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All Journal Jurnal EECCIS Indonesian Journal of Forestry Research
Regen, Rendra
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Agriculture, Biological Sciences & Forestry Engineering

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Automation Mangrove Identification with Case Based Reasoning Process Vatresia, Arie; Johar, Asahar; Regen, Rendra; Kennedy, John
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 16 No. 2 (2022)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v16i2.1470

Abstract

Mangroves are ecosystems with unique functions in the environment. Because of its physical properties, mangroves are able to play a role as a wave retardant as well as retaining intrusion and abrasion of the sea. Mangroves themselves have various types of species that are spread throughout Indonesia and not yet widely known to people in general. In identifying the mangrove species itself cannot be done arbitrarily, it requires an expert who truly understands the mangrove species. This research was conducted with the aim of adopting the knowledge of mangrove experts to identify mangrove species into expert systems. The method used is case based reasoning method using the KNN algorithm which is used to calculate the similarity value between cases that will be applied to the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu. This system is built using HTML, CSS, Javascript, Php, and Mysql programming languages and is designed using UML diagrams. The results of this study itself are, it has been successfully applied the case based reasoning method in the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu
MINING FIRE HOTSPOTS OVER NUSA TENGGARA AND BALI ISLANDS Vatresia, Arie; Regen, Rendra; Putra Utama, Ferzha; Oktariani, Widhia
Indonesian Journal of Forestry Research Vol. 9 No. 1 (2022): Indonesian Journal of Forestry Research
Publisher : Association of Indonesian Forestry and Environment Researchers and Technicians

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59465/ijfr.2022.9.1.73-85

Abstract

Forest fires are still one of the most common problems in Indonesia. In fact, many of these forest fires origin from human activities, namely fires that are intentionally raised for a purpose such as widening the land to prepare for the planting season in the Nusa Tenggara Island. Forest fire events can be identified by observing hotspot data which are monitored through remote sensing satellites. Hotspot is an area that has a relatively higher surface temperature than the surrounding area based on certain temperature thresholds monitored by remote sensing satellites. The objective of this research is to cluster hotspots in the Nusa Tenggara and Bali Islands from year 2013 to 2018 using the K-Means Clustering Method with 28,519 hot spot data. By knowing this result, the ministry can use this data for patrol priority management. This research successfully clustered three types of hotspot classes based on the risk of fire with details as follow; High Risk Class contains 12,212 data with ranges of mean values of confidence in the range of 49.3–100%, brightness in the range of 305.1–421.3o K and FRP in the range of 2.5–714.3; Medium Risk contains 12,250 data mean values of confidence with a range of 20.3–74.3%, brightness in the range of 301.06–341.86o K and FRP in the range of 3.6–141.4; and Low Risk contains 4,057 data with a range of mean values of confidence in the range of 0–39.8%, brightness in the range of 300–365.86oK and FRP in the range of 3.5–275.6.
Co-Authors Arie Vatresia Asahar Johar Kennedy, John Oktariani, Widhia Utama, Ferzha Putra