Judul: Pemodelan Perubahan Penutupan/Penggunaan Lahan dengan Pendekatan Artificial Neural Network dan Logistic Regression (Studi Kasus: DAS Citarum, Jawa Barat)
Abstrak: Perubahan penutupan/penggunaan lahan terjadi atau sedang terjadi karena banyak faktor. Perubahan penutupan/penggunaan lahan dapat berakibat positif dan berakibat negatif. Tujuan dari penelitian ini adalah untuk mengetahui perubahan penggunaan lahan yang terjadi di DAS Citarum dari tahun 2000 sampai 2010 dan memodelkan perubahan penutupan/penggunaan lahan dengan pendekatan Artificial Neural Network (ANN) dan Logistic Regression (Logit). Selama periode ini, penutupan lahan yang mengalami penurunan antara lain hutan primer (12.364 ha), hutan sekunder (15.641 ha), sawah (31.873 ha), tambak (2105 ha), dan kebun teh (31 ha) sedangkan yang mengalami penambahan luas antara lain agroforestri (1.819 ha), pertanian (6.670 ha), permukiman pedesaan (41.574 ha), permukiman perkotaan (1.559 ha), tanaman hortikultur (10.308 ha), dan rumput (39 ha).Kedua model pendekatan ANN dan Logit dapat memprediksi penutupan/penggunaan lahan tahun 2012 dengan nilai Kappa yang sangat baik, yaitu 0,962 untuk ANN dan 0,964 untuk Logit.
Keyword: Pemodelan perubahan pentupan/penggunaan lahan, Artificial Neural Networks, Logistic Regression

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: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity