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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Pendidikan Indonesia Sistemasi: Jurnal Sistem Informasi Knowledge Engineering and Data Science Jurnal Teknologi Informasi dan Pendidikan Sains, Aplikasi, Komputasi dan Teknologi Informasi International Journal of Applied Business and Information Systems Belantika Pendidikan AGRITEXTS: Journal of Agricultural Extension JIPB (Jurnal Inovasi Pembelajaran Biologi) International Journal of Robotics and Control Systems Journal of Disruptive Learning Innovation (JODLI) JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Jurnal Manajemen Dan Akuntansi Medan Central Community Development Journal Jurnal Pengabdian Kepada Masyarakat
Muhammad Iqbal Akbar
Universitas Negeri Malang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology 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 Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : knowledge engineering and data science

 1 
The Effect of Resampling on Classifier Performance: an Empirical Study Utomo Pujianto; Muhammad Iqbal Akbar; Niendhitta Tamia Lassela; Deni Sutaji
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p87-100

Abstract

An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier used in the classification process is Naïve Bayes Classifier, Decision Tree, and Neural Network. The classification results in accuracy, precision, recall, and f-measure values are tested using paired t-tests to determine the significance of the classifier's performance from datasets that were not resampled and those that had applied the resampling. The paired t-test is also used to find a combination between the classifier and the resampling that gives significant results. This study obtained two results. The first result is that resampling on imbalanced class datasets can substantially affect the classifier's performance more than the classifier's performance from datasets that are not applied the resampling technique. The second result is that combining the Neural Network Algorithm without the resampling provides significance based on the accuracy value. Combining the Neural Network Algorithm with the SMOTE technique provides significant performance based on the amount of precision, recall, and f-measure.
Can Multinomial Logistic Regression Predicts Research Group using Text Input? Harits Ar Rosyid; Aulia Yahya Harindra Putra; Muhammad Iqbal Akbar; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p150-159

Abstract

While submitting proposals in SISINTA, students often confuse or falsely submit their proposals to the less relevant or incorrect research group. There are 13 research groups for the students to choose from. We proposed a text classification method to help students find the best research group based on the title and/or abstract. The stages in this study include data collection, preprocessing data, classification using Logistic Regression, and evaluation of the results. Three scenarios in research group classification are based on 1) title only, 2) abstract only, and 3) title and abstract. Based on the experiments, research group classification using title-only input is the best overall. This scenario gets the most optimal results with accuracy, precision, recall, and f1-score successively at 63.68%, 64.91%, 63.68%, and 63.46%. This result is sufficient to help students find the best research group based on the text titles. In addition, lecturers can comment more elaborately since the proposals are relevant to the research group’s scope.
Co-Authors Agung Wibowo Aji Prasetya Wibawa Akhimullah Akmal Fattah Alfian - Daud Amaliya, Sholikhatul Anik Nur Handayani Annisa Tullulu Kaunar Ashar, Muhammad Ashar, Muhammad Aulia Yahya Harindra Putra Ayudhitama, Annisa Putri Azhar Ahmad Smaragdina Daud, Alfian - Deni Sutaji Deny Setiawan Deny Setiawan Dessy Rif’a Anzani Dicki Dwi Prasetya Dila Umnia Soraya Falah, Moh Zainul Felix Andika Dwiyanto Harits Ar Rosyid Harviansyah, Yushivan Rendi Hidayati, Nunik Hidayatul Ma'rifah I Made Wirawan I Putu Dody Lesmana I Wayan Sumberartha Islamiah Kamil juwita oktaviani Kaunar, Annisa Tullulu Kurniawan, Wendy Cahya Liang, Yeoh Wen M Nasir Tamalene Mardiyanti , Lely Meiliyah Ariani, Meiliyah Mimien Henie Irawati Moh Zainul Falah Nidhom, Ahmad Mursyidun Niendhitta Tamia Lassela Ningrum, Gres Dyah Kusuma Nurdin, Muhammad Iman Pramono, Muhammad Adi Retno Indah Rokhmawati Rina Yuliastuty Asmara Roni Herdianto Siti Imroatul Maslikah Sofia Ery Rahayu Soraya Norma Mustika Sueb Sujito Sujito Suminah Suminah Triyanna Widiyaningtyas Utomo Pujianto Wachidatul Linda Yuhanna Wawan Suprianto Nadra Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Zaeni, Ilham Ari Elbaith