Article Per Year (5 Year)
p-Index From 2020 - 2025
2.873
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teknik Elektro dan Komputer TRIAC PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JHCLS SmartComp Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics INJECT Interdisciplinary Journal of Communication
Christian Sri kusuma Aditya, Christian Sri kusuma
Teknik Informatika, Institut Teknologi Sepuluh Nopember
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Implementasi Transfer Learning pada Convolutional Neural Network dengan Arsitektur VGG dalam Klasifikasi Down Syndrome di Asia Uwar, Tarissa Rizky Salsabiila; Aditya, Christian Sri Kusuma
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7150

Abstract

Early detection of Down syndrome is crucial for enabling early intervention and providing healthcare education for children. Down syndrome is associated with specific facial features, such as distinct characteristics of the eyes, nose, lips, face shape, hair, and skin color, which can be analyzed using computer vision techniques. This study aims to classify Down syndrome, especially in the Asian Region, which includes countries with medium/low SDI. The study proposed a CNN based on the VGG16 and VGG19 architectures by implementing transfer learning and augmentation. Augmentation is performed to balance the number of images between classes, while transfer learning is used to train the model first on ImageNet data. The dataset used consists of two categories, Down syndrome and Healthy. The results indicate that the VGG16 model has higher sensitivity and is able to classify more cases of Down syndrome, but has a fairly large prediction error. However, VGG19 model has a better specificity value and has a smaller potential for prediction error. The best model in this study was selected based on the highest validation accuracy value, where VGG19 achieved an accuracy of 93% in its best iteration, and VGG16 achieved an accuracy of 91%. These findings suggest that the proposed models, particularly VGG19, exhibit optimal performance in classifying Down syndrome, especially in the Asian region, with a lower prediction error rate.
Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree Nadya, Fhara Elvina Pingky; Ferdiansyah, M.Firdaus Ibadi; Nastiti, Vinna Rahmayanti Setyaning; Aditya, Christian Sri Kusuma
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48145

Abstract

Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.
Co-Authors Agus Zainal Arifin AINA, LISTYA NUR Aji, Bayu Puspito Alif Akbar Fitrawan, Alif Akbar ASHURI, PUTRI INTAN Atsqalani, Hasbi CAHYANI, INDAH ARDHIA Chandranegara, Didih Rizki Chastine Fatichah Diana Purwitasari Dinanthi, Devi Aprilya Evi Dwi Wahyuni Ferdiansyah, Danny Ferdiansyah, M.Firdaus Ibadi Firdausita, Nuris Sabila Fuad Nasvian, Moch Gayatri, Agnes Ayu Maharani Dyah Hamdan Nafiatur Rosyida Handoko, Chanavaro Bayu K., Halif Fachrizal Erliawan Kusumo, Wisnu Prayogo Lailiyah, Asmaul Mamluatul Hani’ah Muhammady, Dignifo Nauval Nadya, Fhara Elvina Pingky Nugraha, Haidar Aldy Eka Nur Hayatin Nur Putri Hidayah Purwandhani, Septiannisa Alya Shinta Ramadanti, Elisa S, Vinna Rahmayanti Sajiatmoko, Aletta Agigia Novta Salma, Rania Alfita Setyarini, Dela Ananda Sunan, Rohid Aji Ujilast, Novia Adelia Uwar, Tarissa Rizky Salsabiila Wicaksono, Galih Wasis Wiyono, Briansyah Setio Yuda Munarko Yufis Azhar