Nora Trivetisia
Institut Teknologi Telkom Purwokerto

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Perbandingan Algoritme Naïve Bayes dan C4.5 Pada Pengklasifikasian Tingkat Pemahaman Belajar Mahasiswa Dalam Pembelajaran Daring Nora Trivetisia; Rima Dias Ramadhani; Merlinda Wibowo
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1081

Abstract

Online learning is a learning system that has been widely implemented since the Covid-19 Pandemic. This learning system is synonymous with the use of internet-based learning media. In practice, teachers often have difficulty knowing how far their students can understand the material being taught. Therefore, it is necessary to do a classification to make it easier for teachers to assess the level of understanding in terms of health, motivation, and teaching methods. Many classification algorithms can be used so that analysis is needed to find the best algorithm. This study focuses on comparative observations of two classification algorithms, namely Naïve Bayes and C4.5. The dataset used is the result of a student questionnaire at the Telkom Purwokerto Institute of Technology in the form of a Likert scale. The steps taken were data preprocessing and then classification using Naïve Bayes and C4.5. The result is that Naïve Bayes is superior to C4.5 with a Naïve Bayes testing accuracy of 99% compared to C4.5 with 91% accuracy. So, it can be concluded that Naïve Bayes is superior to C4.5 in this case.Keywords: Online Learning; Naïve Bayes; C4.5; Classification; Data Mining AbstrakPembelajaran daring adalah salah satu sistem pembelajaran yang ramai diterapkan sejak Pandemi Covid-19. Sistem pembelajaran ini identik dengan penggunaan media belajar berbasis internet. Dalam pelaksanaannya pengajar sering mengalami kesulitan untuk mengetahui sejauh mana mahasiswanya bisa menangkap materi yang diajarkan. Oleh karena itu, perlu dilakukan klasifikasi untuk mempermudah pengajar dalam menilai tingkat pemahaman dari segi kesehatan, motivasi, dan cara pengajaran. Banyak algoritme klasifikasi yang dapat digunakan sehingga dibutuhkan analisis untuk mencari algoritme terbaik. Penelitian ini berfokus pada pengamatan komparasi terhadap dua algoritme klasifikasi yaitu Naïve Bayes dan C4.5. Dataset yang digunakan adalah hasil kuesioner mahasiswa Institut Teknologi Telkom Purwokerto berbentuk skala Likert. Tahapan yang dilakukan adalah preprocessing data lalu dilakukan klasifikasi menggunakan Naïve Bayes dan C4.5. Hasilnya Naïve Bayes lebih unggul dari C4.5 dengan akurasi untuk pengujian Naïve Bayes sebesar 99% dibanding C4.5 dengan akurasi 91%. Maka, dapat disimpulkan bahwa Naïve Bayes lebih unggul daripada C4.5 pada kasus ini.Kata kunci: Pembelajaran Daring; Naïve Bayes; C4.5; Klasifikasi; Data Mining
Analisis Komparasi Algoritma Machine Learning untuk Sentiment Analysis (Studi Kasus: Komentar YouTube “Kekerasan Seksual”) Chandra Ayunda Apta Soemedhy; Nora Trivetisia; Nawang Anggita Winanti; Dwi Puspa Martiyaningsih; Tri Wulandari Utami; Sudianto Sudianto
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022): JPIT, Mei 2022
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3547

Abstract

Cases of sexual violence in the last decade have been rampant in Indonesia. Cases of sexual violence are increasingly exposed, along with the increasing use of social media. One of them is violence against women. Cases of sexual violence often cause various kinds of stigma in the community, so this study aims to determine the public's response to cases of sexual harassment using sentiment analysis. The data used is sourced from YouTube comments with the title "Kasus Bunuh Diri NW: Bripda Randy Tersangka, Penanganan Polisi Dikritik | Narasi Newsroom." The method used is Machine Learning algorithms such as the SVM algorithm, Naive Bayes, and Random Forest. The results of comparing the three Machine Learning algorithms, Random Forest, obtained the best accuracy rate of 78% compared to the other two algorithms in conducting sentiment analysis on YouTube comments about sexual harassment discussions.