Judul: Deteksi Kerusakan dan Revegetasi Pasca Kebakaran Hutan dan Lahan dengan Pendekatan Decision-Tree Machine Learning: Studi Kasus di Jambi
Abstrak: Informasi mengenai kondisi tutupan hutan dan lahan pasca kebakaran merupakan salah satu bagian penting dalam kegiatan pemantauan setelah terjadinya kebakaran hutan. Penelitian ini bertujuan untuk menentukan fitur, atribut, dan parameter dalam mendeteksi kerusakan dan revegetasi akibat kebakaran berbasis pada citra multiwaktu dengan citra sintetik menggunakan pendekatan Pohon Keputusan (Decision Tree) Pembelajar Mesin (Machine Learning). Penelitian ini menemukan bahwa parameter paling akurat adalah Gini Index pada K1 dengan kombinasi pemangkasan (pruning), tanpa pra-pangkas (pre-pruning), kedalaman pohon sebesar 60, pra-pangkas alternatif (pre-pruning alternative) sebesar 50, sampling split dengan stratified sampling, dan ukuran daun sebesar 41 atau K2 dengan kombinasi tanpa pemangkasan (without pruning), tanpa pra-pangkas (pre pruning), kedalaman pohon sebesar 80, pra-pangkas alternatif (pre-pruning alternative) sebesar 30, sampling split dengan automatic sampling, dan ukuran daun sebesar 100. Kajian ini memberikan overall akurasi (OA) sebesar 95,7% dan kappa accuracy (KA) sebesar 94,6%. Dari tujuh citra indeks yang diuji dengan pohon keputusan, ditemukan tiga indeks yang paling signifikan pada BNDVI (Blue Normalized Differenced Vegetation Index), NBBI (Normalized Built-up & Bare Land Index), dan NDWIG (Normalized Differenced Wetness Index, green-based)., Information on post-fire forest and land cover conditions is important to monitoring activities after forest fires. The study’s objective is to determine features, attributes, and parameters in detecting damage and vegetation growth due to fires based on multi-temporal images with synthetic images using a Machine Learning approach. This study found that the most accurate parameters were the Gini Index at K1 with a combination of pruning, no pre-pruning, tree depth of 60, pre-pruning alternative of 50, sampling split with stratified sampling, and leaf size of 41 or K2 with a combination of no pruning, no pre-pruning, tree depth of 80, pre-pruning alternative by 30, sampling split by automatic sampling, and leaf size by 100. Overall accuracy (OA) gives a value of 95,7% and kappa accuracy (KA) of 94,6%. The classification results of the seven index images tested with decision trees obtained the three most significant indices on BNDVI (Blue Normalized Difference Vegetation Index), NBBI (Normalized Built-up &; Bare Land Index), and NDWIG (Normalized Differenced Wetness Index, green-based).
Keyword: indeks vegetasi, mesin pembelajar, pasca kebakaran, pohon keputusan, decision tree, index vegetation, machine learning, post-fire

Judul: Comparasion of Support Vector Machines and Maximum Likelihood Method for Detecting Damage and Growth Post-Forest and Land Fires: A Case Study in Jambi
Abstrak: Studi ini meneliti tentang citra asli dan citra sintetik, yaitu citra komposit, citra Principal Component Analysis, dan citra indeks vegetasi menggunakan algoritma Mesin Vektor Pendukung (SVM) dan Peluang Maksimum (MLC) dalam mendeteksi kerusakan dan pertumbuhan akibat adanya kebakaran hutan dan lahan. Tujuan penelitian ini adalah mengetahui metode dan citra yang optimal untuk deteksi perubahan vegetasi setelah kebakaran. Pendekatan klasifikasi terbimbing digunakan untuk mengklasifikasikan setiap citra dengan algoritma SVM dan MLC. Citra komposit dengan algoritma MLC menghasilkan KA sebesar 76,7% sedangkan pada algoritma SVM sebesar 84,3%. Untuk citra sintetik, citra PCA dengan algoritma MLC menghasilkan KA sebesar 79,4%, sedangkan pada algoritma SVM sebesar 80,7%. Citra indeks vegetasi pada algoritma MLC menghasilkan KA sebesar 80,4%, sedangkan pada algoritma SVM sebesar 84,0%. Nilai akurasi citrakomposit dengan algoritma SVM merupakan yang terbaik., This study examined raw and synthetic images, i.e composite imagines, Principal Component Analysis imagines, and vegetation index imagines using Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) algorithms in detecting damage and growth post-forest and land fires. The objective of this study was to find out the most optimal method and images for detecting vegetation changes after fires. The supervised classification approach was used to classify each imagery using the SVM and MLC algorithms. The raw images with the MLC algorithm provided a KA of 76,7% while the SVM algorithm was 84,3%. For synthetic images, the PCA images with the MLC algorithm produced a KA of 79,4%, while the SVM algorithm was 80,7%. The vegetation index images with the MLC algorithm produced a KA of 80,4%, while the SVM algorithm was 84,0%. The accuracy of composite imagery with the SVM algorithm was the highest.
Keyword: Maximum Likelihood Classifier, Principal Component Analysis, Support Vector Machine, Vegetation Index

Judul: Optimization of Alginate-Chitosan Microcapsules Formation for Leydig Cells Encapsulation
Abstrak: Hypogonadism is a clinical condition characterized by low concentrations of testosterone. It is caused by malfunction of Leydig cells in producing testosterone. Encapsulation method will protect the transplanted cells from the immune system rejection. The aims of this study was to optimize the formation of microcapsules for Leydig cells encapsulation. The microcapsules were made of alginate and chitosan as the first and the second coating agents, respectively. The result showed that the minimum concentration of alginate was 1.5% (w/v) with a viscosity of 33.8 cPs, resulted spherical microcapsules with diameters of 230-270 µm. The optimum concentration of chitosan as the second coating agent was 0.5% (w/v), resulted spherical microcapsule with mechanical stability in 4 hours. Leydig cells can be trapped inside the capsule with a density that proportional with concentration of cells used in the encapsulation.
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