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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Communications in Science and Technology IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Narra J Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Igi Ardiyanto
Universitas Gadjah Mada
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Transportation

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Studi Analisis Perbandingan Algoritme Pathfinding pada Simulasi Unity 3D Nuryono, Aninditya Anggari; Ardiyanto, Igi; Wibirama, Sunu
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2183.661 KB)

Abstract

Pathfinding digunakan suatu objek untuk mencari jalur dari satu tempat ke tempat lain berdasarkan keadaan peta dan objek lainnya. Dalam pathfinding dibutuhkan algoritme yang dapat dengan cepat memproses dan menghasilkan arah yang terpendek untuk mencapai suatu lokasi tujuan. Algoritme pathfinding yang diulas adalah algoritme A*dan A* smooth Algoritme A* memiliki fungsi heuristik. Algoritme A* smooth merupakan modifikasi dari algoritme A*. Algoritme A* smooth ini bekerja dengan melakukan modifikasi raycast A*. Algoritme A* memanfaatkan node dengan petak-petak kecil. Setiap algoritme ini diimplementasikan ke dalam game object Unity 3D. Setiap game object akan bergerak secara bersamaan untuk menuju titik tujuan dengan posisi awal dan tujuan yang berbeda-beda dengan menghindari banyak halangan. Hasil uji yang didapat adalah algoritme A* smooth lebih unggul dibandingkan dengan algoritme A* dan NavMesh. Waktu tempuh yang dibutuhkan game object dengan algoritme A* smooth lebih cepat 1,6 detik dan 9,6 detik dibandingkan dengan algoritme A* dan NavMesh.
Decoding brain tumor insights: Evaluating CAM variants with 3D U-Net for segmentation Hardani, Dian Nova Kusuma; Ardiyanto, Igi; Adi Nugroho, Hanung
Communications in Science and Technology Vol 9 No 2 (2024)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.9.2.2024.1477

Abstract

Brain tumor segmentation is critical for effective diagnosis and treatment planning. While, conventional manual segmentation techniques are seen inefficient and variable, highlighting the need for automated methods. This study enhances medical image analysis, particularly in brain tumor segmentation by improving the explainability and accuracy of deep learning models, which are essential for clinical trust. Using the 3D U-Net architecture with the BraTS 2020 dataset, the study achieved precise localization and detailed segmentation with the mean recall values of 0.8939 for Whole Tumor (WT), 0.7941 for Enhancing Tumor (ET), and 0.7846 for Tumor Core (TC). The Dice coefficients were 0.9065 for WT, 0.8180 for TC, and 0.7715 for ET. By integrating explainable AI techniques, such as Class Activation Mapping (CAM) and its variants (Grad-CAM, Grad-CAM++, and Score-CAM), the study ensures high segmentation accuracy and transparency. Grad-CAM, in this case, provided the most reliable and detailed visual explanations, significantly enhancing model interpretability for clinical applications. This approach not only enhances the accuracy of brain tumor segmentation but also builds clinical trust by making model decisions more transparent and understandable. Finally, the combination of 3D U-Net and XAI techniques supports more effective diagnosis, treatment planning, and patient care in brain tumor management.
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering Setiowati, Sulis; Adji, Teguh Bharata; Ardiyanto, Igi
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

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Abstract

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The KMeans+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.
Enhancing dermoscopic pigmented skin lesion classification: A refined approach using the pre-trained Inception-V3 architecture Nugroho, Erwin S.; Ardiyanto, Igi; Nugroho, Hanung A.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1852

Abstract

Skin cancer is one of the most prevalent cancers worldwide, with early diagnosis being critical for improving survival rates. Dermoscopy, a non-invasive imaging tool, is widely used for identifying pigmented skin lesions. However, its accuracy is heavily dependent on expert interpretation, which introduces variability and limits accessibility in resource-constrained settings. This highlighted the need for automated solutions to enhance diagnostic consistency and aid in early detection. The aim of this study was to develop a refined machine-learning framework for classifying pigmented skin lesions using dermoscopy images. We employed an enhanced Inception-V3 model, a state-of-the-art convolutional neural network, integrated with a simplified soft-attention mechanism, advanced data augmentation techniques, and Bayesian hyperparameter tuning. These innovations improved the model’s ability to accurately focus on and identify relevant lesion features, marking a significant advancement in the field. Using the ISIC-2019 dataset, a publicly available resource containing dermoscopy images classified into eight diagnostic categories, we implemented preprocessing steps such as resizing, cleaning, and data balancing. Additionally, ImageNet transfer learning and Bayesian optimization were applied to refine the model. The inclusion of a soft-attention mechanism further enhanced the model’s capacity to identify patterns within lesion images. Our model exhibited outstanding performance on the ISIC-2019 dataset, achieving a sensitivity of 98.5%, specificity of 99.62%, precision of 97.42%, accuracy of 97.38%, an F1 score of 97.34%, and an area under the curve (AUC) of 0.99. These metrics underscored the model’s superior capability in accurate and reliable classification of pigmented skin lesions, surpassing current benchmarks and demonstrating significant advancements over existing methodologies.
Evaluating the effectiveness of facial actions features for the early detection of driver drowsiness in driving safety monitoring system Rahmawati, Yenny; Woraratpanya, Kuntpong; Ardiyanto, Igi; Adi Nugroho, Hanung
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1594

Abstract

Traffic accidents caused by drowsiness continue to pose a serious threat to road safety. Many of these accidents can be prevented by alerting drivers when they begin to feel sleepy. This research introduces a non-invasive system for detecting driver drowsiness based on visual features extracted from videos captured by a dashboard-mounted camera. The proposed system utilizes facial landmark points and a facial mesh detector to identify key areas where the mouth aspect ratio, eye aspect ratio, and head pose are analyzed. These features are then fed into three different classification models: 1D-CNN, LSTM, and BiLSTM. The system’s performance was evaluated by comparing the use of these features as indicators of driver drowsiness. The results show that combining all three facial features is more effective in detecting drowsiness than using one or two features alone. The detection accuracy reached 0.99 across all tested models.
Co-Authors Adhistya Erna Permanasari Ahmad Fauzi Mabrur Ahmad Nasikun Aras, Rezty Amalia Atmaja Perdana, Chandra Ramadhan Dian Nova Kusuma Hardani Elfrida Ratnawati Erwin Setyo Nugroho Hanung Adi Nugroho Luky Hidayat Made Rahmawaty, Made Muhammad Fawaz Saputra Nasikun, Ahmad Noor Akhmad Setiawan Nugroho, Arseto Satriyo Nugroho, Erwin S. Nugroho, Hanung A. Nurcahyani Wulandari Nuryono, Aninditya Anggari Oktoeberza, Widhia KZ Persada, Anugerah Galang Rizky Naufal Perdana Rudy Hartanto Setialana, Pradana Silvester Tena Siti Helmyati Sulis Setiowati Sulis Setiowati Sunu Wibirama Teguh Bharata Adji Tri Lestari Woraratpanya, Kuntpong Yenny Rahmawati Yuli Triyani Yulyanti, Vesi