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All Journal ComEngApp : Computer Engineering and Applications Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Herteno, Rudy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Optimasi Recursive Feature Elimination menggunakan Shapley Additive Explanations dalam Prediksi Cacat Software dengan klasifikasi LightGBM Hartati, Hartati; Herteno, Rudy; Faisal, Mohammad Reza; Indriani, Fatma; Abadi, Friska
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1159

Abstract

Software defect refers to issues where the software does not function properly. The mistakes in the software development process are the reasons for software defects. Software defect prediction is performed to ensure the software is defect-free. Machine learning classification is used to classify defects in software. To improve the classification model, it is necessary to select the best features from the dataset. Recursive Feature Elimination (RFE) is a feature selection method. Shapley Additive Explanations (SHAP) is a method that can optimize feature selection algorithms to produce better results. In this research, the popular boosting algorithm LightGBM will be selected as a classifier to predict software defects. Meanwhile, RFE-SHAP will be used for feature selection to identify the best subset of features. The results and discussion show that RFE-SHAP feature selection slightly outperforms RFE, with average AUC values of 0.864 and 0.858, respectively. Moreover, RFE-SHAP produces more significant results in feature selection compared to RFE. The RFE feature selection T-Test results are Pvalue = 0.039 < α = 0.05 and tcount = 3.011 > ttable = 2.776. On the contrary, the RFE-SHAP feature selection T-Test results are Pvalue = 0.000 < α = 0.05 and tcount = 11.91 > ttable = 2.776.
Co-Authors Adawiyah, Laila Aflaha, Rahmina Ulfah Akhtar, Zarif Bin Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Alfando, Muhammad Alvin Andi Farmadi Angga Maulana Akbar Antoh, Soterio Aryanti, Agustia Kuspita Athavale, Vijay Anant Azizah, Azkiya Nur Azizah, Siti Roziana Bahriddin Abapihi Dodon Turianto Nugrahadi Dwi Kartini, Dwi Faisal, M. Reza Fatma Indriani Fauzan Luthfi, Achmad Favorisen R. Lumbanraja Febrian, Muhamad Michael Friska Abadi Ghinaya, Helma Halim, Kevin Yudhaprawira Hartati Hartati Hermiati, Arya Syifa Huynh, Phuoc-Hai Irwan Budiman Itqan Mazdadi, Muhammad Junaidi, Ridha Fahmi Lilies Handayani Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi MULIADI -, MULIADI Ngo, Luu Duc Noor Hidayah Noryasminda Nur Hidayatullah, Wildan Nurdiansyah Nurdiansyah Pratama, Muhammad Yoga Adha Putri, Nitami Lestari Radityo Adi Nugroho Raidra Zeniananto Ramadhan, As`'ary Reza Faisal, Mohammad Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Saputro, Setyo Wahyu Saragih, Triando Hamonangan Setyo Wahyu Saputro Siti Aisyah Solechah Suryadi, Mulia Kevin Ulya, Azizatul Vina Maulida, Vina Wahyu Saputro, Setyo Zamzam, Yra Fatria