Afika Rianti, - (2023) SISTEM REKOMENDASI ARTIKEL JURNAL MACHINE LEARNING MENGGUNAKAN TOOL LOOKER STUDIO. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Indonesia merupakan negara yang belum banyak mempelajari tentang kecerdasan buatan. Hal ini menyebabkan sedikitnya jumlah publikasi terkait bidang kecerdasan buatan termasuk ranah di dalamnya seperti machine learning yang menyebabkan kesulitan bagi pembaca dalam menemukan artikel jurnal yang sesuai. Dalam kasus ini, sistem rekomendasi dapat dimanfaatkan untuk memberikan rekomendasi yang relevan. Tujuan dari penelitian ini adalah: 1) mengembangkan website sistem rekomendasi artikel jurnal machine learning menggunakan tool Looker Studio; dan 2) menguji website sistem rekomendasi artikel jurnal machine learning menggunakan tool Looker Studio. Jenis penelitian yang digunakan adalah R&D dengan desain penelitian menggunakan RAD. Dataset yang digunakan terdiri dari 100 artikel jurnal machine learning. Berdasarkan penelitian yang telah dilakukan, diperoleh kesimpulan: 1) pengembangan website sistem rekomendasi artikel jurnal machine learning dibangun menggunakan tool Looker Studio untuk memberikan fitur rekomendasi yang dihubungkan ke website melalui embedding via URL. Berdasarkan hasil pengujian terhadap fitur tersebut, diperoleh hasil rata-rata presisi sebesar 98,33%; 2) hasil pengujian website sistem rekomendasi artikel jurnal machine learning menggunakan teknik survei melalui instrumen SUS, menunjukkan skor 71,42 yang berarti website memiliki kinerja rata-rata dengan kategori bagus serta acceptability dapat diterima. Dengan demikian, website telah layak dan dapat digunakan dengan baik oleh mahasiswa yang memiliki ketertarikan di bidang kecerdasan buatan selaku pengguna website. ----- Indonesia is a country that has not studied much about artificial intelligence. This has resulted in a small number of publications related to the field of artificial intelligence including areas within it such as machine learning which caused difficulties for readers in finding relevant journal articles. In this case, a recommendation system can be utilized to provide relevant recommendations. The aims of this research are: 1) develop a machine learning journal articles recommendation system website using Looker Studio tool; and 2) test the machine learning journal articles recommendation system website using Looker Studio tool. The type of research used is R&D with a research design used RAD. The dataset used consists of 100 machine learning journal articles. Based on the research that has been done, the conclusions are: 1) the development of the machine learning journal articles recommendation system website is built using the Looker Studio tool to provide a recommendation feature which is linked to the website using embedding via URL. Based on the test results of that feature, the average of precision is 98.33%; 2) The results of testing the machine learning journal articles recommendation system website used survey technique with SUS instrument, showed a score of 71.42, which means it has an average performance in a good category and acceptability as acceptable. Thus, the website is feasible and can be used properly by students who have an interest in artificial intelligence as website users.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | ID SINTA Dosen Pembimbing: Nuur Wachid Abdul Majid: 6054692 Ahmad Fauzi: 6122861 Google Scholar : Nuur Wachid Abdul Majid : https://scholar.google.co.id/citations?user=EOVUYkEAAAAJ&hl=en Ahmad fauzi : https://scholar.google.com/citations?user=b6BGJbEAAAAJ&hl=en |
| Uncontrolled Keywords: | Sistem Rekomendasi, Artikel Jurnal Machine Learning, Looker Studio, RAD, Website |
| Subjects: | L Education > L Education (General) L Education > LC Special aspects of education T Technology > T Technology (General) |
| Divisions: | UPI Kampus Purwakarta > S1 Pendidikan Sistem Teknologi dan Informasi |
| Depositing User: | Afika Rianti |
| Date Deposited: | 13 Jul 2023 04:01 |
| Last Modified: | 13 Jul 2023 04:01 |
| URI: | http://repository.upi.edu/id/eprint/92722 |
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