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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan JIEET (Journal of Information Engineering and Educational Technology) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Bertuah : Jurnal Syariah dan Ekonomi Islam Journal of Applied Data Sciences Jurnal Informatika Teknologi dan Sains (Jinteks) ILTEK : Jurnal Teknologi Jurnal Informatika Polinema (JIP) Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Jurnal Informatika Dan Tekonologi Komputer Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)

Achmad Junaidi

Universitas Pembangunan Nasional Veteran Jawa Timur

Author-ID : 1643370

Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Cloud-Based High Availability Architecture Using Least Connection Load Balancer and Integrated Alert System Prinafsika; Achmad Junaidi; Muhammad Muharrom Al Haromainy
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

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.

Website Security Testing Using PTES Method and OWASP Top 10 Approach Mochammad Yoga Firnanda; Henni Endah Wahanani; Achmad Junaidi
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.v8i1.2564

Rapid technological advancements have greatly benefited the industrial sector, making technology essential for business operations. However, this reliance also introduces vulnerabilities, particularly in Enterprise Resource Planning (ERP) systems, which are critical for managing business processes and sensitive data. Due to their complexity and integration, ERP systems are prime targets for cyberattacks, emphasizing the need for robust security testing. This research aims to identify, evaluate, and exploit vulnerabilities in the ERP website of PT. XYZ, specifically targeting pages accessible by users with the SPV Marketing role. The Penetration Testing Execution Standard (PTES) methodology was used to guide the process from intelligence gathering to exploitation and reporting. PTES also ensures that testing is conducted legally during the pre-engagement phase. Tools such as Google Dorking, Netcraft, Wappalyzer, and Nmap were employed for intelligence gathering. For threat modeling, ISO 27005 was employed to identify vulnerabilities, while ISO 25010 served as a standard for security quality. A ZAP scan revealed 23 security vulnerabilities, including 18 that fall under the OWASP Top 10, such as Broken Access Control and Injection. Simulated attacks successfully identified Cross-Site Scripting (XSS), Session Hijacking, and Cross-Site Request Forgery (CSRF). Based on the findings, the recommendations focus on enhancing ERP system security according to the OWASP Top 10 guidelines, ensuring clarity for the development team. This study highlights the need for improved ERP security and offers a structured PTES-OWASP framework applicable across sectors. Future research may integrate multiple tools to enhance vulnerability detection.

Optimasi Hiperparameter LSTM Menggunakan PSO untuk Peramalan Bawang Merah dan Bawang Putih Mutiq Anisa Tanjung; Anggraini Puspita Sari; Achmad Junaidi
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.v8i1.2569

This research develops a shallot and garlic price prediction model using a Long Short-Term Memory (LSTM) network optimized through the Particle Swarm Optimization (PSO) method. Indonesia experiences an annual increase in demand for these two commodities. This research focuses on optimizing LSTM parameters, such as the number of units in each layer, learning rate, batch size, time step, and number of training epochs using PSO. Various trials were conducted with different PSO parameter settings and data partitioning scenarios to find the best configuration in predicting prices. The results show that the LSTM model optimized with PSO produces an RMSE value of 436,969 for shallots and 173,866 for garlic. In addition to RMSE, the Mean Absolute Percentage Error (MAPE) and R² metrics also show high prediction accuracy. The 90:10 data partitioning scenario showed the best evaluation results, indicating that more data improves the accuracy of the LSTM in learning price patterns. Scatter plots comparing predicted prices with actual prices show a good match, although there is some variation in certain price ranges. This study also highlights the effect of data partitioning on model performance. The LSTM-PSO approach proved effective in improving the accuracy of price predictions and has practical implications for farmers and policy makers in decision making. The model has the potential to be a decision support tool in the agribusiness sector, with the possibility of further development with external factors.

