Publisher

BUMIGORABUMIGORA

Journal MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa KomputerMATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Abstract

The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online. The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.

Conclusion

Sentiment analysis using the SVM algorithm produces significant accuracy with shorter computations than other classification methods.The classification obtained is tested again through the confusion matrix.The test results of tweets show positive sentiments including residence, price range, using promo, type paid, halal food, and by system, while negative sentiments include ethnicity, culture, vegetarianism, and places.

Future Research

Penelitian lebih lanjut dapat dilakukan dengan mengeksplorasi penggunaan algoritma deep learning seperti Recurrent Neural Networks (RNN) atau Transformers untuk meningkatkan akurasi analisis sentimen pada data tweet yang memiliki karakteristik bahasa informal dan seringkali mengandung singkatan atau slang. Selain itu, studi dapat diperluas dengan mengintegrasikan data dari berbagai platform media sosial selain Twitter, seperti Instagram atau Facebook, untuk mendapatkan gambaran yang lebih komprehensif mengenai preferensi dan perilaku konsumen. Terakhir, penelitian dapat difokuskan pada pengembangan model yang mampu mengidentifikasi sentimen secara lebih granular, tidak hanya positif atau negatif, tetapi juga emosi yang lebih spesifik seperti kegembiraan, kekecewaan, atau kemarahan, sehingga memberikan informasi yang lebih berharga bagi para pelaku bisnis makanan dalam merancang strategi pemasaran yang lebih efektif dan personal.

Metadata

File size	422.71 KB
Pages	12
DMCA	Report