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All Journal Fountain of Informatics Journal JISKa (Jurnal Informatika Sunan Kalijaga) Smatika Jurnal : STIKI Informatika Jurnal
Trifebi Shina Sabrila
Universitas Muhammadiyah Malang
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor Trifebi Shina Sabrila; Veronica Retno Sari; Agus Eko Minarno
Fountain of Informatics Journal Vol 6, No 2 (2021): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v6i2.5536

Abstract

Analisis sentimen merupakan salah satu bidang dari pengolahan data berbentuk teks untuk mengidentifikasi isi yang terkandung dalam teks pada dataset dengan membagi dataset ke dalam dua kelas yaitu sentimen positif dan sentimen negatif. Pada penelitian ini akan dilakukan analisis sentimen terhadap data yang diperoleh dari jejaring sosial Twitter mengenai penanganan Covid-19 oleh pemerintah di Indonesia yang menuai banyak pro dan kontra oleh masyarakat di Indonesia. Tujuan dari penelitian ini adalah untuk mengetahui kecenderungan masyarakat terkait topik tersebut. Metode klasifikasi yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dengan ekstraksi fitur Word Embedding. Pengklasifikasian yang dilakukan dengan menggunakan algoritma Support Vector Machine (SVM) dengan menggunakan ekstraksi fitur Word Embedding yaitu Word2Vec menghasilkan akurasi sebesar 85%, presisi 86% , recall 85%, dan nilai AUC sebesar 0.92. Sementara pada algoritma K-Nearst Neighbor (KNN) dengan ekstraksi fitur yang sama, dihasilkan akurasi sebesar 76%, presisi 77%, recall 76% dan nilai AUC sebesar 0.87. Hasil perbandingan dari kedua metode menunjukkan bahwa algoritma Support Vector Machine (SVM) mendapatkan performa yang lebih baik dibandingkan algoritma K-Nearest Neighbor (KNN).
Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO Trifebi Shina Sabrila; Yufis Azhar; Christian Sri Kusuma Aditya
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 7 No. 1 (2022): Januari 2022
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.782 KB) | DOI: 10.14421/jiska.2022.7.1.10-19

Abstract

Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.
Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter Mujaddid Izzul Fikri; Trifebi Shina Sabrila; Yufis Azhar
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 10 No 02 (2020): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v10i02.455

Abstract

Twitter is one of the social media that is widely used by the public as a communication media and obtain information. Through this social media, users can submit various opinions or comments on an issue. The opinions and comments that users submit through the tweets they send can be used for sentiment analysis. Therefore, in this study sentiment analysis of tweets related to the University of Muhammadiyah Malang (UMM) was carried out to determine public opinion about this campus. The analysis was carried out by classifying tweets that contain people’s sentiments regarding UMM. The classification method used in this study is Naïve Bayes and Support Vector Machine (SVM) by weighting the term using TF-IDF. The result of the two methods shows that Naïve Bayes gets better accuracy than SVM with an accuracy of 73,65%
Co-Authors Agus Eko Minarno Christian Sri Kusuma Aditya Mujaddid Izzul Fikri Veronica Retno Sari Yufis Azhar