Article Per Year (5 Year)
p-Index From 2019 - 2024
0.562
P-Index
This Author published in this journals
All Journal JSAI (Journal Scientific and Applied Informatics) SATIN - Sains dan Teknologi Informasi
Yusra
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Klasifikasi Sentimen Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode K-Nearest Neighbor Sayed Omas Tutus Arifta Sayed; Yusra; Muhammad Fikry
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5414

Abstract

There are many types of social media to gather information, share information and share news, one of which is Twitter. In this study, sentiment-based classification is carried out in two categories, namely positive and negative, by applying the k-Nearest Neighbor method to the figure of the governor of Central Java, Mr. Ganjar Pranowo. K-Nearest Neighbor is a method of classifying objects based on training data that uses the smallest distance or similarity from the object. At the learning stage, this algorithm stores only characteristic vectors and classifies the learning data. During the classification stage, the same features are calculated for the test data, the class of which is unknown. The distance from this new vector to the training data vector is calculated and the next K is taken. This study aims to obtain the value of accuracy using 4,000 data with negative and positive sentiment, each of which amounted to 2,000. After the tweet data is successfully retrieved from Twitter, the data is still raw and requires a preprocessing stage to produce clean data and ready for processing at a later stage. Calculation of the value of accuracy by classifying public sentiment on Twitter against Ganjar Pranowo using the K-Nearest Neighbor method in testing accuracy produces a pretty good accuracy value of 81% precision 81% recall 81%.
Klasifikasi Sentimen Masyarakat di Twitter terhadap Kenaikan Harga Bahan Bakar Minyak dengan Metode Modified K-Nearest Neighbor Sofiah; Yusra; Muhammad Fikry; Lola Oktavia
SATIN - Sains dan Teknologi Informasi Vol 9 No 1 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (963.796 KB) | DOI: 10.33372/stn.v9i1.988

Abstract

Kenaikan harga Bahan Bakar Minyak menjadi salah satu tranding topic di kalangan masyarakat Indonesia, baik di dunia nyata maupun dunia maya khususnya di media sosial Twitter. Perkembangan teknologi informasi yang sangat pesat memudahkan masyarakat dalam menyebarkan informasi di sosial media. Naiknya harga BBM memunculkan opini masyarakat yang mengandung sentimen positif dan negatif. Penelitian ini dilakukan untuk mengetahui sentimen publik terkait kebijakan pemerintah yang menaikkan harga BBM serta menerapkan metode Modified K-Nearest Neighbor dalam pengklasifikasian sentimen pengguna Twitter terhadap kenaikan harga BBM. Modified K-Nearest Neighbor merupakan salah satu metode klasifikasi berdasarkan kemunculan kelas terbanyak pada data latih. Data yang digunakan adalah tweet dalam bahasa Indonesia berdasarkan kata kunci “kenaikan BBM” dengan jumlah dataset sebanyak 3.000 tweet. Pembobotan kata dengan menggunakan TF-IDF untuk melakukan klasifikasi sentimen ke dalam dua kelas positif dan negatif. Hasil dari penelitian ini adalah klasifikasi sentimen terhadap kenaikan harga BBM. Akurasi tertinggi didapat 83.33% pada data opini menggunakan perbandingan 90:10 dan K=3.
Co-Authors Lola Oktavia Muhammad Fikry Sayed Omas Tutus Arifta Sayed Sofiah