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Brilliance: Research of Artificial Intelligence

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Published by Information Technology and Science

ISSN : - EISSN : 28079035 DOI : https://doi.org/10.47709

Core Subject : Science, Education,

Decision Sciences, Operations Research & Management Mathematics Other

Brilliance: Research of Artificial Intelligence is The Scientific Journal. Brilliance is published twice in one year, namely in February, May and November. Brilliance aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about Artificial Intelligence. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and colleagues will review. Articles sent to the Brilliance may not be published elsewhere. The manuscript must follow the author guidelines provided by Brilliance and must be reviewed and edited. Brilliance is published by Information Technology and Science (ITScience), a Research Institute in Medan, North Sumatra, Indonesia.

Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer

Articles 594 Documents

56 57 58 59 60

Ablation-Based Machine Learning Framework for Body Mass Index Classification Sechdyna Aura Tursyna; Harrizki Arie Pradana
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.8851

Body Mass Index (BMI) status classification can support population screening, but the relative predictive value of survey-based behavioral data and laboratory biomarkers remains unclear. This study develops an ablation-based health analytics framework to quantify the contribution of these feature domains and a derived lifestyle profile. Nineteen NHANES 2021–2023 component datasets were integrated, producing an analytical sample of 3,631 adults aged 18–80 years. The four-class target comprised Underweight, Normal, Overweight, and Obese categories; predictors included 28 behavioral and 11 biomarker variables. A two-stage framework applied K-Means lifestyle profiling to training data and Gradient Boosting classification across five controlled ablation configurations. Missing data processing, scaling, and Gaussian noise-augmented oversampling were fitted or applied only to the training set to minimize leakage. The primary two-stage full model achieved an AUC of 0.700 (95% CI: 0.643–0.751), accuracy of 0.556, macro F1 of 0.407, and Cohen’s kappa of 0.329. The biomarker-only configuration obtained the highest AUC (0.713), while the behavioral-only model retained moderate discrimination (AUC=0.635). The lifestyle cluster added only 0.002 AUC. Permutation importance identified diastolic blood pressure, uric acid, HDL cholesterol, alanine aminotransferase, and albumin as leading predictors, and a three-class formulation increased macro F1 to 0.545. The results indicate that biomarkers should be prioritized when laboratory resources are available, whereas behavioral variables remain useful for preliminary screening in lower-resource settings.

Decision Support System For Work Training Selection At Department Of Labor Asahan AHP Method Yunika Afrianti; William Ramdhan; Wan Mariatul Kifti
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9256

The increasingly rapid development of information technology has significantly impacted various sectors, including the government sector. One area of government that greatly requires information technology support is the management of job training programs organized by the Asahan Regency Manpower Office. In its implementation, the number of job training applicants often exceeds the available quota, necessitating an appropriate Decision Support System process. The AHP method is a multi-criteria decision-making method capable of breaking down complex problems into a hierarchical structure consisting of objectives, criteria, and alternatives. However, to date, no systematic analysis has been conducted on the selection of training applicants. This study aims to apply the Analytical Hierarchy Process (AHP) method to a Decision Support System as a basis for prioritizing job training participants based on predetermined criteria. The data used in this study include prospective job training participants and the assessment criteria used by the Asahan Regency Manpower Office, such as education, age, work experience, and income. Data were collected through observation, interviews, and literature review using a decision support system approach. The results of this study indicate that Nurhayati received the highest Preference Score (Yi) of 0.8462, placing her in first place, followed by Andi Saputra with a Preference Score (Yi) of 0.8157 and Dewi Sartika with a Preference Score (Yi) of 0.7620. The conclusion of this study is that the application of the Analytical Hierarchy Process (AHP) method can significantly contribute to accurate decision-making.

Classification Of Palm Oil Fruit Maturity Using CNN And Multiclass SVM In Ara Bungong Village Zahrul Laina; Zahratul Fitri; Cut Agusniar; Nurdin Nurdin; Rini Meiyanti
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9265

The determination of the ripeness level of fresh fruit bunches (FFB) of oil palm is still largely performed manually, making it susceptible to subjectivity and classification errors that can affect harvest quality and consistency. This study aims to develop an automated classification system for oil palm fresh fruit bunch ripeness by combining a Convolutional Neural Network (CNN) as a feature extractor and a Multiclass Support Vector Machine (SVM) as the classification algorithm. The dataset consisted of 300 images of oil palm fresh fruit bunches categorized into three classes: unripe, ripe, and overripe. The research stages included image data collection, image preprocessing, feature extraction using CNN, classification using Multiclass SVM, and performance evaluation based on the confusion matrix, accuracy, precision, recall, and F1-score. The CNN was employed to automatically extract representative visual features from the input images, while the Multiclass SVM classified the extracted feature vectors into the corresponding ripeness categories. The experimental results showed that the proposed model achieved an accuracy of 90%, precision of 90%, recall of 90%, and an F1-score of 90% in classifying the ripeness levels of oil palm fresh fruit bunches. These findings indicate that the combination of CNN and Multiclass SVM effectively recognizes the visual characteristics of each ripeness level and provides reliable classification performance. The developed system is expected to serve as an alternative decision-support tool for determining the optimal harvesting time in a more objective, consistent, and efficient manner, thereby supporting productivity improvement and reducing the potential for human error during harvest assessment.

SAW Method In Determining Social Assistance Recipients At The Bandar Pasir Mandoge Subdistrict Office Selvia Angreini; Yori Apridonal M; Amalia Amalia
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9276

This study aims to design and implement an information system to improve the efficiency and accuracy of data processing in organizational activities. The research was motivated by problems found in the existing system, where data management was still conducted manually, resulting in frequent errors, data redundancy, and delays in report generation. These limitations negatively affected the overall performance and decision-making process within the organization. To address these issues, a system was developed using a structured methodology that includes requirements analysis, system design, implementation, and testing. The proposed system integrates data processing, transaction management, and reporting into a single platform, enabling real-time data access and more effective information management. System testing was conducted using functional testing methods to ensure that all features operate according to user requirements and system specifications. The results show that the system successfully improves operational efficiency by reducing processing time, minimizing human error, and increasing data accuracy. Furthermore, the system provides timely and well-structured reports that support better and faster decision-making processes. Based on the evaluation results, it can be concluded that the developed system is feasible and effective in solving the identified problems. The implementation of this system is expected to enhance overall organizational performance and can be further developed by integrating additional features to meet future needs and technological advancements.

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Muhammad Khoiruddin Harahap

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Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Muhammad Khoiruddin Harahap

Contact Email choir.harahap@yahoo.com

Phone +6282251583783

Journal Mail Official publikasi@itscience.org

Editorial Address Medan

Location Unknown, Unknown INDONESIA

Abstract

Abstract

Abstract

Abstract

Contact Name
Muhammad Khoiruddin Harahap

Contact Email
choir.harahap@yahoo.com

Phone
+6282251583783

Journal Mail Official
publikasi@itscience.org

Editorial Address
Medan

Location
Unknown,

Unknown

INDONESIA