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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan ILKOM Jurnal Ilmiah
Anggi Alberto
Universitas Gunadarma
Computer Science & IT

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Analisis Sentimen Produk Permainan menggunakan Metode TF-IDF dan Algoritma K-Nearest Neighbor Rifki Kosasih; Anggi Alberto
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 1 (2021): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i1.3893

Abstract

Pada situs belanja online, terdapat kolom komentar atau rating dari pembeli yang telah melakukan transaksi pada produk tersebut. Dengan adanya fitur penilaian produk berdasarkan rating tersebut, pihak pembeli dapat mengetahui seberapa baik atau buruknya produk tersebut. Akan tetapi muncul permasalahan dimana ada beberapa pembeli memberikan komentar negatif dengan rating sebesar lima bintang ataupun sebaliknya, hal tersebut menyebabkan fitur penilaian produk berdasarkan rating menjadi kurang baik. Oleh karena itu untuk dapat mengetahui kualitas produk tersebut dilakukan analisis sentimen dengan metode TF-IDF dan K-Nearest Neighbor (KNN) berdasarkan ulasan dari pembeli. Data yang dikumpulkan adalah 1000 ulasan yang dibagi menjadi 700 data latih dan 300 data uji. Tahapan selanjutnya dilakukan teks preprocessing seperti case folding (mengubah huruf besar menjadi kecil), tokenizing (pemisahan kalimat menjadi kata tunggal), stopword (menghilangkan kata sambung hasil tokenizing yang tidak ada hubungannya dalam analisis sentimen),  stemming (mengubah kata ke bentuk kata dasar) dan pembobotan kata dengan TF-IDF. Tahapan terakhir adalah melakukan klasifikasi dengan menggunakan metode K Nearest Neighbor (K-NN). Berdasarkan hasil klasifikasi diperoleh tingkat akurasi sebesar 79,3333%.
Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier Rifki Kosasih; Anggi Alberto
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.721.101-109

Abstract

In every product sold on the E-commerce platform, there is a review column from consumers who have made transactions on the products. These reviews are in the form of comments and ratings (stars from one to five) written and given by consumers based on their assessment of the products purchased. With the product evaluation feature based on the rating, the consumer can find out how good or bad the quality of the product is. However, a problem arises when some consumers give negative comments with five stars or vice versa. This causes the product assessment feature based on the rating to be less good so that it does not represent the real value. Therefore, to determine the quality of the product, sentiment analysis was carried out using the TF-IDF method and the Naive Bayes Classifier based on reviews from buyers. The data collected is 1000 reviews which are divided into 700 training data and 300 test data. The next stage is the preprocessing text such as case folding (converting uppercase letters to lowercase), tokenizing (separating sentences into single words), stopwords (removing tokenizing conjunctions that have nothing to do with sentiment analysis), stemming (changing words into basic word forms), and word weighting with TF-IDF. The last step is to classify. Based on the classification results obtained an accuracy rate of 80.2223%.
Co-Authors Rifki Kosasih