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All Journal Jurnal Pseudocode Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Information System Journal (INFOS) Jurnal Pengabdian Mitra Masyarakat (JPMM) International Journal of Advanced Science Computing and Engineering Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Yoga Pristyanto
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Klasifikasi Penyakit Diabetes Pada Imbalanced Class Dataset Menggunakan Algoritme Stacking Yoga Pristyanto; Acihmah Sidauruk; Atik Nurmasani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3442

Abstract

Diabetes is a disease that has the potential to cause death. Based on a report from the IDF (International Diabetes Federation), it was stated that in 2019 there were 463 million people in the world suffering from this disease. According to the Ministry of Health, Indonesia is a country that is included in the top 10 highest in the world by the number of people with diabetes. Machine learning models can be a solution for the early detection of diabetes based on history and available data. The majority of the research that has been done chiefly uses a single classifier. The single classifier model has a weakness when faced with class imbalance conditions in the dataset. Therefore, this study uses the Stacking Model for the classification and prediction process on the diabetes dataset. The goal is to improve the performance of a single classifier. In addition, the Stacking Model is expected to be one of the solutions for the classification of diabetes in the imbalanced class dataset. Based on two test experiments that have been carried out using two different datasets. The Stacking algorithm can produce an accuracy value of 89%, TPR value of 89%, TNR value of 85%, and G-Mean of 86.98% in the first dataset and can produce an accuracy value of 96%, TPR value of 96%, TNR value of 94%, and G-Mean of 94.99% in the second dataset. These results are better than single classifiers such as C4.5, K-NN, and SVM of the four indicators evaluated in both diabetes datasets. Thus, the proposed algorithm, namely Stacking (C4.5-SVM), can be a solution for classifying diabetes datasets with unbalanced class distribution conditions.
Klasifikasi Data Review IMDb Berdasarkan Analisis Sentimen Menggunakan Algoritma Support Vector Machine Gita Cahyani; Wiwi Widayani; Sharazita Dyah Anggita; Yoga Pristyanto; Ikmah Ikmah; Acihmah Sidauruk
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4023

Abstract

Advances in Web 2.0 technology encourage the creation of personal website content involving sentiments such as blogs, tweets, web forums, and various types of social media. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved, nominations received, and reviews from visitors. The number of movies and reviews on IMDb causes users or visitors to check the reviews to find out the film rating, so it takes time for users who have no experience using IMDb. Sentiment analysis can be a solution to label positive and negative reviews. One of the algorithms used in sentiment analysis is the Support Vector Machine (SVM) algorithm. This study aimed to test the accuracy of the SVM algorithm in the classification of sentiment review films on IMDb. The tests carried out using the Support Vector Machine algorithm resulted in an accuracy value of 86.5%. The SVM algorithm can also produce a precision value of 90.67% and a recall value of 91.62%.
Co-Authors Acihmah Sidauruk Aditya Ahmad Zein Aditya Yoga Pratama Anggita, Sharazita Dyah Anna Baita Atik Nurmasani Atik Nurmasani Cherfly Kaope Dipa Wirantanu Eza Nanda Fadhilah Dwi Ananda Gagah Gumelar Gita Cahyani Heri Sismoro Ikmah Ikmah Irfan Pratama Istikomah Lucky Adhikrisna Wirasakti Lucky Adhikrisna Wirasakti Marcheilla Trecya Anindita Mulia Sulistiyono Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Sumarni Adi Wiwi Widayani Zulfikar Mukarabiman