Ardiansyah, Moch. Yugas (2019) Penerapan Term Frequency - Modified Inverse Document Frequency Pada Analisis Sentimen Ulasan Barang Menggunakan Metode Learning Vector Quantization. Sarjana thesis, Universitas Brawijaya.
Abstract
Pada toko-toko online terdapat suatu ulasan barang yang berisi komentar berupa umpan balik dari pembeli sebelumnya yang berguna untuk pembeli selanjutnya maupun penjual pada toko online. Ulasan biasanya terdiri dari komentar negatif atau komentar positif. Namun umumnya ulasan tersebut memiliki jumlah yang sangat banyak. Dalam mengatasi masalah tersebut dibutuhkan sentimen analisis. Penelitian ini menggunakan metode Learning Vector Quantization dan Term Frequency-Modified Inverse Document Frequency. Metode LVQ dipilih karena memiliki keunggulan dapat meringkas dataset menjadi vector codebook. Data yang digunakan terdiri 250 komentar positif dan 250 komentar negatif. Data tersebut akan dilakukan proses preprocessing, pembobotan kata menggunakan TF-mIDF dan hasilnya diklasifikasikan menggunakan metode LVQ. Hasil pada pengujian parameter LVQ diperoleh nilai akurasi sebesar sebesar 75,11%, recall sebesar 75,11% precision sebesar 77,80%, f-measure sebesar 76,43% dengan nilai parameter learning rate 10-3, dec α 10-6, dan nilai maksimum epoch 19. Berdasarkan hasil pengujian akhir, diperoleh nilai dari metode Learning Vector Quantization dengan pembobotan TF-mIDF menghasilkan rata-rata akurasi sebesar 72,47%, recall sebesar 72,47%, precision sebesar 76,39%, dan f-measure sebesar 74,33% dan menggunakan metode Learning Vector Quantization dengan pembobotan TF-IDF menghasilkan rata-rata akurasi sebesar 54,80%, recall sebesar 54,80%, precision sebesar 54,30%, dan f-measure sebesar 52,61%
English Abstract
In online stores there are reviews of items that contain comments about feedback from previous buyers that are useful for subsequent buyers as well as sellers at online stores. Reviews Usually consist of negative comments or positive comments. The number of reviews is very much. In overcoming this problem, sentiment analysis is needed. This study uses the Learning Quantization Vector and Term Frequency-Modified Inverse Document Frequency methods. The LVQ method was chosen because it has the advantage of being able to summarize the dataset into a codebook vector. The data used consisted of 250 positive comments and 250 negative comments. The data will be preprocessing, weighting the word using TFmIDF and consequently using the LVQ method. The results of testing the LVQ parameters obtained an accuracy value of 75.11%, recall of 75.11% precision of 77,80%, f-measure of 76.43% with parameter values of learning rate 10-3, dec α 10-6, and values maximum epoch 19. Based on the final test results, obtained the value of the Learning Vector Quantization method with TF-mIDF resulted in an average accuracy of 72.47%, recall of 72.47%, precision of 76.39%, and f-measure of 74.33 % and using the Learning Vector Quantization method with TF-IDF resulted in an average accuracy of 54.80%, recall of 54.80%, precision of 54.30%, and f-measure of 52.61%.
Other obstract
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| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FILKOM/2019/457/051905840 |
| Uncontrolled Keywords: | sentimen analisis, ulasan barang, komentar, Learning Vector Quantization, Term Frequency – Modified Inverse Document Frequency, sentiment analysis, reviews, comments, Learning Vector Quantization, Term Frequency - Modified Inverse Document Frequency. |
| Subjects: | 000 Computer science, information and general works > 006 Special computer methods > 006.7 Multimedia systems > 006.78 Programs > 006.787 6 Wireless communication systems |
| Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
| Depositing User: | Nur Cholis |
| Date Deposited: | 24 Aug 2020 06:55 |
| Last Modified: | 25 Oct 2021 02:34 |
| URI: | http://repository.ub.ac.id/id/eprint/172013 |
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