Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs

  • Hoiriyah Hoiriyah (1) Universitas Islam Madura
  • Helva Mardiana (2*) Universitas Islam Madura
  • Miftahul Walid (3) Universitas Islam Madura
  • Aang Kisnu Darmawan (4) Universitas Islam Madura

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v19i1.4176

Keywords: Sentiment Analysis, MSMEs, Marketplace, Lexicon-based, Naïve bayes

Abstract

MSMEs (micro, small, and medium enterprises) play an essential role in the Indonesian economy, contributing to 60% of the country's GDP (gross domestic product), creating jobs, and increasing non-oil and gas exports. However, MSMEs in Indonesia face various challenges, including access to technology, digital marketing tools, financial resources, limited market distribution, and low technological literacy. Marketplaces provide an essential marketing channel for MSMEs to increase their competitiveness and sales. Sentiment analysis can assist businesses in making informed decisions about which marketplace to use to increase customer satisfaction. Apart from the importance of the marketplace for MSMEs in Indonesia, research on sentiment analysis for marketplace recommendations is still minimal. Therefore, this study aims to analyze six popular marketplaces in Indonesia using Lexicon-based and naïve Bayes research methods to provide the best marketplace recommendations for MSME marketing. The results showed that Blibli.com had the highest accuracy, followed by Tokopedia, Tiktokshop, Lazada, Shopee, and Bukalapak. Blibli.com received positive reviews with 96.33%, followed by Tokopedia with 95.25%, Tiktokshop with 94.61%, and Lazada with the highest accuracy. 94.22%, Shopee 92.18%, and Bukalapak 89.57%. This research has two significant contributions. First, making a scientific contribution by applying a combination model of lexicon-based and naïve Bayes to analyze market sentiment in Indonesia Second, offering a practical contribution by providing recommendations to MSME actors and policymakers in choosing the best marketplace for MSMEs marketing purposes in Indonesia. By utilizing the recommended marketplace, MSMEs can optimize their marketing strategy and increase their competitiveness in the digital marketplace.

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References

Ahdiat, A. (2022). Indonesia Punya UMKM Terbanyak di ASEAN, Bagaimana Daya Saingnya? Retrieved October 11, 2022, from databooks.katadata.co.id website: https://databooks.katadata.co.id/datapublish/2022/10/11/indonesia-punya-umkm-terbanyak-di-asean-bagaimana-daya-saingnya

Amaliah, F., & Nuryana, I. K. D. (2022). Perbandingan Akurasi Metode Lexicon Based Dan Naive Bayes Classifier Pada Analisis Sentimen Pendapat Masyarakat Terhadap Aplikasi Investasi Pada Media Twitter. JINACS (Journal of Informatics and Computer Science), 3(3), 384–393.

Asri, Y., Suliyanti, W. N., Kuswardani, D., & Fajri, M. (2022). Pelabelan Otomatis Lexicon Vader dan Klasifikasi Naive Bayes dalam menganalisis sentimen data ulasan PLN Mobile. PETIR: Jurnal Pengkajian Dan Penerapan Teknik Informatika, 15(2), 264–275. https://doi.org/10.33322/petir.v15i2.1733

Darmawan, T. D. (2022). Analisis Sentimen Areview Pelanggan E-Commerce di Indonesia Menggunakan Algoritma Naive Bayes. Universitas Dinamika.

Doni. (2021). Pimpin Rapat Hilirisasi Ekonomi Digital, Presiden Instruksikan Percepatan Digitalisasi UMKM. Retrieved February 10, 2021, from kominfo.go.id website: https://www.kominfo.go.id/content/detail/34994/pimpin-rapat-hilirisasi-ekonomi-digital-presiden-instruksikan-percepatan-digitalisasi-umkm/0/berita

Faesal, A., Muslim, A., & Ruger, A. H. (2020). Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means. Jurnal MATRIK, 19(2), 207–213. https://doi.org/10.30812/matrik.v19i2.640

Haranto, F. F., & Sari, B. W. (2019). Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom dan Biznet. Jurnal Pilar Nusa Mandiri, 15(2), 171–176. https://doi.org/10.33480/pilar.v15i2.699

Hartatik, Tamam, M. B., & Setyanto, A. (2020). Prediction for Diagnosing Liver Disease in Patients using KNN an Naive Bayes Algorithm. International Conference on Cybernetics and Intelligent System (ICORIS). https://doi.org/10.1109/ICORIS50180.2020.9320797

Hasugian, A. H., Fakhriza, M., & Zukhoiriyah, D. (2023). Analisis Sentimen Pada Review Pengguna E-Commerce Menggunakan Algoritma Naïve Bayes Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD. Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD, 6(1), 98–107. Retrieved from https://ojs.trigunadharma.ac.id/index.php/jsk/index%0AAnalisis

Johnson, M., & Smith, K. (2022). Exploring Social Media Data Analysis Techniques: A Review of Netlytic. Journal of Social Media Research, 10(1), 20–35.

