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M. Zakki Abdillah
Department Of Information Systems, Universitas Nasional Karangturi, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Design and Implementation of Kernel-Based Quantum Classification Algorithms for Data Analysis in Software Engineering using Quantum Support Vector Machine (QSVM) Abdillah, M. Zakki; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5030

Abstract

With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.
Co-Authors Abdul Hakim Prima Yuniarto Abdul Razak Naufal Achmad Dimyati Ahmad Nugroho Ahmad Nugroho Ahmad Nugroho Akrim Teguh Suseno Al’Amin, M. Astrid Novita Putri, Astrid Cicilia Yolisday Darmawan, Arya Daroji Daroji Devi Astri Nawangnugraeni Devi Astri Nawangnugraeni Dimas Benyamin Dwi Prasojo, Nikolas Erba Lutfina Erba Lutfina Felix Surya Sanjaya Keisan Feoder Yufanka Khoirudin Khoirudin Laily Amalia Maria Yuliana Belaon Muhammad Ramdan NAWANGNUGRAENI, DEVI ASTRI Nugroho, Ahmad Nur Wakhidah Pranata, Ivan Gautama Suprayitno Rafael Ercole Hermanto Robby O. Cahyadi Setiawan Saifur Rohman Cholil Sekarlangit Sekarlangit, Sekarlangit Setyo, Victoricky Kurnia Siska Narulita Solikhin Solikhin Solikhin Solikhin Suseno, Akrim Teguh Toni Wijanarko Adi Putra Tranhuji Gumbara Wijaya, Tan Steven Yonatan, Christopher Nikolas