Pemeringkatan Pencarian pada Buku Pedoman Akademik Filkom UB Menuju Merdeka Belajar dan Free E-Book Pembelajaran Sebagai Prototype Local Smart Micro Search Engine Menggunakan Algoritma Pagerank dan TF-IDF

Penulis

Imam Cholissodin, Akhmad Sa’rony, Rona Salsabila, Ilham Firmansyah, Guedho Augnifico Mahardika, Andreas Pardede, Zaien Bin Umar Alaydrus

Abstrak

Buku Pedoman Akademik FILKOM Universitas Brawijaya merupakan suatu kebutuhan informasi akademik yang cukup penting, dan juga buku penunjang pembelajaran seperti Free e-Book bagi para mahasiswa. Untuk memperoleh informasi yang relevan terhadap query yang diberikan seringkali belum sesuai dengan kebutuhan pencarian pengguna. Pengguna harus menguasai secara keseluruhan untuk mengetahui dokumen mana yang paling sesuai, dan proses ini akan memakan waktu yang banyak. Sistem ini mampu memberikan rekomendasi dokumen sesuai dengan hasil perhitungan pemeringkatan teks. Proses pemeringkatan teks dapat diselesaikan dengan algoritma PageRank, di mana dokumen yang memiliki bobot pemeringkatan terkecil, memiliki kata terbanyak pada dokumen tersebut. Algoritma ini telah dibuktikan mampu memeberikan feedback dokumen yang relevan melalui dua tahap pengujian. Evaluasi yang dilakukan terhadap dua buah pengujian menghasilkan rata-rata nilai recall tertinggi yaitu 80.6% pada data ke-1, dan data ke-2 didapatkan korelasi terbaik antara precision, recall dan f-measure sebesar 0,98, 0,99, 0,99.

 

Abstract

The Brawijaya University FILKOM Academic Handbook is an important academic information need, as well as learning support books such as Free e-Books for students. To obtain information that is relevant to the query given is often not in accordance with the wishes of the user. Users must master the whole to find out which documents are most suitable, which is where the process will take a lot of time. This system is able to provide document recommendations in accordance with the results of the text ranking calculation. The process of ranking the text can be solved by the PageRank algorithm, where documents that have the smallest ranking weight, have the most words in the document. This algorithm has been proven to be able to provide feedback on relevant documents through two stages of testing. he evaluation conducted on the two tests resulted in the highest average recall value of 80.6% on the 1st dataset, and 2nd dataset the best correlation was obtained between precision, recall and f-measure of 0.98, 0.99, 0.99.


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Referensi


BRIN, S., & PAGE, L., 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Diambil kembali dari The Anatomy of Search Engine: http://infolab.stanford.edu/~backrub/google.html

CHOLISSODIN I., SETIAWAN B.D., 2013. Sentiment Analysis Dokumen E-Complaint Kampus Menggunakan Additive Selected Kernel SVM. Seminar Nasional Teknologi Informasi Dan Aplikasinya (SNATIA).

CHUNG, F., 2014. A Brief Survey of PageRank Algorithms. Ieee Transactions On Network Science And Engineering, Vol. 1, No. 1.

F. ALI, I. ULLAH AND S. KHUSRO, 2016. An Empirical Investigation of PageRank and Its Variants in Ranking Pages on the Web. International Conference on Frontiers of Information Technology (FIT), Islamabad, pp. 354-359, doi: 10.1109/FIT.2016.071.

FILKOM, 2020. FILKOM UB Selenggarakan Konsolidasi Penyusunan Kurikulum Kampus Merdeka. Di ambil dari Web FILKOM: https://filkom.ub.ac.id/page/read/news/filkom-ub-selenggarakan-konsolidasi-penyusunan-kurikulum-kampus-merdeka/07c25e5

J. BERKHOUT, 2016. Google's PageRank algorithm for ranking nodes in general networks. 13th International Workshop on Discrete Event Systems (WODES), Xi'an, pp. 153-158, doi: 10.1109/WODES.2016.7497841.

KEMDIKBUD, 2020. Reformasi Pendidikan Nasional Melalui Merdeka Belajar. Di ambil dari Web kemdikbud: https://www.kemdikbud.go.id/main/blog/2020/05/reformasi-pendidikan-nasional-melalui-merdeka-belajar

MIHALCEA, R., 2004. Graph-based Ranking Algorithms for Sentence Extraction, Appliedto TextS ummarization. Di ambil dari Association for Computational Linguistic: https://www.aclweb.org/anthology/P04-3020.

NYEIN, S. S., 2011. Mining Contents in Web Page Using Cosine Similarity. ieeexplore.ieee.org.

WAHIB, A., PASNUR P., SANTIKA, P. P., ARIFIN, A. Z., 2015. Perangkingan Dokumen Berbahasa Arab Menggunakan Latent Semantic Indexing. 6, p.83-91.

THOMAS, R., 2013. Information Retrieval Models. London: Synthesis Lectures On Information Concepts, Retrieval, And Services.

USHA, M., & DR. N. NAGADEEPA., 2018. Combined Two Phase Page Ranking Algorithm for Sequencing the Web Pages. Proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018) . India: IEEE Xplore Compliant.

Z. HAO, P. QIUMEI, Z. HONG AND S. ZHIHAO, 2015. An Improved PageRank Algorithm Based on Web Content. 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Guiyang, pp. 284-287, doi: 10.1109/DCABES.2015.78.




DOI: http://dx.doi.org/10.25126/jtiik.2021854384