Analisis Sentimen pada Ulasan Aplikasi Access by KAI Berbahasa Indonesia Menggunakan Word-Embedding dan Classical Machine Learning

R. Damanhuri; Vito Ahmad Husein

doi:10.14710/jmasif.15.2.62383

DOI: https://doi.org/10.14710/jmasif.15.2.62383

Analisis Sentimen pada Ulasan Aplikasi Access by KAI Berbahasa Indonesia Menggunakan Word-Embedding dan Classical Machine Learning

R. Damanhuri , Vito Ahmad Husein

Department of Informatics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

Received: 28 Feb 2024; Revised: 8 Sep 2024; Accepted: 20 Sep 2024; Published: 30 Nov 2024.

Editor(s): Kabul Kurniawan

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Indonesia memiliki aplikasi perkeretaapian bernama Access by KAI yang dirilis oleh PT Kereta Api Indonesia (KAI). Masyarakat dapat mengunduh dan mengulas aplikasi ini melalui Google Play Store. Rating Access by KAI menurun dari tahun 2022, menandakan bahwa aplikasi belum memenuhi ekspektasi pengguna meskipun telah diperbarui. Ulasan pada platform Google Play Store dapat dianalisis untuk menggali informasi penting, salah satunya adalah sentimen. Penelitian ini melakukan analisis sentimen pada ulasan Access by KAI menggunakan word embedding dengan model Word2Vec untuk ekstraksi fitur dan classical machine learning dengan Naive Bayes dan Logistic Regression untuk algoritma klasifikasi. Metode Logistic Regression lebih baik daripada Naive Bayes dalam hal accuracy dan precision dengan nilai sebesar 68.83% dan 75.49% secara berurutan. Namun, metode Naive Bayes memiliki keunggulan dalam hal recall dengan nilai sebesar 45.07%. Pada penelitian ini, ulasan Access by KAI memiliki sentimen dominan negatif sejumlah 335 data dari total 400 data tes. Kata “mudah” dan “suka” relevan sebagai alasan ulasan bersentimen positif, sedangkan Kata “aplikasi”, “bayar”, dan “tiket” relevan sebagai alasan ulasan bersentimen negatif.

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Keywords: Analisis sentimen; ulasan; access by kai; word embedding; machine learning

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Section: Research Article

Language : ID

In Vol 15, No 2 (2024): In Press

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