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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi
Hastanto, Dika
Universitas Bandar Lampung
Computer Science & IT

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The Prediction of Subsidized Fertilizer Stock Using Least Square Support Vector Machine on The Kartu Petani Berjaya Aplication Hastanto, Dika; Romadhan, Dwi
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 14, No 2 (2024): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v14i2.3976

Abstract

Agriculture is one of the biggest commodities in Lampung, so that this also causes a lot of use and allocation of subsidized fertilizers. In terms of this it is very important to know how much amount of subsidized fertilizer needed in the future to prepare subsidized fertilizer stocks. The data needed was the time series data from subsidized fertilizer redemption data, using Least Square Support Machine and Autoregressive Integrated Moving Average methods to make a prediction model for subsidized fertilizer redemption. The result was hoped that we can find out how many harvests are in Lampung and the future subsidized fertilizer rations. This research was expected to provide benefits to the relevant parties.
Analisis Deforestasi Lahan Karet Menggunakan Google Earth Engine Romadhan, Dwi; Hastanto, Dika; Susanty, Wiwin
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 15, No 1 (2025): June
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v15i1.4283

Abstract

Deforestasi global terus menjadi isu kritis karena konversi hutan ke lahan pertanian dan Perkebunan. Di Indonesia, implementasi EUDR (European Union Deforestation Regulation) menuntut pemantauan deforestasi lahan produksi, termasuk lahan karet. Penelitian ini bertujuan mengevaluasi luasan deforestasi pada dua kebun karet masyarakat di wilayah PT Mardec Way Kanan, Lampung, menggunakan platform Google Earth Engine (GEE) dan dataset Hansen Global Forest Change. Area objek ditentukan berdasarkan polygon (masing-masing empat koordinat) di sekitar kebun karet. Metode meliputi pengambilan citra tutupan hutan awal (treecover2000) dan data kehilangan hutan (loss) dari Hansen (2000–2023), perhitungan luas deforestasi dengan fungsi reduceRegion(), serta perhitungan loss area untuk kedua kebun. Hasil simulasi menunjukkan bahwa kebun 1 mengalami deforestasi sebesar 1,54 ha dari total 33,07 ha (sekitar 4,6%), sementara kebun 2 mengalami deforestasi 1,24 ha dari total 21,95 ha (sekitar 5,6%) yang menggambarkan proporsi deforestasi terhadap luas total kebun. Dapat disimpulkan GEE efektif mendeteksi deforestasi lahan karet secara spasial dan kuantitatif. Temuan ini relevan untuk kepatuhan EUDR, mendorong pemantauan deforestasi berbasis GEE secara berkala dan pengembangan sistem monitoring otomatis.
Co-Authors Romadhan, Dwi Wiwin Susanty