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All Journal Statistika Jurnal Berkala Ilmu Perpustakaan dan Informasi BACA: Jurnal Dokumentasi dan Informasi
Aris Yaman
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Journal : Jurnal Berkala Ilmu Perpustakaan dan Informasi

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Identifikasi kecakapan inovasi lembaga riset di Indonesia berbasis dokumen Yaman, Aris; Sartono, Bagus; Sholeh, Agus M.
Berkala Ilmu Perpustakaan dan Informasi Vol 16 No 2 (2020): Desember
Publisher : Perpustakaan Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/bip.v16i2.424

Abstract

Introduction. Duplication in inventions produced by research institutions in Indonesia becomes an issue. It is important to map the specialization of the invention in research institutions. This study examines the mapping of the innovation in research institutions in Indonesia. Data Collection Method. This study uses a patent-based technology document analysis method to map the potential of technology. The data used is patent data registered in the Direktorat Jenderal Kekayaan Intelektual (DJKI) database. Data Analysis. Metadata analysis was conducted by using the K-Means Klastering method with R software. Results and Discussions. The findings in the pre-analysis show that when the independent variable involved in the model are very large, the Localized feature selection method can effectively select variables without losing much information. There are 5 dominant technology groups that can be produced by research institutions in Indonesia, namely 1) Technology related to the development of measurement and testing instrument technology; 2) Technologies related to food and food ingredients; and 3) microstructural test equipment / detectors; 4) radar technology; 5) Technology in agriculture. Conclusion. The findings show that there are still overlapping inventions by several research institutions in the same technology cluster. K-means clustering with LFSBSS pre analysis has a clear performance in the technology cluster space.
Co-Authors Ambar Yoganingrum Anik Djuraidah Bagus Sartono Pika Silvianti Sholeh, Agus M. Slamet Riyanto Yaniasih Yaniasih