Performance Comparison of Gaussian Mixture Model, Hierarchical Clustering, and K-Medoids in Passenger Data Clustering Thalita Syahlani Putri; I Gede Susrama Mas Diyasa; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3013

The rapid growth of urban populations and increasing reliance on public transportation in Indonesia present challenges in managing passenger demand effectively. In Surabaya, the steady rise in Suroboyo Bus passengers underscores the need for data-driven strategies to optimize fleet allocation, scheduling, and infrastructure development. Identifying passenger density patterns through clustering provides a systematic basis for decision-making. This study aims to address a local research gap by comparing three clustering algorithms Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Model (GMM), and K-Medoids on empirical passenger data. Unlike previous studies that emphasize route optimization or demand forecasting, this research highlights a comparative evaluation to determine the most effective method for handling fluctuating and outlier-prone transportation data. The dataset was obtained from the Surabaya City Transportation Office for the Purabaya–Perak route during a two-week period in 2024. Data preprocessing included attribute selection, transformation of time into numerical format, outlier detection using the Interquartile Range (IQR), and Z-Score normalization. Clustering results were assessed with the Silhouette Score and visualized using scatter plots and histograms. Findings show that K-Medoids achieved the highest Silhouette Score (0.4222), surpassing AHC (0.3657) and GMM (0.3024). K-Medoids produced more balanced clusters and stronger resilience to outliers, while AHC provided interpretable hierarchical structures, and GMM modeled complex patterns but with weaker separation. In conclusion, K-Medoids is recommended as the most suitable approach for passenger density clustering. Academically, this study contributes a comparative framework for clustering in transportation research, while practically offering insights to support data-driven public transport management in developing cities.

Implementation of HMM-GRU for Bitcoin Price Forecasting Rayya Ruwa'im Nafie; Anggraini Puspita Sari; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3137

Bitcoin’s extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov Model–Gated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.

Prediction of Air Pollution Standard Index Using CEEMDAN-LSTM Rafie Ishaq Maulana; Muhammad Muharrom Al Haromainy; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3164

Air pollution has become a critical environmental issue, particularly in urban areas such as DKI Jakarta, where pollutant concentrations frequently reach the highest levels in Indonesia. Accurate prediction of the Air Pollution Standard Index (ISPU) is essential for mitigating the adverse health and environmental impacts of poor air quality. However, ISPU data exhibit nonlinear, volatile, and non-stationary characteristics, posing challenges for conventional prediction models. To overcome these challenges, this study proposes a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Long Short-Term Memory (CEEMDAN–LSTM) model, applied to daily ISPU data from 2010 to 2025 comprising 5,686 records. CEEMDAN was selected over conventional decomposition methods such as EEMD and VMD due to its ability to suppress mode-mixing and extract more stable Intrinsic Mode Functions (IMFs) through adaptive noise addition, thereby enhancing signal interpretability and learning efficiency. The ISPU time series was decomposed into multiple IMFs, and the resulting components were reconstructed and modeled using an optimized LSTM architecture obtained through Bayesian hyperparameter tuning. The optimal configuration batch size of 54, dropout rate of 0.37, and hidden units of 6, 33, and 34 achieved an RMSE of 14.0, reflecting a substantial improvement over the baseline LSTM model. The results demonstrate that integrating CEEMDAN with LSTM effectively reduces signal complexity, stabilizes convergence, and improves forecasting accuracy for non-stationary air quality data in DKI Jakarta. This modeling framework provides a robust foundation for developing predictive early-warning systems, supporting evidence-based environmental policy, and enhancing public health preparedness in rapidly urbanizing regions.

Classification of Jombang Batik Motifs Using Ensemble Convolutional Neural Network Riza Satria Putra; Muhammad Muharrom Al Haromainy; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3204

Batik, recognized by UNESCO as an Intangible Cultural Heritage, presents complex visual patterns that challenge automated classification systems. The intricate variations in texture, color, and geometry across motifs often lead to inconsistent performance in single Convolutional Neural Network (CNN) models, which struggle to generalize across subtle inter-class differences. To address these limitations, this study implements an Ensemble CNN framework to classify six Ploso Jombang batik motifs Garudan, Merak Kinasih Keyna Galeri, Ploso Bersemi, Jombang Berseri, Sulur Kangkung, and Burung Hong from a dataset of 2,134 images. The proposed approach integrates three pre-trained architectures EfficientNetB0, ResNet18, and VGG16 through a stacking ensemble strategy to leverage complementary feature extraction capabilities. Experimental results demonstrate that EfficientNetB0 achieved the highest individual accuracy (94%), while VGG16 recorded the lowest (60%). When combined, the ensemble configurations EfficientNetB0 + VGG16 and EfficientNetB0 + ResNet18 achieved peak test accuracies of approximately 96.88% on 321 test samples, reflecting a 2.88% improvement over the best single model. Confusion Matrix analysis confirmed robust model stability, with 100% accuracy for motifs such as Ploso Bersemi and Sulur Kangkung. These results validate that ensemble learning effectively mitigates overfitting and enhances generalization by aggregating diverse visual representations. The proposed model thus provides a reliable computational framework for automated batik classification and digital cultural preservation, supporting Indonesia’s efforts to document, catalog, and sustain its traditional heritage through artificial intelligence–driven methods.

