Judul: Detecting Cocoa Agroforestry using Decision Tree of Machine  Learning Algorithm Through SPOT 7 Imagery: A Case Study of Malangke and West  Malangke Districts, North Luwu
Abstrak: Kajian ini mengulas tentang pembangunan teknik algoritma pohon keputusan dari pembelajar mesin (decision tree of machine learning) dengan fokus utama mengidentifikasi tanaman kakao agroforestri dan kakao monokultur. Tujuan utama dari penelitian ini adalah menemukan algoritma pohon keputusan terbaik untuk mendapatkan informasi sebaran spasial kakao agroforestri dan kakao monokultur. Penelitian ini menguji tiga kriteria pengambilan keputusan yaitu Information Gain, Gain Ratio, dan Gini Indeks menggunakan peubah-peubah spektral yang diturunkan dari SPOT 7 dan peubah-peubah geo-sosio biofisik. Kajian ini menemukan bahwa integrasi antara empat peubah spektral dan lima peubah geo-sosio biofisik menghasilkan performa model terbaik dengan overall accuracy (OA) 93,54%. Kajian ini menemukan bahwa kriteria terbaik dalam mendeteksi kakao adalah information gain dan citra sintetis NDVI menjadi peubah paling berpengaruh terhadap model. Kajian ini menemukan bahwa minimal producer’s dan user’s accuracy dari algoritma yang dihasilkan adalah 94,20% dan 93,76%., This study describes the development of the "decision tree of machine learning" algorithm development technique with the main focus on identifying agroforestry cocoa and monoculture cocoa plants. The main objective of this study is to find the best decision tree algorithm to obtain accurate spatial distribution of agroforestry and monoculture cacao. This study examined three criteria of the decision tree, namely the Information gain, the Gain ratio, and the Gini index algorithms, using spectral variables derived from SPOT 7 and bio-socio geophysics variables. This study found that integrating four spectral variables and five bio-socio-geophysics attributes resulted in the best model performance with an overall accuracy (OA) of 93,54%. This study found that the best criterion for detecting cocoa is information gain, and the NDVI was identified as the most influential attribute of the model. This study found that the minimum producer's and user's accuracy of the resulting algorithm is 94,20% and 93,76%.
Keyword: agroforestri, indeks vegetasi, kakao, pembelajar mesin, pohon keputusan, agroforestry, cacao, decision tree, machine learning, vegetation index

Judul: Developing a Decision Tree Algorithm for Cocoa Agroforestry  Detection in Three Typologies: Case Studies in Masamba District
Abstrak: Tulisan ini menerangkan tentang pembangunan algoritma klasifikasi machine learning pohon keputusan dalam mengidektifikasi agroforestri kakao dan kakao monokultur pada tiga tipologi menggunakan data penginderaan jauh dan data geospasial. Tujuan dari penelitian ini adalah untuk menemukan peubah (atribut) serta parameter algoritma pohon keputusan dalam mendeteksi kakao khususnya agroforestri berbasis citra resolusi tinggi SPOT 6/7. Kajian ini menguji peubah sosio-biogeofisik dan peubah spektral yang diturunkan dari citra SPOT SPOT 6/7. Penelitian ini menemukan bahwa peubah paling berpengaruh adalah NDVI untuk tipologi 1 dan 2, serta elevasi untuk tipologi 3. Penelitian ini juga menemukan bahwa kriteria dari algoritma pohon keputusan terbaik adalah information gain untuk tipologi 1 dan 2, serta gini index untuk tipologi 3. Kombinasi tersebut menghasilkan kappa accuracy (KA) sebesar 97,30%, 98,80%, dan 99,21% untuk tiap tipologi., This paper describes the development of a decision tree machine learning classification algorithm to identify cocoa agroforestry and cocoa monocultures in three typologies using remote sensing data and geospatial data. The objective of this study was to find variables (attributes) and parameters of the decision tree algorithm in detecting cocoa, especially agroforestry, based on SPOT 6/7 high resolution imagery. This study examines the socio-biogeophysical variables and spectral variables derived from SPOT SPOT 6/7 images. This study found that the most influential variables were NDVI for typology 1 and 2, and elevation for typology 3. This study also found that the best decision tree algorithm criteria were information gain for typology 1 and 2 and gini index for typology 3. The combination produces Kappa Accuracy (KA) of 97,30%, 98,80% and 99,21% for each typology. This study concludes that the vegetation index variable is more reliable in identifying built-up land cover classes than socio-biogeophysical variables.
Keyword: pohon keputusan, mesin pembelajar, indeks vegetasi, sosio-biogeofisik, decision tree, machine learning, vegetation index, socio-biogeophysics

Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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