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All Journal JURNAL HUTAN LESTARI JIM Pendididikan Bahasa dan Sastra Indonesia JOIV : International Journal on Informatics Visualization Borneo Engineering: Jurnal Teknik Sipil Jurnal Yustitia Journal of Applied Civil Engineering and Infrastructure Technology (JACEIT) Islamika Granada Kande : Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia Jurnal Suara Pengabdian 45 Global Journal Basic Education Jurnal Ilmu Ekonomi Mulawarman (JIEM) Socius: Social Sciences Research Journal kawanad Jurnal Teknik Sipil Elementary Education Research International Journal of Artificial Intelligence and Science
Muhammad Idham
Mahasiswa S2
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences Transportation

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Test Case Prioritization for Software Product Line: A Systematic Mapping Study Idham, Muhammad; Halim, Shahliza Abd; Jawawi, Dayang Norhayati Abang; Zakaria, Zalmiyah; Erianda, Aldo; Arss, Nachnoer
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1340

Abstract

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line – Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research.
A Comparative Study of Logistic Regression and Support Vector Machine for COVID-19 Symptom Prediction Ferdiansyah, Ferdiansyah; Putra, Briandy Tri; Yulianingsih, Evi; Fatmasari, Fatmasari; Idham, Muhammad
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.8

Abstract

The rapid spread of COVID-19 has created a critical need for accurate and efficient tools to predict symptoms and aid in early diagnosis. This study aims to compare the effectiveness of two machine learning models, Logistic Regression and Support Vector Machine (SVM), in predicting COVID-19 symptoms based on patient data. The dataset used contains key COVID-19 symptoms, which were processed and modeled using both techniques. Logistic Regression was evaluated alongside SVM using three different kernels: Linear, Sigmoid, and Radial Basis Function (RBF). The models' performance was measured using the Confusion Matrix to assess accuracy. Logistic Regression achieved an accuracy of 96.78%, while the SVM with the RBF Kernel slightly outperformed it with an accuracy of 96.85%. The SVM with the Sigmoid Kernel performed the least effectively, with an accuracy of 95.19%. These findings suggest that both models are highly effective for symptom prediction, with the RBF Kernel showing the best overall performance in handling complex, non-linear data patterns.
Co-Authors Adelina, Cut Adilla, Lica Adnan., Adnan Alamsyah, Teuku Aldo Erianda, Aldo Armia Armia - Arss, Nachnoer Asrin, Yudi Atika, Zuhra Bahmid Bukhari Bukhari Bukhari. Bukhari, Bukhari. Bukhari., Bukhari Desliana, Desliana Emiel Salim Siregar Fah rizal Fajarriani, Nanda Fatmasari Fatmasari Febrio, Doni Ferdiansyah Ferdiansyah Firhan, Muhammad Fitri, Yani G, Gusmaneli Gunawan, Khatami Halim, Shahliza Abd Harmika, Zuwairiah Harun, Mohd. Husna, Nadia Idham, Muh Al ikhwandi Intan Safiah Islami, Raudatul Jawawi, Dayang Norhayati Abang Juliansyah Roy Lizar, Lizar M. Husin, M. Husin Malau, Rado Maruli Mardhatillah, Putra Mariana., Mariana Melia Andriyani Melia, Anasia Mislinawati, Mislinawati Muhammad Hafiz Napitupulu, Celly Naomi Sarah Br Nasution, Muhammad Azizi Ningsih, Ria Mardi Nurrahmah Nurrahmah Nurul Akmal Puluhulawa, Indriyani Putra, Briandy Tri Ramadhani, Hikmah Ramli Rangkuti, Syaiful Zuhri Revain Junata Gultom Rosma Ely, Rosma Safhida, Maya Sahdan, Patria Soedirman Z., Soedirman.z Sofyan Zainal Sri Diana Sulaiman Sulaiman Umi Kalsum Usman, Hikmawati Yulianingsih, Evi Zahara, Alfi Zakaria, Zalmiyah Zayyana, Nasywa Haura