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All Journal Jurnal Teknik ITS Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series ASRO JOURNAL - STTAL PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science
Eko Wahyu Wibowo
Institut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Integration of synthetic minority oversampling technique for imbalanced class Noviyanti Santoso; Wahyu Wibowo; Hilda Hikmawati
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp102-108

Abstract

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.
Kapabilitas Proses Pelatihan Metode Statistika bagi Mahasiswa Pendidikan IPS Universitas Negeri Malang: Capability Process Analysis of Statistical Methods Training for Students of Social Science Education, Malang State University Lucia Ari Dinanti; Zakiatul Wildani; Sri Pingit Wulandari; Wahyu Wibowo; Mike Prastuti; Iis Dewi Ratih
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 7 No. 4 (2022): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v7i4.3030

Abstract

Statistical methods are required as a tool for data analysis in student-led research projects, such as thesis/final projects. However, many students, particularly those from the Faculty of Social Sciences, Department of Social Studies Education, State University of Malang (FIS-UM), do not comprehend statistical methodologies yet. One way to overcome this problem is to conduct data analysis training on statistical methods. For this reason, the Quality and Productivity Engineering Laboratory, Department of Business Statistics, Faculty of Vocational, ITS, organized a community service program in the form of training for Social Science Education students. Initially, this program was planned to be held using a combination of online and offline training. However, the program was carried out using only online training because of some restrictions on community activities in Java and Bali. In this case, six materials are offered with software practice and assignments. The movement had 202 participants from three batches: 2018, 2019, and 2020. However, participants from batch 2018 can not finish the training due to some reasons. The average learning score (RHB) for the class of 2020 was 84.56, while the RHB for the class of 2019 was 80.85. There was no significant difference in RHB between these two groups using the t-test at a significance level of 5%. In addition, the training process of batch 2020 is capable since the X and MR charts of average learning scores demonstrate a controlled process, with a capability index of 1.55.
Co-Authors ABDUL MUNIF Arinda Nur Lathifah Budi Priyono Dhira Audhia Pratiwi Erma Oktania Permatasari Hilda Hikmawati I Nyoman Budiantara Iis Dewi Ratih Jonathan Dian Lucia Ari Dinanti Marsha Fitrantie Mike Prastuti Noviyanti Santoso Putri Rachmawati Rahajeng Dwi Anjas Pratiwi Sandra Yuni Wulandari Siti Rochimah Sri Pingit Wulandari Vita Ratnasari Wahyu Indri Astuti Wildani, Zakiatul