Analisa Perbandingan Metode Certainly Faktor – Naive Bayes Terhadap Diagnose Penyakit Pneumonia
Keywords:
Comparison; Certainly Faktor; Naïve Bayes; PneumoniaAbstract
The development of science and technology is increasing rapidly, along with the progress of the times. Technology is becoming increasingly sophisticated and plays an important role in every aspect of life. currently growing rapidly. One disease that often attacks humans is pneumonia. Pneumonia is one of the diseases handled by the Estomihi Hospital on Jl. Sisingamangaraja No. 235 Kec. Medan City, Medan City. Pneumonia is a disease caused by viral bacteria and infects the respiratory tract. Diagnosis of pneumonia by using the certainty factor and naive Bayes methods is part of the expert system method which has a different way of working, resulting in a different percentage of confidence in the information. Given these differences, it is necessary to have an analysis that compares these two expert system methods to find and determine the best decision in diagnosing pneumonia. The percentage of Combination CF Value is only 55% while the percentage of Bayes Value is 67.6%.
References
F. Mulyani and N. Haliza, “Analisis Perkembangan Ilmu Pengetahuan dan Teknologi (Iptek) Dalam Pendidikan,” J. Pendidik. dan Konseling, vol. 3, no. 1, pp. 101–109, 2021, doi: 10.31004/jpdk.v3i1.1432.
M. Muhlis, A. P. Dyah, and R. Novalinda, “PERBAIKAN KLINIS PADA PNEUMONIA KOMUNITAS DI RS SWASTA KOTA YOGYAKARTA RELATIONSHIP WITH THE ACCURACY OF ANTIBIOTIC PRESCRIPTION BASED ON THE GYSSENS METHOD TO CLINICAL IMPROVEMENT IN COMMUNITY PNEUMONIA,” vol. 6, no. 1, pp. 1–19, 2022.
R. L. Abdjul and S. Herlina, “Asuhan Keperawatan Pada Pasien Dewasa Dengan Pneumonia?: Study Kasus Indonesian Jurnal of Health Development,” J. Heal. Dev., vol. 2, no. 2, pp. 102–107, 2020.
M. Marlina, W. Saputra, B. Mulyadi, B. Hayati, and J. Jaroji, “Aplikasi sistem pakar diagnosis penyakit ispa berbasis speech recognition menggunakan metode naive bayes classifier,” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 58–70, 2017, doi: 10.31849/digitalzone.v8i1.629.
A. S. L. T. T. H. Hafizah, “Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), no. Vol 1, No 6 (2022): EDISI NOVEMBER 2022, pp. 888–897, 2022, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jsi/article/view/5732/1938
Y. L. Nainel, E. Buulolo, and I. Lubis, “Penerapan Data Mining Untuk Estimasi Penjualan Obat Berdasarkan Pengaruh Brand Image Dengan Algoritma Expectation Maximization (Studi Kasus: PT. Pyridam Farma Tbk),” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 2, p. 214, 2020, doi: 10.30865/jurikom.v7i2.2097.
A. Rivandi, E. Bu’ulolo, and N. Silalahi, “Penerapan Metode Regresi Linier Berganda Dalam Estimasi Biaya Pencetakan Spanduk (Studi Kasus: PT. Hansindo Setiapratama),” Pelita Inform. Inf. dan Inform., vol. 7, no. 3, pp. 263–268, 2019.
P. Purwadi, P. S. Ramadhan, and N. Safitri, “Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 18, no. 1, pp. 55–61, 2019.
R. H. Sukarna and Y. Ansori, “Implementasi Data Mining Menggunakan Metode Naive Bayes Dengan Feature Selection Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. Ilm. Sains dan Teknol., vol. 6, no. 1, pp. 50–61, 2022, doi: 10.47080/saintek.v6i1.1467.
F. O. Lusiana, I. Fatma, and A. P. Windarto, “Estimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Simalungun,” J. Informatics Manag. Inf. Technol., vol. 1, no. 2, pp. 79–84, 2021, [Online]. Available: https://hostjournals.com/
Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
G. Gunadi and D. I. Sensuse, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth )?:,” Telematika, vol. 4, no. 1, pp. 118–132, 2012.
A. Z. Siregar, “Implementasi Metode Regresi Linier Berganda Dalam Estimasi Tingkat Pendaftaran Mahasiswa Baru,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 2, no. 3, pp. 133–137, 2021, [Online]. Available: https://tunasbangsa.ac.id/pkm/index.php/kesatria/article/view/73
S. S. S, A. T. Purba, V. Marudut, M. Siregar, T. Komputer, and P. B. Indonesia, “SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN PEMBERIAN PINJAMAN,” vol. 3, pp. 25–30, 2020, doi: 10.37600/tekinkom.v3i1.131.
Y. Yuliana, P. Paradise, and K. Kusrini, “Sistem Pakar Diagnosa Penyakit Ispa Menggunakan Metode Naive Bayes Classifier Berbasis Web,” CSRID (Computer Sci. Res. Its Dev. Journal), vol. 10, no. 3, p. 127, 2021, doi: 10.22303/csrid.10.3.2018.127-138.
J. Infokum, “Data Mining Using a Support Vector Machine , Decision Tree , Logistic Regression and Random Forest for,” vol. 10, no. 2, pp. 792–802, 2022.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisa Perbandingan Metode Certainly Faktor – Naive Bayes Terhadap Diagnose Penyakit Pneumonia
Published
Issue
Section
Copyright (c) 2024 Erniwati Zalukhu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).