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All Journal The Asian Journal of Technology Management (AJTM) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi TIJAB (The International Journal of Applied Business) Journal of Information Technology and Computer Engineering Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Sistem Pendukung Keputusan dengan Aplikasi Indonesian Journal of Education and Social Science Jurnal Ilmiah Computer Science The Indonesian Journal of Computer Science Control Systems and Optimization Letters
Zangana, Hewa Majeed
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Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Artificial Intelligence in Service Quality Improvement: A Bibliometric Analysis Majid, Nurkholish; Ahmad Khumaeni; Zangana, Hewa Majeed
TIJAB (The International Journal of Applied Business) Vol. 10 No. 1 (2026): MARCH 2026
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/tijab.v10.I1.2026.49186

Abstract

Background: Advances in computer technology have enabled the development of machines with capabilities known as Artificial Intelligence (AI). Although its adoption in manufacturing industries remains limited in many countries, AI is increasingly being applied in the service sector and other industries. Objective: This study aims to map recent research trends on the use of AI in improving service quality. Method: The data were analyzed using bibliometric mapping with VOSviewer software, focusing on studies about AI in services and service quality over the past five years. Results: This study maps the use of AI in corporate service automation. AI enables consumer self-service through robotic systems, particularly in sectors such as hospitality, lodging, travel, and tourism. Furthermore, the adoption of AI services by companies helps reveal patterns in consumer behavior. The keyword analysis identified five main clusters: medical, agriculture, government, tourism, and service quality. Conclusion: AI contributes to the enhancement of automation-based systems in sectors such as tourism and travel. However, AI use also faces challenges across sectors, including medical, business services, agriculture, hospitality, and government or public services. Keywords: Artificial Intelligence, Service Quality
A Lightweight Hybrid Template-Matching–CNN Framework with Attention-Guided Fusion for Robust Small Object Detection Zangana, Hewa Majeed; Omar, Marwan; Mirza, Mohammed Aquil; Cao, Xinwei; Wani, Sharyar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14751

Abstract

Small object detection in aerial and surveillance imagery remains challenging due to low resolution, occlusion, and background clutter. This study introduces a novel hybrid detection framework that fuses template matching with a deep learning detector (Faster R-CNN) through an attention-guided decision fusion mechanism. The novelty lies in (i) a dual-stage fusion pipeline that integrates precise structural cues from template matching with deep semantic features, and (ii) a custom scale-aware focal loss, adapted from Focal Loss to emphasize hard and small objects by dynamically increasing penalties for low-confidence predictions. Evaluated on a Pascal VOC subset (1000 images, 5 classes), the proposed system achieves an mAP improvement of 3.5% over the Faster R-CNN baseline and surpasses YOLO-Lite and R-CNN variants in precision and recall. The hybrid design adds only a minimal computational overhead (0.45 s/image vs. 0.42 s for Faster R-CNN), demonstrating favorable efficiency–accuracy trade-offs suitable for scalable deployment. These findings highlight the framework’s robustness, particularly in scenes containing occlusion, clutter, or visually small targets. Limitations regarding template dependency are discussed, along with future directions for automatic template generation and real-time video adaptation.
Co-Authors Abdulazeez, Adnan M Abdullah , Dilovan Taha Abdullah, Dilovan Taha Abualigah, Laith Ahmad Khumaeni Al-Karaki, Jamal N. Ali , Natheer Yaseen Ali, Natheer Yaseen Amaliah, Fithrotul Irda Anastasia Lidya Maukar Anik Vega Vitianingsih As’ad Shidqy Aziz Ayaz khalid Mohammed B, Nur Vidia Laksmi Bazeed, Sameer Mohammed Salih Beitollahi, Hakem Cao, Xinwei DWI CAHYONO Firmansyah, Deden Hassan, Noor Salah Husain, Mamo Muhamad Li, Shuai Lidya Maukar, Anastasia Ma'rifani Fitri Arisa Mahmood Mustafa, Firas Mirza, Mohammed Aquil Mohammed , Ayaz Khalid Mohammed, Aquil Mirza Mohammed, Ayaz Khalid Mohammed, Harman Salih Muhamad, Sabat Salih Mustafa , Firas Mahmood Mustafa, Firas Mahmood Natheer Yaseen Ali Nurkholish Majid Obeyd, Salah Ramadan Oliva, Diego Omar , Marwan Omar, Marwan Omar, Naaman Rizal, Moch Arif Samsul Salih, Azar Abid Sallow , Zina Bibo Sallow, Amira Bibo Sallow, Zina Bibo Salsabilah, Azka Slamet Riyadi, Slamet Riyadi Sulaiman, Maryam A. Wani, Sharyar Widi Aribowo Zeebaree, Subhi R. M.