM to uncover welfare clustering patterns. Based on the evaluation using the Bayesian Information Criterion (BIC) and silhouette score, the optimal configuration was achieved with two clusters, a tolerance of 1e-2, a maximum iteration of 200, and a silhouette score of 0.3403. The first cluster represented regions with higher welfare conditions, while the second indicated relatively lower welfare. These findings demonstrate that the PCA–GMM integration not only improves clustering accuracy but also enhances interpretability of welfare distribution across regions. Future studies may combine PCA with non-linear dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to preserve local structures within complex datasets. Such integration is expected to reveal subtler and more dynamic welfare patterns, offering deeper insights into regional development disparities.
Analysis and Design of a Web-Based Online Booking System for Laboratory Examination Services Nathanael, Jovan Arsenio; Kuswanto, Verri
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.v8i3.3317

Abstract

The laboratory examination registration process in many clinics is still conducted manually, resulting in inefficiencies, limited accessibility, and fragmented data management. This study aims to design and evaluate an online laboratory examination booking system to address these limitations and improve service accessibility. The system was developed using a prototype-based approach, enabling iterative refinement through continuous user feedback during the design and evaluation stages. This approach supports early validation of system requirements and functional suitability when compared to conventional linear development methods. The proposed system provides integrated access to laboratory examination information, including available examination types, pricing, schedules, and downloadable examination results, which can be accessed anytime and anywhere without requiring patients to revisit the clinic. System evaluation was conducted through usability testing involving patients and officers using the System Usability Scale. The results indicate high usability levels, with patients achieving a score of 91% and officers achieving 92%, reflecting excellent system acceptance and ease of use. These findings demonstrate measurable improvements in registration efficiency and user satisfaction compared to the previous manual process. Furthermore, the application of the prototype method proved effective in identifying functional gaps at an early stage, resulting in a more user-centered and adaptable system design. This study contributes a practical solution for improving laboratory examination registration services and empirical evidence supporting the effectiveness of prototype-based development in healthcare information systems.
Desain dan Pengembangan Aplikasi Pengelolaan Properti Mode Offline Menggunakan Sinkronisasi Otomatis dan CQRS Event Sourcing Adiputra, Muhammad Ariq Hawari; Swari, Made Hanindia Prami; 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.v8i3.3332

Abstract

The advancement of information technology has accelerated the digitization of project management, particularly in the supervision and monitoring of construction progress previously handled manually through paper-based documents and Excel spreadsheets. Such manual processes have led to delays in reporting, data duplication, and reduced data accuracy. This study aims to design and implement a web- and mobile-based project management and property sales system featuring Offline-First Synchronization, Command Query Responsibility Segregation (CQRS), and Event Sourcing to maintain the integrity of progress data and empower field supervisors to operate without an internet connection. The research method follows the waterfall model, comprising needs analysis, system design with a clear separation of command and query, and the implementation of event log storage as the single source of truth for every data change, using Laravel as the backend and React Native with MMKV for local storage. Testing results demonstrate that the system ensures data consistency through automatic synchronization once network connectivity is available and can reconstruct project development progress using stored event data. Performance benchmarking showed that CQRS bulk operations reduced processing time to 0.053 seconds, outperforming traditional CRUD bulk operations at 0.073 seconds, while query latency in event sourcing read models averaged 0.101 seconds, only slightly higher than 0.089 seconds in direct database queries. The system also achieves reliable auditability and supports efficient task update and historical recalculation via event replay. The findings confirm that applying CQRS and Event Sourcing within an offline-first architecture improves reliability, auditability, and efficiency in field project monitoring.
Development of Blockchain-Based Escrow System with IPFS Protocol for Secure Digital Transactions Sitompul, Pelean Alexander Jonas; Wahanani, Henni Endah; Junaidi, Achmad
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.v8i3.3337

