Judul: Pemanfaatan Citra Alos Avnir-2 Untuk Interpretasi Tanaman Kelapa Sawit Pada Berbagai Umur (Studi Kasus PTPN VIII Cimulang, Kabupaten Bogor).
Abstrak: BPS data in 2013 showed palm plantation area are 10.5865 million hectares. Large growing area indicate that importance of remote sensing data. Bigest growing area make a problem to monitoring of oil palm plantations, so need remote sensing data required to facilitate the monitoring of oil palm plantations. The use of satellite imagery has been widely used for the monitoring of oil palm plantations, but to monitoring of the age of oil palm trees is not much to do. On this research age estimation of palm oil was analyzed using ALOS AVNIR-2. These results indicate the visual interpretation techniques of oil palm trees can be divided into 5 years, 9 years, 11 years, and 12 years with an accuracy of 90.95% . The fourth age classes may be suspected of age with the band 2 and band 3 on AVNIR ALOS-2. Reflectance patterns on both bands have a downward trend, where the older the plant, the reflectance values obtained were smaller.
Keyword: ALOS AVNIR-2, Age, Palm Oil, Productivity, Transformed Divergence

Judul: Determination of Oil Palm&#8217;s Age Using Artificial Neural Network Method and Sentinel-2 Based Vegetation Indices
Abstrak: Kelapa sawit menjadi varietas unggul dalam total produksi dan distribusi minyak nabati global pada tahun 2019/2020, yaitu mencapai volume sebesar 81,54 juta metrik ton (40%). Pengaruh positif kelapa sawit memberikan pertumbuhan yang sangat besar pada sektor sosial dan ekonomi. Penentuan umur tanaman sawit berbasis data penginderaan jauh sangat penting dalam manajemen perkebunan diantaranya perhitungan dosis pupuk dan penentuan waktu untuk peremajaan. Tujuan penelitian ini mengembangkan model yang dapat menentukan umur tanaman kelapa sawit pada lahan mineral dengan metode Artificial neural network MLPClassifier (ANN-MLPC) berbasis indeks vegetasi dari citra satelit Sentinel-2. Penelitian dilakukan pada Kebun Pendidikan dan Penelitian Kelapa Sawit IPB-Cargill Jonggol. Pengambilan data dilakukan dengan mengunduh citra satelit Sentinel-2 yang diolah menjadi nilai reflektansi tingkat grid. Nilai reflektansi tingkat grid digunakan sebagai variabel independen, sedangkan informasi umur aktual digunakan sebagai variabel dependen. Pembagian proporsi dataset dengan jumlah sampel 1500 data, dimana 80% digunakan untuk data training dan 20% untuk data testing. Berdasarkan hasil evaluasi, model yang dibangun dengan algoritma ANN-MLPC diperoleh nilai akurasi sebesar 97,47%, nilai presisi sebesar 93,87%, recall sebesar 93,81%, serta F1-score sebesar 93,84%. Hasil tersebut menunjukkan model machine learning yang digunakan mampu menentukan umur tanaman sawit pada lahan mineral secara akurat dan presisi., Palm oil is the leading variety in total global vegetable oil production and distribution in 2019/2020, reaching a volume of 81.54 million metric tons (40%). The positive influence of oil palm provides enormous growth in the social and economic sectors. Determining the age of oil palm plants based on remote sensing data is very important in plantation management including calculating fertilizer doses and determining the time for replanting. The purpose of this research is to develop a model that can determine the age of oil palm plants on mineral land with the Artificial neural network MLPC Classifier (ANN-MLPC) method based on vegetation indices from Sentinel-2 satellite images. The research was conducted at the IPB-Cargill Jonggol Oil Palm Education and Research Farm. Data collection was carried out by downloading Sentinel-2 satellite images which were processed into grid-level reflectance values. The grid-level reflectance value was used as the independent variable, while the actual age information was used as the dependent variable. The proportion of datasets with a total sample size of 1500 data, where 80% and 20% of the total data samples are used traing and testing respectively. Based on the evaluation results, the model built with the MLPClassifier Artificial neural network algorithm has accuracy, precision, recall and F1-score values of of 97,47%, 93,87%, 93,81%, and 93,84% respectively. These results show that the machine learning model used is able to determine the age of oil palm plants on mineral land accurately and precisely.
Keyword: Data citra, kecerdasan buatan, satelit, usia tanaman sawit

Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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