Judul: Classification of Oil Palm Harvest Age Based on Electrical Properties Using Principal Component Analysis and Mahalanobis Distance
Abstrak: Kandungan minyak buah kelapa sawit berkaitan dengan kematangan buah. Biasanya kandungan minyak dan kematangan sawit diduga dari warna dan jumlah buah lepas tandan dengan hasil yang variatif karena bersifat subjektif. Penelitian ini bertujuan menentukan karakteristik sifat listrik buah kelapa sawit pada berbagai umur panen dan mengklasifikasikan umur panen kelapa sawit berdasarkan sifat listriknya. Sampel yang digunakan sebanyak 60 buah kelapa sawit dengan 10 umur panen. Nilai impedansi listrik diukur pada frekuensi 50 Hz – 5 MHz. Sifat kimia diukur secara destruktif. Spektrum impedansi dibagi 2 kelompok yaitu seluruh spektrum impedansi (50 Hz – 5 MHz) dan spektrum impedansi (50 – 100 Hz). Kemudian dilakukan pretreatment spektrum dan diklasifikasi menggunakan principal component analysis dan mahalanobis distance (MD). Hasil klasifikasi 10 umur panen berdasarkan impedansi listrik pada frekuensi 50 Hz – 5 MHz cukup baik dengan total varian kumulatif sebesar 80,00% menggunakan PC1 dan PC2, sedangkan dengan 3 kelompok umur panen menghasilkan total varian kumulatif sebesar 88,00%. Klasifikasi 3 kelompok umur panen menggunakan MD menghasilkan akurasi pengelompokkan sebesar 93,30%. Sedangkan klasifikasi 10 umur panen berdasarkan impedansi listrik pada frekuensi 50 – 100 Hz menunjukkan hasil lebih baik, dengan total varian kumulatif PC1 dan PC2 sebesar 97,00%. Klasifikasi menggunakan mahalanobis distance menghasilkan akurasi tinggi mencapai 100,00%.
Keyword: klasifikasi, mahalanobis distance, sifat listrik, kelapa sawit, principal component analysis

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: A Continuous Vaccination Strategy in the SVIR Epidemic Model
Abstrak: Vaccination is conducted to minimize the spread of a disease. The vaccination is usually done several times within a fixed time interval. In the SVIR model, it is assumed that individuals do not get immediate immunity following a vaccination program. So according to process of vaccination on SVIR model, there are two strategies, i.e. the continuous vaccination strategy (CVS) and the pulse vaccination strategy (PVS). In this study, only the CVS strategy in epidemic model SVIR is considered. Results of the study indicate that dynamics of the CVS system depends on the basic reproductive number. If the basic reproductive number is less than one, then the disease-free fixed point is asymptotically stable. It means that the disease will eventually disappear from the population. Conversely, if the basic reproductive number is greater than one, then the endemic fixed point is asymptotically stable, which means that the disease will remain exist in the population. Simulation result shows that the vaccination would minimize the spread of disease by reducing the basic reproductive number. If the time for the vaccinated recipients to obtain the immunity, as well as a possibility of vaccinated recipients to be infected are neglected, then the disease will be eradicated. This condition is called an over-evaluating effect of vaccination.
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