Abstract

Digital transactions are essential to modern economic activities, yet challenges related to trust, transparency, and security persist. This research develops a blockchain-based escrow system integrated with the InterPlanetary File System (IPFS) to address these issues through a decentralized, tamper-resistant architecture. The primary aim is to create an escrow platform that minimizes human intervention while ensuring data integrity, thereby overcoming limitations found in traditional escrow mechanisms that rely heavily on legality and banking institutions. This study demonstrates the feasibility of blockchain technology enhancement to existing escrow models, especially for traders conducting high-value digital transactions. The system enables secure interactions between buyers, sellers, and viewers through a decentralized application (dApp) that assigns user roles and executes transaction logic. Funds are securely locked within the smart contract, while digital assets are stored in IPFS. In cases of dispute, the viewer can cancel the transaction, triggering an automated refund to the buyer and deletion of associated asset data to maintain fairness and security. Smart contract development and testing are carried out using the Hardhat framework before deployment to networks such as the Ethereum-based Sepolia Testnet. The results show that the proposed system reduces transaction risks, increases user trust, and enhances transparency throughout the digital transaction process. This research introduces a practical framework for decentralized escrow systems and provides valuable insights for industries seeking secure, blockchain-driven transaction solutions. The system developed in this study serves as a reference model for integrating traditional transaction with blockchain technology, encouraging broader adoption and future exploration of decentralized systems.
History Learning Game of the Three-Day Battle Surabaya with Branching Narrative Himawan, Rantau; Putra, Chrystia Aji; Sihananto, Andreas Nugroho
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.v8i3.3346

Abstract

This study presents the development of an educational history game about the Three-Day Battle in Surabaya, designed using a branching narrative approach to enhance students’ engagement and historical understanding. Traditional history learning in Indonesia often relies on memorization and lacks interactive media, leading to low student motivation. To address this issue, the game integrates a decision-based narrative structure that allows players to explore consequences, experience alternative paths, and engage with historical events through meaningful choices. The game was developed using the Unity Engine with iterative refinement involving playtesting and feedback-based adjustments to dialogue flow, minigame mechanics, and visual presentation. The evaluation involved 15 participants and employed the GUESS-18 instrument. The results indicate strong user reception, with high scores in Narrative Understanding and Game Engagement, while Playability and Aesthetics received moderate ratings, highlighting areas for visual and interaction improvements. Despite the short testing duration, the game demonstrated potential to support historical learning by increasing immersion and reinforcing students’ understanding of key events and cause–effect relationships during the Surabaya conflict. This study contributes to the field of educational game development by demonstrating the pedagogical value of branching narratives and providing a practical model that can be adapted to other historical topics in future research.
Comparison of ARCH and GARCH Models for Ethereum Return Volatility Makarim, Rizqi Akbar; Maheswari, Desinta; Pramustiwi, Aqila Dina; Rahmawati, Kartika Ayu; Ghaisani, Salma Fatila
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.v8i3.3363

Abstract

The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.
Detection of ARP Poisoning on Wireless LAN Using Machine Learning: Random Forest and AdaBoost Ersamazaya, Rafi Dhafin; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
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.v8i3.3364

