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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Bulletin Format : Jurnal Imiah Teknik Informatika Jurnal Ilmiah FIFO Jurnal Riset Informatika Jurnal Ilmu Teknik dan Komputer Jurnal Pengabdian Masyarakat Nasional
Ponco Sulistyo, Febryo Ponco Sulistyo
Jurusan Teknik Informatika, Fakultas Ilmu Komputer, Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Journal : Jurnal Riset Informatika

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Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes Febryo Ponco Sulistyo; Endang Sri Palupi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.554

Abstract

Klasifikasi Faktor Blighted Ovum atau janin tidak berkembang dilakukan mengingat kasus Blighted Ovum banyak terjadi pada ibu hamil. Blighted Ovum merupakan 60% dari penyebab keguguran, di Indonesia ditemukan 37% dari setiap 100 kehamilan. Klasifikasi menggunakan Naïve Bayes berbasis Particle Swarm Optimization (PSO) yang hanya membutuhkan data training yang kecil untuk menentukan estimasi parameter yang diperlukan dalam proses pengklasifikasian dan penggunaan Particle Swarm Optimization dapat meningkatkan nilai akurasi serta memecahkan masalah optimasi. Dengan proses pemilihan data variable dan data attribute untuk membuat kuisioner sebagai metode pengambilan data. Hasil klasifikasi blighted ovum pada wanita hamil menggunakan algoritma Naïve Bayes dengan framework Rapid Miner mendapatkan nilai akurasi sebesar 71,56% dengan Area Under Curve (AUC) 0,674 termasuk dalam kategori klasifikasi yang baik. Setelah menggunakan optimasi PSO nilai akurasi naik menjadi 79,82% dengan Area Under Curve 0,764 termasuk kategori klasifikasi yang baik. Naïve bayes merupakan metode yang cocok untuk klasifikasi, dan PSO bisa membuat nilai akurasi dan AUC lebih baik lagi.
Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes Febryo Ponco Sulistyo; Endang Sri Palupi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.491 KB) | DOI: 10.34288/jri.v5i3.238

Abstract

Classification of Blighted Ovum Factors or undeveloped fetuses is carried out considering that many cases occur in pregnant women. Blighted Ovum is 60% of the causes of miscarriage. In Indonesia, it is found in 37% of every 100 pregnancies. Classification uses Naïve Bayes based on Particle Swarm Optimization (PSO), which only requires small training data to determine the parameter estimates needed in the classification process, and the use of Particle Swarm Optimization can increase accuracy and solve optimization problems with the process of selecting variable data and attribute data to create a questionnaire as a data collection method. The results of the classification of blighted Ovum in pregnant women using the Naïve Bayes algorithm with the Rapid Miner framework obtained an accuracy value of 71.56% with an Area Under Curve (AUC) of 0.674 included in the excellent classification category. After using the PSO optimization, the accuracy value rose to 79.82% with an Area Under the Curve of 0.764, including a good classification category. Naïve Bayes is a suitable method for classification, and PSO can improve the accuracy and AUC values .
Co-Authors Arief Agus Sukmandhani Chairul Anhar Tanof Eko Prasetyo Pratomo Endang Sri Palupi Indah Ramadhani Jumaryadi, Yuwan Mustika, Fandi Ali Rifqi Muhammad