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All Journal Journal of Education and Learning (EduLearn) JOIV : International Journal on Informatics Visualization Journal of Applied Business and Technology
Baharum, Zirawani
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences Other

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Journal : Journal of Applied Business and Technology

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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.
Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk Nasien, Dewi; Owen, Steven; Fenly, Fenly; Johanes, Johanes; Lombu, Frendly; Leo, Leo; Baharum, Zirawani
Journal of Applied Business and Technology Vol. 6 No. 3 (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.v6i3.201

Abstract

This study aims to optimize the classification of obesity risk at an early stage using Principal Component Analysis (PCA), which is an important technique in machine learning. PCA is used to reduce the dimensionality of data, maintain important information without losing data, and has the advantage of reducing complexity which usually increases the risk of overfitting. The obesity dataset will be classified using algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting Linear, and XGBoost. Specifically, each algorithm is chosen because of its respective advantages: KNN for nonlinear data, SVM for high-dimensional data, and Random Forest and XGBoost for complex data patterns. Evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to assess the performance of the algorithm. The results show that the Random Forest and XGBoost algorithms provide the best performance in terms of accuracy, especially when all dataset features are used without PCA reduction. This study is expected to be a consideration in determining the best algorithm for obesity classification, supporting early detection, and facilitating decision making in health analysis.
Co-Authors Abdulmaged, Alyaa Idrees Adiya, M. Hasmil Ahmad, Nahdatul Akma Aida Mustapha, Aida Alde Alanda, Alde Aldo Erianda, Aldo Aldulaimi, Mohammed Hasan Ali, Muhammad Asri Mohd Alias, Nursyahidah Alo, Salam Omar Anggara, Devi Willeam Ariff, Mohamed Imran Mohamed Arriffin, Maizatul Najihah Arshad, Mohammad Syafwan Defni, - Dewi Nasien Fenly, Fenly Ghazal, Taher M. Gusman, Taufik Hairulnizam Mahdin, Hairulnizam Hassan, Azman Hassan, Mustafa Hamid Heru Nurwarsito Hezam, Abdulkareem A. Ismail, Shahrinaz Jaber, Mustafa Musa Johanes Johanes, Johanes Jubair, Mohammed Ahmed Kamri, Khairol Anuar Khaleefah, Shihab Hamad Khattak, Umar Farooq Leo, Leo Lojungin, Eva Cabrini Lombu, Frendly Mostafa, Salama A Mostafa, Salama A. Owen, Steven Rahmadhani, Ummi Sri Rahmat Hidayat Rita Afyenni Salikon, Mohd Zaki Saringat, Mohd Zainuri Shahdan, Tengku Shahrom Tengku Su-Cheng Haw Yacob, Azliza Zailan, Zulaikha N.I.