Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Health (JoH) Jurnal Ners
Mifta Aulia Ramadhani
Politeknik Perkapalan Negeri Surabaya
Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Algoritma Support Vector Machine (SVM) Untuk Diagnosis Kesehatan Manusia Berbasis Web Application Mifta Aulia Ramadhani; Agus Khumaidi
Jurnal Ners Vol. 9 No. 1 (2025): JANUARI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i1.31481

Abstract

Rumah sakit mempunyai peranan penting dalam kesehatan masyarakat. Namun, rumah sakit mempunyai banyak kekurangan salah satunya adalah dari segi pelayanan. Oleh karena itu, untuk meningkatkan efisiensi pelayanan rumah sakit dilakukan perancangan sistem diagnosis kesehatan manusia melalui aplikasi web berbasis kecerdasan buatan yaitu support vector machine. Support Vector Machine (SVM) merupakan algoritma supervised learning yang bekerja dengan cara mencari hyperplane antara dua kelas data hingga mendapatkan margin terbesar. SVM mempunyai beberapa keunggulan serta performa yang baik, seperti kemampuan generalisasi yang tinggi dan mempunyai fungsi kernel untuk digunakan pada dataset yang berdimensi tinggi sehingga sering digunakan di berbagai penelitian. Pengumpulan data dilakukan dengan penyebaran kuisioner kepada masyarakat umum dengan jumlah responden sebanyak 1.164 orang serta wawancara dengan expert judgement untuk menentukan 10 penyakit dan 40 gejala penyakit. Hasil penelitian menunjukkan tingkat akurasi pengujian diagnosis penyakit pasien mencapai 99%. Inovasi ini memungkinkan diagnosis gejala penyakit manusia dilakukan dengan lebih tepat dan cepat, sehingga diharapkan dapat meningkatkan produktivitas rumah sakit dan derajat kesehatan masyarakat Indonesia secara keseluruhan. Kata Kunci: Diagnosis Penyakit, Support Vector Machine (SVM), Rumah Sakit.
Perancangan Sistem Diagnosis Kesehatan Manusia Melalui Screening Digital Berbasis Desktop Application Menggunakan Metode Forward Chaining dan Neural Network: Design of a Human Health Diagnosis System Through Desktop Application-Based Digital Screening Using Forward Chaining and Neural Network Methods Indis Dwi Agustin; Nurahmad Hadi Cahyadi; Mifta Aulia Ramadhani; Mujtaba Fa’akuli Zazila; Am Maisarah Disrinama
Journal of Health Vol. 11 No. 1 (2024): Journal of Health (JoH) - January
Publisher : LPPM STIKES Guna Bangsa Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (253.921 KB) | DOI: 10.30590/joh.v11n1.702

Abstract

One of the problems with hospital services is that queues are important because they affect hospital productivity. Hospital queues can be caused by the large number of patients and the length of time the patient is treated. According to the Regulation of the Minister of Health of the Republic of Indonesia Number 30 of 2022, the standard of patient satisfaction with health services must reach ≥ 90% where one indicator is the length of waiting time. Long waiting times or queues can cause medical services to be less than optimal, especially for patients who have emergency complaints. Therefore, to increase the productivity of hospital services, a human health diagnosis system was designed through digital screening to make it easier for doctors to diagnose patient illnesses. The design of this disease diagnosis system was carried out using methods using forward chaining and neural network methods. This innovation is also equipped with severity level detection and treatment recommendations to patients. This research aims to create a knowledge model that can predict patient disease. The effectiveness test was carried out by taking 15 samples of respondents who had different symptoms. The results of this research were that the accuracy level of patient disease diagnosis testing reached 86.6% with the functional capability of the designed diagnostic application functioning at 100%. With this innovation, diagnosis of symptoms of human disease can be carried out precisely and precisely so that it can help medical personnel in increasing hospital productivity and the level of health in every community in Indonesia.
Co-Authors Agus Khumaidi Am Maisarah Disrinama Indis Dwi Agustin Mujtaba Fa’akuli Zazila Nurahmad Hadi Cahyadi