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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 ILKOMNIKA: Journal of Computer Science and Applied Informatics 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) Journal of Vocational Education and Information Technology (JVEIT) Jurnal Penelitian Sistem Informasi ILTEK : Jurnal Teknologi Jurnal Informatika Polinema (JIP) Horizon: Indonesian Journal of Multidisciplinary 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 Humanities 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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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.
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.
STRATEGI AKSELERASI SERTIFIKASI HALAL BAGI UMKM SEKTOR KULINER DI INDONESIA SEBAGAI UPAYA PENGUATAN EKOSISTEM INDUSTRI HALAL BERKELANJUTAN Dunuroi Assuryani; Achmad Junaidi
Bertuah Jurnal Syariah dan Ekonomi Islam Vol. 7 No. 1 (2026): Bertuah Jurnal Syariah dan Ekonomi Islam
Publisher : Institut Agama Islam Negeri Datuk Laksemana Bengkalis, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56633/jsie.v7i1.1462

Abstract

Indonesia menghadapi tantangan krusial dalam menyongsong kewajiban mandatori halal pada Oktober 2026, khususnya pada sektor UMKM kuliner yang merupakan pilar ekonomi nasional. Penelitian ini bertujuan untuk merumuskan strategi akselerasi sertifikasi halal melalui penguatan ekosistem industri halal yang berkelanjutan. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan menyaring 200 literatur ilmiah menjadi 25 referensi inti melalui perangkat lunak Publish or Perish (PoP) dalam rentang tahun 2021-2026. Hasil penelitian menunjukkan bahwa hambatan utama UMKM saat ini telah bergeser dari kendala biaya menuju rendahnya literasi digital dan kompleksitas rantai pasok. Sebagai solusi, penelitian ini menawarkan model strategi "High-Tech, High-Touch" yang mengintegrasikan efisiensi platform SIHALAL dengan humanisme pendampingan Proses Produk Halal (PPH). Selain itu, penguatan ekosistem dilakukan melalui sinergi Pentahelix yang melibatkan pemerintah, akademisi, pelaku usaha, komunitas, dan media. Secara filosofis, strategi ini merupakan manifestasi prinsip Maqashid Syariah dalam aspek Hifdzun Nafs dan Hifdzun Mal. Kesimpulannya, akselerasi sertifikasi halal yang integratif dan kolaboratif tidak hanya mempercepat kepatuhan regulasi, tetapi juga memperkuat daya saing UMKM Indonesia dalam Global Halal Value Chain.
Implementasi Kerangka Kerja MITRE D3FEND dalam Mitigasi Serangan Ransomware LockBit 3.0 Syahbagus Radithya Haryo Santoso; Henni Endah Wahanani; Achmad Junaidi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3636

