Judul: Aplikasi  Algoritma Pohon Keputusan Pembelajar Mesin (Decision Tree of Machine  Learning) dalam Deteksi Agroforestri Kakao: Studi Kasus Kecamatan Baebunta  dan Baebunta Selatan, Luwu Utara
Abstrak: Tulisan ini menjelaskan tentang pembangunan algoritma pohon keputusan dari pembelajar mesin menggunakan data penginderaan jauh dan data geospasial untuk mengidentifikasi tanaman kakao agroforestri dan kakao monokultur. Tujuan dari penelitian ini adalah untuk menentukan peubah, atribut, dan parameter dalam mengidentifikasi agroforestri kakao dan kakao monokultur menggunakan citra SPOT 7 dengan fitur indeks vegetasi dan parameter geo-sosio-biofisik menggunakan mesin pembelajaran pohon keputusan. Penelitian ini menemukan bahwa peubah yang paling berpengaruh adalah NDVI dan elevasi. Penelitian ini juga menemukan bahwa parameter yang paling berpengaruh adalah Information Gain dengan kombinasi tanpa pemangkasan (pruning), tanpa pra-pangkas (pre pruning), kedalaman pohon 51, pra-pangkas alternatif (pre-pruning alternative) 21, ukuran daun 21, dan jenis sampel yang digunakan adalah Random Sampling. Kajian ini menghasilkan nilai Overall Accuracy (OA) sebesar 93,56% dan Kappa Accuracy (KA) sebesar 92,9%. ATHALLAH SYAFIQ PADANTYA. Application of the Decision Tree of Machine Learning Algorithm in the Detection of Cocoa Agroforestry: a Case Study of Baebunta and South Baebunta Districts, North Luwu. Supervised by I NENGAH SURATI JAYA. This paper describes the development of a decision tree of machine learning algorithm using remote sensing data and geospatial data to identify agroforestry and monoculture cocoa plants. The purpose of this research is to determine variables, attributes, and parameters in identifying cocoa agroforestry and cocoa monoculture using SPOT 7 with vegetation index features and geo-socio-biophysical parameters using machine learning decision tree. This study found that the most influential variables were NDVI and elevation. This study also found that the most influential parameter was Information Gain with a combination of no pruning, no pre-pruning, tree depth of 51, pre-pruning alternative of 21, leaf size of 21, and the type of sample which is random sampling. This study resulted an Overall Accuracy (OA) value of 93,56% and a Kappa Accuracy (KA) value of 92,9%.
Keyword: indeks vegetasi, geo-sosio-biofisik, pohon keputusan, mesin pembelajar

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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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