Kemp, S. (2021). Digital 2021 : Indonesia. Retrieved February 18, 2023, from Datareportal We Are House website: https://datareportal.com/reports/digital-2021-indonesia

Kurniawan, A., Adinugroho, S., & Features, B. (2019). Analisis Sentimen Opini Film Menggunakan Metode Naïve Bayes dan Lexicon Based Features. 3(9), 8335–8342.

Mahdi, M. I. (2022). Berapa Jumlah UMKM di Indonesia? Retrieved January 19, 2022, from dataindonesia.id website: https://dataindonesia.id/sektor-riil/detail/berapa-jumlah-umkm-di-indonesia

Mardiana, T., Syahreva, H., & Tuslaela. (2019). Komparasi Metode Klasifikasi Pada Analisis Sentimen Usaha Waralaba Berdasarkan Data Twitter. Jurnal Pilar Nusa Mandiri, 15(2), 267–274. https://doi.org/10.33480/pilar.v15i2.752

Pamungkas, R. B. (2023). Hati-Hati Inilah Tantangan UMKM di Indonesia Pada Tahun 2023. Retrieved January 5, 2023, from Niagahoster.co.id website: https://www.niagahoster.co.id/blo/tantangan-umkm-indonesia/

Pratiwi, S. Y. A., & Nudin, S. R. (2021). Analisis Sentimen terhadap Facebook Marketplace Menggunakan Metode Lexicon Based dan Support Vector Machine. JIFTI-Jurnal Ilmiah Teknologi Informasi Dan Robotika, 3(2), 9–15.

Rianti, D. L., Umaidah, Y., & Voutama, A. (2021). Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi, 6(1), 98–105.

Risnasari, M. (2022). Konsep Dasar Data Mining Teori dan Praktik dengan PYTHON (1st ed.). Malang: CV Literasi Nusantara Abadi.

Tamam, M. B., Hozairi, Walid, M., & Bernado, J. F. A. (2023). Classification of Siggn Language in Real Time Using Convolutional Neural Network. Applied Information System and Management (AISm, 6(1), 39–46.

Umbu, A., Ama, T., Mulya, D. N., Astuti, Y. P. D., Bias, I., & Prasadhya, G. (2022). Analisis Sentimen Customer Feedback Tokopedia Menggunakan Algoritma Naïve Bayes. Jurnal Sistem Komputer Dan Informatika (JSON), 4(September), 50–55. https://doi.org/10.30865/json.v4i1.4783

Utama, H. S., Rosiyadi, D., Aridarma, D., & Prakoso, B. S. (2019). Sentimen Analisis Kebijakan Ganjil Genap di Tol Bekasi Menggunakan Algoritma Naive Bayes dengan Optimalisasi Information Gain. Jurnal Pilar Nusa Mandiri, 15(2), 247–254. https://doi.org/10.33480/pilar.v15i2.705

Wilandini, D., & Purwantoro. (2022). Penerapan Algoritma Naive Bayes dalam Mengklasifikasikan Media Sosial Untuk Mengamati Trend Kuliner. Jurnal Teknologi Terpadu, 8(1), 31–39. Retrieved from https://journal.nurulfikri.ac.id/index.php/jtt

Wirma, S. (2022). Data Mining Dengan Metode Naïves Bayes Classifer dalam Memprediksi Tingkat Kepuasan Pelayanan Dokumen Kependudukan. Jurnal Informatika Ekonomi Bisnis, 4(3), 119–123. https://doi.org/10.37034/infeb.v4i3.155

Published
2023-09-01
How to Cite
Hoiriyah, H., Mardiana, H., Walid, M., & Darmawan, A. (2023). Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs. Jurnal Pilar Nusa Mandiri, 19(2), 65-76. https://doi.org/10.33480/pilar.v19i1.4176
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Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2023
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Copyright (c) 2023 Hoiriyah Hoiriyah, Helva Mardiana, Miftahul Walid, Aang Kisnu Darmawan

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