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All Journal Techno.Com: Jurnal Teknologi Informasi EMARA : Indonesian Journal of Architecture JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMATICS ComTech: Computer, Mathematics and Engineering Applications Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurnal Informatika dan Sistem Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Buletin Poltanesa Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) Eduvest - Journal of Universal Studies EMARA : Indonesian Journal of Architecture
Daniel Martomanggolo Wonohadidjojo
Universitas Ciputra Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Neuroscience Social Sciences

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Sistem Kendali Jarak Jauh untuk Smart Home Melalui Aplikasi Android Menggunakan NodeMCU dan Firebase Wonohadidjojo, Daniel Martomanggolo; Santoso, Hansel
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1285

Abstract

Smart home merupakan rumah dengan beberapa perangkat pintar yang terpasang di dalam rumah. Perangkat pintar ini merupakan bagian dari Internet of Things (IoT), dengan tujuan untuk membantu pemilik rumah dalam memantau dan mengendalikan keadaan dalam rumah. Perangkat pintar dapat dioperasikan oleh pemilik rumah di dalam maupun di luar rumah dengan menggunakan cloud storage. Dalam penelitian ini hal tersebut diimplementasikan dengan perangkat keras NodeMCU. Setiap sensor yang terhubung dengan NodeMCU mengirimkan data ke pusat untuk dikirimkan ke cloud storage yang bernama Firebase menggunakan metode WebSocket. Data yang disimpan di Firebase akan dikirim ke aplikasi Android yang dibangun menggunakan Flutter. Dengan demikian sistem ini mampu membantu pemilik rumah untuk memantau dan mengendalikan perangkat elektronik di dalam rumah menggunakan aplikasi Android dimana Aplikasi ini dihubungkan dengan NodeMCU menggunakan cloud real-time database.
CLASSIFICATION OF BONE FRACTURES IN THE WRIST AND HAND USING DENSENET AND XCEPTION Nusantara, Michelle Swastika Bianglala; Wonohadidjojo, Daniel Martomanggolo
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9201

Abstract

This study aims to apply Convolutional Neural Network (CNN) using DenseNet and Xception to classify fracture in the wrist and hand bones, while utilizing transfer learning to enhance model's performance. Accurate diagnosis and successful treatment of bone fractures depend on early identification, which lowers the likelihood of long-term issues such avascular necrosis or non-union. The research utilized data from two publicly available musculoskeletal radiography datasets and employed deep learning techniques with the Keras framework. DenseNet was selected for wrist image analysis due to its dense connectivity, which preserves information from previous layers, while Xception was chosen for hand bone image analysis because of its ability to identify complex patterns using depthwise separable convolutions. Transfer learning was implemented to accelerate training and improve accuracy. The DenseNet model achieved a test accuracy of 97.5% for wrist classification, while the Xception model reached 92% accuracy for hand bone classification. By tailoring CNN architectures to specific radiographic images and employing transfer learning, this study demonstrates significant potential for improving diagnostic precision in clinical situations. Furthermore, the findings can support medical personnel in detecting bone fractures more efficiently and accurately, ultimately expediting clinical decision-making and improving patient care.
A Comparative Analysis of Deep Learning Models for Knee Osteoarthritis Severity Grading Mulijono, Steffany Florence Sugiarto; Wonohadidjojo, Daniel Martomanggolo
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14967

Abstract

The Kellgren-Lawrence (KL) grading system is commonly used to evaluate knee osteoarthritis (OA), but it can be subjective and subject to variation among assessors. Our study looked at three Convolutional Neural Network (CNN) methods for OA severity classification from a dataset of 15,770 X-ray images to overcome this difficulty and create a more objective technique. Under the same preprocessing conditions, we contrasted a baseline custom CNN, DenseNet201, and a hybrid model with a CBAM attention mechanism. With an overall accuracy of 65%, a weighted precision and recall of 65%, and an F1-score of 64%, the hybrid model, which uses DenseNet201 as a fixed feature extractor, performed the best. This was better than both the baseline model (59% accuracy) and the standalone DenseNet201 (59% accuracy). Although the hybrid architecture has a lot of promise, we also had to deal with issues like overfitting. Our thorough comparison demonstrates how this hybrid strategy can successfully combine strong pre-trained features with the flexibility required for particular tasks. Although more clinical validation is necessary, this shows that automated systems like ours could improve diagnostic consistency in OA grading.   Keywords - Knee Osteoarthritis, Kellgren-Lawrence Grading, Deep Learning, Attention Mechanism, CBAM
DETECTION AND CLASSIFICATION OF GRAM-STAINED BACTERIA IN MICROSCOPIC IMAGES USING YOLOV8 WITH CBAM Sanjaya, Karyna Budi; Wonohadidjojo, Daniel Martomanggolo
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10891

Abstract

Bloodstream infection accounts for approximately 11 million deaths annually, and yet conventional blood culture methods require 40-48 hours to complete pathogen identification which delays definitive therapeutic decisions. Gram staining does provide preliminary bacterial classification within hours, but manual interpretation still remains a labor-intensive task and is prone to variability. This study develops an automated bacterial detection and classification system by integrating CBAM into the YOLOv8 architecture. The model was trained on Gram-stained microscopic images across four bacterial categories: Gram-positive cocci, Gram-negative cocci, Gram-positive bacilli, and Gram-negative bacilli. Dataset preprocessing involved quality selection, noise reduction, and targeted augmentation to address severe class imbalances. The inclusion of CBAM improved feature discrimination and localization performance, with an increase of 1.4% in mAP@0.5:0.95 (from 70.8% to 72.2%). The proposed model also reduced cross-class misclassifications, particularly among morphologically similar cocci. These findings demonstrate that integrating lightweight attention mechanisms can enhance bacterial detection reliability in microscopic imaging and support the development of automated systems for faster, more consistent preliminary bacterial identification.
Co-Authors Citra, Caecilia Citra Claudia Kenyta Dyah Kusuma Wardhani Dyah Kusuma Wardhani Eric Harianto Hans Richard Alim Natadjaja Hansel Santoso Indra Maryati Jiemesha, Micheila Jonathan Andrei Wibawa Kurniawan, Ardian Laura Mahendratta Tjahjono Lestari, Caecilia Citra Likorawung, Marsha Alexis Mellysa Margarita Susilo Mulijono, Steffany Florence Sugiarto Mychael Maoeretz Engel Natadjaja, Hans Richard Alim Nehemia Sugianto Nusantara, Michelle Swastika Bianglala Rinabi Tanamal Sanjaya, Karyna Budi Santoso, Hansel Sugianto, Nehemia Susan Susilo, Mellysa Margarita Wiradinata, Marcell Jeremy Yosua Setyawan Soekamto Yusuf Ariyanto Yuwono Marta Dinata