Judul: Lignin Content Estimation in the Rice Bran Image Mixed with Husks Using Run Length and KNN Method
Abstrak: Pemalsuan dedak padi terjadi cukup tinggi karena harga dedak padi yang mahal. Kualitas dedak padi yang bercampur sekam dapat menurun karena kandungan serat kasar dan lignin bersifat anti-nutrisi. Kandungan lignin dapat diestimasi dengan tekstur citra dedak padi yang bercampur sekam. Penelitian ini bertujuan untuk melakukan analisis tekstur pada citra dedak padi yang bercampur sekam menggunakan metode ekstraksi ciri run length dengan klasifikasi k-nearest neighbour (KNN). Citra dedak padi yang bercampur sekam diambil menggunakan mikroskop digital Dino Capture dengan perbesaran 200 kali dan menghasilkan keluaran citra berukuran 640×480 piksel dengan format bitmap. Praproses dilakukan dengan mengubah citra RGB ke dalam grayscale, lalu hasil citra grayscale dilakukan image enhancement menggunakan histogram equalization. Pembagian data uji dan data latih menggunakan 3 model k-fold cross validation dengan k = 5. Hasil KNN menggunakan 7 fitur run length menunjukkan akurasi terbaik sebesar 74.55% pada sudut 135° dan k = 5.
Keyword: dedak padi, k-nearest neighbour, lignin, run length, sekam

Judul: Estimasi Kandungan Lignin pada Dedak Padi Menggunakan KNN dengan Praproses PCA Berdasarkan Citra Grayscale
Abstrak: Pemalsuan dedak padi yang telah tercampur sekam bisa menurunkan kualitas pakan. Pengujian kandungan sekam dapat diukur dari kandungan lignin melalui reaksi pewarnaan menggunakan larutan phloroglucinol. Warna merah yang semakin pekat menunjukkan semakin tinggi kandungan sekam pada dedak padi. Oleh karenanya, pada penelitian ini akan dilakukan estimasi kandungan lignin pada dedak padi yang telah diwarnai tersebut dengan ekstraksi fitur berbasis Principal Component Analysis (PCA) dengan metode klasifkasi K-Nearest Neighbor (KNN). Citra yang digunakan adalah citra grayscale yang diperoleh dari transformasi citra RGB. Penelitian ini menghasilkan akurasi prediksi tertinggi sebesar 77.27%., Counterfeiting of rice bran mixed with husks can reduce feed quality. The husk content test can be measured from the lignin content through a staining reaction using a phloroglucinol solution. The darker red color indicates the higher the husk content in the rice bran. Therefore, this study will estimate the lignin content of rice bran mixed with husks using feature extraction based on Principal Component Analysis (PCA) with the K-Nearest Neighbor (KNN) classification method. The image used is a grayscale image obtained from the RGB image transformation. This research resulted in the highest prediction accuracy of 77.27%.
Keyword: grayscale, K-Nearest Neighbor, Principal component analysis, rice bran

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.
Keyword: