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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer At-Taqaddum Management Analysis Journal Jurnal Kedokteran Gigi Universitas Padjadjaran Jurnal Ilmiah Peuradeun Jurnal STIE Semarang (Edisi Elektronik) Tunas: Jurnal Pendidikan Guru Sekolah Dasar Mudir : Jurnal Manajemen Pendidikan bit-Tech JURNAL CAPITAL : Kebijakan Ekonomi, Manajemen dan Akuntansi Darul Ilmi: Jurnal Ilmu Kependidikan dan Keislaman Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat: Humanity and Medicine International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sehat Indonesia (JUSINDO) Wijaya Putra Law Review Kartika: Jurnal Studi Keislaman Scoring: Journal of Film Music Neurona Addabani: Interdisciplinary Journal of Islamic Education Jurnal Pengabdian Masyarakat Management Analysis Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Inovasi Sosial: Jurnal Pengabdian Masyarakat House of Wisdom: Journal on Library and Information Sciences Journal of Neurointervention and Stroke (JNeViS)
Achmad Junaidi, Achmad
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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.
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.
Comparative Analysis of LSTM and GRU Algorithms for Inflation Rate Forecasting Ardiyansyah, Moh. Angga; 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.v8i3.3370

Abstract

Inflation is a critical economic indicator that directly affects price stability, purchasing power, and the formulation of fiscal and monetary policies. In East Java, inflation has demonstrated considerable year-to-year volatility, creating significant challenges for policymakers in maintaining regional economic stability. This situation highlights the need for forecasting models that are both accurate and capable of adapting to complex economic data patterns. This study presents a comparative analysis of two deep learning algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting year-on-year (YoY) inflation in East Java using data from January 2005 to December 2024. The dataset was processed using Min–Max normalization and a 12-month sliding window to capture long-term dependencies and seasonal variations. Model performance was evaluated using RMSE, MAE, and MAPE. The findings demonstrate that no single model performs best across all metrics. The LSTM4 model with a [128,128] architecture achieved the lowest MAE and MAPE values, indicating superior average predictive accuracy and stronger capability in learning complex long-term inflation patterns. In contrast, the GRU1 [64,64] model produced the lowest RMSE and the shortest training time, highlighting its efficiency in minimizing extreme prediction errors and reducing computational cost. These results offer valuable insights for policymakers in East Java: LSTM is more suitable for applications requiring high prediction accuracy, whereas GRU is preferable for real-time or resource-efficient forecasting systems, especially in fast-changing economic environments.
Aplikasi OMR untuk Pemeriksaan Lembar Jawaban menggunakan DexiNed Prastyo, Kus Dwi; Junaidi, Achmad; 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.v8i3.3425

Abstract

Digital image processing is a field of computer science that focuses on analyzing and interpreting digital images to extract meaningful information. One of its applications is Optical Mark Recognition (OMR), a technology used to detect marks on documents. OMR is commonly utilized for evaluating answer sheets. However, conventional OMR systems typically rely on specialized scanners that are expensive and lack flexibility. Although Computer-Based Testing (CBT) offers the convenience of automated scoring, its implementation heavily depends on the availability of technological infrastructure such as computers, internet connectivity, and a stable power supply. This study develops a real-time Optical Mark Recognition (OMR) application capable of performing answer sheet assessment directly on the client side. The system utilizes the DexiNed method for edge detection of the answer areas. The application is web-based and built using JavaScript and OpenCV.js to process images directly from the user's device camera. Testing was carried out under various scenarios, including different lighting intensities, scanner positions, pencil types, and shading quality. The results show that the application can detect marked answers with an accuracy up to 100%, although some limitations were observed under certain technical conditions. Weaknesses were found in low lighting conditions using a 5 watt lamp at a distance of 3 meters, light reflections, and the camera angle was not aligned with the answer sheet. Overall, the application provides an efficient and flexible alternative for answer sheet assessment without requiring dedicated scanning devices, making it suitable for educational institutions with limited infrastructure.
Design of Thesis Topic Recommendation System Using TF-IDF and Cosine Similarity Arrisalah, Muhammad Baihaqi; Haromainy, Muhammad Muharrom Al; 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.v8i3.3579

Abstract

Selecting a thesis topic is a critical stage in a student’s academic journey and frequently poses substantial cognitive and procedural challenges. This study reports the design and implementation of the Computer Science Thesis Recommendation System (SRSIK Hub), a web-based decision-support platform aimed at improving the efficiency and accuracy of thesis topic selection. The primary novelty of this research lies in the systematic integration of Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity within a large-scale academic corpus to model fine-grained semantic relevance between student interests and prior thesis documents, enabling more precise and transparent recommendations than conventional keyword-based searches. The system adopts a content-based filtering approach and processes approximately 4,000 thesis records collected from multiple university repositories. Textual data are preprocessed and transformed using TF-IDF vectorization, while Cosine Similarity is employed to rank candidate topics according to relevance. System effectiveness was evaluated using the WebUse Framework involving 75 student respondents. The evaluation yielded an overall score of 4.44 out of 5, indicating high usability, strong information quality, and reliable system functionality. This performance score demonstrates that the proposed recommendation model is not only technically sound but also practically applicable in real academic settings, where it can significantly reduce topic selection time and uncertainty for students. The results confirm that SRSIK Hub effectively supports students in identifying research topics aligned with their academic interests and competencies. Beyond local deployment, the system is transferable to other institutions for scalable thesis recommendation support.
Evaluating Web Application Security Using OWASP Top 10 and NIST SP 800-115 Vierino, Farrel Tiuraka; 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.v8i3.3702

Abstract

Cybersecurity assurance for public-facing government websites remains critical amid accelerating digital transformation. This study adopts an exploratory–evaluative research design to systematically examine and validate the security posture of the Surabaya Public Slaughterhouse (RPH Surabaya) website through an integrated application of OWASP Top 10 (2021) as a vulnerability taxonomy and NIST SP 800-115 as a procedural testing framework. The methodology follows structured planning, discovery, attack, and reporting phases. Discovery combined reconnaissance tools (Nslookup, Whois, Nmap, Dirsearch, Wappalyzer, and Google Dorking) with OWASP ZAP scanning, while attack validation employed Burp Suite, SQLMap, and browser-based developer analysis within a controlled Kali Linux environment. Thirteen potential vulnerabilities were detected, of which ten were empirically confirmed after manual verification. Confirmed weaknesses were predominantly categorized as Security Misconfiguration, including missing Anti-CSRF protections, directory browsing exposure, absent Content Security Policy and anti-clickjacking headers, outdated JavaScript libraries, insecure cookie attributes (missing HttpOnly and SameSite), lack of Strict-Transport-Security and X-Content-Type-Options headers, and user-controllable HTML attributes. The contribution lies in demonstrating a reproducible dual-framework validation pipeline that distinguishes scanner alerts from confirmed exploitability, thereby strengthening methodological rigor in public-sector web security assessment. These findings indicate systemic configuration-level risk exposure that may elevate susceptibility to XSS, CSRF, clickjacking, and injection-related threats relative to comparable public-institution websites. However, the assessment is limited to a single institutional website and an unauthenticated testing scope, constraining generalizability and deeper application-layer analysis.
Uncovering Hidden Security Risks in Government Web Portals Using Penetration Testing and Attack Modeling Salsabila, Belia Putri; Endah Wahanani, Henni; 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.v8i3.3776

Abstract

Government web portals that consolidate public services and process personally identifiable data are prime targets for cyber adversaries. However, many existing assessments rely on single-framework methodologies that provide limited adversarial context and insufficient prioritization guidance. This study evaluates the security posture of System X, a public-facing government portal in Indonesia, using a grey-box penetration testing approach that integrates OWASP Top 10:2021, CVSS v3.1, and MITRE ATT&CK. Automated scanning using OWASP ZAP and Nessus identified 12 potential vulnerabilities, which were subsequently validated through manual testing using Burp Suite, cURL, SQLmap, and browser developer tools. The validation process confirmed nine True Positives and three False Positives, resulting in a 25% false positive rate, consistent with prior studies on government web applications. The identified vulnerabilities fall within Broken Access Control, Security Misconfiguration, and Identification and Authentication Failures, with CVSS Base Scores ranging from 4.2 to 6.1. Unlike traditional severity-based assessments, the integration of MITRE ATT&CK enables adversarial behavior mapping and reveals dependency relationships between vulnerabilities. For example, a single Content Security Policy (CSP) misconfiguration was found to enable multiple attack techniques (T1059.007), demonstrating that addressing one root cause can mitigate several related vulnerabilities simultaneously. This integrated approach enhances vulnerability prioritization by providing both severity and attacker-context insights, offering more actionable remediation strategies compared to single-framework methods. The findings contribute to improving practical security assessment methodologies for government systems and support evidence-based cybersecurity decision-making.
Co-Authors Abadi , Totok Wahyu Abda Abda Adwirianny, Ashita Hulwah Agung Prasetyawan AHMADI Amar, Muhammad Ana Afida Anggraeni, Dya Anggraini Puspita Sari Apriyono, Apriyono Ardiyansyah, Moh. Angga Arrisalah, Muhammad Baihaqi Aryanto, Tossy Awerman Awerman Buhori, A Ary Firman Chotib, Moc Dimas Satria Prayoga Diyasa, I Gede Susrama Mas Elsa Adelia, Febri Enggardipta, Raras Ajeng Erik Iman Heri Ujianto Fatullah, Ryan Reynickha Febrianti, Sania Fetty Tri Anggraeny Fidyah Yuli Ernawati, Fidyah Yuli Firza Prima Aditiawan Fitriyah, Eliyatul Hartarini, Yovita Mumpuni Hendrayati, Selvia Henni Endah Wahanani Ibnu Elmi AS Pelu Isworo, Muhamad Raihan Ramadhani Juhairiyah Khairil Anwar Kurniawan, Muh. Irsyad Dwi Lesmana, Dimas Bayu Putra Maimun marisdina, selly Marsha Ayunita Irawati Mohamad Rifqy Roosdhani Muhammad Muharrom Al Haromainy Muhammad Noor Muhlisa, Safitri Nainggolan, Putri Drani Nindhita, Yoga Octavinawaty, Lenny Okparasta, Andika Oktaviandi, Ardy Permanasari, Wahyu Melinda Permani, Dyah Pradana, Adi Pramesti, Adella Anggia Prastyo, Kus Dwi Pribadi Santosa Rafiqi, Ilham Dwi Rahmat Fauzi Ramadhoni, Pinto Desti Rini Nindela, Rini Robita, Achmad Rosa, Marta Salsabila, Belia Putri Samsul Arifin Sari, Allan Ruhui Fatmah Sari, Annisa Fitria Sari, Oki hardiyanti Rukmana Sa’dud Darain, A. Silvia Hendrayanti Sitompul, Pelean Alexander Jonas Sopi, Sopi Subekti, Heny Sukarsono Sukarsono Tri Endra Untara, Tri Endra Tsanie, Maria Latifa Tunjung Nugraheni Vierino, Farrel Tiuraka Wahyu Gunawan, Rafif Ilafi Wahyudi Wahyudi Yulita Kristanti Yusril Yusril Zumrotun Nafiah