Abstract

ARP poisoning is a prevalent security threat in Wireless Local Area Networks (WLANs), enabling attackers to manipulate ARP tables and perform man-in-the-middle attacks. This study develops a machine learning-based detection system to identify ARP poisoning incidents in real-time, using Random Forest, AdaBoost, and a hybrid Random Forest-AdaBoost ensemble model. Data was collected from a public Wi-Fi environment in Surabaya, consisting of 11,225 ARP traffic records, augmented with simulated ARP poisoning attacks. Data preprocessing included exploratory analysis, feature engineering, encoding, and dataset balancing to improve model performance. Experimental results demonstrate that the hybrid ensemble model achieved the highest accuracy (99.92% on validation and 99.94% on testing), but its inference time of 517.30 ms rendered it unsuitable for real-time deployment. In contrast, the AdaBoost model achieved similar accuracy with significantly faster inference latency (7.82–14.93 ms), making it the most efficient model for live monitoring. The optimized AdaBoost classifier was then deployed through a Telegram-based alert system integrated with Scapy for continuous packet inspection and immediate attack notifications. This study contributes to the advancement of real-time intrusion detection mechanisms for WLAN environments by demonstrating the effectiveness of ensemble learning in ARP poisoning detection. Furthermore, it emphasizes the importance of balancing detection accuracy with computational efficiency for practical deployment in dynamic network environments. The findings offer insights into developing scalable, low-latency security solutions and lay the groundwork for future research on adaptive, real-time detection frameworks.
Implementation of MobileNetV3-Large in Rhizome Classification Nurdiansyah N.A, M. Ryan; Via, Yisti Vita; 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.v8i3.3365

Abstract

Rhizomes are widely used in Indonesia as cooking spices and herbal ingredients, yet their visual similarity often causes misidentification when recognition relies on manual inspection, leading to inconsistent product quality and economic losses. This study presents an automatic rhizome image classification system based on the MobileNetV3-Large architecture to distinguish eight Indonesian rhizome types (bangle, ginger, kencur, kunci, turmeric, galangal, temu ireng, and temulawak) from RGB images. The dataset is organised by class and processed with a pipeline that includes resizing to 224×224 pixels, image flipping and rotation, brightness adjustment, zoom, and normalisation, before being split into training, validation, and testing subsets with an 80:10:10 ratio. MobileNetV3-Large pretrained on ImageNet is used as a feature extractor with a three layer dense classification head and dropout regularisation, trained using the Adam optimiser with a learning rate of 0.0001 and a checkpoint mechanism to store the best validation model. The proposed model achieves 97.50% accuracy, 97.65% precision, 97.50% recall, and 97.51% f1-score on the test set, indicating stable and balanced performance across all rhizome classes despite their similarity. Compared with earlier rhizome classification approaches based on handcrafted features, which typically report lower accuracies on fewer classes, and with heavier VGG or ResNet backbones, this work provides, to the best of the authors’ knowledge, the first evaluation of MobileNetV3-Large for multi class rhizome classification and shows that it offers a practical and computationally efficient baseline for image based rhizome identification on resource constrained mobile or embedded devices.
Expert System Implementation Using Certainty Factor Method for Early Pregnancy Disease Detection Hariyanti, Nanda Syarla; Nurlaili, Afina Lina; Aditiawan, Firza Prima
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.v8i3.3366

Abstract

Pregnancy requires continuous and accurate monitoring to prevent complications that may endanger both the mother and the fetus. Data from the 2024 Maternal Perinatal Death Notification (MPDN) system recorded an increase in maternal mortality, largely driven by delays in early diagnosis and late referral to appropriate healthcare facilities. These conditions highlight the need for decision-support technologies capable of providing timely and consistent early risk detection. This study develops HerBump, a web-based expert system designed to support the early identification of common pregnancy-related diseases by integrating the Certainty Factor (CF) method with expert medical knowledge. The novelty of this work lies in the use of CF to represent the degree of confidence from both experts and users, which helps improve diagnostic accuracy compared with conventional rule-based systems, especially in cases where symptoms are overlapping, incomplete, or vary between individuals. Evaluation results show that HerBump can generate early diagnostic outputs accurately and efficiently, supported by a System Usability Scale (SUS) score of 98.3 (Excellent) and Blackbox Testing that confirms all features function correctly across different scenarios. More broadly, the system has meaningful implications for maternal health, as it can support earlier interventions, enhance the consistency of risk assessments, and potentially help reduce maternal and infant mortality through faster and more reliable early detection. Its simple and scalable design also enables potential use in resource-limited areas, including regions with shortages of healthcare workers, with future development opportunities through expanded disease coverage and more diverse datasets to strengthen diagnostic reliability.

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