Performance Evaluation of YOLOv5su and SVM With HOG Features for Student Attendance Face Recognition Achmad Rozy Priambodo; Achmad Junaidi; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3215

The rapid evolution of Artificial Intelligence (AI) and Computer Vision has revolutionized conventional attendance systems by introducing automated and intelligent alternatives. Traditional approaches such as manual entry and fingerprint-based systems are often inefficient, error-prone, and unsuitable for large-scale student management. This study evaluates a hybrid face recognition framework that combines You Only Look Once version 5 su, Histogram of Oriented Gradients (HOG), and Support Vector Machine (SVM) to automate student attendance. The YOLOv5su algorithm performs fast and lightweight face detection, while HOG extracts gradient-based facial descriptors classified by SVM. Experiments were conducted using a facial image dataset consisting of 500 original images from 10 classes (50 images per class), which were augmented to 3,500 images with variations in pose, expression, and illumination. The proposed YOLOv5sU–HOG–SVM model achieved 97.1% detection accuracy and 97% recognition accuracy, with mean precision, recall, and F1-score values of 0.98, outperforming conventional CNN-based hybrid models in both accuracy and computational efficiency. These results demonstrate that the combination of YOLOv5su, HOG, and SVM provides a novel balance between detection speed and recognition robustness, making it suitable for real-time academic attendance management. Future work should integrate transformer-based facial feature extraction to further enhance robustness under extreme conditions and larger-scale datasets.

Mobile Legends Match Outcome Prediction Based on Players Statistics Using CatBoost and XGBoost Ciptaagung Firjat Ardine; Eka Prakarsa Mandyartha; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3259

Mobile Legends: Bang Bang (MLBB) is a mobile-based Multiplayer Online Battle Arena (MOBA) game with a vast global community and professional ecosystem. Despite the extensive use of machine learning in desktop-based MOBAs such as Dota 2 and League of Legends, predictive modeling for MLBB remains underexplored. This study addresses this research gap by developing and comparing two advanced gradient boosting algorithms CatBoost and XGBoost for predicting match outcomes based on individual player statistics. The dataset, collected through web scraping from the official MPL Malaysia Season 14 website, comprises 1,430 player-level records representing professional-level competitive matches. Both models were trained and evaluated using 5-Fold Cross Validation to ensure stability and robustness. The results indicate that CatBoost achieved the highest predictive accuracy, with an average of 96.15%, outperforming XGBoost, which attained 94.75%. However, XGBoost exhibited exceptional computational efficiency, completing the prediction process 99.62% faster 0.76 seconds compared to CatBoost’s 3 minutes and 21 seconds. These findings highlight the trade-off between accuracy and processing speed in esports predictive modeling. The study demonstrates the potential of gradient boosting approaches for MLBB-specific analytics, providing a novel contribution to the limited body of research on mobile esports prediction. Accordingly, CatBoost is more suitable for analytical or strategic contexts where precision is essential, while XGBoost is better aligned with real-time predictive systems that demand rapid computation and scalability.

