Analisis Topik Skripsi Menerapkan Pemodelan Latent Dirichlet Allocation
Abstract
At XYZ University, the alignment of thesis titles with the vision of each study program has never been analyzed. The sheer volume of thesis titles makes conventional analysis challenging. Therefore, an automated and rapid analysis technique is needed. Topic modeling with the Latent Dirichlet Allocation (LDA) model offers a solution for facilitating topic analysis. This study compares topic modeling results with and without stemming in data preprocessing. Topic modeling without stemming yielded higher coherence scores compared to stemming, with scores of 0.336 for the Informatics Engineering program and 0.446 for the Information Systems program. Based on the topic modeling analysis, it was found that the thesis topics have a clear focus aligned with the vision and mission of each study program.
Keywords: Stemming; coherence score;Thesis titles; Vision and mission; Topic modeling
Abstrak
Pada perguruan tinggi XYZ, judul-judul skripsi yang telah ada belum pernah dilakukan analisis kesesuaian topik dengan visi masing-masing program studi. Jumlah judul-judul skripsi yang begitu banyak akan sulit jika dianalisis secara konvensional. Sehingga diperlukan teknik yang dapat melakukan analisis otomatis dengan cepat. Teknik pemodelan topik dengan model Latent Dirichlet Allocation (LDA) dapat menjadi solusi untuk mempermudah menganalisis topik. Penelitian ini membandingkan hasil pemodelan topik antara hasil dengan menerapkan stemming dan tanpa stemming pada pra-pemrosesan data. Pemodelan topik tanpa proses stemming menghasilkan Coherence Score yang lebih tinggi dibandingkan dengan proses stemming, yaitu 0,336 untuk program studi Teknik Informatika dan 0,446 untuk program studi Sistem Informasi. Berdasarkan analisis pemodelan topik, ditemukan bahwa topik-topik skripsi memiliki fokus yang jelas sesuai dengan visi dan misi masing-masing program studi.
Keywords
References
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