Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor

Ikhsan Habib Kusuma, Nuri Cahyono

Abstract


Abstract E-commerce's rapid growth has resulted in an increase in online transactions and shifts in consumer behavior. In Indonesia, the use of e-commerce has grown rapidly, with many online platforms emerging. Understanding public sentiment towards e-commerce in Indonesia is crucial for businesses to improve their services and maintain customer satisfaction. In this review, study propose a methodology for feeling investigation of popular assessment on the utilization of web-based business in Indonesia, utilizing directed learning calculations. The study involved collecting data from the website Google Play Store. The study performed data preprocessing, including removing stop words, tokenization, and stemming, before applying the K-Nearest Neighbor (K-NN) algorithm to classify sentiments into positive or negative. The evaluation was conducted using confusion matrix and classification report. The results showed that the proposed approach was effective in analyzing public sentiment towards e-commerce in Indonesia, with an accuracy rate of 82%. The study concluded that the proposed strategy could help businesses enhance their services and better satisfy customers' requirements and expectations.

Keywords Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.

 

Abstrak - Perkembangan e-commerce yang pesat telah menyebabkan peningkatan transaksi online dan perubahan perilaku konsumen. Di Indonesia, penggunaan e-commerce tumbuh pesat dengan banyak platform online bermunculan. Memahami sentimen masyarakat terhadap e-commerce di Indonesia sangat penting bagi bisnis untuk meningkatkan layanan dan menjaga kepuasan pelanggan. Oleh karena itu, dalam penelitian ini peneliti mengusulkan sebuah pendekatan untuk melakukan analisis sentimen opini publik mengenai penggunaan salah satu e-commerce di Indonesia dengan menggunakan algoritma K-Nearest Neighbor. Pengumpulan data dilakukan dari website Google Play Store dengan tujuan untuk memperoleh pandangan dan pengalaman masyarakat terkait penggunaan salah satu e-commerce di Indonesia. Setelah data terkumpul, dilakukan proses preprocessing untuk membersihkan data, termasuk menghilangkan stopwords, tokenisasi, dan stemming. Setelah itu, algoritma K-Nearest Neighbor (K-NN) digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Evaluasi dilakukan dengan menggunakan confusion matrix dan classification report untuk menilai keakuratan algoritma. Hasil penelitian menunjukan bahwa pendekatan yang diusulkan efektif dalam menganalisis sentimen masyarakat terhadap e-commerce di Indonesia, dengan tingkat akurasi 82%. Penelitian ini memiliki implikasi penting bagi bisnis e-commerce di Indonesia dalam meningkatkan layanan dan memenuhi kebutuhan serta harapan pelanggan secara lebih baik.

Kata Kunci - Sentimen Analisis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.


Keywords


Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.

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DOI: https://doi.org/10.30591/jpit.v8i3.5734

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