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Sekolah Tinggi Ilmu Farmasi “YAYASAN PHARMASI” Semarang
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Journal : Jurnal Nasional Komputasi dan Teknologi Informasi

 1 
Analisis Sentimen Terhadap Aplikasi Parak Acil Online Berdasarkan Ulasan Masyarakat Menggunakan Metode Support Vector Machine (SVM) Mutmainah, Mutmainah; Cipta, Subhan Panji; Mambang, Mambang; Zulfadhilah, Muhammad; Naparin, Husni; Syapotro, Usman
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.7962

Abstract

Abstrak - Perkembangan teknologi informasi mempermudah akses layanan publik, termasuk aplikasi Parak Acil Online yang dikembangkan oleh Pemerintah Kota Banjarmasin untuk pengurusan dokumen administrasi. Sejak diluncurkan, aplikasi ini telah digunakan oleh puluhan ribu warga. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi dan mengevaluasi performa Support Vector Machine dalam klasifikasi ulasan. Metode penelitian yang digunakan adalah Support Vector Machine untuk mengklasifikasikan ulasan pengguna. Hasil analisis menunjukkan bahwa algoritma Support Vector Machine (SVM) dalam mengklasifikasikan data ulasan mendapatkan akurasi tertinggi pada pembagian data latih dan data uji 70:30 sebesar 85,1%, presisi 78,2%, dan recall 97,2%. Dari klasifikasi dan visualisasi, didapatkan kata-kata yang sering muncul pada sentimen positif yaitu “good”, “easy”,  dan “helpful” serta kata-kata yang sering muncul pada sentimen negatif yaitu “difficult”, “take” dan “feature”. Sentimen masyarakat terhadap aplikasi Parak Acil Online menunjukkan bahwa mayoritas ulasan masyarakat terhadap aplikasi ini bersifat positif, dan performa analisis sentimen menggunakan metode Support Vector Machine yang digunakan dalam penelitian ini terbukti efektif dalam mengklasifikasikan sentimen dari ulasan pengguna. Diharapkan penelitian ini dapat membantu pengembang dan pemangku kebijakan dalam meningkatkan kualitas aplikasi Parak Acil Online serta memahami kebutuhan masyarakat.Kata kunci: Analisis Sentimen, Aplikasi Parak Acil Online, Support Vector Machine, Textblob. Abstract - The advancement of information technology has facilitated access to public services, including the Parak Acil Online application developed by the Banjarmasin City Government for managing administrative documents. Since its launch, this application has been used by tens of thousands of residents. This study aims to analyze user sentiment towards the application and evaluate the performance of Support Vector Machine (SVM) in classifying reviews. The research method used is Support Vector Machine (SVM) to classify user reviews. The analysis results show that the Support Vector Machine (SVM) algorithm achieves the highest accuracy in classifying review data with a 70:30 train-test split, reaching 85.1% accuracy, 78.2% precision, and 97.2% recall. Classification and visualization reveal that frequently occurring words in positive sentiment include "good," "easy," "helpful," and "fast," while frequently occurring words in negative sentiment include "difficult," "document," "take," and "feature." The sentiment of the public towards the Parak Acil Online application indicates that the majority of reviews are positive. The performance of sentiment analysis using the Support Vector Machine method employed in this study has proven effective in classifying sentiment from user reviews. It is hoped that this research can assist developers and policymakers in improving the quality of the Parak Acil Online application and understanding community needs.Keywords: parak acil online application, sentiment analysis, support vector machine, textblob.
Co-Authors A, Muhammad Tahir Abdjul, Nur Kurnia Agung Budi Santoso Agus Hermanto Agus Purwowidodo Akhmadi Akhmadi Al-Fikriah, Najwa Azkiatul Fadilah Anggraini, Fitria Anugrah, Dinda Rahmawati Anuli, Wadan Anuli, Wadan Y Ardianto Ardianto Asma, Andi Asrul Asrul Bagiana, Kadek Binol, Astria Cipta, Subhan Panji Darise, Gina Nurvina DEWI SARTIKA Egayani, Egayani Ekayanti, Ekayanti F, Yuvianti D Farhansyah, Muhammad Franyoto, Yuvianti Dwi Gonibala, Rukmina Halimatussa'diah, Halimatussa'diah Hanna Sundari Hilyatin, Nadia I Kadek Bagiana Ika Puspitaningrum Ilham, Abrari Ilhamsyah Indartini, Mintarti Juhana, Juhana Jusriadi Kadir Kadir Kalayo Hasibuan Kamil, Athiyyah Rosyadah Nurwidjaya Karim, Jamalia Kusumastuti, Frida Restu Rizki Lahabu, Nabila Nasria Ramdjani Lakari, Fitri Lamasese, Nur Ifnaita Lewar, Emanuel Suban Bala Lia Kusmita Lukita, Cahya Mabrur, M. Fadli Makalalag, Ditha Regita Mambang Mamonto, Cici Prishelia Mamonto, Suci Marliani Matawae, Muhammad Firmansyah Mayang Sari Mikrayanti, Mikrayanti Mokoagow, Deby Amalia Muchlis Muchlis Muhammad Zulfadhilah Mulyadin, Edi Murtalib, Murtalib Naparin, Husni Nikmah, Masfi'atun Nur Baeti, Nur Nur Fadli Utomo Nurnaningsih Nurnaningsih, Nurnaningsih Oktaviana Purnamasari Panudju, Andreas Tri R, Ungsari Rajak, Andri Ramadhan Tosepu Ramadhona, Cindy Lutvi Reksamunandar, Rhyan Prayuddy Rosmini Rosmini Sahibe, Nurhayati Sanusi, Fauji Saputera, Selamet Saputra, M. Wahyu Hadi Sari, Nursina Satriani Satriani Septian, Esa Setiawati, Maria Caecilia Nanny Sigit Setiawan Silviana, Dewi Siti Munisih Soendoro, Aries K. Sowanto, Sowanto Sri Endang Sri Haryanti Sri Wulandari Surani, Dewi Suryani, Emma Syaharani, Cinta Nadhifa Syapotro, Usman Tria Patrianti Trian Pamungkas Alamsyah, Trian Pamungkas Tungkagi, Calvina Izumi Putri Tusi, Juandri Seprianto Umi Marfuah Vanchapo, Antonius Rino Waladiri, Djuhria Wardhana, Dewi Wawo, Balbina Antonelda Marled Wildaniyati, Arini Wiyli Yustanti Wulandari Wulandari Yuanyuan Wang, Yuanyuan Zainal, Nur Fitriani Zou, Guijiao