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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Law Reconstruction Jurnal Pendidikan Informatika dan Sains JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Al-Khidmah CYBERNETICS BULETIN AL-RIBAATH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Teknika Jurnal Abdi Insani JIKA (Jurnal Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JUTECH : Journal Education and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Alkadri, Syarifah Putri Agustini
Universitas Muhammadiyah Pontianak
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology 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 Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Journal of Artificial Intelligence and Engineering Applications (JAIEA)

 1 
Classification of Fetal Health Using the K-Nearest Neighbor Method and the Relieff Feature Selection Method Anita; Asrul Abdullah; Syarifah Putri Agustini Alkadri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.794

Abstract

Understanding fetal health early can reduce risks to the pregnancy and the womb. Identifying correlations among factors influencing fetal well-being helps medical professionals clarify key impacts. Quantified relationships between features and labels also guide future research. This study focuses on three aspects: evaluating KNN model performance with and without ReliefF feature selection, analyzing the impact of feature removal, and assessing ReliefF's ability to identify critical features for fetal health classification.The research begins by framing fetal health classification as a supervised machine learning task using labeled datasets. A cardiotocographic dataset from the UCI Machine Learning Repository supports data collection. Initial analysis identifies data types and detects outliers, followed by preprocessing, feature selection, and KNN model training. Model testing uses metrics such as accuracy and recall. Results show the KNN model with ReliefF features achieves an accuracy of 0.896. Testing a pruned model by removing high-importance features slightly reduces accuracy to 0.866. These findings confirm ReliefF's effectiveness in identifying essential features and optimizing model efficiency without compromising quality. This study underscores ReliefF's role in improving KNN performance for fetal health classification.
New Employee Selection System using WP and SAW Methods Based on Web at PT Lanang Agro Bersatu Ria Sapitri; Syarifah Putri Agustini Alkadri; Putri Yuli Utami
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.808

Abstract

Employees are valuable assets for a company, requiring careful selection based on educational background and experience to ensure proper placement and avoid issues. At PT Lanang Agro Bersatu, the selection process involves approximately 30 candidates monthly. This study developed a web-based employee selection system using the Weighted Product (WP) and Simple Additive Weighting (SAW) methods. The system aims to calculate weight values for criteria such as Education, Work Experience, Age, Health, GPA, Academic Tests, and Psychological Tests, providing accurate rankings to simplify decision-making. The top candidate, Khusnul Wasillah, achieved the highest preference value of 0.1563, calculated through combined SAW and WP methods. System testing using black box and equivalence partitioning methods showed 100% accuracy.
Decision Support System for Selection of Achieving Students Using MetDecision Support System for Selection of Achieving Students Using Method Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Web Based Isra Pebrianti; Syarifah Putri Agustini Alkadri; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.829

Abstract

The selection of outstanding students identifies the best students based on grades and achievements to recommend them for college entrance. This process often encounters challenges due to numerous determining factors, leading to potential biases in decision-making. A Decision Support System (DSS) helps address this by utilizing data and decision models to resolve structured and unstructured problems. This study applies the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method, using criteria such as attendance, attitude scores, knowledge and skills component values, extracurricular/organizational involvement, and achievements. The DSS identified 40 outstanding students at SMA Negeri 1 Tayan Hulu, with the highest preference score of 0.0819 achieved by Indah Prasetyaning Tias. Functional testing was conducted using the black-box method with Equivalence Partitioning, and accuracy testing through MAPE showed a calculation accuracy rate of 2.79%.
Co-Authors Acep Supriyanto Agustian Agustian Ajmi, Nur Dzakiyyah Alda Cendekia Siregar Alkhairi, Muhammad Ghozy Andalas Rivaldi Permana Anita Asrul Abdullah Barry Ceasar Octariadi Dila Adellia Dini Oktaviani Eko Julianto Fakhruzi, Izhan Hasim, Wahyudi Hazilina, Hazilina Heni Kriswanti Isra Pebrianti Istikoma, Istikoma Kalsum, Dayang Nur Kristin Damay Asmara Masroni Maysa, Ade Medi, Medi Muhammad Dwi Ramadhianto Novianti Novianti Otafyani, Mega Putri Yuli Utami Rachmat Wahid Saleh Insani Ria Sapitri Rizka Amalia Rizky Wahyu Prasetio Ruhama, Ufi Ryani Yulian Setiaji, Wanda Primadita Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sumirah Sumirah Sumirah Sumirah Utami, Putri Yuli vera vibiola Vika Ummu Hani Wahyu Nugroho Wiguna, Seftyan Yulrio Brianorman