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All Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
Niko Akbar
Universitas Jambi
Computer Science & IT Education

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Development of a Web-Based Application for Predicting Stroke Patient Emergency Levels Using the Naïve Bayes Algorithm Nurul Abdillah; Oki Dahwanu; Hamzah Alghifari; Niko Akbar
EDUTIC Vol 13, No 1: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i1.34303

Abstract

Stroke is a serious disease that requires prompt and appropriate treatment; therefore, determining the level of patient Emenrgency Levels is critically important. This study aims to develop a web-based application for predicting the Emenrgency Levels level of stroke patients using the Naïve Bayes algorithm as a classification method. The research data were obtained from the medical records of stroke patients at RSUP Dr. M. Djamil Padang during March and April 2025, with a total of 222 data samples. The attributes used in this study include age, gender, address, length of stay, ward class, BPJS insurance membership status, and comorbidities, with Emenrgency Levels status as the class attribute classified into Emenrgency and non-Emenrgency. The application was developed as a web-based system to facilitate easy access for medical personnel in utilizing the prediction system. The experimental results indicate that the Naïve Bayes algorithm achieved an accuracy of 77.48% with an error rate of 22.52%. The findings of this study are expected to assist medical personnel in supporting faster and more objective decision-making regarding the Emenrgency Levels level of stroke patients.
KLASIFIKASI DAN PREDIKSI KELUARGA BERISIKO STUNTING DI PROVINSI JAMBI MENGGUNAKAN METODE KNN DAN NAIVE BAYES Niko Akbar; Hamzah Alghifari; Nurul Abdillah; Oki Dahwanu
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 7, No 1: JUNI 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v7i1.8522

Abstract

Di Indonesia Prevalensi stunting sebesar 37,2%, naik dari 35,6% pada tahun 2019 dan 36,8%, dengan mayoritas dipengaruhi oleh penduduk setempat. Kementerian Kesehatan Indonesia memperkirakan bahwa prevalensi stunting akan mencapai 38,9% pada tahun 2020. Permasalahannya Beberapa Data yang diambil dan dipakai berupa data sekunder yang di ada diwebsite opendata provinsi jambi yang berjudul Faktor Penapisan Keluarga Berisiko Stunting di Provinsi Jambi. Dengan menggunakan algoritma KNN dan Naïve Bayes, maka didapatkan hasilnya berupa kedua algoritma cocok untuk pengklasteran dan Scoring dari algoritma menunjukkan hasil yang berbeda. Karena keakurasiannya sesuai dengan perhitungan manual yang telah dijabarkan pada bagian pengolahan data. Beberapa hasil dari cross-validasi menyatakan bahwa nilai accuracy 85,29%, nilai precision berupa 83,33%, nilai recall 85,29%.
Co-Authors Alghifari, Hamzah Nurul Abdillah Oki Dahwanu