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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Inspiration
Muchammad Chandra Cahyo Utomo
Institut Teknologi Kalimantan
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Other

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Perbandingan Fitur Pada Platform Kuis Terpopuler Muchammad Chandra Cahyo Utomo; M. Gilvy Langgawan Putra; Dwi Arief Prambudi
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 11, No 1 (2021): Jurnal Inspiration Volume 11 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v11i1.2596

Abstract

Because of the COVID-19 outbreak, many schools are applying an online learning activity at home. Because the students are learning at home, the teacher must bring the online exercise or something like that to measure the student’s knowledge. Google Forms, Microsoft Office Forms, Kahoot, and Quizizz can provide online quizzes, but these platforms have their own value point. This study is comparing and mapping the value points of these online quizzes’ platforms. The three main focuses in the comparison, are the variety of questions that can be displayed, the ease of understanding and processing the score of the quiz results, and the limitations in taking the quiz, such as when to do it and the duration of the quiz. Also, this study will give the recommendation of the best platform depending on your needs. Based on the comparison, Google Forms is the most featured platform for various purposes. But if you want to provide a new and enjoyable learning experience for your students, we recommend that you use the Kahoot platform. Keywords: Forms, Google, Kahoot, Microsoft, Quizizz
Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes Syamsul Mujahidin; Bagus Prasetio; Muchammad Chandra Cahyo Utomo
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 10, No 3 (2022): Vol. 10, No 3, September 2022
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v10i3.118299

Abstract

Youtube merupakan platform video terbesar di dunia dengan total pengguna sebanyak 1,5 miliar pada tahun 2018. Youtube menjadi salah satu platform penyedia informasi, salah satunya yakni kenaikan harga minyak mentah dunia hingga berada di atas US$100 per barel. Berdasarkan permasalahan tersebut, penulis melakukan penelitian terkait analisis sentimen dari komentar pengguna Youtube mengenai kenaikan harga BBM menggunakan metode Gaussian naïve bayes. Percobaan dilakukan menggunakan 3053 dataset dengan pelabelan menggunakan lexicon dan split data 8:2. Penerapan vektorisasi kata menggunakan word embedding Fasttext dan Bag of word sebagai pembanding terhadap akurasi. Percobaan dilakukan dengan kombinasi perbedaan dimensi size pada proses pembuatan language model fasttext. Berdasarkan hasil penelitian yang telah dilakukan, didapatkan nilai akurasi tertinggi pada percobaan dengan dataset tanpa filtering stopword dan model fasttext size 100 dengan akurasi sebesar 74%. Berdasarkan hasil evaluasi, sistem yang dibangun dapat mengklasifikasikan sentimen atau opini publik ke dalam sentimen positif dan sentiment negatif secara otomatis.Kata kunci : BBM, Fasttext, Lexicon, Gaussian naïve bayes, Word embedding Youtube is the largest video platform in the world with a total of 1.5 billion users in 2018. Youtube is one of the information provider platforms, one of which is the increase in world crude oil prices to above US$100/barrel. Based on these problems, the authors conducted research related to sentiment analysis from Youtube user comments regarding the increase in fuel prices using the Gaussian nave Bayes method. The experiment was carried out using 3053 datasets with labeling using lexicon and 8:2 data split. The vectorization uses Fasttext and BoW as a comparison of accuracy. The experiment was carried out with a combination of size dimensions fasttext. Based on the results of the research, the highest accuracy value was obtained in experiments with a dataset without stopword and fasttext size 100 with an accuracy of 74%. The system built can classify public sentiment into positive and negative sentiments automatically. Keywords: Fuel, Fasttext, Lexicon, Gaussian naïve bayes, Word embedding
Co-Authors Bagus Prasetio Dwi Arief Prambudi M. Gilvy Langgawan Putra Syamsul Mujahidin