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All Journal Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengabdian Magister Pendidikan IPA JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Bumigora Information Technology (BITe) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Networking and Security - IJNS Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Nanggroe: Journal Of Scholarly Service Jurnal Riset Teknologi dan Inovasi Pedidikan (JARTIKA) Nanggroe: Journal of Scholarly Service Journal Of Artificial Intelligence And Software Engineering Journal of Information Systems Management and Digital Business Jurnal Pengabdian kepada Masyarakat IPTEKS ORAHUA : Jurnal Pengabdian Kepada Masyarakat JATISKOM: Jurnal Aplikasi Teknologi Informasi dan Sains Komputer Jurnal Pengabdian Pada Masyarakat IPTEKS
Ria Rismayati
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
Machine Learning Approaches For Classification Of Infectious Diseases Using Smote Shofwan, Ari; Sulistianingsih, Neny; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v%vi%i.6960

Abstract

Infectious diseases such as acute nasopharyngitis, acute pharyngitis, and acute tonsillitis remain major public health issues, especially in primary healthcare facilities with limited resources like Puskesmas Gunungsari. This study aims to develop a machine learning-based classification model to detect infectious diseases using patient medical data. The evaluated models include Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network, with performance assessed using k-fold cross-validation ranging from 5 to 10 folds. Evaluation results show that the Decision Tree consistently achieved the best performance, with an accuracy of approximately 91.7% to 91.9% and an F1-score ranging from 91.9% to 92.3% on cross-validation data, as well as a test accuracy of 94.7% and an F1-score of 95.0%. The Random Forest model also demonstrated good and stable performance, with accuracy between 90.5% and 90.7%. Meanwhile, SVM and Neural Network produced lower results, with maximum accuracy of around 77.0% and 71.7%, respectively. Overall, the findings demonstrate that the Decision Tree model is the most effective for supporting early diagnosis of infectious diseases at Puskesmas Gunungsari, providing superior classification capabilities compared to other models.
Sentiment Analysis of Service and Facility Satisfaction at Computer Lab of Universitas Bumigora Using Indobert Mundika, Eko; Martono, Galih Hendro; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6798

Abstract

Computer laboratories have a strategic role in supporting the technology-based learning process at Bumigora University. To understand the extent to which the available services and facilities meet students' expectations, this study conducted a sentiment analysis of student reviews using the IndoBERT model, an artificial intelligence-based Natural Language Processing (NLP) approach. Data was obtained from a questionnaire focusing on aspects of laboratory services and facilities, then analyzed to classify opinions into positive, negative, and neutral sentiments. The analysis results show the dominance of positive sentiments, indicating that computer laboratories have generally met student expectations, especially in supporting practicum activities. The IndoBERT model used was able to achieve 85% accuracy, demonstrating its effectiveness in reliably identifying opinion trends. These findings provide a comprehensive picture of student perceptions, and serve as an important basis for managers in formulating strategies to improve the quality of laboratory services and facilities so that a conducive and relevant learning experience can be maintained.
Comparison of Random Forest, Decision Tree, and XGBoost Models in Predicting Student Academic Success Nurbaeti, Nurbaeti; Sulistiyaningsih, Neny; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7138

Abstract

Students' academic success is influenced by various academic and non-academic factors. Machine learning (ML) offers an effective approach to predicting academic outcomes by analyzing complex data patterns. However, most previous studies are limited to graduation prediction and rarely incorporate non-academic features or multiple feature selection techniques. This study aims to compare the performance of three ML algorithms Random Forest, Decision Tree, and XGBoost in classifying students’ academic success using a dataset from the UCI Machine Learning Repository, consisting of 4424 records and 37 features. The data underwent cleaning, label transformation, and feature selection using PCA, SelectKBest, and Variance Threshold. Models were trained using a holdout method (80% training, 20% testing) and evaluated based on accuracy, precision, recall, and F1-score. The results show that Random Forest with Variance Threshold achieved the highest accuracy (0.77) and F1-score (0.84) on majority classes. XGBoost followed with 0.75 accuracy, while Decision Tree showed the lowest performance. All models struggled to classify the minority class, indicating challenges related to data imbalance. This research highlights the importance of algorithm choice and effective feature selection in academic classification tasks. It also emphasizes the need for data balancing strategies to reduce class bias. The findings can help educational institutions design data-driven interventions to improve learning outcomes and reduce dropout rates.
Co-Authors Agus Pribadi Agus Pribadi Agus Pribadi, Agus Ahmad Ahmad Ahmad Zuli Amrullah Ahmat Adil Andi Sofyan Anas Arinul Haq, M. Syaoqi Diah Supatmiwati Dian Darmawan Dwi Saputra Fatimatuzzahra Fatimatuzzahra Febri Mamluatul Ulumi Galih Hendro Martono Halimatuzzahrah Halimatuzzahrah Hasbullah Hasbullah Hatina, Adelia Azzahrah Hilda Hastuti Husaini, Rahayun Amrullah I Made Yadi Dharma Indyah Hartami Santi Ismarmiaty Ismarmiaty Ismarmiaty, Ismarmiaty Kartarina, Kartarina La Dzunnafi Arridlwany Lalu Aldila Maulana Fajar Lely Kurniawati Miftahul Jannah Muhammad Aldi Zarkashy Muhammad Innuddin MUHAMMAD TAJUDDIN Muhammad Yunus Muhammad Yunus Mundika, Eko Neny Sulistianingsih Ni Gusti Ayu Dasriani Ni Ketut Sri Winarti, Ni Ketut Sri Ni Luh Putu Merawati Ni Luh Putu Merawati Ningsih, Neny Sulistia Nur Utami Wahdaniati Nurbaeti Nurbaeti Parihin Parihin Phyta Rahima Pytha Rahima Rahmat Suci Rahmiati, Baiq Fitria Rifqi Hammad S, Suriyati Santi, Indyah Hartami Shofwan, Ari Sulistiyaningsih, Neny Suriyati Suriyati Suriyati, Suriyati Syahrir, Moch. Syahroni Hidayat Wahyu Kamil Syarifaturrahman, Wahyu Kamil