ANALISIS PRODUKTIVITAS KEBUN KOPI 2016 – 2019 DENGAN METODE SPASIAL TEMPORAL (STUDI KASUS: SELURUH INDONESIA)

Akhtaufa Irfansyah Putra, Yanto Budisusanto, Udiana Wahyu Deviantari

Abstract


In 2019 Indonesia was ranked 4th (fourth) after Brazil, Vietnam, and Colombia as the world's largest coffee producer, 760,963 tons produced from 1.2 million hectares of land consisting of smallholder plantations (Perkebunan Rakyat / PR), large private plantations (Perkebunan Besar Swasta / PBS), and large state plantations (Perkebunan Besar Negara / PBN). The productivity of the plantation can be seen from comparing the area of the plantation with the production of coffee, the national productivity has only reached 0.77 tons of greenbean coffee/ha/year. This figure is considered still very small when compared to its potential which reaches 3tons of greenbean coffee/hectare/year. Temporal mapping method can be used to find out the level of productivity more easily based on location and time. Thus, the priority of handling coffee area becomes more focused and on target. The web-based GIS platform provides flexibility in terms of access and attractive appearance. The information presented is more communicative and easy to understand for interested users. This study uses ArcGIS Online with an operation dashboard to visualize the results of this study. The results showed that in the last two years arabica productivity in 2019 and 2018 was the highest in West Phak - Phak district with a figure of 1,575 kg/ha in 2019 and 1,580 kg/ha and the lowest was in Tabanan Regency at 29 kg/ha in 2019 and located in Wonosobo Regency at 26 kg/ha in 2018. For robusta in 2019 the highest was in Ogan Komering Ulu Timur district with a figure reaching 5,186 kg/ha and in 2018 the highest was in Kota Pagar Alam district with a figure reaching 2,890 kg/ha. ha and the lowest was in Muna Regency at 57 kg/ha in 2019 and in Nias Regency at 12 kg/ha in 2018.

Keywords: ArcGis, Coffee, Coffee Producer, Operation Dashboard, Yield,


Keywords


ArcGis, Coffee, Coffee Producer, Operation Dashboard, Yield,

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DOI: https://doi.org/10.17509/k.v20i2.52362

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