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All Journal Knowledge Engineering and Data Science BACA: Jurnal Dokumentasi dan Informasi
Zaenal Akbar
Pusat Penelitian Fisika – Lembaga Ilmu Pengetahuan Indonesia
Computer Science & IT Engineering Library & Information Science

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SOCIAL NETWORK ANALYSIS OF MANGOSTEEN TECHNOLOGY DEVELOPMENT CLUSTER IN INDONESIA BASED ON PATENT DOCUMENT APPLICATION Yaman, Aris; Aris Kartika, Yulia; Tsurayya, Silmi; Ankafia, Adi; P. Manik, Lindung; Akbar, Zaenal; Indrawati, Ariani
BACA: Jurnal Dokumentasi dan Informasi Vol. 43 No. 1 (2022): BACA: Jurnal Dokumentasi dan Informasi (Juni)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.baca.v43i1.828

Abstract

Functional food consumption is on the rise and has a significant market value. Indonesia is one of the largest mangosteens (a functional food source commodity) exporting countries globally. Unfortunately, the mangosteen export is still in fresh fruit condition, not in other forms that have a higher value. Policymakers need to identify critical technologies in the development of mangosteen commodities. This study uses a patent-based technology document analysis method to map the potential of technology. The data used is patent data that has been registered with the Indonesian Patent Office and the WIPO Patentscope database. The analysis was carried out using computational methods, namely a Social Network Analysis with the Girvan-Newman algorithm. According to the study's findings based on global patent data, there are three major technology clusters used in mangosteen patents: 1) 24 percent for technology related to developing preparations for medical, dental, or toilet purposes (A61K). 2) 20% for food and food ingredient technology or non-alcoholic beverages (A23L). The remaining 43 percent is spread across many other IPC technology codes, including technology related to drug preparations (A61P). It is in line with the results of patent data analysis in Indonesia, which also shows that there are three dominant technology groups applied to mangosteen in Indonesia, namely 1) Technology related to the development of medical, dental, and toilet technology (A61K) of 47 percent; 2) Technology related to food and food ingredients or non-alcoholic drinks (A23L) by 18 percent, and 3) Technology related to drug preparations (A61P) by 13 percent and the remaining 22 percent spread over several other IPC technology codes. According to Social Network Analysis, the world's dominant technology cluster for mangosteen is technology related to the development of food and food ingredients or non-alcoholic beverages (A23L). The technology associated with medical, dental, and toilet technology is the most important mangosteen technology cluster in Indonesia (A61K).
Non-Gaussian Analysis of Herbarium Specimen Damageto Optimize Specimen Collection Management Yaman, Aris; Kartika, Yulia Aris; Indrawati, Ariani; Akbar, Zaenal; Manik, Lindung P.; Wardani, Wita; Djarwaningsih, Tutie; Mahendra, Taufik; Saleh, Dadan R.
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, location, storage, and remounting status on the level of damage to herbarium specimens, especially those in the genus Excoecaria. Gaussian modeling is not good enough to model the counted data phenomenon (the amount of damage to herbarium specimens). Negative binomial regression (NBR) provides a better model when compared to generalized Poisson regression and ordinary Gaussian regression approaches. NBR detects non-uniformity in the storage process, causing damage to herbarium specimens. Natural damage to herbarium specimens is caused by differences in species and the origin of specimens.
Co-Authors Ankafia, Adi Aris Kartika, Yulia Aris Yaman Indrawati, Ariani Kartika, Yulia Aris Mahendra, Taufik Manik, Lindung P. P. Manik, Lindung Saleh, Dadan R. Silmi Tsurayya, Silmi Tutie Djarwaningsih, Tutie Wita Wardani