https://ejournal.ittelkom-pwt.ac.id/index.php/infotel <h2>About Jurnal INFOTEL</h2> <table border="0"> <tbody> <tr> <td><img src="https://ejournal.ittelkom-pwt.ac.id/public/site/images/journaladmin/cover_infotel.png" alt="telecommunication journal" width="180" height="250"></td> <td align="justify" valign="top"> <div style="background-color: #ebfeec; border: 1px solid #bae481; border-radius: 5px; text-align: justify; padding: 10px; font-family: sans-serif; font-size: 14px; margin-left: 10px;">Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of <strong>informatics, telecommunication, and electronics</strong>. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. <strong>Starting in 2018, Jurnal INFOTEL uses English as the primary language.</strong></div> </td> </tr> </tbody> </table> <div style="text-align: justify;"> <p>&nbsp;</p> <h4><strong>Important For Authors (Volume 16, No. 4, November 2024)<br></strong></h4> <p>Reminder for all the authors, you are expected to submit papers that:<br> 1. are original and have not been submitted to any other publication.<br>2. use the template specified by Jurnal INFOTEL.<br>3. use a reference manager <em>e.g.</em> Mendeley or others when managing the references.<br>4. Add all authors and complete affiliation in the metadata.<br>5.&nbsp;The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.<br>6.&nbsp;Willing to make improvements at the pre-review stage, a maximum of 14 days after the pre-review was carried out<br>Thank you.</p> <p>&nbsp;</p> </div> LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO en-US JURNAL INFOTEL 2085-3688 <p>Authors who publish with this journal agree to the following terms:</p> <ul> <li style="text-align: justify;">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li style="text-align: justify;">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li style="text-align: justify;">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work&nbsp;</li> </ul> https://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1159 <p>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 <em>P_value</em> = 0.039 &lt; <em>α</em> = 0.05 and <em>t_coun</em>_t = 3.011 &gt; <em>t_table</em> = 2.776. On the contrary, the RFE-SHAP feature selection T-Test results are <em>P_value</em> = 0.000 &lt; α = 0.05 and <em>t_coun</em>_t = 11.91 &gt; <em>t_table</em> = 2.776.</p> Hartati Hartati ##submission.copyrightStatement## http://creativecommons.org/licenses/by-sa/4.0 2025-02-28 2025-02-28 17 1 1 16 10.20895/infotel.v17i1.1159