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All Journal Jurnal Penelitian dan Pengembangan Pendidikan Pendidikan Bahasa dan Sastra Indonesia Poetika: Jurnal Ilmu Sastra Marwah: Jurnal Perempuan, Agama dan Jender Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Mimbar Sekolah Dasar AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Pendas : Jurnah Ilmiah Pendidikan Dasar Surya Abdimas DIKDAS MATAPPA: Jurnal Ilmu Pendidikan Dasar Bahastra: Jurnal Pendidikan Bahasa dan Sastra Indonesia Kumawula: Jurnal Pengabdian Kepada Masyarakat Sasando : Jurnal Bahasa, Sastra Indonesia, dan Pengajarannya Aksara: Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia Metafora: Jurnal Pembelajaran Bahasa Dan Sastra Jurnal Locus Penelitian dan Pengabdian Sinar Dunia: Jurnal Riset Sosial Humaniora dan Ilmu Pendidikan Jurnal Bima: Pusat Publikasi Ilmu Pendidikan Bahasa dan Sastra Jurnal Informatika: Jurnal Pengembangan IT Literasi (Jurnal Pendidikan Dasar) Literature and Literacy Jurnal Pendidikan Guru Profesional
Ika Septiana
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Journal : Jurnal Informatika: Jurnal Pengembangan IT

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Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024 septiana, ika; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6640

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.
Co-Authors Agus Wismanto Ahmad Ari Aldino Ahmad Rifai Ahmad Ripai Aini, Miladia Nur Alita, Debby Annisa, Zuraida Jihan Asropah Asropah Budiawan, Raden Yusuf Christina Wahyuningsih Eny Junyanti Fatmawati, Fika Febryansyah, Rido Fitriyani, Irma Rizqia Harjito - Indriati, Theodora Jumarni Jumarni Khotimah, Hidayatul Kukuh Andriawan Sulistio Putra Lulud Oktaviani Maziana, Nabilla Putri Mei Fita Asri Untari Muhajir Muhajir, Muhajir Muhammad Fakhri Rosyada Mukhlis Mukhlis Nazla Maharani Umaya ningtyas, Kurnia rimadhanti Nisa, Erika Wahidatun Nur’aini, Anisa Pudjowati, Anis Tri Rahayu, Winarni Rahmaningrum, Friska Safitri, Indah Shofia Rani, Ika Shofia Rani Siti Wahyuni Sofya Yulianti, Risa Dwi Sri Suciati SUAIDAH SUAIDAH, SUAIDAH Sudaryono Teguh Wibowo Ulfiana, Ulfiana Uluwwi, Muhammad Ulul Warkito, Petrus Joko Winaryuni, Nevia Indah