Implementasi Text Summarization pada Ulasan Aplikasi Mobile JKN Menggunakan TF-IDF dan Cosine Similarity
Abstract
ABSTRACT
Mobile JKN is an application developed by Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan that was developed for easier access to national health services. As of November 2024, this application has been downloaded 50 million times on the Google Play Store, and about 720 thousand reviews have been given by users. The reviews provided by users who have downloaded the Mobile JKN app are very useful and important for potential users and developers. However, the huge volume of reviews is a challenge in reading them one by one and can cause information overload. Based on the occurring problems, the author will apply text summarization to summarize the reviews of the JKN Mobile application by implementing the Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity methods. The author added the Maximum Marginal Relevance (MMR) method because the TF-IDF and Cosine Similarity methods cannot produce a summary. Summarization is done by taking the most relevant reviews from among a collection of other reviews. This research resulted with the average Accuracy value of 29.6%, Precision 55.6%, and Recall 39.8%, with the highest value of Accuracy 38.4%, Precision 79.2%, and Recall 42.7%.
Keywords: Cosine Similarity, Text Summarization, TF-IDF, MMR
ABSTRAK
Aplikasi Mobile JKN merupakan aplikasi yang dikembangkan oleh Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan yang dikembangkan untuk kemudahan akses layanan kesehatan nasional. Per November 2024, aplikasi ini telah diunduh sekitar 50 juta kali di Google Play Store dan sekitar 720 ribu ulasan telah diberikan oleh para pengguna. Ulasan-ulasan yang diberikan oleh pengguna yang telah mengunduh aplikasi Mobile JKN sangat bermanfaat dan penting bagi calon pengguna dan pengembang. Akan tetapi, volume ulasan yang sangat besar menjadi tantangan dalam membacanya satu per satu dan dapat menimbulkan information overload. Berdasarkan permasalahan yang terjadi, maka penulis akan menerapkan text summarization untuk meringkas ulasan-ulasan aplikasi Mobile JKN dengan mengimplementasikan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan Cosine Similarity. Penulis menambahkan metode Maximum Marginal Relevance (MMR) karena metode TF-IDF dan Cosine Similarity tidak dapat menghasilkan ringkasan. Peringkasan dilakukan dengan mengambil ulasan-ulasan yang paling relevan dari antara kumpulan ulasan lainnya. Penelitian ini menghasilkan nilai rata-rata Accuracy 29,6%, Precision 55,6%, dan Recall 39,8% dengan nilai tertinggi Accuracy 38,4%, Precision 79,2%, dan Recall 42,7%.
Kata Kunci: Cosine Similarity, Text Summarization, TF-IDF, MMR
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