Published: 2022-01-03
Sistem Pakar Diagnosa Penyakit Kulit Kucing Menggunakan Metode Naive Bayes Berbasis Web
DOI: 10.35870/jtik.v6i3.466
Downloads
Abstract
This study aims to implement the Naive Bayes method in a skin disease diagnosis system in cats. The Naive Bayes method in this application system uses disease probability calculations, calculates the probability of symptoms, calculates the posterior probability of disease, and calculates the maximum value of each disease. This method is used to analyze the results of the diagnosis of skin diseases in cats based on the symptoms of skin diseases. Data analysis on this application system is based on the results of tests carried out 50 times to produce disease diagnosis results from the user and calculated by the system to produce the same diagnostic results as calculations performed manually. With this facility, cat owners can provide first aid to help their cat before being taken to the clinic and further examined by a veterinarian.
Keywords
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 6 No. 3 (2022)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2022
-
License: CC BY 4.0
-
Copyright: © 2022 Authors
-
DOI: 10.35870/jtik.v6i3.466
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Farhan Dwiramadhan
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
Mohammad Iwan Wahyuddin
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
Deny Hidayatullah
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
-
Samhan, L.F., Alfarra, A.H. and Abu-Naser, S.S., 2021. An Expert System for Knee Problems Diagnosis. International Journal of Academic Information Systems Research (IJAISR), 5(4).
-
Burhani, H.R., Fitri, I. and Andrianingsih, A., 2021. Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(3), pp.291-299.
-
Yunas, R.A.D., Triayudi, A. and Sholihati, I.D., 2021. Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(3), pp.338-345.
-
Harefa, K., 2020. Sistem Pendukung Keputusan Kelayakan Pemberian Pinjaman dengan Metode Analytical Hierarchy Process (AHP) dan Simple Additive Weighting (SAW). Jurnal Informatika Universitas Pamulang, 5(2), pp.136-145.
-
Gaihre, A., Wu, Z., Yao, F. and Liu, H., 2019, June. Xbfs: exploring runtime optimizations for breadth-first search on gpus. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (pp. 121-131).
-
Spampinato, D.G., Sridhar, U. and Low, T.M., 2019, June. Linear algebraic depth-first search. In Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (pp. 93-104).
-
Kartika, D. and Junaidi, A., 2018. Aplikasi Diagnosa Penyakit Lambung Dengan Metode Forward Chaining. Jurnal Teknologi Informatika dan Komputer, 4(2), pp.71-77.
-
Rizal, S. and Wali, M., 2020. The Impact of Using Technology (Technostress) with the Forward Chaining Method as a Decision Support System: The Impact of Using Technology (Technostress) with the Forward Chaining Method as a Decision Support System. Jurnal Mantik, 4(1), pp.521-527.
-
Adrianto, L.B., Wahyuddin, M.I. and Winarsih, W., 2021. Implementasi Deep Learning untuk Sistem Keamanan Data Pribadi Menggunakan Pengenalan Wajah dengan Metode Eigenface Berbasis Android. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(1), pp.89-96.
-
Fadhilah, A.M., Wahyuddin, M.I. and Hidayatullah, D., 2021. Analisis Faktor yang Mempengaruhi Perokok Beralih ke Produk Alternatif Tembakau (VAPE) menggunakan Metode K-Means Clustering. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(2), pp.219-225.
-
Ferdiansyah, Rizki, W. Muflikhah, Lailil. Adinugroho, Sigit. “Sistem Pakar Diagnosis Penyakit Pada Kambing Menggunakan Metode Naive Bayes dan Certainty Factorâ€. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 2, hlm. 451-458, Februari 2018.
-
Satya, Tungga, D. Hidayat, Nurul. Sutrisno. “Sistem Pakar Diagnosis Penyakit Sapi Ternak Potong Menggunakan Metode Naïve Bayes – Certainty Factorâ€. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 10, hlm. 3406-3410, Oktober 2018.
-
Ambica, A., dkk. 2013. An Efficient Expert System for Diabetes by Naïve Bayesian Classifier. Dadi Institute of Engineering and Technology (Affiliated to JNTUK). Andhra Pradesh.
-
KUSRINI. 2008. Aplikasi Sistem Pakar Menentukan Faktor Kepastian Pengguna dengan Metode Kuantifikasi Pertanyaan. Yogyakarta: Andi.
-
Kusrini. (2008). Sistem Pakar Teori dan Aplikasi. Yogyakarta: Penerbit Andi Offset. ISBN: 978-979-763-172-7.
-
Insani, Muhammad Ilham, Alamsyah, Anggyi Trimawan Putra. “Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methodsâ€.Vol. 5, No. 2, Nov 2018.
-
Sunardi dan Desi Saputra. 2011. Sistem Pakar Untuk Mendiagnosa Penyakit Pada Kucing Melalui Perangkat Mobile. Tugas Akhir. Palembang : STMIK GI MDP.
-
Nugroho, Fride. 2012. Sistem Pakar Untuk Mendiagnosa Penyakit Pada Kucing. Tugas Akhir. Semarang : UDINUS.
-
Informatikalogi.com (2017). Algoritma Naive Bayes.
-
Wijaya, B. A., & Tanjung, J. P. (2020). An Expert System For Diagnosis Eye Diseases On Human Using Certainty Factor Method Based Web.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.