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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Riset Informatika INTI Nusa Mandiri
Endang Sri Palupi
Universitas Bina Sarana Informatika
Computer Science & IT

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Journal : Jurnal Riset Informatika

 1 
ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION Endang Sri Palupi
Jurnal Riset Informatika Vol 4 No 1 (2021): Period of December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (703.608 KB) | DOI: 10.34288/jri.v4i1.279

Abstract

During the pandemic, most schools, campuses, and places of education conducted online teaching and learning activities. Many teaching and learning activities are carried out using the Zoom, Google, WebEx, or Microsoft Teams applications. All of this can be done through a PC or laptop, or using a cellphone, so the need for PCs and cellphones increases, both new and used goods. Even though during the pandemic the economic situation was declining, many companies suffered losses, resulting in a reduction in employees and causing a high unemployment rate, the need for Android phones remains high. In addition to online distance learning facilities, Android phones can also be used for online sales through e-commerce, market places, social media, and other digital platforms. Currently, Android phones have many choices and according to the funds we have, with various brands and specifications. Many brands issue android cellphone products with pretty good specifications and affordable prices, so that even though purchasing power has decreased due to the pandemic, sales of android cellphones are still high. In this study, the author predicts the highest sales of android cellphones using the Naïve Bayes method and the K-Nearest Neighbor method based on Particle Swarm Optimization accuracy of 81.33%.
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 Ponco Sulistyo, Febryo Ponco Sulistyo Richardus Eko Indrajit Said Mirza Pahlevi