ANALISIS SENTIMEN UNTUK PENILAIAN PELAYANAN SITUS BELANJA ONLINE MENGGUNAKAN ALGORITMA NAIVE BAYES

ANGGRAENI, LULU (2022) ANALISIS SENTIMEN UNTUK PENILAIAN PELAYANAN SITUS BELANJA ONLINE MENGGUNAKAN ALGORITMA NAIVE BAYES. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Online shopping is a shopping site that is favored by the public at this time because it offers various types of goods from what is needed to not needed, not only that, online shopping does not need to bother going to the store location, and of course buying and selling transactions are easy. Many people are satisfied with the services offered by the online shopping sites that people choose and many are dissatisfied. The opinion given by the public on the assessment of online shopping sites will be revealed on social media, one of which is twitter. This study will conduct a sentiment analysis on the assessment of online shopping customer opinions, namely Lazada, Bukalapak, Blibli and Tokopedia on Twitter which can later be used to determine the assessment of online shopping stores so that people are not wrong in choosing an online shopping store site. From the post of customer opinion, sentiment labeling will be carried out which produces 3 classes in the form of positive, negative and neutral after that pre-processing is carried out, namely data cleansing, case folding, tokenizing, stop word removal and stemming. The data that already has this data class will be divided into training data and test data with 3 scenarios of the nave Bayes classifier process and then the test data is used as a test for the nave Bayes classification. The results of the classification using the nave Bayes method showed an average accuracy of 45.92%. Key words: Online Marketplace, Sentiment Analysis, Twitter, Classification, Naive Bayes Belanja online merupakan situs belanja yang digemari oleh masyarakat pada saat ini dikarenakan menawarkan berbagai macem jenis barang dari yang diperlukan sampai tidak diperlukan, bukan hanya itu belanja online pun tidak perlu repot untuk dating kelokasi toko, dan pastinya transaksi jual beli pun mudah. Banyak masyarakat yang puas akan pelayanan yang dipersembahkan oleh situs belanja online yang masyarakat pilih dan banyak juga yang tidak puas. Pendapat yang diberikan masyarakat terhadap penilaian situs belanja online akan terungkap di media sosial, salah satunya adalah twitter. Penelitian ini akan melakukan analisis sentimen terhadap penilain opini pelanggan belanja online, yaitu Lazada, Bukalapak, Blibli dan Tokopedia pada twitter yang nantinya dapat digunakan untuk menentukan penilaian toko belanja online supaya masyarakat tidak salah dalam memilih situs toko belanja online. Dari postingan opini pelanggan yang di dapat akan dilakukan pelabelan sentiment yang menghasilkan 3 kelas berupa positive, negative dan netral setalah itu dilakukan pre-processing yaitu cleansing data, case folding, tokenizing, stop word removal dan stemming. Data yang sudah memiliki kelas data ini, akan di bagi menjadi data latih dan data uij dengan 3 skenario proses penglasifikasi naïve bayes lalu data uji digunakan sebagai pengujian pada penglasifikasian naïve bayes. Hasil klasifikasi dengan metode naïve bayes rata-rata menujukkan akurasi sebesar 45,92%. Kata kunci: Situs belanja online, analisis sentiment, twitter, klasifikasi, Naive Bayes

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 22 136
NIM/NIDN Creators: 41518110062
Uncontrolled Keywords: Situs belanja online, analisis sentiment, twitter, klasifikasi, Naive Bayes
Subjects: 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
200 Religion/Agama > 260 Christian Social Theology/Teologi Sosial Kristen > 268 Religious Education/Pendidikan Agama Kristen, Pengajaran Agama Kristen > 268.7 Services/Pelayanan
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 > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 20 Oct 2022 03:26
Last Modified: 20 Oct 2022 03:26
URI: http://repository.mercubuana.ac.id/id/eprint/70637

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