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All Journal Dinamik Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Informatika Upgris JNKI (Jurnal Ners dan Kebidanan Indonesia) (Indonesian Journal of Nursing and Midwifery) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Teknologi Informasi MURA Indonesian Journal of Learning Education and Counseling Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Brahmana : Jurnal Penerapan Kecerdasan Buatan Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Brilliance: Research of Artificial Intelligence Jurnal Rekam Medis dan Manajemen Informasi Kesehatan Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi Mura Science Technology and Management Journal (STMJ) Scientific Journal of Informatics Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
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Journal : Jurnal Teknik Informatika (JUTIF)

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.
Artificial Intelligence in Monetary Response: The Role of Investor Sentiment in the Effectiveness of Bank Indonesia’s Interventions Sumantiawan, Dody Indra; Abdillah , M. Zakki; Muhammad Kholilurrahman
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5184

Abstract

Exchange rate stability is a core pillar of macroeconomic resilience, especially for emerging economies like Indonesia. The effectiveness of Bank Indonesia’s (BI) monetary interventions in stabilizing the Rupiah depends not only on policy instruments but also on market perceptions and investor sentiment. This study examines the relationship between investor sentiment and the effectiveness of BI’s interventions by integrating Natural Language Processing (NLP), event study, and moderated regression analysis. The dataset spans 2023–2025 and includes daily exchange rate data, an investor sentiment index derived from financial forums and business news using VADER and TextBlob algorithms, and BI intervention records. An event study with a ±5 day window evaluates the short-term impact of interventions on exchange rate returns, while moderated regression analyzes the interaction between sentiment and interventions. Results indicate that BI interventions produce short-term exchange rate recovery, with a cumulative average abnormal return (CAAR) of 0.55% on the third day after intervention. Regression findings show that investor sentiment significantly influences Rupiah movements (p < 0.01), and the interaction between sentiment and interventions is also significant (p < 0.05), indicating greater effectiveness under positive or neutral sentiment. These findings underscore that intervention success is closely tied to market psychology. Therefore, BI should incorporate AI-driven sentiment analysis into policy design to enhance intervention effectiveness and strengthen public communication credibility. This study enriches the literature on behavioral macroeconomics and offers a data-driven framework for adaptive monetary policymaking in the digital economy.