Klasifikasi Ulasan Pengguna Aplikasi: Studi Kasus Aplikasi Ipusnas Perpustakaan Nasional Republik Indonesia (PNRI)

Andina Septiani
Indra Budi


DOI: https://doi.org/10.29100/jipi.v7i4.3216

Abstract


Menurunnya jumlah tren pengguna baru aplikasi iPusnas berpengaruh terhadap penurunan pencapaian nilai target laporan LKIP Pujasintara PNRI 2020 2024. Hal tersebut berkaitan dengan nilai peringkat ulasan pengguna aplikasi di Google Playstore yang dinilai masih lebih rendah dibandingkan aplikasi sejenis lainnya. Electronic Word of Mouth (EWOM) yang sangat berpengaruh terhadap keputusan calon pengguna baru aplikasi dalam mempertimbangkan aplikasi terbaik yang sejenis, karena melibatkan tinjauan nilai peringkat dan ulasan pengguna. Beberapa penelitian terdahulu membuktikan bahwa kesulitan selalu dihadapi ketika melakukan analisis atau penggalian informasi penting dalam ulasan pengguna aplikasi secara manual. Analisis ulasan sangat berguna untuk mengembangkan fitur layanan aplikasi agar dapat meningkatkan kepuasan pengguna dan peringkat nilai aplikasi, sehingga diperlukan alat bantu klasifikasi ulasan pengguna secara otomatis dengan mencari model terbaik yang sesuai. Penelitian ini menerapkan metodologi CRISP-DM, tetapi hanya sampai tahap evaluasi. Algoritma klasifikasi yang digunakan adalah Nae Bayes, Logistic Regression, Support Vector Machine (SVM), serta kombinasi fitur tf-idf unigram, bigram, dan trigram. Hasil penelitiannya adalah kombinasi fitur tf-idf unigram (F1) dengan algoritma SVM mencapai nilai terbaik untuk setiap nilai evaluasi precision, recall, dan f1-score masing-masing sebesar 87%. Nilai evaluasi terendah precision 55% dari hasil kombinasi fitur F2 dengan SVM, recall 42% dan f1-score 32% dari kombinasi fitur F3 dengan logistic regression.


Keywords


Klasifikasi Ulasan; Algoritma Classifier; Machine Learning; Text Mining

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