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All Journal Journal of Information Systems Engineering and Business Intelligence Jambura Journal of Informatics
Katili, Moh. Zulkifli
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Computer Science & IT

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Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus Katili, Moh. Zulkifli; Yeni Herdiyeni; Hardhienata, Medria Kusuma Dewi
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.94-109

Abstract

Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order.   Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector
Implementasi Metode AHP-TOPSIS dalam Sistem Pendukung Rekomendasi Mahasiswa Berprestasi Katili, Moh. Zulkifli; Amali, Lanto Ningrayati; Tuloli, Mohamad Syafri
Jambura Journal of Informatics VOL 3, NO 1: APRIL 2021
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v3i1.10246

Abstract

Menentukan mahasiswa berprestasi untuk diikutkan pada perlombaan atau untuk seleksi beasiswa merupakan masalah yang selalu dialami oleh pihak fakultas/jurusan. Untuk mengatasi masalah tersebut dibutuhkan penggunaan aplikasi untuk membantu dalam pengambilan keputusan. Tujuan penelitian ini adalah untuk merancang aplikasi Sistem Pendukung Keputusan (SPK) untuk pemberian rekomendasi mahasiswa berprestasi. Penelitian ini menggunakan metode pengembangan sistem model Waterfall dan metode AHP-TOPSIS untuk penentuan kriteria data mahasiswa berupa nilai matakuliah, kegiatan yang diikuti, ataupun prestasi yang dimiliki. Untuk memastikan fungsionalitas, sistem aplikasi telah diuji melalui Uji Black-box dan White-box. Penelitian ini menghasilkan aplikasi SPK untuk pemberian rekomendasi mahasiswa berprestasi yang dapat disesuaikan dengan kriteria dan kebutuhan pihak fakultas/jurusan selaku pengguna. Deciding which outstanding students to be enrolled in competitions or scholarships is a problem that faculties or departments always experience. Hence, an application is needed to help in the decision-making process. This research aims to design a decision support system (DSS) application to give recommendations regarding outstanding students. The Waterfall Model development method and the AHP-TOPSIS method were employed to determine the criteria for the students' data, such as subject scores, activities being participated in, and achievements. The application system has already been tested through the Black-box and White-box tests to ensure its functionality. It resulted in a DSS application that gives recommendations about outstanding students, which may be adjusted according to the criteria and needs of the faculty/department as the users.
Co-Authors Hardhienata, Medria Kusuma Dewi Lanto Ningrayati Amali mohammad syafri tuloli