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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTECOMS: Journal of Information Technology and Computer Science Data Science: Journal of Computing and Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknimedia: Teknologi Informasi dan Multimedia Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Jurnal Bangkit Indonesia Jurnal Pendidikan Indonesia (Japendi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Dhani Ariatmanto
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science

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Deteksi Karakter Aksara Jawa Menggunakan YOLO11 Pendekatan Deep Learning untuk Pelestarian Warisan Budaya Digital Eko Rahmad Darmawan; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.450

Abstract

Javanese script represents a significant cultural heritage of the Indonesian archipelago that faces extinction threats due to Latin alphabet dominance and minimal users capable of writing with this traditional script. This research aims to develop a Javanese character detection system using You Only Look Once version 11 (YOLO11) algorithm to support cultural preservation efforts through efficient digitalization. The research methodology employs an experimental approach with deep learning, where the Javanese script dataset consisting of 20 basic characters plus background class was obtained from Kaggle and preprocessed using Roboflow with data augmentation techniques. The YOLO11 model was implemented with SGD optimizer, 640px image size, and trained for 500 epochs to achieve optimal convergence. YOLO11 architecture integrates advanced components such as C3K2 blocks, Spatial Pyramid Pooling-Fast (SPPF), and Cross-scale Pixel Spatial Attention (C2PSA) to enhance multiscale feature extraction capabilities. Model performance evaluation utilized confusion matrix with accuracy, precision, recall, and F1-score metrics. Research results demonstrate that the YOLO11 model achieved an overall accuracy of 81.00% with macro-averaged precision of 86.28%, macro-averaged recall of 87.25%, and macro-averaged F1-score of 86.41%. Model performance distribution shows 7 classes with high performance (F1-score ≥ 90%), 9 classes with medium performance (80-90%), and 4 classes with low performance (<80%). The "nga" class achieved perfect performance of 100%, while the "ha" class showed the lowest performance with an F1-score of 68.09%. This research successfully improved accuracy compared to previous methods using backpropagation neural networks (74%) and conventional backpropagation (59.5%), although challenges remain in detecting characters with similar shapes and handling background class. The main contribution is the first implementation of YOLO11 for Javanese script detection, opening opportunities for developing more efficient and accurate ancient literature digitalization systems.
Perbandingan Metode Ekstraksi Fitur LBP, GLCM, dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN Roy Jordy; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.452

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.
Analisis Perbandingan Kinerja Model YOLO11 dan YOLOv8 dalam Identifikasi Penyakit pada Daun Tomat Muhammad Arif Kholis Majid; Dhani Ariatmanto
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.459

Abstract

Diseases on tomato leaves can reduce the quality and quantity of agricultural yields, as well as affect market prices. This study compares the effectiveness of the YOLO11 and YOLOv8 models in detecting diseases on tomato leaves with traditional CNN-based models such as VGG-16 and Inception-V3. The results show that the YOLO11 model provides the best accuracy of 99.4%, followed by YOLOv8 with 98.5%, both excelling in real-time detection. CNN-based models like VGG-16 and Inception-V3 have high accuracy (99% and 93.8%), but are slower in computation. The ensemble model of VGG-16 and NASNet Mobile achieves an accuracy of 98.7%, but is slightly lower than YOLO11. The YOLO model is more efficient in detection speed, making it a better choice for field applications. This study shows that YOLO11 offers the best combination of accuracy and detection speed for a real-time plant disease detection system.
Sistem Kendali Mikroklimat Greenhouse Cerdas Berbasis Hybrid GA-LSTM dan Fuzzy Logic Controller Rainer Maghma Royali; Dhani Ariatmanto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i2.1798

Abstract

Controlling temperature and humidity in greenhouses is a complex challenge due to its non-linear nature and dependence on external weather conditions. Conventional control methods often experience energy inefficiency and delayed responses to drastic changes. This study proposes a hybrid approach by combining Long Short-Term Memory (LSTM) optimized using Genetic Algorithm (GA) for temperature prediction, and Fuzzy Logic Controller (FLC) for actuator decision-making. Genetic Algorithm is employed to find optimal hyperparameters (number of neurons and batch size) in the LSTM architecture. Experimental results demonstrate the GA-LSTM model's capability in predicting temperature with high accuracy, yielding an R² score of 0.9881 and Root Mean Square Error (RMSE) of 1.1273°C. These accurate predictions are subsequently used as input to the FLC to regulate exhaust fan speed and mist pump status. Simulations demonstrate the system's capability in making energy-efficient decisions—activating actuators only when conditions are predicted to deviate from ideal values—while remaining responsive to extreme temperature anomalies.
Explainable DDoS Detection with a CNN-LSTM Hybrid Model and SHAP Interpretation Amali Amali; Anggi Muhammad Rifa'i; Edy Widodo; Ahmad Turmudi Zy; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6865