Abstract

Cybersecurity threats are escalating due to the evolution of LockBit 3.0 ransomware, which has disrupted national vital sectors. This study aims to demonstrate the implementation of the MITRE D3FEND framework to mitigate these attacks within a Windows 11 environment. An experimental method using a technical comparative analysis approach was applied and validated through 50 test iterations to ensure data reliability. The results indicate that the baseline unprotected system is completely vulnerable to the entire LockBit 3.0 attack chain. However, the deployment of MITRE D3FEND controls proactively enhances system resilience, achieving a 75% effectiveness score by successfully executing passive detection and real-time active blocking at critical attack vectors. This study concludes that a digital artifact-based defense strategy significantly hardens cyber infrastructure, while recommending future developments in artificial intelligence (AI) based adaptive mitigation automation.Kata kunci: MITRE D3FEND; LockBit 3.0; Cybersecurity; Ransomware; Mitigation AbstrakAncaman keamanan siber meningkat akibat evolusi ransomware LockBit 3.0 yang melumpuhkan berbagai sektor vital nasional. Penelitian ini bertujuan mendemonstrasikan implementasi kerangka kerja MITRE D3FEND dalam memitigasi serangan tersebut pada Windows 11. Metode eksperimen diterapkan melalui pendekatan analisis komparatif teknis yang divalidasi lewat 50 kali iterasi pengujian guna menjamin reliabilitas data. Hasil pengujian menunjukkan bahwa sistem standar tanpa proteksi sepenuhnya rentan terhadap seluruh rangkaian serangan LockBit 3.0. Namun, penerapan kontrol pertahanan MITRE D3FEND terbukti proaktif meningkatkan resiliensi sistem dengan skor efektivitas mencapai 75% melalui keberhasilan fungsi deteksi pasif serta pemblokiran aktif secara real-time di titik-titik krusial serangan. Penelitian ini menyimpulkan bahwa strategi pertahanan berbasis artefak digital secara signifikan memperkeras keamanan infrastruktur siber, sekaligus merekomendasikan pengembangan otomatisasi mitigasi adaptif berbasis kecerdasan buatan (AI) di masa depan. 
Co-Authors Achmad Rozy Priambodo Afifudin, Muhammad Agung Mustika Rizki, Agung Mustika Akbar, Refansya Rachmad Akmal, Mohammad Faizal Al Fathoni, Hanif Allan Ruhui Fatmah Sari Andreas Nugroho Sihananto Andreas Nugroho Sihananto Anggraini Puspita Sari Anggraini Puspita Sari Anggraini Puspita Sari Ar Romandhon, Mitzaqon Gholizhan Arif Saifudin, Muhamad Ariq Musyaffah Ghufron, Althaf Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa Belia Putri Salsabila beni tiyas kristanti Ciptaagung Firjat Ardine Clara Diva Paramitha Dafauzan Bilal Syaifulloh Darmawan, Marcellinus Aditya Vitro Dinda Friska Oktaviana Dunuroi Assuryani Dwi Arman Prasetya Efendi, Ridwan Eka Prakarsa Mandyartha Ekamartha, Ken Narendra Erik evranata Pardede Erik Iman Heri Ujianto Eva Yulia Puspaningrum Eva Yulia Puspaningrum Farrel Tiuraka Vierino Fauzan Novriandy, Muhammad Fetty Tri Anggraeny Firza Prima Aditiawan Galan Ahmad Defanka Galan Ahmad Defanka Hafiyan Fazagi Adnanto Hakim, Albi Akhsanul Henni Endah Wahanani Henni Endah Wahanani I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Isworo, Muhamad Raihan Ramadhani Izzatul Fithriyah Kartini Kartini kristanti, beni tiyas Kus Dwi Prastyo Lesmana, Benedictus Rafael Mandyartha, Eka Prakarsa Maulana, Hendra Mochammad Afdal Susilo Aji Mochammad Afdal Susilo Aji Mochammad Yoga Firnanda Moh. Angga Ardiyansyah Mohammad Haydir Awaludin Waskito Mohammad Syarifuz Zaim Muh. Irsyad Dwi Kurniawan Muhammad Azka Zaki Muhammad Baihaqi Arrisalah Muhammad Muharrom Al Haromainy Muhammad Rafi Muhtaddin Noor Mustika Rizki, Agung Mutiq Anisa Tanjung Muttaqin, Faisal Nugroho Sihananto, Andreas Nurlaili, Afina Lina Pelean Alexander Jonas Sitompul Pratama, Novandi Kevin Prinafsika PW, Benar Setya Rachmadhany Iman Rafie Ishaq Maulana Rafif Ilafi Wahyu Gunawan Rahmanda Putri, Endin Ratantja Kusumajati, Fatwa Rayya Ruwa'im Nafie Ridwan Efendi Riza Satria Putra Rizki, Agung Mustika Royan Fajar Sultoni Ryan Reynickha Fatullah Sajiwo, Achmad Fauzihan Bagus Sebrina, Aida Fitriya Shahab, Muhammad Syaugi Syahbagus Radithya Haryo Santoso Thalita Syahlani Putri Tinambunan, Fernanda Wahyu Melinda Permanasari Wardah Gracillaria Suharyono, Farra William Lijaya Therry, Renaldy