Judul: Clustering Jamur Asterinaceae Menggunakan Metode K-Means
Abstrak: Fungi are one of the many cultivated crops by farmers in upland areas. Fungi are beings who are heterotrophic eukaryotes, which absorbs organic matter from the environment so that his life is parasitic and saprophyte. Diversity of these fungi cause difficulty to distinguish between one type and another. Before identifying fungi taxa Asterinaceae Australia, a grouping or clustering method is required to recognize the fungi by their characteristics. In grouping these fungi, characteristics possessed are first converted into binary form. K-Means clustering method is then utilized. There are three main stages for the clustering purpose, namely normalization, K-Means method, and Davies Bouldin Index calculation. The results show that the best clustering is obtained with 10 clusters.
Keyword: K-Means., fungi Asterinaceae, Davies Bouldin Index

Judul: Seleksi ciri morfologi untuk membangun kunci identifikasi Asterinaceae berdasarkan Principal Component Analysis
Abstrak: Characters of each fungal species may show similarities to others, such that they are related to each other. In this case, the characters of 90 species of Asterina, 2 species of Asterolibertia, 7 species of Lembosia, and 2 species of Parasterinella (Asterinaceae) were studied. These species are delimited based on 116 morphological characters with three different character states (71 nominal, 33 ordinal, and 12 numerical). Recognizing each species using all those characters is rather difficult. Therefore, selection of representative characters is needed. Selection was done by reducing the number of features to the minimum without losing the essential information using Principal Component Analysis (PCA) approach. Eventhough PCA is usually applied for one type of data, in this research PCA was experimented for mixed data types. The purpose was to select the most representative morphological traits. Selected traits were then used as parameters in the construction of interactive Asterinaceae identification system using the principal component scores. The result of this research shows that PCA can be used to reduce the traits with different data types. Its 23 characters out of 116 characters were selected. The accuracy of the identification system based on those 23 traits was 100%, as all Asterinaceae species can be identified
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Judul: Analysis of NaTFSA Electrolytes on Sodium Ion  Batteries Using the DC-DFTB-MD Method
Abstrak: Pelarut air digunakan dalam sistem elektrolit NaTFSA pada baterai ion natrium karena sifatnya yang relatif aman. Namun, menghasilkan tegangan yang cukup rendah sehingga memiliki keterbatasan dalam pengaplikasiannya. Untuk mengatasi hal tersebut dikembangkan elektrolit berkonsentrasi tinggi. Pada penelitian ini, pengaruh konsentrasi elektrolit NaTFSA terhadap sifat struktur, dinamika, dan elektronik dianalisis menggunakan metode dinamika molekul pada level mekanika kuantum. Analisis mikroskopik struktur dan dinamika elektrolit NaTFSA menunjukkan bahwa seiring kenaikan konsentrasi, koefisien difusi air menurun sedangkan konduktivitas elektrolit cenderung meningkat sebanding dengan mobilitas ion-ion., The aqueous solvent is used in the NaTFSA electrolyte system in sodium ion batteries because it is relatively safer. However, the voltage window of the sodium ion battery is narrower than that of the lithium ion battery, thus it still limits its practical implementation. To overcome this problem, a high concentration of electrolyte was developed. In this present work, the salt concentration effect on structural, dynamic and electronic properties were analyzed using molecular dynamics methods at the quantum mechanical level. Microscopic analysis of the structural and dynamical properties of the NaTFSA electrolyte showed that as the concentration increased, the water diffusion coefficient decreased while the electrolyte conductivity tended to increase.
Keyword: Conductivity, DC-DFTB, Diffusion constants, NaTFSA electrolytes, Sodium ion batteries