Pemodelan Topik dengan LDA untuk Temu Kembali Informasi dalam Rekomendasi Tugas Akhir

  • Diana Purwitasari Institut Teknologi Sepuluh Nopember
  • Aida Muflichah Institut Teknologi Sepuluh Nopember
  • Novrindah Alvi Hasanah Institut Teknologi Sepuluh Nopember
  • Agus Zainal Arifin Institut Teknologi Sepuluh Nopember
Keywords: topic extraction, recommendation system, latent dirichlet allocation (LDA)–gibbs sampling, k-means clustering


Undergraduate thesis as the final project, or in Indonesian called as Tugas Akhir, for each undergraduate student is a pre-requisite before student graduation and the successfulness in finishing the project becomes as one of learning outcomes among others. Determining the topic of the final project according to the ability of students is an important thing. One strategy to decide the topic is reading some literatures but it takes up more time. There is a need for a recommendation system to help students in determining the topic according to their abilities or subject understanding which is based on their academic transcripts. This study focused on a system for final project topic recommendations based on evaluating competencies in previous academic transcripts of graduated students. Collected data of previous final projects, namely titles and abstracts weighted by term occurences of TF-IDF (term frequency–inverse document frequency) and grouped by using K-Means Clustering. From each cluster result, we prepared candidates for recommended topics using Latent Dirichlet Allocation (LDA) with Gibbs Sampling that focusing on the word distribution of each topic in the cluster. Some evaluations were performed to evaluate the optimal cluster number, topic number and then made more thorough exploration on the recommendation results. Our experiments showed that the proposed system could recommend final project topic ideas based on student competence represented in their academic transcripts.


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Haviluddin, S. J. Patandianan, G. M. Putra, and H. S. Pakpahan, 2021. “Implementasi Metode K-Means untuk Pengelompokkan Rekomendasi Tugas Akhir,” Inform. Mulawarna J. Ilm. Ilmu Komput., 16 (1), pp. 13–18.

P. M. Prihatini, I. K. Suryawan, and I. N. Mandia, 2017. “Feature Extraction for Document Text Using Latent Dirichlet Allocation,” 2nd Int. Jt. Conf. Sci. Technol., Bali, Indonesia 27-28 September 2017. IOP Publishing Ltd.

L. Farokhah and R. Aditya, 2017. “Implementasi K-Means Klustering untuk Rekomendasi Tema Tugas Akhir pada Stmik Asia Malang,” J. Teknol. dan Manaj. Inform., 3 (2), pp. 142–148.

M. R. Muttaqin and M. Defriani, 2020. “Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa,” Ilk. J. Ilm., 12 (2), pp. 121–129.

M. Sholehhudin, M. Fauzi Ali, and S. Adinugroho, 2018. “Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi ( Studi Kasus : Universitas Brawijaya ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., 2 (11), pp. 5518–5524.

V. Kurnia Bakti and J. Indriyatno, 2017. “Klasterisasi Dokumen Tugas Akhir Menggunakan K-Means Clustering, sebagai Analisa Penerapan Sistem Temu Kembali,” KOPERTIP J. Ilm. Manaj. Inform. dan Komput., 1 (1), pp. 31–34.

L. Yasni, I. M. I. Subroto, and S. F. C. Haviana, 2018. “Implementasi Cosine Similarity Matching Dalam Penentuan Dosen Pembimbing Tugas Akhir,” Transmisi, 20 (1), pp. 1–7.

B. K. Triwijoyo and K. Kartarina, 2019. “Analysis of Document Clustering based on Cosine Similarity and K-Main Algorithms,” J. Inf. Syst. Informatics, 1 (2), pp. 164–177.

Z. Shahbazi and Y.-C. Byun, 2020. “Analysis of Domain-Independent Unsupervised Text Segmentation using LDA Topic Modeling over Social Media Contents,” Int. J. Adv. Sci. Technol., 29 (6), pp. 5993–6014.

A. R. Destarani, I. Slamet, and S. Subanti, 2019. “Trend Topic Analysis using Latent Dirichlet Allocation (LDA) (Study Case: Denpasar People’s Complaints Online Website),” J. Ilm. Tek. Elektro Komput. dan Inform., 5 (1), pp. 50–58.

H. Shimodaira, 2015. “Similarity and recommender systems,” Japan Similarity Recomm. Syst., 20 January 2015, pp. 1–25.

C. Juliane, R. Dzulkarnaen, and W. Susanti, 2019. “Metode McCall’s untuk Pengujian Kualitas Sistem Informasi Administrasi Tugas Akhir (SIATA)”, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 3(3), 488 - 495.

How to Cite
Purwitasari, D., Aida Muflichah, Novrindah Alvi Hasanah, & Agus Zainal Arifin. (2021). Pemodelan Topik dengan LDA untuk Temu Kembali Informasi dalam Rekomendasi Tugas Akhir. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 421 - 428.
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