Judul: Study of Support Vector Machine and Maximum Likelihood Methods of Pleides-1B Imagery Based on Mean-Shift Algorithm Segmentation: Case Study in North Kalimantan Dryland Forest
Abstrak: Studi ini meneliti tentang algoritma segmentasi mean-shift menggunakan pendekatan klasifikasi parametrik yaitu peluang maksimum (MLC) dan pendekatan klasifikasi non-parametrik mesin vektor pendukung (SVM) pada citra satelit resolusi sangat tinggi Pleiades-1B dalam mendeteksi penutupan tajuk ekosistem hutan lahan kering. Pendekatan klasifikasi MLC dan SVM digunakan untuk mengklasifikasi hasil segmentasi mean-shift menjadi tajuk dan bukan tajuk. Klasifikasi segmentasi citra Pleiades-1B menggunakanMLC menghasilkan akurasi kappa (KA) tertinggi sebesar 50% pada kombinasi K-08, sedangkan menggunakan SVM menghasilkan KA sebesar 73% pada kombinasi K-07. Akurasi tertinggi hasil segmentasi didapat menggunakan parameter range radius (Hr) senilai 20 dan akurasi terendah menggunakan Hr senilai 18. Hasil studi menunjukkan bahwa hasil terbaik klasifikasi segmentasi mean-shift yaitu menggunakan klasifikasi nonparametrik SVM dengan parameter segmentasi Hr senilai 20. Kata Kunci: akurasi kappa, mean-shift, MLC, SVM, penutupan tajuk., This study examined the mean-shift segmentation algorithm using the parametric classification approach of Maximum likelihood (MLC) and the non-parametric classification approach of Support Vector Machine (SVM) on very high-resolution satellite images of Pleiades-1B in detecting canopy closure of dryland forest ecosystems. The MLC and SVM classification approaches were used to classify the results of mean-shift segmentation into canopy and non-canopy. The segmented Pleiades-1B image classification using the MLC provided the highest KA of 50% in combination K-08, while using SVM resulted a KA of 72% with the combination of K- 07. The highest accuracy of segmentation classification results was obtained using a range radius (Hr) parameter of 20, while the lowest accuracy was with Hr of 18. The study results show that the best result of mean-shift segmentation classification is using SVM non-parametric classification with a segmentation parameter Hr of 20.
Keyword: Canopy closure, Kappa accuracy, Mean-shift, MLC, SVM

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: 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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