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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 223 Documents
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Comparison of Adaptive Boosting and Categorical Boosting in Heart Attack Diagnosis Amran, Ali; Suryani, Suryani; Fathinah, Nadiva Azro; Desiani, Anita; Ramayanti, Indri
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.9051

Abstract

Heart disease is one of the leading causes of death worldwide, and therefore, accurate early detection methods are needed to help reduce mortality rates. One approach that can be applied is machine learning using classification techniques based on ensemble boosting algorithms. This study aims to compare the performance of two ensemble algorithms, namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost), in classifying heart attack disease. The labels used in this study are positive and negative. The evaluation process was conducted using two testing techniques: percentage split with a ratio of 80% training data and 20% testing data, and 10-fold cross-validation. Model performance was evaluated based on accuracy, precision, and recall to comprehensively measure classification capability. The results show that in the percentage split method, CatBoost achieved the highest accuracy of 98.88%, while in k-fold cross-validation it reached 98.43%. Nevertheless, AdaBoost also demonstrated good performance, with all evaluation metrics exceeding 90%. Therefore, the best-performing model in this study is CatBoost with the k-fold cross-validation technique on the heart attack dataset.
Product Sales Analysis based on sales level using the K-Means Clustering method Kinasih, Aisha Bethary; Christianto, Paminto Agung; Amalia, Nurul
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.7710

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a highly strategic role in driving Indonesia’s economic growth. Nevertheless, most business actors have not yet utilized digital technology to its full potential. One such example is Toko Nabila Daster, which recorded 475 sales transactions during the period of January–June 2025, but has not conducted an analysis to identify products with high, medium, or low sales levels. This situation may result in stock accumulation and ineffective promotional strategies. The objective of this study is to group products based on their sales levels using the K-Means Clustering method. The optimal number of clusters is determined through the Elbow Method, while the quality of clustering is assessed using the Davies-Bouldin Index (DBI). The results of the analysis indicate the formation of product clusters that distinguish best-selling, moderately selling, and low-selling categories. These findings are expected to serve as a foundation for business decision-making, particularly in designing promotional strategies and managing inventory more efficiently.
Implementation of WebSocket in an IoT-Based Smart Home Door Security System Using ESP32-CAM with Face Recognition Safriadi, Safriadi; Nasir, Muhammad; Erdiansyah, Umri
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.9052

Abstract

The advancement of Internet of Things technology, especially in the field of information technology, opens up opportunities in the development of smarter, more efficient, and flexible home security systems. Frequently used systems such as fingerprints and RFID still have limitations in flexibility, scalability, and effectiveness against threats. To overcome these problems, an IoT-based home door security system was developed using ESP 32 - CAM and face recognition technology. This system utilizes the Haar Cascade Classifier algorithm for face detection and the Local Binary Pattern Histogram for face recognition. Test results show a fast response, communication stability, and an increase in accuracy of 66.07% with 10 datasets, 86.07% with 50 datasets, and 93.03% with 100 datasets. This shows that the more datasets used, the higher the system's accuracy in recognizing user faces.

Page 23 of 23 | Total Record : 223

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