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All Journal Jurnal Teknologi Industri Pertanian Jurnal Radioisotop dan Radiofarmaka Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir Hunafa: Jurnal Studia Islamika Jurnal Informatika dan Teknik Elektro Terapan Jurnal Kimia dan Kemasan Diponegoro Law Review Media Kesehatan Politeknik Kesehatan Makassar community: Pengawas Dinamika Sosial Al-Falah: Jurnal Ilmiah Keislaman dan Kemasyarakatan VISI PUSTAKA: Buletin Jaringan Informasi Antar Perpustakaan Jurnal Ekonomi Manajemen Sistem Informasi JURNAL USM LAW REVIEW Jurnal Teknik Industri Terintegrasi (JUTIN) Bioscientist : Jurnal Ilmiah Biologi Jurnal Riset dan Inovasi Pembelajaran Jurnal Ilmiah Teunuleh: The International Journal of Social Sciences Action Research Literate (ARL) Indonesian Journal of Research and Educational Review (IJRER) Hunafa: Jurnal Studia Islamika Journal of Comprehensive Science Makara Journal of Technology International Journal of Contemporary Islamic Law and Society Talimi Jurnal Penelitian, Pengembangan Pembelajaran dan Teknologi (JP3T) Journal Juridisch Jurnal Gizi Ilmiah Jurnal Kesehatan Nusantara
Abidin Abidin
Pusat Teknologi Radioisotop dan Radiofarmaka Gd. 11 Puspiptek, Serpong, 15314
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Journal : Jurnal Informatika dan Teknik Elektro Terapan

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ANALISIS SENTIMEN ULASAN APLIKASI SIREKAP MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Abidin, Abidin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.7768

Abstract

SIREKAP is the official application of the General Election Commission (KPU) used for digital vote tabulation in the 2024 elections. Since its release on the Google Play Store, the application has received various responses from users, ranging from praise to complaints. This study aims to analyze user sentiment toward the SIREKAP application by applying the Support Vector Machine (SVM) algorithm. The data analyzed consists of 15,000 Indonesian-language reviews obtained through web scraping techniques. The research process includes text preprocessing (cleaning, tokenizing, and stemming), sentiment labeling into positive, negative, and neutral categories, and training the SVM model with a linear kernel. Model performance evaluation was conducted using a confusion matrix based on accuracy, precision, and recall metrics. The results showed that the SVM model was able to classify sentiment with high accuracy, reaching 99%. This study provides an overview of public perception of the SIREKAP application and can serve as evaluation material for developers and election organizers in the future.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Muthalib Abdullah Kelib, Abdullah Adang H. Gunawan Adang Hardi Gunawan Adawiah, Jumratul Adhi Susilo Adi Fadli Alwi Alwi Andi Nuddin Asep Wasid Aslam, Mohd Mizan Ayu Dwi Putri Rusman Basrin Ombo Chalid, Ibrahim Dadang Hafid Djoharly Caniago Dodoteng, Harisman E. Lestari, E. E. Sarmini Endang Sarmini Enny Lestari Eriyadi, Pebri Evi Sovilawati Fatmawati, Fatmawati Gunawan, Syahrul H. Setiawan Hasan, Edi Hastutiningsih, Ida Herlina Herlina Hotman Lubis Indra Saptiama Jutriano, Juan Kadarisman Kadarisman Karyadi Karyadi Laksmi Andri Astuti laodemuhamadsaefudin M Natsir M. Nasir Marlina, Marlina Muhammad Akbar Muhammad Husni Murniati Ruslan Mursyidin Mursyidin Mustafa Norlailah, Norlailah Purwoko Purwoko Ratlan Pardede Ristandi, Alvian Rohadi Awaludin Rosiana, Dhini Sapruddin Sapruddin Shine Pintor Siolemba Patiro Sitti Musyahidah Soegianto Soegianto Sofyan, Syafran Sri Aguswarini Sri Aguswarini Lestiawati Sri Bagiawati Sri Hastini Sri Setiyowati Sriyono Sriyono Sudarmanto, Kukuh Tasnim Tasnim Tasrif Akib, Tasrif Titasari, Andini Putri Triani Widyaningrum Tulus Suryanto Wajdi, Muhammad Widyastuti Widjaksana Yayan Tahyan Zaenal Arifin