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All Journal EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JAREE (Journal on Advanced Research in Electrical Engineering) Geoid - Journal of Geodesy and Geomatics
Rachmadi, Reza Fuad
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science

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Klasifikasi Citra Satelit menggunakan Lightweight Ensemble Convolutional Network Rachmadi, Reza Fuad; Prioko, Kentani Langgalih; Nugroho, Supeno Mardi Susiki; Purnama, I Ketut Eddy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14346

Abstract

Citra satelit dapat digunakan salah satunya sebagai pengamatan kondisi atmosfer dan permukaan pada bumi. Dengan semakin berkembangnya teknologi citra satelit, waktu untuk pengambilan citra satelit menjadi lebih efisien. Makalah ini melakukan eksperimen menggunakan klasifier ensemble convolutional network untuk melakukan pengenalan kondisi atmosfer pada citra satelit. Empat buah arsitektur Convolutional Neural Network (CNN) digunakan dalam eksperimen ini, yaitu MobileNetV2, ResNet18, ResNet18Half, dan SqueezeNet. Keempat arsitektur CNN tersebut dipilih karena mempunyai jumlah parameter yang tidak terlalu besar (lightweight) serta dapat diterapkan pada banyak perangkat keras tertanam. Eksperimen yang dilakukan dengan menggunakan dataset USTC SmokeRS memperlihatkan bahwa klasifier ensemble memperoleh hasil yang baik dengan akurasi rata-rata tertinggi sebesar 97.06 %.
Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image Rumala, Dewinda Julianensi; Rachmadi, Reza Fuad; Sensusiati, Anggraini Dwi; Purnama, I Ketut Eddy
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.853

Abstract

Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.
Tracking Socer Player Based on Deepsort Algorithm with YOLOV8 FrameWork Khabibullah, Zein Bilal; Yuniarno, Dr. Eko Mulyanto; Rachmadi, Reza Fuad
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 9, No 1 (2025): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i1.413

Abstract

Abstract—Tracking is a set procedure that entails assigninganidentificationtoacertainobjectandsubsequentlycon-sistently recognizing that object without altering the assignedidentification over a sequence of frame images and associatingitaccordingly.Whenperformingresearchonobjecttracking,especially in sports where the object of interest is a human, aresilient technology is necessary to facilitate the tracking process.When the state-of-the-art object detection approach, YOLOV8,is combined with the DeepSORT algorithm, it is anticipated toproduce highly accurate and exact outcomes in the trackingand detection of objects. Challenges in multi-object trackinginclude robustness, oculusion, and identity shifts. In our research,we take advantage of a fusion of YOLOV8 and DeepSORTalgorithmstoachieveahighlyreliableandprecisetrackingsolution. The implementation of the Kalman filter-based motionprediction in DeepSORT allows for the achievement of smoothtrajectories, whereas the YOLOV8 deep neural network usedassists in precisely recognizing the appearance of objects on thefield. The result of our experiment shown the tracking we get is38% HOTA, 47% DetA, 31% AssA, 68% DetPre, 35% AssRE,61% AssPr amd 79% LOcA.Index Terms—Tracking, DeepSORT, YOLO, MOT, Socce
Incremental Learning Approaches for Dermoscopic Image Classification in Teledermatology Hernanda, Arta Kusuma; Asayanda, Fikra Agha Rabbani; Ait-Souar, Iliès; Rachmadi, Reza Fuad; Purnama, I Ketut Eddy
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3016

Abstract

This study investigates the application of incremental learning techniques to enhance the classification of skin diseases in dermoscopic images. The research aims to develop a model capable of continuous adaptation to new data while retaining previously acquired knowledge. Two datasets were utilized: acne images and the HAM10000 dataset comprising various skin lesions. The methodology involved initially training a ResNet-18 convolutional neural network on 1,052 samples across eight classes, followed by an incremental learning phase incorporating 800 additional data points. Rigorous preprocessing steps were implemented to ensure data quality, including cropping, resizing, and normalization. Results demonstrate that the base model achieved 87% accuracy on the test set, which improved to 90% after the incremental learning process. Detailed analysis revealed significant improvements in precision, recall, and F1-scores for several skin disease classes, notably for challenging categories such as Basal Cell Carcinoma (bcc) and Dermatofibroma (df). Confusion matrix analysis and Grad-CAM visualizations provided insights into the model's decision-making process and its focus on clinically relevant features. The study also implemented a Streamlit application to demonstrate real-time classification capabilities and the system's adaptability in a simulated clinical environment. These findings have potential clinical applications, particularly in teledermatology systems where adaptive algorithms can accommodate new dermatological data over time. The study highlights the potential of incremental learning in creating accurate, adaptable, and clinically relevant AI models for skin disease classification in evolving medical practices.
Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework Sasmita, Wirat Moko Hadi; Sumpeno, Surya; Rachmadi, Reza Fuad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6293

