Herbal medicinal plant is a traditional medicinal plant that is used to cure a disease. Most of modern people did not know yet the benefits that will be gotten from herbal plants for the health. This research developed a supporting decision application system of herbal medicinal plants for various diseases. ROC (Rank Order Centroid) method was used to count the total number of criteria value and Oreste method was used to rank the alternative herbal medicinal plants with criteria which influence it, namely disease, blood pressure, tall, weight, user’s condition (other diseases), age, kinds of plants, substance and efficacy of plants themselves. Final result of this system was that there were some alternative herbal medicinal plants which were appropriate to user’s disease. In this research, the researcher conducted white box testing by using path base testing to make complex logical estimates to define current action and conducted black box testing by using equivalence partitioning technique which divided domain input, decided testing case by explaining kinds of mistakes. The results of proper test for the system which were done by using questionnaire were gotten 86.75% for testing of functional system, 87% for interface and accessing testing, and 87.33% for testing of advantages system.

This study aims to describe the level of achievement on child development by early childhood education institutions, based on national education standards. This kind of research has not been done yet by other researchers. The method used is documentation study / literature study using secondary data from 76 early childhood education (ECE) institutions from nine sub-districts collected through standardized institution accreditation instruments. Data collection is carried out by assessors of the National Accreditation Board, through the steps of desk evaluation, visitation and validation. Data is processed using percentage statistical techniques then analyzed using indicators of the level of achievement on child development, namely; (1) achievement of six aspects of child development includes religious and moral, social and language, cognitive, motoric, and artistic values. (2) Classifying classes based on the age of the child. (3) Documentation of achievement on developments at the daily, weekly, semester and annual levels.The results of the study show that the level of achievement on child development in early childhood education (ECE) institutions that is only fulfilled is (17%) while those that have not been fulfilled (83%).

Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.

Abstrak:Berdasarkan data yang dipublikasikan oleh Badan Geologi Amerika Serikat yaitu United States Geological Survey (USGS) dalam programnya memonitor aktivitas gempa di seluruh dunia, pada tahun 2013 hingga tahun 2018 tercatat telah terjadi gempa di pulau Sumatera  sebanyak 1.443 kali dengan berbagai magnitudo dan kedalaman. Dampak yang disebabkan oleh gempa bumi berbeda-beda bergantung pada magnitudo dan kedalamannnya. Untuk mengetahui pengelompokan  pola gempa bumi di wilayah pulau Sumatera dapat dilakukan dengan salah satu teknik data mining yaitu clustering. Sistem ini dibuat dengan bahasa pemrograman PHP, Tools Google Maps API dan metode pengembangan Waterfall. Pada penelitian ini dilakukan clustering data titik gempa menggunakan metode Fuzzy Possibilistic C-Means. Sistem ini berhasil mengelompokkan data titik gempa wilayah pulau Sumatera dari tahun 2013 hingga tahun 2018 menjadi 3 kluster. Kluster 1 memuat gempa dangkal dengan kedalaman 3.69 km – 50 km dan kekuatan 2.7 SR – 6.6 SR, kluster 2 memuat gempa dangkal dengan kedalaman 50.09 km – 56.15 km dan kekuatan 4 SR – 5.3 SR, dan kluster 3 memuat gempa dangkal, menengah, dan dalam dengan kedalaman 56.34 km - 365.24 km dan kekuatan 4 SR – 6 SR. Dari hasil clustering data titik gempa pulau Sumatera dari tahun 2013 hingga tahun 2018 dengan metode Fuzzy Possibilistic C-Means data terkluster berdasarkan kedalaman saja, sedangkan untuk kekuatan gempa bumi menyebar di setiap kluster.

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