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All Journal PROSIDING SEMINAR NASIONAL INFORMAL: Informatics Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Ulumuna: Jurnal Studi Keislaman Geneologi PAI : Jurnal Ilmiah Bidang Pendidikan Agama Islam Al Iman: Jurnal Keislaman dan Kemasyarakatan JURMA : Jurnal Program Mahasiswa Kreatif Jurnal Ilmu Keperawatan dan Kebidanan Iqtishaduna : Jurnal Ilmiah Mahasiswa Hukum Ekonomi Syariah Jurnal Ilmiah Maternal JUBIDA-Jurnal Kebidanan ATTAQWA: Jurnal Pendidikan Islam dan Anak Usia Dini Mazinda: Jurnal Akuntansi, Keuangan, dan Bisnis MOTORIK Jurnal Ilmu Manajemen Dan Pendidikan
Ummi Kulsum
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : INFORMAL: Informatics Journal

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Penanganan Data Tidak Seimbang Menggunakan Hybrid Method Resampling Pada Algoritma Naive Bayes Untuk Software Defect Prediction Moch Lutfi; Arief Tri Arsanto; Muhammad Faishol Amrulloh; Ummi Kulsum
INFORMAL: Informatics Journal Vol 8 No 2 (2023): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v8i2.41090

Abstract

Software defect prediction is software data that is used to identify a software module and can also be used to predict software defects. Before carrying out further trials, it is necessary to carry out special handling, especially by using algorithm models as predictions of software defects with the aim of obtaining information from the device being developed. Therefore, it is necessary to predict software defects using appropriate classification and prediction methods, so that the resulting accuracy results are better. In this study, the naïve Bayes algorithm was used as a classification with a resampling technique approach to handle unbalanced data, including SMOTEENN and SMOTETomek. The best accuracy results in the research conducted were 92.5% on the Nasa Repository PC4 dataset
Co-Authors Anisa Farah Ardy Anny Rosiana Masithoh Arief Tri Arsanto Atun Wigati Azyan Zafirah Sulhair Bakri Blegur Diah Andriani Kusumastuti Dwi Astuti Elinda I'in Fauziah Feronika Parastuti Firman Santoso Hasbullah Hasbullah Holilur Rahman ika tristanti Ika Widiowati Inayah Swasti Ratih Khofidlotur Rofi’ah Labibah Moch. Lutfi Mudafiatun Isriyah Muhammad Faishol Amrulloh Muhammad Fauzen Adiman Muhammad Hafiz Mujibno, Mujibno Nasriyah Nasriyah Nina Kurniawati Noor Hidayah Prihaten Maskhuliah Relly Sugianto Rina Septiani Septi Marsiningsih Sri Herianingrum Sri Inayanti Rasang Susanto Susanto Tamimah Tamimah, Umi Ayu Syufiawati Weni Kurnia Rahmawati