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All Journal Jurnal Komtika (Komputasi dan Informatika) Jurnal Sains Komputer dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal Komtika (Komputasi dan Informatika)
Fitriana, Frizka
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes Fitriana, Frizka; Utami, Ema; Al Fatta, Hanif
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5185

Abstract

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.
Performance Analysis of SVM In Emotion Classification: A Comparative Study Of TF-IDF and Countvectorizer Fitriana, Frizka; Setiawan, Hendrik
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8396

Abstract

In today’s digital era, emotion analysis of social media comments plays a critical role in gaining deeper insights into user sentiment. This study aims to compare two text representation methods TF-IDF and CountVectorizer in enhancing the performance of the Support Vector Machine (SVM) algorithm for emotion classification. The dataset employed in this research is a subset of GoEmotions, consisting of 1,000 YouTube comments labeled with 27 distinct emotion categories. The dataset was split into training and testing sets with an 80:20 ratio. Both text representation methods were tested separately using a linear kernel in the SVM algorithm. The models were evaluated based on accuracy, precision, recall, and F1-score. The classification results show that TF-IDF slightly outperformed CountVectorizer in terms of accuracy (35% vs. 32%). However, CountVectorizer exhibited marginally better performance in precision and F1-score. These findings suggest that the choice of text representation significantly impacts emotion classification outcomes. This research contributes to the development of text-based emotion analysis systems for social media platforms.
ANALISIS ALGORITMA SUPPORT VECTOR MACHINE PADA OPINI MASYARAKAT MENGGUNAKAN TWITTER TERHADAP VAKSIN COVID-19: Support Vector Machine Algorithm Analysis On Public Opinion Using Twitter Against Covid-19 Vaccine Fitriana, Frizka
Jurnal Sains Komputer dan Teknologi Informasi Vol. 6 No. 2 (2024): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v6i2.7034

Abstract

Penyebaran virus covid-19 yang terjadi dan juga terdapat bahaya yang mengintainya maka butuh penangan untuk mencegah hal ini semakin berbahaya. Terdapat beberapa cara mencegah penyebaran virus covid-19 yaitu dengan pengembangan vaksin. Vaksin yang dikembangkan ini tidak hanya melindungi penderita covid-19 tetapi bisa juga mengurangi penyebaran penyakit covid-19.  Dengan mempertimbangankan pentingnya vaksin covid-19 ini Pemerintah Republik Indonesia telah mengeluarkan vaksinasi untuk masyarakat Indonesia pada akhir tahun 2020. Hal tersebut sudah dikonsultasikan kepada Indonesian Technical Advisory Group on Immunization (ITAGI) yang bertugas memberikan nasehat kepada Menteri Kesehatan. Dengan adanya vaksin covid-19 tentunya menuai pendapat berbagai pihak. Ada yang menerima vaksin covid-19 ini diterapkan ada juga yang menganggap hal ini tidak baik, selain itu ada juga masyarakat yang bersikap netral. Karena hal tersebut penulis melakukan penelitian mengenai analisis sentimen terhadap opini masyarakat media sosial twitter menggunakan algoritma Support vector machine. Pada penelitian ini akan menguji algoritma Support vector machine apakah sudah efektif dalam menguji 1000 data dengan hal yang diuji mengenai performa akurasi, waktu training dan juga nilai MAE. Dengan hal itu didapatkan hasil pengujian model algoritma Support vector machine memiliki nilai tingkat performa akurasi dengan nilai 85%, performa waktu training dengan nilai 40,60 detik, dan nilai MAE dengan nilai 0,001334 pada 1000 data.
SISTEM PENJADWALAN KULIAH BERBASIS WEBSITE PADA PERGURUAN TINGGI POLITEKNIK SAMPIT: Website-Based Lecture Scheduling System At Sampit Polytechnic College Fitriana, Frizka
Jurnal Sains Komputer dan Teknologi Informasi Vol. 7 No. 1 (2024): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v7i1.8794

Abstract

Proses penjadwalan merupakan hal yang krusial di dalam sebuah instansi pendidikan, Politeknik Sampit yang merupakan perguruan tinggi vokasi yang baru dikenal atau dalam proses berkembang masih terkendala dalam hal penjadwalan kuliah mengingat kurangnya ruangan atau fasilitas sehingga mengakibatkan penjadwalan kuliah dalam proses pembelajaran seringkali mengalami bentrok seperti ruangan yang ingin digunakan dalam waktu bersamaan. Untuk itu sistem penjadwalan kuliah berbasis Website di Politeknik sampit ini menggunakan metode skuensial linear atau biasa disebut metode waterfall. Sistem ini juga menggunakan bahasa pemrograman Hypertext Preprocessor (PHP) dan CSS (Cascading Style Sheet). Hasil penelitian menunjukan bahwa, sistem penjadwalan ini memenuhi syarat kepraktisan yang meliputi kemudahan penggunaan, kecepatan penyampaian informasi, serta cepat dalam penyimpanan dan pengambilan informasi yang dinilai praktis.
Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes Fitriana, Frizka; Utami, Ema; Al Fatta, Hanif
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5185

Abstract

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.
Co-Authors Ema Utami Hanif Al Fatta Hendrik Setiawan, Hendrik