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All Journal Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Seminar Nasional Teknologi Informasi Komunikasi dan Industri Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Applied Engineering and Technological Science (JAETS) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Unri Conference Series: Community Engagement Seandanan: Jurnal Pengabdian pada Masyarakat Journal of Applied Business and Technology
Adiya, M. Hasmil
Institut Bisnis Dan Teknologi Pelita Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Journal of Applied Business and Technology

 1 
Literature Review: Mediation Effects of Debt Maturity on Good Corporate Governance in Enhancing Financial Performance M. Hasmil Adiya; Masyuri Hamidi; Rida Rahim; Fajri Adrianto
Journal of Applied Business and Technology Vol. 4 No. 2 (2023): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v4i2.125

Abstract

This study proposes a research model to investigate the mediating effect of debt maturity on the relationship between good corporate governance (GCG) and financial performance. The qualitative approach employed is a systematic literature review (SLR) technique, analyzing 11 articles selected from 142 related ones found on the Google Scholar database within the 2010-2021 research range based on inclusion and exclusion criteria. Debt maturity was found to play a crucial role in shaping the effectiveness of GCG mechanisms in managing financial risks and improving financial performance. The proposed research model has implications for researchers and practitioners, providing a basis for future empirical studies exploring the interplay between GCG, debt maturity, and financial performance. For practitioners, it offers a theoretical framework for developing policies and practices promoting effective GCG and optimal debt maturity structures to enhance financial performance. The findings inform the development of policies and procedures that promote good corporate governance and optimal debt maturity structures, leading to improved financial performance. Additionally, the proposed research model may be applied to other contexts, contributing to a better understanding of the role of debt maturity in the relationship between GCG and financial performance.
Increasing Trust in AI with Explainable Artificial Intelligence (XAI): A Literature Review Nasien, Dewi; Adiya, M. Hasmil; Anggara, Devi Willeam; Baharum, Zirawani; Yacob, Azliza; Rahmadhani, Ummi Sri
Journal of Applied Business and Technology Vol. 5 No. 3 (2024): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v5i3.193

Abstract

Artificial Intelligence (AI) is one of the most versatile technologies ever to exist so far. Its application spans as wide as the mind can imagine: science, art, medicine, business, law, education, and more. Although very advanced, AI lacks one key aspect that makes its contribution to specific fields often limited, which is transparency. As it grows in complexity, the programming of AI is becoming too complex to comprehend, thus making its process a “black box” in which humans cannot trace how the result came about. This lack of transparency makes AI not auditable, unaccountable, and untrustworthy. With the development of XAI, AI can now play a more significant role in regulated and complex domains. For example, XAI improves risk assessment in?finance by making credit evaluation transparent. An essential application of?XAI is in medicine, where more clarity of decision-making increases reliability and accountability in diagnosis tools. Explainable Artificial Intelligence?(XAI) bridges this gap. It is?an approach that makes the process of AI algorithms comprehensible for people. Explainable Artificial Intelligence (XAI) is the bridge that closes this gap. It is a method that unveils the process behind AI algorithms comprehensibly to humans. This allows institutions to be more responsible in developing AI and for stakeholders to put more trust in AI. Owing to the development of XAI, the technology can now further its contributions in legally regulated and deeply profound fields.
Automated Waste Classification Using YOLOv11 A Deep Learning Approach for Sustainable Recycling Nasien, Dewi; Adiya, M. Hasmil; Farkhan, Mochammad; Rahmadhani, Ummi Sri; Samah, Azurah A.
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.205

Abstract

The rapid increase in waste generation due to urbanization and population growth has necessitated more efficient waste management solutions. Traditional waste sorting methods rely on manual labor, which is time-consuming, error-prone, and inefficient at large scales. This paper proposes an automated waste classification system using YOLOv11, the latest iteration of the YOLO family, which is known for its high speed and accuracy in object detection. By leveraging a custom dataset containing 10,464 labeled waste images from various categories—such as biodegradable, plastic, metal, paper, and glass—this study trains and evaluates a deep learning model capable of real-time waste identification and categorization. Experimental results demonstrate that YOLOv11 achieves high detection accuracy, with an overall classification accuracy of 94% and a mean average precision (mAP) exceeding previous methods. The model effectively differentiates between various waste types, though some misclassifications occur, particularly between visually similar materials like transparent plastic and glass. Performance metrics, including precision and recall, indicate the robustness of the proposed system in real-world applications. This research highlights the potential of YOLOv11 for integration into smart waste management systems, such as automated sorting machines and AI-powered recycling bins, to enhance efficiency and reduce environmental impact. Future work will focus on optimizing model performance by incorporating additional training data, applying advanced image augmentation techniques, and exploring hybrid approaches such as texture analysis and spectral imaging to improve classification accuracy. The implementation of this technology is expected to streamline waste recycling processes, minimize contamination in recyclable materials, and contribute to sustainable waste management practices.
Co-Authors ahmad kamal, ahmad Ahmad Mulyadi Aldo Erianda, Aldo Alyauma Hajjah Anggara, Devi Willeam Angriawan, Sherkhing Baharum, Zirawani Butar-Butar, Rio Juan Hendri Dahliyusmanto, Dahliyusmanto Deny Jollyta Desnelita, Yenny Devi Willieam Anggara Dewi Nasien Erlin Fajri Adrianto Farkhan, Mochammad Feri Candra Gustientiedina Helly Aroza Siregar Ihsan, M. Nurul Iis Afrianty Ismail, Shahrinaz Jaber, Mustafa Musa Johan Johan Jubair, Mohammed Ahmed M Noval Dwi Pratama Marlim, Yulvia Nora Masyhuri Hamidi, Masyhuri Mostafa, Salama A Nadira Nurul Atika Rahmadhani, Ummi Sri Rahmadian Yuliendi, Rangga Rianda, Gilang Rida Rahim Roni Sanjaya Samah, Azurah A. Siddik, M. Sirait, Andrio Pratama ST, M.A.B, Okto Fenta Riandi Sukabul, Ahmad Suroyo Suroyo Suroyo Suroyo Wicaksono, Mahfuzan Hadi Wilda Susanti Willy Andrio Harefa Yacob, Azliza Yulianti, Deni Yusnita Rahayu