ANALISIS SENTIMEN KEPUASAN PENGGUNA APLIKASI JAMSOSTEK MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE

PRATAMA, FAJAR ALVIYASIN (2024) ANALISIS SENTIMEN KEPUASAN PENGGUNA APLIKASI JAMSOSTEK MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The increased usage of the Workers' Social Security Guarantee (JAMSOSTEK) application as a platform for accessing information and services related to social security demands a thorough evaluation of user satisfaction. This research proposes a sentiment analysis using the Naive Bayes and Support Vector Machine (SVM) methods to understand user perceptions of the JAMSOSTEK application. The data used in this study includes reviews and feedback from users collected from various online sources. The Naive Bayes and SVM methods are employed to classify review sentiments into positive, negative, or neutral. The research findings will demonstrate how the comparison between the Naive Bayes and SVM methods provides a deeper understanding of user satisfaction with the JAMSOSTEK application. The sentiment analysis results using the Naïve Bayes and Support Vector Machine (SVM) algorithms show performance differences across various data split ratios. Naïve Bayes with a 90:10 ratio yields an accuracy of 93%. However, at an 80:20 ratio, the accuracy slightly decreases to 92%. SVM shows higher results with ratios of 90:10, 80:20, and 70:30 achieving accuracies of 95%. Thus, SVM generally demonstrates better performance compared to Naïve Bayes in terms of accuracy. Kata Kunci : Sentiment Analysis, Jamsostek Mobile, Data Classification, Naïve Bayes, Support Vector Machine Peningkatan penggunaan aplikasi Jaminan Sosial Tenaga Kerja (JAMSOSTEK) sebagai platform untuk akses informasi dan layanan terkait jaminan sosial menuntut evaluasi mendalam terhadap kepuasan pengguna. Penelitian ini mengusulkan sebuah analisis sentimen menggunakan metode Naive Bayes dan Support Vector Machine (SVM) untuk memahami persepsi pengguna terhadap aplikasi JAMSOSTEK. Data yang digunakan dalam penelitian ini mencakup ulasan dan feedback pengguna yang dikumpulkan dari berbagai sumber online. Metode Naive Bayes dan SVM digunakan untuk mengklasifikasikan sentimen ulasan menjadi positif, negatif, atau netral. Hasil penelitian akan menunjukkan bagaimana perbandingan metode Naive Bayes dan SVM memberikan pemahaman yang lebih mendalam tentang kepuasan pengguna aplikasi JAMSOSTEK. Hasil analisis sentimen menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM) menunjukkan perbedaan performa pada berbagai rasio pembagian data. Naïve Bayes dengan rasio 90:10 menghasilkan akurasi 93%. Tetapi rasio 80:20, akurasi sedikit menurun menjadi 92%. SVM menunjukkan hasil yang lebih tinggi dengan rasio 90:10, 80:20, 70:30 mencapai akurasi 95%. Dengan ini SVM umumnya menunjukkan kinerja lebih baik dibandingkan Naïve Bayes dalam hal akurasi. Kata Kunci : Analisis Sentimen, Jamsostek Mobile, Klasifikasi Data, Naïve Bayes, Support Vector Machine

Item Type: Thesis (S1)
Call Number CD: FIK/SI. 24 105
NIM/NIDN Creators: 41820010023
Uncontrolled Keywords: Analisis Sentimen, Jamsostek Mobile, Klasifikasi Data, Naïve Bayes, Support Vector Machine
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.5 General Purpose Application Programs/Program Aplikasi dengan Kegunaan Khusus
100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: khalimah
Date Deposited: 27 Jul 2024 03:20
Last Modified: 27 Jul 2024 03:20
URI: http://repository.mercubuana.ac.id/id/eprint/89862

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