Analisis Sentimen terhadap Ulasan Pelanggan UB Press menggunakan Metode Learning Vector Quantization
Kata Kunci:
analisis sentimen, ulasan pelanggan, lexicon-based features, tf-idf, learning vector quantizationAbstrak
naskah ini akan diterbitkan di JUST-SI
Referensi
Akbari, M. I. H. A. D., Novianty, A., & Setianingsih, C. (2017). Analisis Sentimen Menggunakan Metode Learning Vector Quantization. E-Proceeding of Engineering, 4(2), 2283–2292. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/825
Ardiansyah, Moch. Y., Fuzi, M. A., & Adinugroho, S. (2019). Penerapan Term Frequency - Modified Inverse Document Frequency pada Analisis Sentimen Ulasan Barang menggunakan Metode Learning Vector Quantization. In Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (Vol. 3, Issue 6).
Bannan-Ritland, B. (2003). The Role of Design in Research: The Integrative Learning Design Framework. Educational Researcher, 32(1), 21–24. https://doi.org/10.3102/0013189X032001021
Batistič, S., Černe, M., & Vogel, B. (2017). Just how multi-level is leadership research? A document co-citation analysis 1980–2013 on leadership constructs and outcomes. The Leadership Quarterly, 28(1), 86–103. https://doi.org/10.1016/j.leaqua.2016.10.007
Bharti, S. K., & Babu, K. S. (2017). Automatic Keyword Extraction for Text Summarization: A Survey. https://arxiv.org/abs/1704.03242
Deolika, A., Kusrini, K., & Luthfi, E. T. (2019). ANALISIS PEMBOBOTAN KATA PADA KLASIFIKASI TEXT MINING. JURNAL TEKNOLOGI INFORMASI, 3(2), 179. https://doi.org/10.36294/jurti.v3i2.1077
Desai, M., & Mehta, M. A. (2016). Techniques for sentiment analysis of Twitter data: A comprehensive survey. 2016 International Conference on Computing, Communication and Automation (ICCCA), 149–154. https://doi.org/10.1109/CCAA.2016.7813707
Fausett, L. V. (1993). Fundamentals of Neural Networks Architectures, Algorithms, and Applications (1st ed.). Pearson. https://dl.matlabyar.com/siavash/Neural%20Network/Book/Fausett%20L.-Fundamentals%20of%20Neural%20Networks_%20Architectures,%20Algorithms,%20and%20Applications%20(1994).pdf
Hariri, F. R., Utami, E., & Amborowati, A. (2015). Learning Vector Quantization untuk Klasifikasi Abstrak Tesis. Creative Information Technology Journal, 2(2), 128. https://doi.org/10.24076/citec.2015v2i2.43
Indrayanto, C. G., Ratnawati, D. E., & Rahayudi, B. (2023). Analisis Sentimen Data Ulasan Pengguna Aplikasi MyPertaminadi Indonesia pada Google Play Storemenggunakan Metode Random Forest. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(3), 1131–1139.
Kurniawan, A., Indriati, & Adinugroho, S. (2019). Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dan Lexicon Based Features. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(9), 8335–8342.
Laxmi, M. D., Indriati, & Fauzi, M. A. (2019). Query Expansion Pada Sistem Temu Kembali Informasi Berbahasa Indonesia Dengan Metode Pembobotan TF-IDF Dan Algoritme Cosine Similarity Berbasis Wordnet. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(1), 823–830. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4223
Lestari, D. W. P., Perdana, R. S., & Adikara, P. P. (2019). Klasifikasi Video Clickbait Pada Youtube berdasarkan Analisis Sentimen Komentar menggunakan Learning Vector Quantization (LVQ) dan Lexicon-Based Features. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(2), 1184–1189. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4326
Munir, M. M., Fauzi, M. A., & Perdana, R. S. (2018). Implementasi Metode Backpropagation Neural Networkberbasis Lexicon Based Featuresdan Bag of WordsUntuk Identifikasi Ujaran Kebencian Pada Twitter. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(10), 3182–3191. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/2573
Park, S.-M., & Kim, Y.-G. (2021). Root Cause Analysis Based on Relations Among Sentiment Words. Cognitive Computation, 13(4), 903–918. https://doi.org/10.1007/s12559-021-09872-3
Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277
Rofiqoh, U., Perdana, R. S., & Fauzi, M. A. (2017). Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features. 1(12), 1725–1732. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/628
Sejarah-Universitas Brawijaya Press. (n.d.).
Sumanjaya, A. A. A., Indriati, & Ridok, A. (2022). Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naive Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(4), 1865–1872.
