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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi 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 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
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Journal : bit-Tech

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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

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.
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

Abstract

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.
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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 Ar Romandhon, Mitzaqon Gholizhan Arif Saifudin, Muhamad Ariq Musyaffah Ghufron, Althaf Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa beni tiyas kristanti Ciptaagung Firjat Ardine Dafauzan Bilal Syaifulloh Darmawan, Marcellinus Aditya Vitro Dunuroi Assuryani Dwi Arman Prasetya Efendi, Ridwan Eka Prakarsa Mandyartha Erik evranata Pardede Eva Yulia Puspaningrum Fauzan Novriandy, Muhammad Fetty Tri Anggraeny Firza Prima Aditiawan Galan Ahmad Defanka Hafiyan Fazagi Adnanto Henni Endah Wahanani I Gede Susrama Mas Diyasa Izzatul Fithriyah Kartini Kartini kristanti, beni tiyas Lesmana, Benedictus Rafael Mandyartha, Eka Prakarsa Maulana, Hendra Mohammad Haydir Awaludin Waskito Muhammad Azka Zaki Muhammad Muharrom Al Haromainy Muhammad Muharrom Al Haromainy Mustika Rizki, Agung Muttaqin, Faisal Nugroho Sihananto, Andreas Nurlaili, Afina Lina Oktaviana, Dinda Friska Paramitha, Clara Diva Pratama, Novandi Kevin Prinafsika PW, Benar Setya Rachmadhany Iman Rahmanda Putri, Endin Ratantja Kusumajati, Fatwa Ridwan Efendi Riza Satria Putra Rizki, Agung Mustika Royan Fajar Sultoni Sajiwo, Achmad Fauzihan Bagus Sebrina, Aida Fitriya Shahab, Muhammad Syaugi Thalita Syahlani Putri Tinambunan, Fernanda Wardah Gracillaria Suharyono, Farra William Lijaya Therry, Renaldy Zaim, Mohammad Syarifuz