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All Journal Journal of Educational Science and Technology POSITIF INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Teknologi dan Manajemen Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Seminar Nasional Teknologi Industri Berkelanjutan NERO (Networking Engineering Research Operation) JAR'S (Journal of Advanced Research in Informatics)
Hendro Nugroho, Hendro
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Journal : NERO (Networking Engineering Research Operation)

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KLASIFIKASI DIAGNOSIS DIABETES MELITUS MENGGUNAKAN METODE NAÏVE BAYES DENGAN SELEKSI FITUR BACKWARD ELIMINATION Nugroho, Hendro; Yuliastuti, Gusti Eka; Pradana, Andrean Firman
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.21110

Abstract

Diabetes mellitus is a dangerous disease caused by high sugar levels (hyperglycemia). Hyperglycemia can cause sufferers to experience chronic disease, damage to organs in the body. Diabetes mellitus is a dangerous disease, so it is very interesting to classify diabetes mellitus using the Naïve Bayes method with Backward Elimination (BE) feature selection. The Diabetes mellitus dataset used in the research consisted of 101 data with 5 attributes consisting of age, Current Blood Sugar (GDS), 2 hours after eating/Post Pradial (PP), Fasting Blood Sugar (GPD) levels, and Low Density Lipoprotein (LDL) . To get classification results, there are several steps taken, namely data input, BE feature selection, 8-Fold Cross Validation, Naïve Bayes and results testing. From the classification results, testing was carried out using the accuracy, precision and recall calculation method. To find out the results of classification performance, four test scenarios were carried out, namely the first scenario, Naïve Bayes combined with BE and 8-Fold Cross Validation, accuracy of 77%, second scenario, Naïve Bayes combined with 8-Fold Cross Validation, accuracy of 78.1%, third scenario, Naïve Bayes combined with BE accuracy is 86% and the fourth scenario of Naïve Bayes classification accuracy is 90%, so the accuracy of Naïve Bayes classification with BE feature selection is better.Keywords: Diabetes melitus, Naïve bayes, Backward Elimination. 8-Flod Cross Validation.
Co-Authors A.D., Chrisna Afriandhi, Mochamad Ilham Albana, Isa Andy Rachman Audrey, Talitha Naifa Augusta, Taufan Azmuri Wahyu Azinar Bayu Rizky Pratama, Bayu Rizky Dian Puspita Hapsari Evi Yuliawati Febri Liantoni, Febri Gusti Eka Yuliastuti Khamidah, Fitri Sofia Nur Lestari, Linda A. Maftahatul Hakimah, Maftahatul Muchamad Kurniawan, Muchamad Muhammad Afif Naili Saidatin Pradana, Andrean Firman Purwanto, Mochamad Yuliadi Putra, Moch Andria Reza Effendi Rahmawati, Lailia Rinci Kembang Hapsari S.Pd. M Kes I Ketut Sudiana . Suparjo Susilowati, Arda Gusema Tukadi Tukadi, Tukadi Tutuk Indriyani Wahyudi, Ahmad Lutfi Wardhana, Septiyawan Rosetya Wicaksono, Redi Nurdin