Judul: Wetland Change Prediction Using Markov Cellular Automata Model In Lore Lindu National Park Central Sulawesi Province, Indonesia (Seminar)
Abstrak: Lore Lindu National Park (LLNP) is located between 119°90’ - 120°16’ East and 1°8’ - 1°3’ South and administratively, located in Poso District and Donggala District, in Central Sulawesi Province, Indonesia. Topographically, Lore Lindu National Park has two enclaves: Lindu Enclave (13.093 ha) and Besoa Enclave (5.085 ha). Lindu enclaves have high growing socio economic activities that can threaten Lore Lindu National Park as a tropical rainforest that requires the stability of rainforest margin area. If agricultural and built up area (settlement area) in Lindu enclaves is always increase every years then the water availability to support Lore Lindu National Park will decrease so the stability of rainforest becomes worst. Wetland as ecological component is important to be maintained, monitored and modeled to support the stability of rainforest margin area. Therefore, Wetland change prediction is needed to predict what will happen in the future. The objectives of this research are: 1) to identify the threatening factors affecting the wetland area in Lindu Enclave area & Lindu Lake, 2) to predict the changes of wetland area in Lindu Enclave area & Lindu Lake using Markov Cellular Automata Model. Land cover 1983 from aerial photography, Landsat TM 2002 and 2006 are used as the spatial model data. Image correction is also performed in order to acquire well ground spatial estimation. Afterward, markov chain is used to get rate value per years of wetland change area in Lindu area. Multicriteria evaluation for wetland area created with soil map, topography map, and land cover map as the variables for suitable wetland area criteria that will be used in cellular automata model. Finally, markov chain probability values for each class and multicriteria evaluation map is merged with cellular automata model with 3 x 3 cell neighbors analysis then spatial wetland change prediction map for 2021 can be created. The results of this research show that Lindu enclaves have two wetland types; natural wetland (marsh and bush land) and construction / man - made wetland (paddy field and fish pond). The changes of wetland area in Lindu enclave area for natural wetland (marsh) was - 66.01 hectares (1983-2002) with – 0.1 % annual change rate and 343.94 hectares area for human made / construction wetland area (paddy field) with 1.0 % annual change rate. The suitable area for wetland was located in every enclave area, however the most suitable area was located at the Southern part of Lindu enclave area.The prediction of spatial distribution for natural wetland area in 2021 will disappear with an annual change rate of -0.1 %. However, human made wetland such as paddy field area has increased to 1,729.06 hectares in 2021 with 0.7 % annual change rate.
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Judul: Prediction of Land Cover Change using Markov Chain and ANN-Markov Models in the Krueng Aceh Watershed
Abstrak: Perubahan tutupan lahan akibat intervensi manusia terhadap lahan pada daerah aliran sungai merupakan faktor penting dalam menentukan perencanaan pembangunan suatu wilayah. Prediksi perubahan tutupan lahan yang baik akan menjadi pertimbangan dalam menentukan strategi pembangunan di masa depan. Prediksi perubahan tutupan lahan dapat dilakukan dengan berbagai metode, tergantung pada kebutuhan data, algoritma pemodelan yang dilakukan dan output apa saja yang diperlukan. Tujuan penelitian ini adalah untuk menganalisis dinamika perubahan dan prediksi tutupan lahan di DAS Krueng Aceh. Metode yang dilakukan menggunakan penginderaan jauh dan SIG. Klasifikasi tutupan lahan menggunakan metode interpretasi visual. Analisis perubahan tutupan lahan dilakukan dengan membandingkan model Markov Chain (MC) dan Artificial Neural Network-Markov (ANN-MC) untuk memahami dinamika spatio-temporal tutupan lahan. Algoritma prediksi probabilitas perubahan ke dua model menggunakan data tutupan lahan 2007 (T1) dan 2017 (T2) untuk memprediksi perubahan tutupan lahan pada 2020 (T3). Pengujian akurasi Kappa dilakukan pada peta hasil klasifikasi citra landsat OLI tahun 2020. Hasil klasifikasi tutupan lahan tahun 2020 mendapatkan nilai Kappa Accuracy sebesar 84%. Analisis perubahan tutupan lahan pada kurun waktu 2007 hingga 2020 menunjukkan bahwa pertanian lahan kering mengalami pengurangan terbesar seluas 12.592 ha. Sedangkan tutupan lahan yang mengalami peningkatan terbesar adalah permukiman dengan penambahan seluas 8.424 ha. Hasil uji akurasi prediksi perubahan tutupan lahan menggunakan Markov Chain (MC) sebesar 88,69% dan Artificial Neural Network-Markov (ANN) sebesar 87,81%., Land cover changes caused by human intervention on land needs occur in watersheds are an important factor to determining the development area planning. A good prediction of land cover change will be a consideration in determining the development strategy of the area in the future. There are various methods we can used to predicting land cover change. It depends on the data, model algorithm, and output type are required. The objective of this reasearch was to analyze the dynamics of land cover change and prediction of land cover in the Krueng Aceh watershed. The approach applied was remote sensing and GIS. Visual interpretation method was used to determine land cover classification. Comparison of Markov Chain (MC) model and Artificial Neural Network-Markov (ANN-M) applied to understand the spatio-temporal dynamics of land cover. The probability algorithm of those models carried out by land cover data in 2007 (T1) and 2017 (T2) was to predicted land cover change in 2020 (T3). Kappa accuracy was calculated using manual interpretation of Landsat 8-OLI in 2020 . The result of land cover classification in 2020 has 84% of kappa value. Land cover change analysis from 2007 to 2020 showed that dryland agriculture decreased 12.592 ha. Meanwhile, biggest area that increased was settlements with additional area 8.424 ha. The result of accuration test for Markov Chain (MC) was 88.69% and Artificial Neural Network-Markov (ANN) was 87,81%.
Keyword: Algoritma, ANN, Markov Chain, Prediksi tutupan lahan

Judul: Sirup Fruktosa sebagai Hasil Samping Pembuatan Tepung Beras Berprotein Tinggi
Abstrak: Tepung beras berprotein t i n g g i (TBPT) adalah salah satu bahan pangan olahan yang dapat digunakan untuk pemenuhan kebutuhan protein. Proses pembuatan TBPT menghasilkan hasil samping berupa h i d r o l i s a t pati. Penelitian ini bertujuan untuk memanfaatkan hasil samping tersebut sebagai bahan baku pembuatan sirup fruktosa.
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