PEMODELEN TOPIK PENELITIAN BIDANG KEPERAWATAN INDONESIA PADA REPOSITORY JURNAL SINTA MENGGUNAKAN METODE TOPIC MODELLING LDA (LATENT DIRICHLET ALLOCATION)
Abstract
Pada saat ini penelitian di bidang kesehatan semakin meningkat baik dari peneliti, pemerintah, akademis bahkan dari kalangan umum. Penelitian dosen, mahasiswa, peneliti dan lain-lain sudah terdigitalisasi dalam repository Jurnal. Adapun Penelitian kesehatan khususnya keperawatan semakin hari semakin bertambah dan menyebabkan kesulitan dalam mencari topik penelitian yang sesuai dengan kebutuhan, sehingga dibutuhkan metode yang dapat membantu untuk mencari topik penelitian yang mengorganisasi informasi penelitian keperawatan yang digunakan untuk referensi pencarian terkait topik penelitian keperawatan secara umum. Researcher dan akademisis menerbitkan artikel penelitian merupakan salah satu program tri dharma perguruan tinggi sehingga setiap hari, bulan dan tahunnya bertambah artikel penelitian di dalam repository jurnal. Untuk mencari dan menemukan penelitian sekarang sangat mudah dapat ditemukan di repository OJS (Open Jurnal System), tetapi permasalahannya adalah mengetahui bagaimana tren di bidang keperawatan berdasarkan tahun. Metode yang digunakan dalam penelitian ini yaitu metode topic modelling LDA (Latent Dirichlet Allocation). Penelitian ini dilakukan dengan tujuan untuk mengidentifikasi bagaimana metode topic modelling LDA dapat melakukan analisis tren topik penelitian dengan pemodelan topik terhadap judul-judul penelitian di bidang keperawatan di Indonesia yang diperoleh dari Repository Jurnal SINTA. Penelitian ini menjadi referensi dalam melakukan penelitian keperawatan di Indonesia berdasakan topik yang sudah dimodelkan.
Kata Kunci: topic modelling, keperawatan, LDA
Abstract
At this time research in the health sector is increasing both from researchers, government, academics and even from the general public. Research lecturers, students, researchers and others have been digitalized. As for health research, especially nursing, is increasing day by day and causes difficulty in finding research topics that suit your needs, so a method is needed that can help to find research topics that organize nursing research information that is used for search references related to nursing research topics in general. Researchers and academics publish research articles as one of the Tri Dharma College programs so that every day, month and year there are more research articles in the journal repository. It is very easy to find and find research now in the OJS repository (Open Journal System), but the problem is knowing how the trends in nursing are based on the year. The method used in this research is the LDA (Latent Dirichlet Allocation) topic modeling method. This research was conducted with the aim of identifying how the LDA topic modeling method can analyze research topic trends by modeling the topics of research titles in the field of nursing in Indonesia obtained from the SINTA Journal Repository. This research become a reference in conducting nursing research in Indonesia based on the topics that have been modeled.
Downloads
References
[2] C. Casman, A. Ahadi Pradana, E. Edianto, and L. O. Abdul Rahman, “Kaleidoskop Menuju Seperempat Abad Pendidikan Keperawatan di Indonesia,” J. Endur., vol. 5, no. 1, p. 115, 2020, doi: 10.22216/jen.v5i1.4291.
[3] A. Nurlayli and M. A. Nasichuddin, “Topic Modeling Penelitian Dosen JPTEI UNY pada Google Scholar Menggunakan Latent Dirichlet Allocation,” vol. 4, no. November, pp. 154–161, 2019, doi: 10.21831/elinvo.v4i2.
[4] F. Martin and M. Johnson, “More Efficient Topic Modelling Through a Noun Only Approach,” Proc. Australas. Lang. Technol. Assoc. Work., pp. 111–115, 2015, [Online]. Available: http://www.nltk.org/howto/wordnet.html.
[5] A. F. Hidayatullah and M. R. Ma’Arif, “Road traffic topic modeling on Twitter using latent dirichlet allocation,” Proc. - 2017 Int. Conf. Sustain. Inf. Eng. Technol. SIET 2017, vol. 2018-Janua, no. November 2017, pp. 47–52, 2018, doi: 10.1109/SIET.2017.8304107.
[6] R. Kusumaningrum, M. I. A. Wiedjayanto, S. Adhy, and Suryono, “Classification of Indonesian news articles based on Latent Dirichlet Allocation,” Proc. 2016 Int. Conf. Data Softw. Eng. ICoDSE 2016, pp. 1–5, 2017, doi: 10.1109/ICODSE.2016.7936106.
[7] M. Fajriyanto, “Penerapan metode bayesian dalam model latent dirichlet allocation di media sosial application of bayesian methods in latent dirichlet allocation model in social media,” pp. 1–6, 2018.
[8] B. W. Arianto and G. Anuraga, “Topic Modeling for Twitter Users Regarding the ‘Ruanggguru’ Application,” J. ILMU DASAR, vol. 21, no. 2, p. 149, 2020.
[9] M. Fitriasih and R. Kusumaningrum, “Analisis Klasifikasi Opini Tweet Pada Media Sosial Twitter Menggunakan Latent Dirichlet Allocation (LDA),” Semin. Nas. Teknol. Inf. dan Komun. 2019 (SENTIKA 2019), vol. 2019, no. Sentika, pp. 177–186, 2019.
[10] Zulhanif, Sudartianto, B. Tantular, and I. G. N. M. Jaya, “Aplikasi Latent Dirichlet Allocation ( Lda ) Pada Clustering Data Teks,” J. Log., vol. 7, no. 1, pp. 46–51, 2017.
[11] A. I. Alfanzar, K. Khalid, and I. S. Rozas, “Topic Modelling Skripsi Menggunakan Metode Latent Diriclhet Allocation,” JSiI (Jurnal Sist. Informasi), vol. 7, no. 1, p. 7, 2020, doi: 10.30656/jsii.v7i1.2036.
[12] I. Surjandari, A. Rosyidah, Z. Zulkarnain, and E. Laoh, “Mining Web Log Data for News Topic Modeling Using Latent Dirichlet Allocation,” Proc. - 2018 5th Int. Conf. Inf. Sci. Control Eng. ICISCE 2018, pp. 331–335, 2019, doi: 10.1109/ICISCE.2018.00076.
[13] M. F. A. Bashri and R. Kusumaningrum, “Sentiment analysis using Latent Dirichlet Allocation and topic polarity wordcloud visualization,” 2017 5th Int. Conf. Inf. Commun. Technol. ICoIC7 2017, vol. 0, no. c, pp. 4–8, 2017, doi: 10.1109/ICoICT.2017.8074651.