Vanaja, S., & Belwal, M. (2018). Aspect-Level Sentiment Analysis on E-Commerce Data. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 1275–1279. https://doi.org/10.1109/ICIRCA.2018.8597286
Visi, Misi, dan Maklumat Pelayanan-Universitas Brawijaya Press. (n.d.).
Akbari, M. I. H. A. D., Novianty, A., & Setianingsih, C. (2017). Analisis Sentimen Menggunakan Metode Learning Vector Quantization. E-Proceeding of Engineering, 4(2), 2283–2292. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/825
Ardiansyah, Moch. Y., Fuzi, M. A., & Adinugroho, S. (2019). Penerapan Term Frequency - Modified Inverse Document Frequency pada Analisis Sentimen Ulasan Barang menggunakan Metode Learning Vector Quantization. In Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (Vol. 3, Issue 6).
Bannan-Ritland, B. (2003). The Role of Design in Research: The Integrative Learning Design Framework. Educational Researcher, 32(1), 21–24. https://doi.org/10.3102/0013189X032001021
Batistič, S., Černe, M., & Vogel, B. (2017). Just how multi-level is leadership research? A document co-citation analysis 1980–2013 on leadership constructs and outcomes. The Leadership Quarterly, 28(1), 86–103. https://doi.org/10.1016/j.leaqua.2016.10.007
Bharti, S. K., & Babu, K. S. (2017). Automatic Keyword Extraction for Text Summarization: A Survey. https://arxiv.org/abs/1704.03242
Deolika, A., Kusrini, K., & Luthfi, E. T. (2019). ANALISIS PEMBOBOTAN KATA PADA KLASIFIKASI TEXT MINING. JURNAL TEKNOLOGI INFORMASI, 3(2), 179. https://doi.org/10.36294/jurti.v3i2.1077
Desai, M., & Mehta, M. A. (2016). Techniques for sentiment analysis of Twitter data: A comprehensive survey. 2016 International Conference on Computing, Communication and Automation (ICCCA), 149–154. https://doi.org/10.1109/CCAA.2016.7813707
Fausett, L. V. (1993). Fundamentals of Neural Networks Architectures, Algorithms, and Applications (1st ed.). Pearson. https://dl.matlabyar.com/siavash/Neural%20Network/Book/Fausett%20L.-Fundamentals%20of%20Neural%20Networks_%20Architectures,%20Algorithms,%20and%20Applications%20(1994).pdf
Hariri, F. R., Utami, E., & Amborowati, A. (2015). Learning Vector Quantization untuk Klasifikasi Abstrak Tesis. Creative Information Technology Journal, 2(2), 128. https://doi.org/10.24076/citec.2015v2i2.43
Indrayanto, C. G., Ratnawati, D. E., & Rahayudi, B. (2023). Analisis Sentimen Data Ulasan Pengguna Aplikasi MyPertaminadi Indonesia pada Google Play Storemenggunakan Metode Random Forest. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(3), 1131–1139.
Kurniawan, A., Indriati, & Adinugroho, S. (2019). Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dan Lexicon Based Features. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(9), 8335–8342.
Laxmi, M. D., Indriati, & Fauzi, M. A. (2019). Query Expansion Pada Sistem Temu Kembali Informasi Berbahasa Indonesia Dengan Metode Pembobotan TF-IDF Dan Algoritme Cosine Similarity Berbasis Wordnet. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(1), 823–830. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4223
Lestari, D. W. P., Perdana, R. S., & Adikara, P. P. (2019). Klasifikasi Video Clickbait Pada Youtube berdasarkan Analisis Sentimen Komentar menggunakan Learning Vector Quantization (LVQ) dan Lexicon-Based Features. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(2), 1184–1189. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4326
Munir, M. M., Fauzi, M. A., & Perdana, R. S. (2018). Implementasi Metode Backpropagation Neural Networkberbasis Lexicon Based Featuresdan Bag of WordsUntuk Identifikasi Ujaran Kebencian Pada Twitter. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(10), 3182–3191. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/2573
Park, S.-M., & Kim, Y.-G. (2021). Root Cause Analysis Based on Relations Among Sentiment Words. Cognitive Computation, 13(4), 903–918. https://doi.org/10.1007/s12559-021-09872-3
Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277
Rofiqoh, U., Perdana, R. S., & Fauzi, M. A. (2017). Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features. 1(12), 1725–1732. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/628
Sejarah-Universitas Brawijaya Press. (n.d.).
Sumanjaya, A. A. A., Indriati, & Ridok, A. (2022). Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naive Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(4), 1865–1872.
Vanaja, S., & Belwal, M. (2018). Aspect-Level Sentiment Analysis on E-Commerce Data. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 1275–1279. https://doi.org/10.1109/ICIRCA.2018.8597286
Visi, Misi, dan Maklumat Pelayanan-Universitas Brawijaya Press. (n.d.).
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