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All Journal Building of Informatics, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science
Andriyani, Dwi Ratna
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Computer Science & IT

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Analisis Sentimen Masyarakat Terhadap Penghapusan Honorer Berdasarkan Opini Dari Twitter Menggunakan Naïve Bayes Classifier Andriyani, Dwi Ratna; Afdal, M; Monalisa, Siti
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3541

Abstract

The removal of honorees is currently a hot topic throughout Indonesia. Sharing how honorary personnel do so that the honorary removal policy is not implemented. Most honorary personnel have served for several years, but the government has issued a circular on the abolition of honorees. Various pros and cons of society regarding the abolition of honorees, such as honorary workers can lose their jobs, not get income, and unemployment is increasing. The purpose of the study is that the government can provide strategies that must be carried out in the event of the removal of honorees, such as appointing all honorees to become Civil Servants or Government Employees with Work Agreements. So the removal of the honoree became one of the trending topics on Twitter social media in 2022. From the results of the analysis conducted, public opinion that uses Twitter is very influential for honorary workers by grouping opinions into three categories, namely positive opinions, neutral opinions, and negative opinions. So the study with text mining used the Naïve Bayes Classifier algorithm with data from Twitter tweets from January 2022 to December 2022 with 2,705 data. The results of this study obtained accuracy with 10 K-fold Cross Validation on K-10, which was 73.01%. And it was found that sentiment polarity against the removal of honorees on positive class sentiment by 10% against agreeing to remove honorees with 285 data tweets, neutral class sentiment by 67% against agreeing and disagreeing with the removal of honorees with 1,801 data tweets, and negative class sentiment by 23% against disagreeing with the removal of honorees with 619 data tweets
Prediksi Risiko Stunting pada Keluarga Menggunakan Naïve Bayes Classifier dan Chi-Square: Prediction of Stunting Risk In Families Using Naïve Bayes Classifier and Chi-Square Gurning, Umairah Rizkya; Octavia, Sania Fitri; Andriyani, Dwi Ratna; Nurainun, Nurainun; Permana, Inggih
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1074

Abstract

Stunting merupakan sesuatu yang berbahaya pada manusia karena dapat menyebabkan terjadinya hambatan pertumbuhan serta perkembangan organ lainnya termasuk otak, jantung dan ginjal. Meningkatnya kasus stunting pada balita memerlukan suatu upaya dalam penanganan dan pencegahan secara dini. Terdapat 17 atribut pada data stunting yang harus diperhatikan, dengan banyaknya atribut tersebut menyebabkan sulitnya menemukan atribut yang paling berpengaruh dalam memprediksi stunting. Pada penelitian ini diterapkan seleksi fitur menggunakan Chi Square dan menerapkan Algoritma Naïve Bayes untuk menemukan atribut yang harus diprioritaskan dalam memprediksi stunting. Hasil prediksi dengan menggunakan Naive bayes saja pada penelitian ini didapatkan nilai akurasi sebesar 94,3 %, nilai recall sebesar 93,9 % dan nilai precision sebesar 93,93% dengan waktu 0,07 detik. Sedangkan dengan menerapkan seleksi fitur Chi square pada penelitian ini diperoleh 5 atribut yang paling berpengaruh terhadap prediksi stunting yang dapat meningkatkan kecepatan pembentukkan model Algoritma Naiva Bayes dengan waktu 0,01 detik, namun tidak dapat meningkatkan akurasi, recall dan presisi. Harapannya instansi terkait dapat lebih memperhatikan dan memprioritaskan ke-5 atribut tersebut sebagai pemantauan prediksi stunting di Kota Dumai.
Co-Authors Gurning, Umairah Rizkya Inggih Permana M. Afdal Nurainun Nurainun Octavia, Sania Fitri Siti Monalisa