Article Per Year (5 Year)
p-Index From 2021 - 2026
4.118
P-Index
This Author published in this journals
All Journal Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika CommIT (Communication & Information Technology) Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Egia Rosi Subhiyakto, Egia Rosi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Klasifikasi Kanker Kulit menggunakan Convolutional Neural Network dengan Optimasi Arsitektur Sinaga, Jesica Trivena; Faudyta, Haniifa Aliila; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6141

Abstract

Skin cancer is a severe condition characterized by the abnormal growth of skin cells, often triggered by ultraviolet exposure and genetic factors. Early detection of skin cancer is essential for improving patient recovery rates, given the high incidence and significant impact of the disease. This study aims to develop a skin cancer classification system using the Convolutional Neural Network (CNN) method with the VGG-16 architecture, known for its effectiveness in medical image analysis. The CNN method was chosen because it can extract complex features from images. At the same time, the VGG-16 architecture was selected for its depth and ability to capture fine details in images—critical for distinguishing between types of skin cancer. The dataset was sourced from the ISIC platform and optimized through data augmentation techniques to address data imbalance issues. The research results indicate that while a basic CNN can provide good accuracy, implementing the VGG-16 architecture significantly increases accuracy. The basic CNN model achieved a training accuracy of 95.68% and a validation accuracy of 89.83%, whereas the CNN with VGG-16 reached a training accuracy of 96.21% and a validation accuracy of 90.89%. These findings suggest that combining CNN with VGG-16 effectively detects skin cancer, with VGG-16 providing a slight accuracy improvement, highlighting this architecture's potential as a more accurate tool to support skin cancer diagnosis.
Implementasi Transfer Learning Menggunakan Convolutional Neural Network untuk Deteksi Jenis Kulit Wajah Septiani, Karlina Dwi; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6154

Abstract

In Indonesia, extreme tropical climate conditions with high humidity and sun exposure increase the risk of facial skin problems for the community. Facial skin that is not properly cared for is often prone to disorders, ranging from dry skin, oily skin, to acne. However, Indonesian people's awareness of the importance of maintaining healthy skin is still relatively low, which is exacerbated by limited time and access to consult a dermatologist. Most people may not know their skin type, even though each skin type requires different care to stay healthy and avoid more serious skin problems. To answer this problem, this study aims to develop an iOS-based application that is able to automatically detect facial skin types using transfer learning with a Convolutional Neural Network (CNN) architecture. The model was developed by training a dataset of facial images to classify skin types such as dry, oily, normal, and acne-prone, and integrated into an iOS application for real-time analysis through user facial images. The evaluation results showed a model accuracy of 87% and an application accuracy of 83.3% in identifying facial skin types. It is hoped that this application will help Indonesian people better understand their skin conditions and obtain appropriate treatment recommendations to maintain healthy skin in a tropical climate.
Perbandingan Kinerja Algoritma K-Nearest Neighbors dan Decision Tree untuk Klasifikasi Diabetes Yunianto, Amar Haris; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6550

Abstract

Diabetes is a chronic metabolic disease that is a major concern in global health due to its increasing prevalence, including in Indonesia, with significant impacts on individual health and health systems. This study aims to compare the performance of K-Nearest Neighbors (KNN) and Decision Tree (DT) algorithms in diabetes classification using the Pima Indians Diabetes Database (PIDD) dataset. Research methods include data collection, pre-processing, missing value handling, outlier detection and handling, and data balancing techniques using Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalance in the dataset. Model implementation is done by optimizing parameters using GridSearchCV, while performance evaluation is done based on accuracy, precision, recall, and F1 score matrices. The results show that the DT algorithm has superior performance compared to KNN, both without SMOTE and with SMOTE. In the model without SMOTE, DT achieved 85.71% accuracy, while KNN only reached 83.12%. After applying SMOTE, the performance of both algorithms improved significantly, with DT achieving 92% accuracy, 94% precision, 90.38% recall, and 92.16% F1 score, while KNN achieved 91% accuracy, 96.59% recall, and 90.43% F1 score. This study revealed that the use of SMOTE effectively improved the model's performance in handling data imbalance, while the DT algorithm showed better performance stability. These findings are expected to make a significant contribution to the development of more accurate prediction models for diabetes diagnosis, while enriching insights into the application of machine learning in the healthcare field.
Co-Authors Abiyyi, Ryandhika Bintang Abu Salam Adhitya Nugraha Andreas Wilson Setiawan Ardytha Luthfiarta Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Bahres, Salim Bin Sulaiman Cahyati, Ade Puput Catur Supriyanto Dzaky, Azmi Abiyyu Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faradeya, Muhammad Az-Zauqy Faudyta, Haniifa Aliila Fauzi Adi Rafrastara Guruh Fajar Shidik Hafidhoh, Nisa'ul Hapsari, Swita Amalia Hulaifah Al Abrori, Zahra Zul Ika Novita Dewi Iskandar, Marcelino Junta Zeniarja Kurniawan, Defri Kusumasari, Dinita Liya Umaroh, Liya Manurung, Ayub Michaelangelo Miyanthi, Sheryn Aulia Mutia Rahmi Pratiwi Ningrum, Novita Kurnia Ningrum, Novita Kurnia Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Paramita, Cinantya Prajanto Wahyu Adi, Prajanto Wahyu Prihatmoko, Radig Gedhe Pulung Nurtantio Andono Puspitaningrum, Hapsari Retno Rahadian, Arief Rohman, Muhammad Syaifur Sabilillah, Ferris Tita Safina, Nabila Safina, Nabila Santoso, Ilham Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Simbolon, Calvin Samuel Sinaga, Jesica Trivena Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Triono, Justin Matthew Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Widi Utomo, Emanuel Pinesthi Winarsih, Nurul Anisa Sri Yunianto, Amar Haris