Article Per Year (5 Year)
p-Index From 2021 - 2026
3.428
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) POROS TEKNIK JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Repositor Jurnal Perempuan & Anak Jurnal Impresi Indonesia Makara Journal of Technology Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Chandranegara, Didih Rizki
Universitas Muhammadiyah Malang
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Diabetes Detection Using Extreme Gradient Boosting (XGBoost) with Hyperparameter Tuning Dinanthi, Devi Aprilya; Ramadanti, Elisa; Aditya, Christian Sri Kusuma; Chandranegara, Didih Rizki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 2 (2024): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/qr3hw926

Abstract

Diabetes is a serious condition that can lead to fatal complications and death due to metabolic disorders caused by a lack of insulin production in the body. This study aims to find the best classification performance on diabetes dataset using Extreme Gradient Boosting (XGBoost) method. The dataset used has 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE, and hyperparameter optimization is performed using GridSearchCV and RandomSearchCV. Model evaluation was performed using confusion matrix as well as metrics such as accuracy, precision, recall, and F1-score. The test results show that the use of GridSearchCV and RandomSearchCV for hyperparameter tuning provides good results. The application of data resampling also managed to improve the overall model performance, especially in the XGBoost method that has been optimized using GridSearchCV, which achieved the highest accuracy of 85%, while XGBoost with RandomSearchCV optimization showed 83% accuracy performance.
User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor Chandranegara, Didih Rizki; Ashari, Anzilludin; Sari, Zamah; Wibowo, Hardianto; Suharso, Wildan
Makara Journal of Technology Vol. 27, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mouse dynamics authentication is a method for identifying a person by analyzing the unique pattern or rhythm of their mouse movement. Owing to its distinctive properties, such mouse movements can be used as the basis for security. The development of technology is followed by the urge to keep private data safe from hackers. Therefore, increasing the accuracy of user classification and reducing the false acceptance rate (FAR) are necessary to improve data security. In this study, we propose to combine the K-nearest neighbor method and simple random sampling and obtain a sample from a dataset to improve the classification of users and attackers. The results show that our proposed method has high accuracy for implement to practical system and reports the best results than previous research with a FAR of 0.037. Therefore, this method can be implemented in a real login system. The high false rejection rate of our proposed method will not be a problem because the most important thing in the login system is denying the attacker system access.
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Chandranegara, Didih Rizki; Vitanti, Vizza Dwi; Suharso, Wildan; Wibowo, Hardianto; Arifianto, Sofyan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1728

Abstract

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.
Co-Authors Agus Eko Minarno Agus Zainal Arifin Ahmad Hanif Nurfauzi Akbar, Muhammad Sulthoni Akbi, Denar Regata Aminudin, Aminudin Aprilya Dinathi, Devi Ashari, Anzilludin Aulyah, Khairunnisa Azzahra, Kiara christian s.k.aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma christianskaditya Dewantoro, Muhammad Bagas Dinanthi, Devi Aprilya Ekanita Rakhmah Eko Budi Cahyono Elan Cahya Niswary Elsandy Wirahadi Kusumah Fachry Abda El Rahman Ferdiansyah, Danny Gayatri, Agnes Ayu Maharani Dyah Hanif, Lathifah Hardianto Wibowo Heni Pujiastuti Ianatut Thoifah Imron, Moch Khasanah, Rahayu Nurul L. Yasril Imam Lathifah Hanif Lathifah, Fildzah Moch Imron Moch. Rosid Noviansyah Ningsih Djamsi Novidianto, Raditya Nuryasin, Ilyas Raffi Ainul Afif Rahayu Nurul Khasanah Rakhmah, Ekanita Ramadanti, Elisa Rista Azizah Arilya Rizaldi, Muhammad Ikhwananda Rizky, Wahyu Saiful Amien Sari, Zamah Setiono, Fauzan Adrivano Setyarini, Dela Ananda Sofyan Arifianto Sumadi, Fauzi Dwi Setiawan Vallent Austin Theasar Kurniawan Vitanti, Vizza Dwi Wanvy Arifha Saputra Wicaksono, Galih Wasis Wildan Suharso Wildan Suharso Yuda Munarko Yufis Azhar Zamah Sari