Abstract

The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach. The proposed method integrates Convolutional Neural Networks (CNN) to capture local traffic patterns and Long Short-Term Memory (LSTM) networks to model temporal dependencies. The CICIDS 2017 dataset, after preprocessing steps including data cleaning, standardization, and class balancing with SMOTE, was used to train and evaluate the model. Experimental results show that the framework achieved 99.98% accuracy and a 99.83% F1-Score, with minimal false positive and false negative rates. This study integrates SHAP to improve model interpretability, aligning feature importance with network security expertise. Future research will focus on real-time deployment, cross-dataset validation, and exploring alternative explainable AI techniques for improved scalability.
A Hybrid Round-Robin Scheduler for GPU Batch Rendering in Constrained Cloud Environments Ibnu Hadi Purwanto; Dhani Ariatmanto; M. Shahkhir Mozamir; Afifah Nur Aini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7117

Abstract

Creating high-quality 2D and 3D assets is essential for digital content, but inefficient scheduling and inaccurate time estimates often hamper the rendering process. Traditional methods, which assume rendering time is directly proportional to frame count, fail to account for variations in scene complexity, resulting in severe estimation errors averaging 97.0% across all tasks. We propose a Hybrid Round-Robin Scheduler (HRRS) that intelligently manages batch rendering tasks through complexity-aware classification. Our method first categorizes tasks by complexity (Low, Medium, High) and routes them to appropriate queues with tiered quantum allocations. It then employs non-linear time estimation models and dynamically adjusts processing priorities based on real-time performance metrics. We evaluated our scheduler against standard algorithms—First-Come-First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR)—using 21 diverse rendering tasks with frame counts ranging from 10 to 420 frames. The results demonstrate that our approach reduces average waiting time by 45.9% (from 29.63s to 16.02s) and cuts bottleneck-induced delays by 78% (from 41s to 9s), while maintaining optimal CPU utilization at 85% and limiting context switches to only nine occurrences. A key finding reveals that complexity, rather than frame count, is the primary driver of processing time; high-complexity tasks required significantly longer processing (averaging 238.27 seconds) compared to medium-complexity tasks (averaging 34.52 seconds), representing a 6.9-fold performance differential. Our hybrid framework effectively overcomes the primary limitations of existing algorithms: it prevents bottlenecks from large tasks (FCFS), avoids the parallelism issues of SJF, and minimizes the performance overhead from frequent switching in Round Robin. This work provides a robust foundation for intelligent resource allocation in cloud rendering environments where task demands are variable and difficult to predict, establishing that effective scheduling requires complexity-aware algorithms rather than universal approaches.
Deteksi Penyakit Gigi dan Mulut Menggunakan Algoritma Inception-V3 Detection of Dental and Oral Diseases Using Inception-V3 Dloifur Rohman Al Ghifari; Ema Utami; Dhani Ariatmanto
Jurnal Pendidikan Indonesia Vol. 6 No. 4 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i4.7649

Abstract

Kesehatan gigi dan mulut sangat penting bagi kesejahteraan umum, namun banyak orang mengabaikan pengobatan karena kurangnya kesadaran atau tantangan diagnostik. Metode diagnostik tradisional sering kali kurang akurat dan efisien. Penelitian ini bertujuan mengembangkan sistem otomatis untuk mengklasifikasikan penyakit gigi dan mulut menggunakan algoritma deep learning Inception-V3 guna meningkatkan akurasi diagnostik. Penelitian menggunakan dataset 8.776 citra oral yang diseimbangkan dengan SMOTE dan diproses dengan teknik augmentasi. Inception-V3 dilatih dan dibandingkan dengan CNN, VGG-16, ResNet50, serta model machine learning tradisional. Model Inception-V3 mencapai akurasi 94%, mengungguli model lain (CNN: 81%, VGG-16: 88.7%, ResNet50: 76.25%) dan menunjukkan stabilitas tanpa overfitting. Studi ini menegaskan potensi Inception-V3 dalam analisis gambar medis, menawarkan alat diagnostik yang andal untuk deteksi dini penyakit gigi dan mulut, sehingga dapat meningkatkan hasil layanan kesehatan.
DETEKSI JERAWAT DI WAJAH MENGGUNAKAN SEGMENTASI GAMBAR Harapan Napitupulu; Dhani Ariatmanto
Jurnal DutaCom Vol 19 No 1
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/h55kdy38

