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Untoro, Meida Cahyo
Teknik Informatika, Institut Teknologi Sumatera
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Journal : IT JOURNAL RESEARCH AND DEVELOPMENT

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Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset Untoro, Meida Cahyo; Yusuf, Muhammad Asyroful Nur Maulana
IT Journal Research and Development Vol. 8 No. 1 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12412

Abstract

Classification is a model for making predictions based on existing data. Imbalanced data leads to misclassification or modeling errors where the data is not relevant and results in poor classification modeling. A poor classification model is caused by imbalanced data in the classification label, and there is a need for data balancing as a solution to resolve this issue. The methods used to handle data imbalance are Random Undersampling and MWMOTE. The goal is to see the implementation of Random Undersampling and MWMOTE working well in addressing the imbalanced dataset and to know the performance and accuracy in modeling. The dataset used is an open source dataset from Kaggle consisting of Diabetes data, Bank Turnover data, Stroke data, and Credit Card data with various data ratios, with the goal of addressing the problem of imbalanced data. Model evaluation was performed using the confusion matrix and decision tree algorithm by looking at the precision, recall, f-measure, and accuracy values from the Random Undersampling and MWMOTE methods. Random Undersampling can address the problem of imbalanced data with a precision of 76.28%, recall of 76.74%, f-measure of 76.48%, and accuracy of 76.21%. MWMOTE can address the problem of imbalanced data with a precision of 86.04%, recall of 87.30%, f-measure of 86.66%, and accuracy of 86.61%. It can be concluded that the MWMOTE method is better than the Random Undersampling method because the average evaluation of the confusion matrix of the Random Undersampling method is smaller than the MWMOTE method.
Co-Authors Afriansyah, Aidil Ahmad Agung Zefi Syahputra Aidil Afriasnyah Algifari, Muhammad Habib Amrulloh, Iqbal Anastasia Puteri Dewi Andika Setiawan Andika Setiawan, Andika Andini, Maria Anggraini, Leslie Annisa Dwi Atika Anugerah Perdana Aprilia Purwanto Aprilianda, Mohamad Meazza Arre Pangestu Athalla, Muhammad Nadhif Bagaskara, Radhinka Bangun, Natasya Ate Malem Ba’its, Alfian Kafilah Buliali, Joko Lianto Dani Al Mahkya Desi Budiarti Dharmawan, Benedictus Budhi Dian Anggraini Drantantiyas, Nike Dwi Grevika Eka Nur'azmi Yunira Eko Dwi Nugroho Eri Yuni Nilasari Faisal, Amir Faza Nur Fuadina Febrianto, Andre Feri Fahrianto Fery Widhiatmoko Fitrawan, Mhd. Kadar Gunawan, Rayhan Fatih Harmiansyah Hidayah, Fathan Rizki Ibn, Ferreyla Setara Ilham Firman Ashari Irawati, Febri Dwi Jerhi Wahyu Fernanda Kesuma, Alvin Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Leonard Rizta Listiani, Amalia M. Syamsuddin Wisnubroto Mahdia Nisrina Maharani M Mandiri, Tobyanto Putra Marbun, Rustian Afencius Maria Oktarise Natania Gultom Mastuti Widianingsih, Mastuti Muhammad Adam Aslamsyah Muhammad Affandi Muhammad Alfarizi Tazkia Muhammad Farhan Muhammad Muttaqin Muhammad Nadhif Athalla Muhammad Yusuf Muhammad Zulfarhan Najie, Muhammad Nasrulloh, M. Anas Nazla Andintya Wijaya Nestiawan Ferdiyanto Nur'azmi, Eka Nurul Fajrin Ariyani Oktaviana Rinda Sari Perdana, Agung Mahadi Putra Prabandari, Pungki Resti Praramadhana, Daffa Praseptiawan, Mugi Pungki Resti Prabandari Putri, Nabila Muthia Raidah Hanifah Raidah Hanifah Retnosari, Hesti Revangga, Dwi Arthur Riyanarto Sarno Samsu Bahri Sianturi, Elsa Elisa Yohana Sidabutar, Ribka Julyasih Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sofian, Ahmad Alif Sophia Nouriska Suranta, Akmal Fauzan Tirta Setiawan Verdiana, Miranti Winda Yulita Wisnubroto, M. Syamsuddin Yulita, Winda Yunira, Eka Nur'azmi Yusuf, Muhammad Asyroful Nur Maulana