Autoimmune Skin Disease Image Classification using EfficientViT-M1 with AdamW Optimizer Hafiyan Fazagi Adnanto; Anggraini Puspita Sari; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3300

Diagnosing autoimmune skin diseases is a clinical challenge because several conditions share overlapping visual characteristics. This study evaluates the EfficientViT-M1 model trained with the AdamW optimizer to classify images from five autoimmune skin disease categories. The dataset contains 3,336 images before augmentation and is divided into 60 percent training, 20 percent validation, and 20 percent testing to ensure stable evaluation and reduce overfitting. The model is trained for 50 epochs with a learning rate of 0.0001, and experiments using batch sizes of 64, 128, and 256 are conducted to analyze the impact of data processing on performance. Performance is measured using accuracy, precision, recall, and F1-score derived from confusion matrix results. The best performance appears at a batch size of 64, achieving 89.25 percent accuracy along with balanced precision, recall, and F1-score. These results show that EfficientViT-M1 can extract relevant lesion features while maintaining computational efficiency. A notable challenge emerges when distinguishing visually similar disease classes, particularly Psoriasis and Lichen, which often share comparable textures and color patterns that contribute to misclassification. This highlights the influence of dataset imbalance and visual overlap on prediction outcomes. The approach offers potential value for clinical practice, especially in underserved areas where automated decision support can help early screening when specialist access is limited. The model demonstrates encouraging potential as a resource-efficient tool for dermatological assessment. Future improvements may include increasing dataset diversity, incorporating clinical metadata, and exploring alternative optimization strategies to enhance diagnostic reliability.

Co-Authors Achmad Rozy Priambodo Afifudin, Muhammad Agung Mustika Rizki, Agung Mustika Akbar, Refansya Rachmad Akmal, Mohammad Faizal Al Fathoni, Hanif Andreas Nugroho Sihananto Andreas Nugroho Sihananto Anggraini Puspita Sari Anggraini Puspita Sari Anggraini Puspita Sari Ar Romandhon, Mitzaqon Gholizhan Ardiyansyah, Moh. Angga Arif Saifudin, Muhamad Ariq Musyaffah Ghufron, Althaf Arrisalah, Muhammad Baihaqi Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa beni tiyas kristanti Ciptaagung Firjat Ardine Dafauzan Bilal Syaifulloh Darmawan, Marcellinus Aditya Vitro Diyasa, I Gede Susrama Mas Dunuroi Assuryani Dwi Arman Prasetya Efendi, Ridwan Eka Prakarsa Mandyartha Erik evranata Pardede Erik Iman Heri Ujianto Eva Yulia Puspaningrum Fatullah, Ryan Reynickha Fauzan Novriandy, Muhammad Fetty Tri Anggraeny Firza Prima Aditiawan Galan Ahmad Defanka Hafiyan Fazagi Adnanto Henni Endah Wahanani Henni Endah Wahanani I Gede Susrama Mas Diyasa Isworo, Muhamad Raihan Ramadhani Izzatul Fithriyah Kartini Kartini kristanti, beni tiyas Kurniawan, Muh. Irsyad Dwi Lesmana, Benedictus Rafael Mandyartha, Eka Prakarsa Maulana, Hendra Mochammad Yoga Firnanda Mohammad Haydir Awaludin Waskito Muhammad Azka Zaki Muhammad Muharrom Al Haromainy Muhammad Muharrom Al Haromainy Muhammad Muharrom Al Haromainy Mustika Rizki, Agung Mutiq Anisa Tanjung Muttaqin, Faisal Nugroho Sihananto, Andreas Nurlaili, Afina Lina Oktaviana, Dinda Friska Paramitha, Clara Diva Permanasari, Wahyu Melinda Prastyo, Kus Dwi Pratama, Novandi Kevin Prinafsika PW, Benar Setya Rachmadhany Iman Rafie Ishaq Maulana Rahmanda Putri, Endin Ratantja Kusumajati, Fatwa Rayya Ruwa'im Nafie Ridwan Efendi Riza Satria Putra Rizki, Agung Mustika Royan Fajar Sultoni Sajiwo, Achmad Fauzihan Bagus Salsabila, Belia Putri Sari, Allan Ruhui Fatmah Sebrina, Aida Fitriya Shahab, Muhammad Syaugi Sitompul, Pelean Alexander Jonas Thalita Syahlani Putri Tinambunan, Fernanda Vierino, Farrel Tiuraka Wahyu Gunawan, Rafif Ilafi Wardah Gracillaria Suharyono, Farra William Lijaya Therry, Renaldy Zaim, Mohammad Syarifuz

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