Analisis Sentimen Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine dan Lexicon Based

Bella, Bella (2024) Analisis Sentimen Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine dan Lexicon Based. S1 Sistem Informasi thesis, STMIK Widya Cipta Dharma.

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Abstract

Bella, 2024, Analisis Sentimen Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine Dan Lexicon Based. Skripsi Program Studi Sistem Informasi, Sekolah Tinggi Manajemen Informatika dan Komputer Widya Cipta Dharma. Pembimbing Utama Wahyuni, S.Kom., M.Kom., Pembimbing Pendamping Ita Arfyanti, S.Kom., M.M. Kata Kunci : Analisis Sentimen, Identitas Kependudukan Digital, Support Vector Machine, Lexicon Based, Pengguna Aplikasi. Aplikasi identitas kependudukan digital menjadi fokus utama dalam penelitian ini, dengan tujuan untuk menganalisis sentimen pengguna terhadap aplikasi tersebut. Penelitian ini menggunakan metode Support Vector Machine (SVM) dan pendekatan lexicon based untuk mengelompokkan sentimen pengguna menjadi kategori positif, negatif, atau netral. Data yang digunakan adalah hasil dari scraping ulasan yang ada di Google Play Store. Metode SVM digunakan untuk klasifikasi sentimen berdasarkan fitur�fitur teks dari tanggapan pengguna, sementara pendekatan lexicon based digunakan untuk memperkuat analisis sentimen dengan memanfaatkan kumpulan kata-kata berlabel sentimen. Hasil penelitian yang dilakukan menunjukkan bahwa metode SVM mampu mengklasifikasikan dengan akurasi sebesar 81%, presisi dan recall untuk sentimen negatif masing-masing 87%, untuk sentimen netral 61% dan 65%, serta untuk sentimen positif 84% dan 80%.Temuan dari penelitian ini diharapkan dapat memberikan wawasan yang berharga bagi pengembang aplikasi identitas kependudukan digital untuk meningkatkan pengalaman pengguna dan menjadi bahan evaluasi untuk kedepannya==========================================================================================================================================================================Bella, 2024, Sentiment Analysis of Digital Population Identity Applications Using Support Vector Machine and Lexicon Based Methods. Bachelor's Thesis, Information Systems Department, Widya Cipta Dharma School of Management and Computer Science, Primary Supervisor Ms. Wahyuni, S.Kom., M.Kom, Assistant Supervisor Ms. Ita Arfyanti, S.Kom., M.M. Keywords: Sentiment Analysis, Digital Population Identity, Support Vector Machine, Lexicon Based, Application Users. The digital population identity application is the main focus of this research, with the aim of analyzing user sentiment towards the application. This research uses the Support Vector Machine (SVM) method and a lexicon based approach to group user sentiment into positive, negative or neutral categories. The data used is the result of scraping reviews on the Google Play Store. The SVM method is used for sentiment classification based on text features from user responses, while the lexicon based approach is used to strengthen sentiment analysis by utilizing a collection of words labeled with sentiment. The results of the research conducted show that the SVM method is able to classify with an accuracy of 81%, precision and recall for negative sentiments respectively 87%, for neutral sentiments 61% and 65%, and for positive sentiments 84% and 80%. Findings from the research It is hoped that this will provide valuable insight for developers of digital population identity applications to improve user experience and serve as evaluation material for the future

Item Type: Thesis (S1 Sistem Informasi)
Additional Information: Pembimbing 1 : Wahyuni, S.Kom., M.Kom Pembimbing 2 : Ita Arfyanti, S.Kom., M.M
Uncontrolled Keywords: Analisis Sentimen, Identitas Kependudukan Digital, Support Vector Machine
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Divisions: Sistem Informasi
Depositing User: Bella Bella
Date Deposited: 08 Aug 2024 03:23
Last Modified: 08 Aug 2024 03:23
URI: http://repository.wicida.ac.id/id/eprint/5744

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