Sistem Pakar Akurasi dalam Mengidentifikasi Penyakit Gingivitis pada Gigi Manusia dengan Metode Naive Bayes

Authors

  • Rifa Yuliza Independent Researcher

DOI:

https://doi.org/10.37034/jsisfotek.v5i1.157

Keywords:

Expert System, Identification, Gingivitis, Dental, Naive Bayes

Abstract

Gingivitis is a condition where the gums become inflamed due to a bacterial infection, causing the gums to swell. Gingivitis if treated too late will trigger more dangerous dental disease. Minimize knowledge about gingivitis and limited time to consult with experts so that people pay less attention to dental and oral health which can indicate gingivitis. The purpose of this study was to determine accuracy in identifying gingivitis disease using the Naive Bayes method, which can help the public to find out information about gingivitis, so a system with experts was built. Expert System is knowledge from experts that is entered into a computer or system that can be used for consultation. The data used in this study were 24 symptom data and 5 types of disease data sourced from interviews with an expert. The experts used in this study were dentists at the Rahmatan Lil Alamin Clinic. The data can be obtained from the results of the medical records of patients who perform examinations with dentists. The data processed in this study is knowledge about the symptoms and types of gingivitis in the teeth obtained from an expert. The result of the test is that the largest value of the total probability is found in the type of gum disease Puberty with a value of 0.000106795855. This research can help the community to conduct consultations easily, find out the symptoms early so that people do not have to go to the hospital with a long distance.

References

Suryani, L. (2021). Hubungan Pengetahuan Kebersihan Gigi dengan Gingivitis Pada Wanita Pubertas di MTSS Babah Krueng. Jurnal Mutiara Ners, 4(1), 1-4. DOI: https://doi.org/10.51544/jmn.v4i1.1216

Skripsa, T. H., Unique, A. A., & Hermawati, D. (2021). Hubungan Pengetahuan dan Tindakan Menjaga Kesehatan Gigi Mulut dengan Keluhan Subyektif Permasalahan Gigi Mulut pada Mahasiswa Kesehatan dan Non Kesehatan. e-GiGi, 9(1). DOI: https://doi.org/10.35790/eg.9.1.2021.32676

Rianti, E., Yenila, F., & Marfalino, H. (2021). Expert System System Deteksi Gingivitis Gigi Menggunakan Certainty Factor. Jurnal Teknologi, 11(2), 50-56. DOI: https://doi.org/10.35134/jitekin.v11i2.51

Khaleel, B. I., & Aziz, M. S. (2021, May). Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases. In Journal of Physics: Conference Series (Vol. 1897, No. 1, p. 012027). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1897/1/012027

Kaura, M. A., Bawa, H. M., Ekuase, E., & Bamgbose, B. O. (2021). Oral hygiene level and prevalence of gingivitis amongst pregnant women in a nigerian teaching hospital. Journal of Dentomaxillofacial Science, 6(1), 10-16. DOI: https://doi.org/10.15562/jdmfs.v6i1.1119

Senjaya, A. A., Arini, N. W., Ratmini, N. K., & Handayani, N. K. A. S. S. (2020). Hubungan Sextan yang Mengalami Gingivitis dengan Usia Kehamilan pada Ibu Hamil Di Puskesmas Manggis Ii Kabupaten Karangasem Tahun 2019. Jurnal Kesehatan Gigi (Dental Health Journal), 7(2), 53-58. DOI: https://doi.org/10.33992/jkg.v7i2.1260

Huang, S., He, T., Yue, F., Xu, X., Wang, L., Zhu, P., ... & Xu, J. (2021). Longitudinal multi-omics and microbiome meta-analysis identify an asymptomatic gingival state that links gingivitis, periodontitis, and aging. MBio, 12(2), e03281-20. DOI: https://doi.org/10.1128/mBio.03281-20

Andesti, C. L., Sumijan, S., & Nurcahyo, G. W. (2020). Expert System in Accuracy to Identify Gingivitis in Humans Using the Certainty Factor Method. Jurnal Informasi dan Teknologi, 97-104. DOI: https://doi.org/10.37034/jidt.v2i3.69

Restari, R. H., Sinurat, S., & Suginam, S. (2020). Rancangan Aplikasi Sistem Pakar Diagnosa Penyakit Mononukleosis Dengan Metode Naive Bayes. JURIKOM (Jurnal Riset Komputer), 7(3), 403-408. DOI: http://dx.doi.org/10.30865/jurikom.v7i3.2179

Băjenescu, T. M. (2020). Comparing artificial intelligence developments of superpowers: China and the US. Journal of Social Sciences, 3(3), 43-50. DOI: https://doi.org/10.5281/zenodo.3971959

Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., ... & Islam, S. R. (2020). Medical diagnostic systems using artificial intelligence (ai) algorithms: Principles and perspectives. IEEE Access, 8, 228049-228069. DOI: https://doi.org/10.1109/ACCESS.2020.3042273

