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All Journal Jurnal Teknik International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JPM (Jurnal Pemberdayaan Masyarakat) ILKOM Jurnal Ilmiah Computer Science and Information Technologies Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Mandiri Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Bandung Conference Series: Industrial Engineering Science Bandung Conference Series: Statistics Bulletin of Social Informatics Theory and Application Journal of Embedded Systems, Security and Intelligent Systems Linier: Literatur Informatika dan Komputer
Manga, Abdul Rachman
Univeristas Muslim Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Journal : computer science and information technologies

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Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset Poetri Lestari Lokapitasari Belluano; Reyna Aprilia Rahma; Herdianti Darwis; Abdul Rachman Manga
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p235-242

Abstract

This study aims to analyze the performance of various ensemble machine learning methods, such as AdaBoost, bagging, and stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that AdaBoost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with AdaBoost being an exception that shows a decrease in accuracy after applying imbalanced data methods.
Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets Abdul Rachman Manga; Aulia Putri Utami; Huzain Azis; Yulita Salim; Amaliah Faradibah
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p205-214

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.
Co-Authors Achrul Abdullah Admojo, Fadhila Tangguh Adriawan Amrullah Ali Munawir Aliefian Raflisyah Amaliah Faradibah Andi Anugrah Aqsa Andi Azizul Hidayat Andi Muh Afdal Andri Rajsya Anik Nur Handayani Anik Nur Handayani Asman Haris Aulia Putri Utami Aziz, Huzain Chaerun Niam Syah Dedy Atmajaya Dedy Atmajaya Dewi Widyawati Dolly Indra Dwiky Aries Nugraha Yusuf Fadlan Hasan Fadly Shabir Fahmi Faradibah, Amaliah Farniwati Fattah Harlinda Lahuddin Herawati Herawati Heru Wahyu Herwanto Heru Wahyu Herwanto Hilal Luthfi Hibatullah Huzain Azis Huzain Azis Ihwana As’ad Irawati Irawati Jalil, Rizqi Ananda JUMARLIS, MILA Kasmira, Kasmira Khalish Ghandur Syamsuddina L, Harlinda lilis nurhayati Listyan Nur Saida Lokapitasari Belluano, Poetri Lestari Mufti, Farid Wajdi Muh Fatwah Fajriansyah M Muh Fatwah Fajriansyah Marlang Muh Syawal Muh Yeyen Dwi Suherman Muhammad Alif Ambas Muhammad Arfah Asis Muhammad Arzhi Azis Muhammad Fiqram Muis, Ismunandar Nabila Vita Dewi Nanda, As'syahrin Nasir, Haidawati Nurhalima Nurhalima Purnawansyah Purnawansyah Rahma Puspitasari Rahma Puspitasari Rahmat A Ramdan Satra Reyna Aprilia Rahma Roesman Ridwan Raja Rosa Andrie Asmara Salim, Yulita Samsul Shabir, Fadly Siska Anraeni Siti Rahmi Kelilauw Sitti Nurhalimah Subhan Ardhiman Syafie, Lukman Syam, Aminurlah Syam, Aminurlah Syamsul Bahri Syamsumarlin Syamsumarlin Tasrif Hasanuddin Tenri Sa'nah Tri Anita Resky Ramadhani Utami, Aulia Putri Wahyu Kadri Rahmat Suat Suat Yudha Islami Sulistya Yulita Salim Yusrizal Damri Zulham Jaya Syafar