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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Barry Caesar Octariadi
Universitas Muhammadiyah Pontianak
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Sistem Pendukung Keputusan Rekomendasi SMA Islam Swasta Di Kota Pontianak Menggunakan Metode SAW Dan TOPSIS Hafi Risandika; Syarifah Putri Agustini; Barry Caesar Octariadi
JURNAL FASILKOM Vol 13 No 02 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i02.5178

Abstract

Perkembangan pendidikan yang ada di Indonesia tepatnya di Kota Pontianak, semakin memperketat persaingan antar sekolah. Berdasarkan data dari kementerian pendidikan, SMA yang ada di Kota Pontianak berjumlah total 54 sekolah dengan jumlah SMA Islam Swasta terdapat 20 sekolah. Banyaknya pilihan SMA Islam Swasta terkadang membuat calon siswa dan siswi kesulitan dalam menjatuhkan pilihan. Tujuan penelitian ini yaitu dengan menerapkan metode Simple Additive Weighting (SAW) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) pada sistem pendukung keputusan dengan kriteria yang telah ditentukan berdasarkan angket yang dibagikan kepada 60 siswa, kriteria yang digunakan yaitu Akreditasi, Biaya Masuk, Fasilitas, Aksesibilitas dan Lokasi. Berdasarkan aplikasi sistem pendukung keputusan yang telah dibuat, sistem mampu memberikan rekomendasi pilihan terbaik SMA Islam Swasta di kota Pontianak sesuai dengan kriteria. Dalam perhitungan manual dan perhitungan sistem, SMAS Islam Bawari menjadi rekomendasi pertama dengan nilai preferensi 0,7715.
Comparison of Naive Bayes and KNN Algorithms for Heart Attack Disease Classification Syahril Arsad; Sucipto; Barry Caesar Octariadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2218

Abstract

This Heart attack is one of the leading causes of death worldwide and requires early diagnosis to reduce fatal risks. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbors (KNN) algorithms in classifying heart attack disease. The dataset used consists of medical records containing clinical parameters such as age, blood pressure, cholesterol level, and heart rate. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The results show that Naive Bayes demonstrates advantages in computational speed and performs well on smaller datasets, achieving an accuracy of 85%. In contrast, KNN provides better performance on larger datasets, reaching an accuracy of 90%, particularly when the optimal K value is applied. These findings indicate that algorithm selection for heart attack classification depends on dataset characteristics and specific implementation needs. This study is expected to contribute to the development of artificial intelligence–based clinical decision support systems for early heart attack diagnosis and improved healthcare outcomes.
Co-Authors Hafi Risandika Sucipto Syahril Arsad Syarifah Putri Agustini Alkadri