Abstract

Helpdesk services are an important component in supporting Information Technology (IT) services. The helpdesk operates as the initial interface for managing and resolving concerns. Helpdesk helps user to get solutions when facing problems while using an IT service. This research focuses on the impact of artificial intelligence (AI)-powered chatbots on the performance of the initial response of government helpdesk services. The chatbot is designed to improve service performance by quickly identifying and classifying reported issues and automatically responding to messages, enabling faster responses. The research proposed a new System Design of a helpdesk system with an AI-based chatbot. The data used comes from Telegram group chat logs, exported in JSON format. We find that the Rasa NLU model with DIET Classifier successfully achieved an accuracy rate of 0.825 in classifying intents, with the precision value of 0.838, recall of 0.829, and F1 score of 0.821 using a Rasa model with cross-validation, where folds is 5 in evaluation. And initial response time was highly improved after using chatbot artificial intelligence from more than 3 hours on the telegram group helpdesk based to an average of 2.15 seconds. These research results suggest AI-Chatbot-based ability to assist the helpdesk team in handling user queries and reports, and improving initial time response.
Penggunaan Deep Learning dan Post-Processing Algoritma Douglas-Peucker untuk Ekstraksi Jaringan Jalan pada Area Urban dari Orthophoto Bimanjaya, Alfian; Handayani, Hepi Hapsari; Rachmadi, Reza Fuad
GEOID Vol. 19 No. 2 (2024)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v19i2.1127

Abstract

Peta dasar skala besar sangat dibutuhkan oleh kota besar/metropolitan seperti Kota Surabaya untuk perencanaan kota dan menunjang pembangunan kota cerdas. Beberapa informasi utama yang paling dibutuhkan dari peta skala besar adalah fitur bangunan dan jaringan jalan. Ekstraksi jaringan jalan merupakan pekerjaan yang menantang karena banyak alasan, termasuk sifat heterogen dari geometri dan spektral, kompleksitas objek yang sulit dimodelkan, dan data sensor yang kurang baik. Intepretasi yang dilakukan oleh operator secara visual masih merupakan pendekatan yang umum digunakan untuk ekstraksi informasi dari orthophoto. Akurasi intepretasi yang dihasilkan tergantung pada keterampilan dan pengalaman dari operator. Sehingga, dapat terjadi inkonsistensi pada data yang dihasilkan oleh operator yang berbeda. Beberapa tahun terakhir ini, ekstraksi otomatis jalan dari orthophoto maupun CSRT menjadi isu penelitian penting dan menantang yang mendapat perhatian lebih besar. Dalam penelitian ini, penulis menerapkan metode deteksi objek berbasis Mask Region-based Convolutional Neural Network (Mask R-CNN) untuk ekstraksi jaringan jalan memanfaatkan orthophoto dan DSM LiDAR di daerah urban Kota Surabaya. Beberapa strategi dirancang dan digabungkan dengan model deteksi objek berbasis Mask R-CNN, termasuk post-processing yang terdiri dari regularisasi poligon algoritma Douglass-Peucker, remove overlap, fill gap, dan penghalusan poligon. Metode yang penulis terapkan menghasilkan kinerja yang cukup baik untuk ekstraksi jalan menghasilkan nilai presisi 90,28%; kelengkapan (recall) 85,85%; skor-F1 88,01%; dan IoU 78,59%; serta overall accuracy 95,25 % dan nilai kappa 90,5%.
Co-Authors Ait-Souar, Iliès Asayanda, Fikra Agha Rabbani Bimanjaya, Alfian Hepi Hapsari Handayani, Hepi Hapsari Hernanda, Arta Kusuma I Ketut Eddy Purnama Khabibullah, Zein Bilal Prioko, Kentani Langgalih Rumala, Dewinda Julianensi Sasmita, Wirat Moko Hadi Sensusiati, Anggraini Dwi Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Yuniarno, Dr. Eko Mulyanto