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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 Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi 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 International Journal Of Computer, Network Security and Information System (IJCONSIST) 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
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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Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam Ujianto, Erik Iman Heri; Diyasa, I Gede Susrama Mas; Junaidi, Achmad; Fatullah, Ryan Reynickha; Permanasari, Wahyu Melinda; Sari, Allan Ruhui Fatmah
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1167

Abstract

This study develops a reliable face-based verification system for online examinations by integrating a face recognition model with a blink detection mechanism to minimize the risk of identity fraud, also known as "contract cheating," and static image manipulation. "Contract cheating" refers to the practice where students hire others to complete their exams or assignments, compromising academic integrity. The growing reliance on online exams has raised concerns about the credibility of facial verification, as conventional methods are often vulnerable to spoofing attempts. To address this issue, the proposed system combines FaceNet, a deep learning model for identity recognition, with Dlib’s eye blink detection to provide a stronger layer of protection. The system was evaluated using 5-fold and 10-fold K-fold cross-validation, and additional testing assessed the impact of different video frame rates on performance. The results show that the system performs effectively in identifying legitimate users and detecting spoofing. FaceNet achieved an accuracy of 96.67 percent, outperforming DeepFace, which showed poorer results in precision, recall, and F1 score for some participants. Both models were evaluated on the same dataset, consisting of 150 images. The preprocessing pipeline, including face detection using MTCNN, cropping, and resizing, was applied consistently to both models to ensure a fair comparison of their performance. The system also demonstrated adaptability, achieving correct classifications at both 15 and 30 frames per second. Anti-spoofing tests based on the eye blink detection system detected all real faces, while static images were classified as spoofing. These results confirm that combining face recognition with liveness detection enhances the security of online examination platforms. The findings demonstrate the system's potential to reduce contract cheating and impersonation fraud, making online examinations more credible. Future work may focus on implementing adaptive thresholding for blink detection and integrating multimodal verification techniques to improve robustness across diverse real-world environments.
Model Hibrida Deret Waktu Adaptif untuk Peramalan Dinamis Permintaan Penumpang Kereta Api Menggunakan Kalman Filter Isworo, Muhamad Raihan Ramadhani; Tri Anggraeny, Fetty; Junaidi, Achmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Peramalan permintaan penumpang kereta api yang akurat sangat penting untuk optimasi operasional dan perencanaan layanan transportasi PT KAI. Model peramalan konvensional sering menghadapi tantangan dalam menangani dinamika permintaan yang kompleks, terutama saat terjadi perubahan pola mendadak dan kejadian khusus seperti hari libur atau kejadian luar biasa. Penelitian ini mengusulkan model hibrida adaptif yang menggabungkan SARIMAX dan Prophet dengan optimasi bobot menggunakan Kalman Filter. Data yang digunakan adalah jumlah penumpang bulanan PT KAI DAOP 8 periode 2016-2023 yang mencakup fluktuasi musiman dan kejadian khusus yang mempengaruhi permintaan penumpang. Hasil menunjukkan bahwa model hibrida adaptif mencapai MAPE 4.32%, lebih baik dibandingkan dengan SARIMAX (7.72%) dan Prophet (6.06%). Kalman Filter berhasil mengoptimalkan bobot secara dinamis, meningkatkan kemampuan adaptasi model terhadap perubahan pola permintaan. Model ini menunjukkan performa akurasi dan stabilitas yang tinggi, serta dapat digunakan untuk meramalkan permintaan penumpang di masa depan dan memberikan rekomendasi untuk perencanaan kapasitas PT KAI yang lebih efektif.   Abstract Accurate forecasting of rail passenger demand is essential for operational optimization and planning of transportation services. Conventional forecasting models often face challenges in handling complex demand dynamics, especially when sudden pattern changes and special events occur. This study proposes an adaptive hybrid model combining SARIMAX and Prophet with weight optimization using the Kalman Filter. The data used is the monthly passenger number of PT KAI DAOP 8 from 2016 to 2023, which includes seasonal fluctuations and special events affecting passenger demand. The results show that the adaptive hybrid model achieved a MAPE of 4.32%, better than SARIMAX (7.72%) and Prophet (6.06%). The Kalman Filter successfully optimized the weights dynamically, improving the model's adaptability to changing demand patterns. This model demonstrates high accuracy and stability, and can be used to forecast future passenger demand and provide recommendations for more effective capacity planning for PT KAI.
Prophet–LightGBM Hybrid Model Implementation in Cafe Menu Sales Prediction: Implementasi Model Hybrid Prophet–LightGBM dalam Prediksi Penjualan Menu Kafe Erik evranata Pardede; Fetty Tri Anggraeny; Achmad Junaidi
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to improve the accuracy of sales forecasting for cafe menu items through the development of a hybrid model that combines the Facebook Prophet and LightGBM algorithms. This hybrid model is designed to leverage the strengths of Prophet in detecting seasonal patterns and trends, as well as the ability of LightGBM to learn from residuals that are not captured by Prophet. The dataset used is sourced from Kaggle, containing cafe menu sales data, which includes information about the menu items, the quantity sold, and the transaction dates. Model evaluation was conducted using MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error) metrics. According to the results, the hybrid model shows significant improvement in forecasting accuracy, with MAPE of 5.83% for one menu item (cake), MAE of 0.84, and RMSE of 0.99, indicating better accuracy compared to the single models. This study is expected to provide valuable contributions to more efficient stock management and the development of more targeted marketing strategies for the cafe industry.
Identifikasi Citra Penyakit Monkeypox dengan Random Forest Serta Ekstraksi Fitur VGG19: Indonesia Muhammad Azka Zaki; Eka Prakarsa Mandyartha; Achmad Junaidi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10132

Abstract

Monkeypox is an infectious disease that can be recognized through images of the patient's skin lesions. A fast and accurate diagnosis method is required to identify Monkeypox. This research aims to identify Monkeypox imagery using the VGG19 feature extraction method, which is then classified using the Random Forest algorithm. The dataset consists of 770 original images, which were expanded to 5,860 images through geometric transformation augmentation. The test results show that the VGG19 feature extraction method with Random Forest classification achieved an accuracy of 95.1%, indicating good performance. This finding suggests the potential of this method as a machine learning approach for detecting Monkeypox and can be further developed with other artificial intelligence approaches.
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.
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

Abstract

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

Abstract

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

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.
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

Abstract

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

Abstract

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.
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 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 Syahbagus Radithya Haryo Santoso Thalita Syahlani Putri Tinambunan, Fernanda Vierino, Farrel Tiuraka Wahyu Gunawan, Rafif Ilafi Wardah Gracillaria Suharyono, Farra William Lijaya Therry, Renaldy Zaim, Mohammad Syarifuz