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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Artificial Intelligence and Engineering Applications (JAIEA) SmartComp Health Dynamics
Barry Caesar Octariadi
Universitas Muhammadiyah Pontianak
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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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.
Implementasi Algoritma Naïve Bayes Untuk Klasifikasi Mahasiswa Berpotensi Drop Out Anugrah Esa Putra; Barry Caesar Octariadi; Rachmat Wahid Saleh Insani
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 3 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i3.10276

Abstract

Tingginya tingkat mahasiswa yang mengalami drop out dalam program studi sering menjadi tantangan bagi institusi pendidikan tinggi, tidak menutup kemungkinan akan terjadi pada Program Studi Ilmu Komputer di Universitas Muhammadiyah Pontianak. Untuk mengatasi masalah ini, diperlukan sebuah sistem yang dapat membantu memprediksi potensi mahasiswa yang berisiko drop out, sehingga dapat dilakukan intervensi yang tepat waktu. Penelitian ini mengembangkan sebuah aplikasi berbasis data mining yang menggunakan algoritma Naïve Bayes untuk memprediksi resiko drop out mahasiswa. Metode penelitian meliputi definisi masalah, pengumpulan data, permodelan, evaluasi, penyebaran model, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes berhasil diimplementasikan dengan tingkat akurasi 91%, presisi 85%, dan recall 96%. Meskipun terdapat beberapa kekurangan dalam akurasi, aplikasi yang telah dibangun diharapkan dapat mengatasi masalah mahasiswa berpotensi drop out.
The Relationship Between User Ability Response (UAR) and Intentions and Attitudes Toward Vector Control Among Users of the Geplakkin Application Azizah Nur Aini; Andri Dwi Hernawan; Elly Trisnawati; Barry Caesar Octariadi
Health Dynamics Vol 3, No 8 (2026): August 2026
Publisher : Knowledge Dynamics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33846/hd30806

Abstract

Background: Geplakkin is a digital application that enables the general public to report, monitor, and obtain information on Aedes mosquito density as part of a monitoring system. The objective of this study was to analyze the relationship between the level of use of the Aedes mosquito app (UAR) and the intention to eliminate mosquito breeding sites, as well as the relationship between the level of use and attitudes toward eliminating mosquito breeding sites among Geplakkin app users in Pontianak. Methods: The study population consisted of households in Pontianak. A total of 240 Geplakkin app users were selected through targeted sampling. The inclusion criterion was owning a smartphone capable of installing and using the app. Data were analyzed using univariate and bivariate analyses. Bivariate analysis was performed using Pearson’s product-moment correlation and Spearman’s rank correlation. Results: The results showed a weak but statistically significant positive relationship between the level of Geplakkin app usage and the intention to eliminate mosquito breeding sites (r = 0.310, p = 0.000). The variable attitude toward mosquito breeding site elimination showed a very weak but statistically significant positive correlation with UAR (r = 0.149, p = 0.021). Both variables exhibited a positive association, indicating that higher UAR values are associated with stronger intentions and more positive attitudes toward eliminating mosquito breeding sites. Conclusion: Continued use of the Geplakkin monitoring app is recommended, as users’ positive responses to the app have the potential to strengthen their intention to eliminate mosquito breeding sites and thereby foster a positive community attitude toward mosquito breeding site elimination (PSN) efforts as part of dengue vector control.
Co-Authors Andri Dwi Hernawan Anugrah Esa Putra Azizah Nur Aini Elly Trisnawati Hafi Risandika Rachmat Wahid Saleh Insani Sucipto Syahril Arsad Syarifah Putri Agustini Alkadri