Analisis Sentimen Temporal Tentang Kuliner Di Kota Surabaya Berbasis Gender Menggunakan Bahasa Indonesia

Asmara, Rengga; Basuki, Achmad; Rasyid, M. Udin Harun Al

doi:10.33050/tmj.v5i1.1260

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Mastan dan Toni menunjukkan bahwa persepsi konsumen terhadap produk dan makanan di rumah makan maupun restaurant perlu dianalisis secara komprehensif agar dapat memberikan rekomendasi yang tepat dan relevan bagi para pengusaha bisnis kuliner di Indonesia [40]. Disisi lain, Asmara et al menegaskan bahwa opini konsumen yang dipublikasikan melalui berbagai media terkait produk dan layanan di rumah makan maupun restaurant mencerminkan citra wisata kuliner Indonesia [41]. Adapun, Fauzi et al menekankan bahwa pendekatan big data melalui kajian analisis sentimen konsumen produk dan layanan Usaha Mikro Kecil dan Menengah (UMKM) diperlukan untuk mengidentifikasi faktor-faktor yang menghambat perkembangan UMKM khususnya di sektor bisnis kuliner [42].…”

Section: Hasil Dan Pembahasanunclassified

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Singgalen

2023

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Culinary is one of the economic activities that support national economic growth and represents the gastronomy of the archipelago in Indonesia. Culinary tourism activities have become famous for domestic and foreign tourists to experience the taste of a food based on the culture of each region. Makassar is one of the regions with diverse types of food and beverages and has a relationship with local socio-cultural values. Considering this, this study aims to analyze consumer sentiment toward food and services in ten restaurants in Makassar based on the recommendations of the Tripadvisor website using the Cross-Industry Standard Process for Data Mining (CRISPD-DM) and Service Quality (SERVQUAL) methods. The stages of CRISP-DM are as follows: the stage of understanding business processes; the stage of understanding data; the stage of preparing data; the modeling stage; the evaluation stage; and the deployment stage. The algorithms used as models are k-Nearest Neighbor (kNN), Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM). The results of this study show that the DT algorithm when using the SMOTE operator where the resulting accuracy value is 93.25%, precision is 88.74%, recall is 99.10%, and f-measure is 93.62%. In addition, the k-NN algorithm without using the SMOTE operator showed an accuracy value of 98.72%, a precision of 98.72%, a recall of 100%, and an f-measure of 99.36%. However, the resulting AUC value is 0.905 (90.5%). Meanwhile, when using the SMOTE operator, the SVM algorithm produces an accuracy value of 99.42%, a precision of 100%, a recall of 98.84%, and an f-measure of 99.42%. Meanwhile, the resulting AUC value is 1,000 (100%). Based on the ROC value, three algorithms can be used as models in the CRISPP-DM and SERVQUAL frameworks: the k-NN algorithm without SMOTE and the DT and SVM algorithms using the SMOTE operator

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Section: Hasil Dan Pembahasanunclassified

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Singgalen

2023

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“…Meskipun demikian, makanan tradisional tetap perlu dipertahankan dan ditingkatkan popularitasnya. Penelitian terkait sentimen masyarakat terhadap kuliner Indonesia telah dilakukan sebelumnya, seperti sentimen jajanan yang tersedia di pusat perkotaan Surabaya [25] dan Bandung [26], penentuan sentimen dengan data hash tag kuliner [27], sentimen pada tempat penyediaan kuliner [28] dan restoran [29]. Namun, pengukuran sentimen masyarakat terhadap makanan tradisional dan makanan internasional belum pernah dilakukan.…”

Section: Pendahuluanunclassified

Text Mining dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat terhadap Makanan Tradisional

Berutu

2022

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Indonesia has several famous traditional foods and is available in some cities. In addition, several international foods also are interesting to Indonesian. This article analyzes the netizen sentiment for these food categories where the data source is Twitter. The foods are rendang, sate, gudeg, pizza, hamburger, and spaghetti. The text mining approach is adopted to process data. The research steps are data crawling, cleaning, filtering, translating, and splitting. Furthermore, the classifier model based on the Naïve Bayes algorithm is developed. The analysis result shows that the gudeg food reaches a high percentage of positive sentiment with 57,9. Then, the high rate of negative sentiment is achieved by the rendang food with 21,9 %. Moreover, hamburger food obtains a high percentage of neutral sentiment. Meanwhile, the evaluation of classifier model performance shows that the model with the hamburger dataset achieves a high score for accuracy, precision, and recall parameters with 0.72, 0.72, and 0.68 sequentially.

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“…Dalam perkembangannya, kajian analisis sentimen menjadi holistik dengan topik dan studi kasus yang komprehensif, sepertihalnya ulasan tentang : pengguna gopay [78]; pemilihan presiden [79], [80]; pembangunan infrastruktur [81]; pembatasan sosial berskala besar [7]; pelayanan jasa internet [82]; pelanggan toko online [24], [83], [84]; pariwisata [85]- [90]; bakal calon [91], [92]; sarana transportasi [93]- [95]; penilaian kinerja dosen [96]; mengukur persepsi produk [97]; media sosial berbahasa indonesia [98]; maskapai [99]- [101]; layanan sistem informasi akademik [102]; layanan perusahaan [2]; layanan haiDJPb [103]; kurikulum [104]; kuliner [105], [106]; konten radikal [107], [108]; komparasi teknik klasifikasi [5], [48], [109], [110]. Dengan demikian, dapat diketahui bahwa kuantitas riset analisis sentimen yang populer ialah ulasan pelanggan toko online, pariwisata, opini publik tentang sarana transportasi, bakal calon presiden, penilaian dosen, mskapai penerbangan, kuliner, konten radikal dan komparasi teknik klasifikasi.…”

Section: Gambar 2 Hasil Klasifikasi Studi Kasus Kajian Analisis Sentimen Sumber : Hasil Penyandian Axial Menggunakan Nvivo 12 Plusunclassified

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Singgalen

2021

Journal-ISI

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This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

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Analisis Sentimen Temporal Tentang Kuliner Di Kota Surabaya Berbasis Gender Menggunakan Bahasa Indonesia

Cited by 4 publications

References 2 publications

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Text Mining dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat terhadap Makanan Tradisional

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

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