Sarazin, A., Bascans, J., Sciau, J. B., Song, J., Supiot, B., Montarnal, A., ... & Truptil, S. (2021). Expert system dedicated to condition-based maintenance based on a knowledge graph approach: Application to an aeronautic system. Expert Systems with Applications, 186, 115767. DOI: https://doi.org/10.1016/j.eswa.2021.115767

Saibene, A., Assale, M., & Giltri, M. (2021). Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications, 177, 114900. DOI: https://doi.org/10.1016/j.eswa.2021.114900

Widodo, Y. B., Anggraeini, S. A., & Sutabri, T. (2021). Perancangan Sistem Pakar Diagnosis Penyakit Diabetes Berbasis Web Menggunakan Algoritma Naive Bayes. Jurnal Teknologi Informatika Dan Komputer MH. Thamrin, 7(1), 112-123. DOI: https://doi.org/10.37012/jtik.v7i1.507

Suherman, B. B. (2021). Sistem Pakar Diagnosa Penyakit Dan Hama pada Tanaman Jagung Menggunakan Metode Naive Bayes. Jurnal Informatika dan Rekayasa Perangkat Lunak, 2(3), 390-398. DOI: https://doi.org/10.33365/jatika.v2i3.1251

Nugroho, F. A., Solikin, A. F., Anggraini, M. D., & Kusrini, K. (2021). Sistem Pakar Diagnosa Virus Corona Dengan Metode Naïve Bayes. Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), 9(1), 81-88. DOI: http://dx.doi.org/10.30646/tikomsin.v9i1.553

Karim, F., Nurcahyo, G. W., & Sumijan, S. (2021). Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes. Jurnal Sistim Informasi dan Teknologi, 221-226. DOI: https://doi.org/10.37034/jsisfotek.v3i4.69

Yang, L., Fu, B., Li, Y., Liu, Y., Huang, W., Feng, S., ... & Bu, H. (2020). Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm. Computer methods and programs in biomedicine, 192, 105458. DOI: https://doi.org/10.1016/j.cmpb.2020.105458

Ramadhan, F. Z., Aditya, G., Nainggolan, P. D. Y., & Adhinata, F. D. (2021). Sistem Pakar Diagnosa Penyakit pada Hewan Kucing Berbasis Web. Jurnal Komtika (Komputasi dan Informatika), 5(2), 122-131. DOI: https://doi.org/10.31603/komtika.v5i2.5301

Hari, T. R. S., & Sumijan, S. (2021). Sistem Pakar dengan Menggunakan Metode Naive Bayes dalam Mengidentifikasi Penyakit Karies pada Gigi Manusia. Jurnal Sistim Informasi dan Teknologi, 233-238. DOI: https://doi.org/10.37034/jsisfotek.v3i4.71

Ramadhana, F., Fauziah, F., & Winarsih, W. (2020). Aplikasi Sistem Pakar untuk Mendiagnosa Penyakit ISPA menggunakan Metode Naive Bayes Berbasis Website. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 4(3), 320-329. DOI: http://dx.doi.org/10.30998/string.v4i3.5441

Amalia, M. M., Ernawati, E., & Wijanarko, A. (2022). Implementasi Metode Naïve Bayes Dalam Sistem Pakar Diagnosis Hama dan Penyakit Pada Tanaman Hias Aglaonema SP. Rekursif: Jurnal Informatika, 10(1), 23-39. DOI: https://doi.org/10.33369/rekursif.v10i1.18953

Narulita, D., & Yuhandri, Y. (2021). Sistem Pakar Dalam Menganalisis Tingkat Akurasi Keparahan Penyakit Erosi Gigi Menggunakan Metode Certainty Factor. Jurnal Informasi dan Teknologi, 239-244. DOI: https://doi.org/10.37034/jidt.v3i4.160

Mohapatra, S., & Anand, K. (2021). An expert system to implement symptom analysis in healthcare. Integration of Cloud Computing with Internet of Things: Foundations, Analytics, and Applications, 57-69. DOI: https://doi.org/10.1002/9781119769323.ch4

Zohra, F. T. (2020). Prediction of Different Diseases and Development of a Clinical Decision Support System using Naive Bayes Classifier. International Journal for Research in Applied Science and Engineering Technology, 8(5), 8-13. DOI: http://doi.org/10.22214/ijraset.2020.5002

Rahman, N. T. (2020). Analisa Algoritma Decision Tree dan Naive Bayes pada Pasien Penyakit Liver. JURNAL FASILKOM (teknologi inFormASi dan ILmu KOMputer), 10(2), 144-151. DOI: https://doi.org/10.37859/jf.v10i2.2087

Downloads

Published

31-08-2022

How to Cite

Yuliza, R. (2022). Sistem Pakar Akurasi dalam Mengidentifikasi Penyakit Gingivitis pada Gigi Manusia dengan Metode Naive Bayes. Jurnal Sistim Informasi Dan Teknologi, 5(1), 27–32. https://doi.org/10.37034/jsisfotek.v5i1.157

Issue

Section

Articles