Abstract

Jerawat merupakan suatu penyakit yang biasa terjadi di wajah bagi anak remaja atau dewasa atau juga disebut masa puberitas dari usia Sembilan tahun sampai delapan belas tahun keatas. Penyakit kulit ini sangat sulit disembuhkan walaupun tidak berdampak pada kesehatan fisik manusia, tapi sangat mempengaruhi mental psikologi jiwa manusia yang ingin tampil pede, bersih, rapih, cantik, dan tampan. Sudah banyak penelitian yang digunakan dalam mendeteksi jerawat dengan berbagai metode yang masih rumit dalam mendeteksi jerawat. Dalam pengobatan wajah berjerawat diperlukan pengidentifikasi jerawat yang cepat mudah dan sederhana dengan tenologi, agar hasil pengobatan dapat dilakukan secara maksimal dan biaya yang murah. Tujuan dari penelitian ini adalah untuk mendeteksi jerawat dengan menggunakan segmentasi gambar yang lebih mudah dan simple untuk dipahami dengan mengkopres gambar menggunakan algoritma K-means lalu disegmentasi. Segmentasi gambar adalah pembagian gambar digital menjadi kelompok piksel diskrit untuk mendeteksi objek dan klasifikasi objek. Hasil dari penelitian ini menunjukan bahwa menggunakan metode segmentasi gambar mampu mendeteksi jerawat dengan identifikasi berbentuk titik dan lingkaran di wajah dengan mencocokan gambar asli dengan gambar yang di segmentasikan. Maka dari itu dengan menggunakan metode ini dapat digunakan dan membantu dalam pengobatan wajah berjerawat menggunakan segmentasi gambar. .  
Size-Controlled Opcode Ablation for Smart Contract Vulnerability Detection Astrid Pranadani; Dhani Ariatmanto
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 2 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI) In Press
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10i2.26398

Abstract

Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
Implementation of an Automatic System for Vehicle License Plate Character Recognition in Indonesia Using YOLOv8 and Tesseract OCR Alief Dewana Iriawan; Andi Sunyoto; Dhani Ariatmanto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 2 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i2.7930

Abstract

This study develops an automatic system for vehicle license plate identification in Indonesia by combining the YOLOv8 object detection algorithm with character recognition using Tesseract OCR. The system is designed to recognize two commonly used license plate colors in Indonesia: white and black. The process begins with training the YOLOv8 model using an annotated dataset consisting of 602 images of Indonesian vehicle license plates. Once the license plate area is accurately detected, the image is cropped based on the bounding box coordinates for the character recognition stage. Before being recognized by Tesseract OCR, the cropped image undergoes a series of preprocessing steps to enhance recognition accuracy. These steps include grayscale conversion, local contrast enhancement using the CLAHE method, segmentation with Otsu thresholding, and color inversion using the bitwise NOT operation. The system is evaluated using metrics such as accuracy, precision, recall, and F1-score. Test results show that the system is capable of detecting and recognizing license plates with high accuracy achieving 96% for white plates and 92% for black plates. Overall, the system achieved a combined accuracy of 94%. These findings indicate that the integration of YOLOv8 and Tesseract OCR, supported by effective image preprocessing, can significantly enhance license plate recognition performance. Nonetheless, challenges remain in recognizing characters on black plates with low contrast, which could be the focus of future improvements.
Co-Authors Afifah Nur Aini Agus Fatkhurohman Ahmad Turmudi Zy Alief Dewana Iriawan Alva Hendi Muhammad Amali Amali Andi Sunyoto Anggi Muhammad Rifa'i Anggit Rianansyah Ari Rudiyan Astrid Pranadani Bambang Soedijono Bima Widianto Dewi Yustika Lakoro Dloifur Rohman Al Ghifari Edy Widodo Eko Rahmad Darmawan Ema Utami Ferda Ernawan Harapan Napitupulu Ibnu Hadi Purwanto M. Iqbal Abdullah Sukri2 M. Shahkhir Mozamir Maulana Brama Shandy Ma’ruf Aziz Muzani Minggu, Emanuel Muhammad Arif Kholis Majid Muhammad Ghozy Alkhairi Rainer Maghma Royali Rizki Firdaus Mulya Roy Jordy Syifa Nur Fauziah