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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JST ( Jurnal Sains Terapan ) INOVTEK Polbeng - Seri Informatika ILKOM Jurnal Ilmiah JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Sistem Informasi dan Informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS Jurnal Algoritma JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang
Triyani Arita Fitri
STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Perbandingan Algoritma XGBoost dan SVM Dalam Analisis Opini Publik Pemilihan Presiden 2024 Safitri, Dea; Susanti; Rahmaddeni; Fitri, Triyani Arita
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4041

Abstract

Pemilihan presiden dipengaruhi oleh berbagai faktor, termasuk latar belakang kandidat, masalah politik, dan preferensi ideologis, menjadikan pemilihan presiden sebagai subjek klasifikasi yang kompleks dan menarik. Menganalisis sentimen publik terhadap kandidat dan isu-isu politik memberikan wawasan penting tentang dinamika politik selama pemilihan. Penelitian ini berfokus pada pemilihan presiden dan membandingkan kinerja dua algoritma klasifikasi populer, XGBoost dan SVM, untuk menentukan metode mana yang lebih efektif. Setelah beberapa preprocessing teks dari 562 tweet, kami menemukan bahwa mayoritas pengguna Twitter cenderung memilih 347 tweet "Prabowo". Model Extreme Gradient Boosting (XGBoost) menunjukkan performa terbaik dengan presisi 78%, presisi 76%, recall 78%, dan skor f1 76%. Hasil ini menunjukkan bahwa XGBoost adalah model terbaik untuk mengklasifikasikan opini publik terkait pemilihan presiden 2024 dan memberikan kontribusi penting untuk memahami efektivitas metode klasifikasi dalam konteks pemilihan presiden.
Sentiment Analysis for Online Learning using The Lexicon-Based Method and The Support Vector Machine Algorithm Anam, M. Khairul; Fitri, Triyani Arita; Agustin, Agustin; Lusiana, Lusiana; Firdaus, Muhammad Bambang; Nurhuda, Agus Tri
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1590.290-302

Abstract

The pros and cons regarding online learning has been a hot topic in society, both on social media and in the real world. Indonesian netizens still post opinions about online learning on social media such as Twitter. This study aims to analyze public comments to determine whether the trend of the comments is positive, negative, or neutral. The classification of netizen opinions is called sentiment analysis. This study applies 2 ways of carrying out sentiment analysis. The first stage employs the SVM algorithm with data labeling automatically obtained from the Emprit Academy drone portal while the second stage is still using the SVM algorithm but the data labeling with lexicon-based method. The results of this study are comparisons of labels obtained automatically from the Emprit Academy drone portal and labeling using lexicon based. The SVM algorithm obtains an accuracy of 90%, while the use of lexicon-based increases the accuracy value by 5% to 95%. It can be concluded that labeling data using a lexicon-based method can improve the accuracy of the SVM algorithm.
Co-Authors Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Ahmad Zamsuri, Ahmad Aldi Saputra Anam, M Khairul Boby Hasbul Fikri Bunga Nanti Pikir Cesmawati Cesmawati Cesmawati, Cesmawati Dewi Sari Wahyuni Edwar Ali Efrizoni, Lusiana Erleni - Erleni -, Erleni Erlin Erlin - Erlin -, Erlin Fatdha, Eiva Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Hadi Asnal, Hadi Hamdani Hamdani - Hamdani . Hanafiah, Anggi Hanafiah, Anggi Handoko Hazira, Nadila Herwin Herwin, Herwin Ismanizan, Ryan Johan Junadhi Junadhi Junadhi, Junadhi Karpen Kharisma Rahayu Koko Harianto Koko Harianto Lusiana Lusiana Efrizoni Lusiana Lusiana M Naofal Arsyad M. Mashudi Muhammad Bambang Firdaus Muhammad Jamaris Nu'man, Nu'man Nurhadi Nurhuda, Agus Tri Nurjayadi Nurjayadi Parlindungan Kudadiri Perty Putriani Saragih Praba Isna Tasya Rahmaddeni Rahmaddeni Rahmawati Rahmiati Rahmiati Rahmiati Rinaldi Yuslami Rini Yanti Rini Yanti, Rini Rio Ferdiansyah Rita Novita Safitri, Dea Susandri, Susandri SUSANTI Susanti Susanti Susanti, Susanti SUSI ERLINDA Syam, Salmaini Safitri T. Sy. Eiva Fatdha Teguh Sujana Torkis Nasution Widi Paramita Wirta Agustin Yuslami, Rinaldi Yusuf Nuron Zuriatul Khairi