Judul: Perbandingan Support Vector Machine dan Partial Least Square Discriminant Analysis untuk Memprediksi Kemiripan Biomassa.
Abstrak: Batu bara dan karbon aktif banyak dimanfaatkan dalam berbagai aplikasi. Dua bahan tersebut menjadi perlu diperhatikan karena batu bara dan karbon aktif memiliki sifat yang tidak dapat diperbaharui. Oleh karena itu, diperlukan suatu bahan lain yang dapat dimanfaatkan sebagai alternatif pengganti bagi batu bara atau karbon aktif. Biomassa sebagai sumber energi terbarukan memiliki potensi untuk menggantikan batu bara atau karbon aktif. Metode Fourier Transform Infrared (FTIR) dan analisis multivariat digunakan untuk mengukur kemiripan biomassa dengan batu bara atau karbon aktif. Analisis multivariat Support Vector Machine (SVM) dan Partial Least Square Discriminant Analysis (PLSDA) menghasilkan performa yang baik dengan rata-rata nilai akurasi, sensitivitas, dan spesifisitas sebesar 100%. Namun, PLSDA tidak memerlukan tuning parameter terlebih dahulu sehingga dipilih untuk memprediksi biomassa. Setelah diprediksi, biomassa menunjukkan kemiripan dengan batu bara sehingga berpotensi sebagai alternatif pengganti bagi batu bara.
Keyword: batu bara, biomassa, karbon aktif, PLSDA, 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: Mercury Toxicity and It’s Effect on Histopathology Change of Nile Tilapia (Oreochromis niloticus).
Abstrak: Nile tilapia (Oreochromis niloticus) is one of fish species that oportunity to contaminated with mercury. The effects of pollutants can damage living organisms organs and can cause death. The effects caused mercury had acute or chronic character. The aims of this study are to determine threshold and acute toxicity of mercury chloride (HgCl2) of Nile tilapia (Oreochromis niloticus), to assess the effect of mercury chloride (HgCl2) on several biometric parameters (survival, growth rate of average daily weighted, liver condition, bile and reproductive variability of Nile tilapia (Oreochromis niloticus), assess the level of damage to the histological structure of the gills, liver and kidney of Nile tilapia (Oreochromis niloticus) caused mercury chloride (HgCl2) exposure. This study was conducted from February to June 2013. Nile Tilapia (Oreochromis niloticus) were used in this study were female with average weight 20 grams or 11-13 cm length range. Toxicant materials used in this study was mercury chloride (HgCl2). Fish were reared in each aquarium 60x40x30 containing 48 liters of water. This study consists of three stages: a preliminary test, acute test, and sub-chronic test. To determine the changes in biometric condition of tilapia approximated by measuring the survival rate (SR), the growth rate of average daily weight , the content of mercury in the fish organs, hepatosomatic index (HSI), the relative bile volume (VER), gonadal soamtic index (IKG), fecundity and oocit diameter, tissue damage caused by mercury in fish organs by making histological preparations stained with hematoxylin and eosin. The median lethal concentration (96 hr, LC50) of Mercury Chloride was calculated as 1,64 mgL--1.The survival rate was highest in the control treatment (46,67 %) . Mercury chloride with concentration 0.196 mgL--1 show significant effect to changes HSI and relative bile volume, and oocyte diameter of Nile tilapia (p < 0,05). Mercury chloride with a concentration of 0,164 mgL-1 and 0,196 mgL-1 have not a significant effect on the growth rate of weight, gonadal somatic index and fecundity of Nile tilapia (p > 0,05). Mercury chloride cause damage to gills (hyperplasia, hypertrophy, proliferation and neukrosis), liver (hyperplasia, hemorhage, shrinkage of hepatocytes, proliferation and neukrosis) and kidney (hyperplasia, hemorrhage, shrinkage of the glomerulus, proliferation and neukrosis).
Keyword: Nile tilapia, Hepatosomatic indexs (HSI), histopathology