Judul: Spatiotemporal Data Model Development on Hotspot Data with an Event-based Spatiotemporal Data Model (ESTDM) Concept
Abstrak: Hotspot data that has been existed all this time is still in the form of usual spatial data which is not enough to keep temporal information and information about how a hotspot evolved. There are so many disadvantages in keeping the hotspot data. Therefore a data model that can keep enough information to represent the hotspot evolution is needed, and also can handle the lack of keeping the previous hotspot data. An Event-based Spatiotemporal Data Model (ESTDM) concept is propose to overcome this problem. Appearance and disappearance process that occurs on hotspot will be used as base for spatiotemporal data model development on hotspot data. This research aims to implement an Event-based Spatiotemporal Data Model (ESTDM) concept into PostgreSQL. As a result, we can get the informations about spatial information and temporal information about geographical information changes to represent hotspot evolution from spatiotemporal query such as show hotspot that reappear after experience disappearance before. These queries make analyze a hotspot easier. The result of these queries can be visualized in a Indonesian map then with histories about period of appearance and disappearance of hotspot.
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Judul: Indexing Spatio-Temporal Pada Data Hotspot
Abstrak: The spatial information of hotspot is always changing over time. The recording of these changes makes it necessary to add dimension of time (temporal) into database. The database which is capable to store spatial and temporal information is known as spatiotemporal database. This database is bigger than a normal database because it stores all versions of an object over time and not allowed data to be physically deleted. A large database size causes it’s search time increases. It makes an access structure is needed in spatiotemporal database. Indexing is one of the access structure applied in the spatiotemporal database that accelerates data searching process. Spatiotemporal indexing method consists of two parts namely spatial indexing which is based on geographical information, and temporal indexing which is based on data timing information. The process of hotspot data indexing will be done in this research by using R-Tree approach for spatial data and Change Temporal Index (CTI) for temporal data. R-Tree is a hierarchical, height-balanced external memory data structure for multidimensional object. The hierarchical of R-Tree based on list of administrative such as regency, provinces and island. Change Temporal Index makes indexed data based on temporal side like valid time. CTI keeps valid data in current database and previous data according to interval valid time in historical database. This research is expected to accelerate the retrieving process in spatiotemporal databases. Spatial indexing makes searching faster up to 70%, temporal indexing makes searching faster up to 35% and spatiotemporal indexing makes searching faster than without indexing up to 50%.
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Judul: Karakteristik Fisikokimia, Organoleptik, dan Antibakteri Sabun Cair Berbasis Minyak Kelapa Murni (Cocos nucifera L) Fermentasi Lactobacillus casei (InaCC B1045).
Abstrak: Minyak kelapa murni dapat diolah kembali menjadi produk lain dengan memanfaatkan kandungan asam laurat yang memiliki sifat antibakteri sehingga dapat dijadikan sebagai bahan baku pembuatan sabun cair. Penelitian ini bertujuan menganalisis karakteristik fisikokimia, organoleptik, dan antibakteri sabun cair berbasis minyak kelapa murni (Cocos nucifera L) hasil fermentasi Lactobacillus casei (InaCC B1045). Karakteristik fisikokimia yang diuji meliputi tegangan permukaan, nilai pH, dan stabilitas busa, sedangkan uji organoleptik meliputi warna, banyak busa, kesan lembut, aroma dan kekentalan. Sabun cair ini rata-rata memiliki pH 12 sehingga belum memenuhi persyaratan yang ditetapkan oleh Standar Nasional Indonesia (SNI). Panelis lebih menyukai sabun cair dengan konsentrasi KOH 34% (VB34) yang dipilih berdasarkan warna, banyak busa dan kelembutan sabun. Aktivitas antibakteri ini menggunakan metode cakram yang ditunjukkan oleh zona bening pada sabun. Aktivitas tersebut tergolong kuat untuk Staphylococcus aureus, yaitu sebesar 18.52 mm (sabun VB34), Staphylococcus epidermidis sebesar 10.94 mm (sabun VB32), sedangkan untuk Eschericia coli tergolong lemah karena tidak terbentuk zona bening.
Keyword: antibakteri, fisikokimia, organoleptik, sabun cair