Article Per Year (5 Year)
p-Index From 2021 - 2026
3.426
P-Index
This Author published in this journals
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

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Insyst : Journal of Intelligent System and Computation

 1 
Classification of Skin Diseases Using Transfer Learning with ResNet-50 Architecture and Data Preprocessing Using Real-ESRGAN and Wiener Filter Jiemesha, Micheila; Wonohadidjojo, Daniel Martomanggolo
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.399

Abstract

The skin is a vital organ which serves as a barrier against external factors, yet it’s highly susceptible to diseases. These diseases are often presented as lesions with similar appearances, making it difficult to be diagnosed and prone to human errors. To address this challenge, this study uses Deep Learning, particularly the ResNet-50 architecture using Transfer Learning, to classify skin diseases from lesion. In this study, data augmentation is implemented to increase dataset size, thus improving model performance and preventing overfitting. Data is then preprocessed using Real-ESRGAN to enhance resolution and the Wiener Filter to sharpen the features. Adam optimizer is used to further enhance the model’s performance. Hyperparameter tuning is also implemented to optimize the model parameters, and dropout regularization is applied to enhance the model's ability to be able to accurately classify unseen data. The model managed to achieve a high accuracy of 99.09%, with 0.96 precision, 0.95 recall, and 0.95 F1-score. These results demonstrate the effectiveness of combining Real-ESRGAN and Wiener Filter with the ResNet-50 architecture and the Adam optimizer in developing a robust model for skin disease classification. This approach offers a promising tool for healthcare professionals which may help reduce human error in dermatological diagnosis.
Sistem Deteksi dan Klasifikasi Truk Air Menggunakan YOLO v5 dan EfficientNet-B4 Kurniawan, Ardian; Wonohadidjojo, Daniel Martomanggolo
Intelligent System and Computation Vol 5 No 2 (2023): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v5i2.356

Abstract

Kegiatan pencatatan dalam usaha pengisian air yang dilakukan dengan menggunakan truk air mengalami masalah karena kesalahan manusia (human error) misalnya pencatatan yang terlewat dan efisiensi waktu yang diperlukan. Untuk itu diperlukan otomatisasi sistem dengan menggunakan teknologi. Untuk mengatasi masalah tersebut, pada penelitian ini digunakan metode yang termasuk dalam Computer Vision dengan penggunaan algoritma Object Detection dan Classification. Pada penelitian ini dibangun suatu sistem yang mengambil frame video menggunakan CCTV yang kemudian dimasukkan pada algoritma object detection dengan arsitektur YOLOv5 (You Only Look Once versi 5). Hasil deteksi kemudian diklasifikasikan  dengan menggunakan algoritma dengan arsitektur EfficientNet-B4. Hasil klasifikasi tersebut akan menentukan secara spesifik truk air yang mana yang sedang melakukan pengisian dan dicatat. Kemudian rekapitulasi hasil pencatatan tersebut dikirimkan dengan menggunakan aplikasi messaging Telegram menggunakan library Tkinter kepada pemilik usaha yang mengambil air tersebut. Rekapitulasi tersebut kemudian digunakan oleh sang pemilik usaha dalam memantau usaha tersebut dan melakukan pembayaran sesuai dengan jumlah pengambilan air. Hasil pengujian untuk object detection dan classification dengan menggunakan evaluation metrics menunjukkan bahwa metode tersebut berhasil melakukan deteksi dan klasifikasi dengan baik. Pengujian keseluruhan sistem menunjukkan bahwa semua tahap pengujian berhasil dilakukan dengan baik. Hal ini menunjukkan bahwa sistem tersebut dapat digunakan untuk mengatasi masalah yang dihadapi.
Implementation of DenseNet Architecture With Transfer Learning to Classify Mango Leaf Diseases Likorawung, Marsha Alexis; Wonohadidjojo, Daniel Martomanggolo
Intelligent System and Computation Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i2.401

Abstract

Mango plants (Mangifera indica) are a significant export commodity in the horticultural industry, offering numerous nutritional and economic benefits. They are rich in essential micronutrients, vitamins, and phytochemicals, contributing to their high demand globally. However, mango plants are susceptible to various diseases that can severely impact their yield and quality. These diseases pose a challenge to mango farmers, many of whom struggle to identify and treat them effectively, leading to potential harvest failures. This study aims to address this challenge by implementing a Deep Learning approach to classify diseases in mango leaves. Specifically, the research utilizes a Convolutional Neural Network (CNN) with DenseNet architecture, known for its efficiency in image classification tasks. The study incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing to enhance detail and improve the model’s performance. Transfer Learning is utilized to optimize the DenseNet model, leveraging a pre-trained model to achieve high accuracy even with a relatively small dataset. The dataset used in this research comprises 4000 labeled images of mango leaves, covering seven disease categories and healthy leaves. These images include common diseases such as Anthracnose, Dieback, Powdery Mildew, Red Rust, Cutting Weevil, Bacterial Canker, and Sooty Mould. The DenseNet model achieved an overall accuracy of 99.5% in classifying mango leaf diseases.
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