Judul: Development of a Data Warehouse Model for Online  Analytical Processing Applications for Karhutla Hotspot Data
Abstrak: Hotspot merupakan salah satu indikator untuk mengidentifikasi terjadinya kebakaran hutan dan lahan (karhutla). Spatial data warehouse hotspot diperlukan untuk membuka peluang dalam melakukan analisis yang lebih mendalam terhadap data hotspot, serta memfasilitasi pengambilan keputusan berbasis spatial yang lebih baik dalam konteks penanggulangan karhutla. Penelitian ini bertujuan membangun modul database untuk mengelola data hotspot yang bersumber dari website SiPongi, membangun spatial data warehouse pada sistem Spatial OLAP untuk Data Hotspot, dan membangun modul extract, transform, dan load (ETL). Metode penelitian meliputi identifikasi fitur dan atribut yang akan dilakukan pada pengembangan modul database hotspot, pembangunan spatial data warehouse, dan pengembangan modul ETL. Tahap terakhir adalah pengujian dengan menggunakan metode pengujian black box. Penelitian menghasilkan spatial data warehouse hotspot dengan menunjukkan bahwa data warehouse dapat melakukan operasi OLAP dengan benar sesuai dengan kebutuhan sistem., Hotspots are an indicator to identify forest and land fires (karhutla). Spatial data warehouse hotspots are needed to open up opportunities to carry out more in depth analysis of hotspot data, as well as to facilitate better spatial-based decision making in the context of forest and land fire prevention. This study aims to build a database module to manage hotspot data sourced from the SiPongi website, build a spatial data warehouse on the Spatial OLAP system for Hotspot Data, and build an extract, transform, and load (ETL) module. The research steps include identification of features and attributes that will be carried out in the development of the hotspot database module, the development of a spatial data warehouse, and the development of the ETL module. Finally, testing is carried out using the black box testing method. The study succeeded in producing a spatial data warehouse hotspot. The data warehouse can provide OLAP operations correctly according to system requirements.
Keyword: data warehouse, extract, transform dan load (ETL), spatial online  analytical processing (SOLAP)

Judul: Data Mining System to Analyze Hotspot Data
Abstrak: Karhutla di Indonesia mengalami peningkatan dari intensitas dan penyebarannya. Salah satu kegiatan untuk mencegah dan mengurangi terjadinya fenomena tersebut adalah dengan menganalisis data titik panas. Data titik panas tersebut dianalisis menggunakan teknik data mining, yaitu spatio-temporal clustering dan sequential pattern mining. Penelitian ini dilakukan untuk menggabungkan kedua teknik tersebut menjadi sebuah pengembangan sistem terintegrasi yang lebih efisien dalam menganalisis data titik panas. Metode yang digunakan pada penelitian ini adalah prototyping. Sistem ini menggunakan data titik panas dari API SiPongi yang disimpan dalam database. Sistem yang dikembangkan berupa aplikasi web menggunakan framework Shiny. Penelitian ini berhasil melakukan penggabungan dua teknik beserta penggunaan data yang dinamis dan berasal dari satu sumber database. Berdasarkan pengujian yang dilakukan terhadap sistem, fitur untuk visualisasi data, yaitu peta, tabel, dan summary memberikan hasil yang sesuai dengan ekspektasi. Fitur lainnya, seperti help dan about juga memberikan hasil sesuai harapan. Hasil pengujian dilakukan menggunakan metode black box testing dan mendapatkan 100% untuk efektivitas sistem., Forest and land fires in Indonesia have increased in intensity and spread. One of the activities to prevent and reduce the occurrence of this phenomenon is to analyze hotspot data. The hotspot data are analyzed using data mining techniques, namely spatio-temporal clustering and sequential pattern mining. This study aims to combines the two techniques into an integrated system that is more efficient in analyzing hotspot data. The method used in this study is Prototyping. The data for this system are collected using the API from SiPongi that is stored in database. The system is a web application using the Shiny framework. This study successfully combined two separate data mining techniques with the use of dynamic data derived from a single source of database. Based on tests conducted on the system, features for data visualization, namely maps, tables, and summaries has met the expectations. Other features, such as help and about also provide results as desired. The system test using the black box method results in 100% system effectiveness
Keyword: Data mining, Forest and land fires, Hotspot, Sequential pattern mining, Spatio-temporal clustering

Judul: Insecticidal Activity of Essential Oils Leaves of Cinnamomum spp. (Lauraceae) against Crocidolomia pavonana and Their Phytotoxicity on Broccoli Seedlings
Abstrak: Essential oils from leaves of 13 species of Cinnamomum were tested for their insecticidal activity on the cabbage head caterpillar Crocidolomia pavonana with leaf immerse method. The results showed that the treatment with 1% (w/v) essential oils leaves of C. celebicum, C. multiflorum, C. sintoc, and C. verum caused 21.45, 93.65, 60.30, and 76.10% mortality, respectively. Based on the results of phytotoxicity and insecticidal activity tests, C. multiflorum oil was further tested at 6 concentration levels. In all treatments, larval mortality has occurred at 24 hours after treatment (HAT) and increased at 48 HAT. LC50 of C. multiflorum oil at 24 and 48 HAT was 0.504 and 0.396%, respectively. The treatment with C. multiflorum oil at ≤0.31% did not inhibit the development of C. pavonana larvae. Determination of the chemical constituents of Cinnamomum oils were carried out by gas chromatography-mass spectrometry (GCMS). Totally, 345 compounds were identified. Methyleugenol constituted the main component (49.4%) of leaf essential oil of C. multiflorum and was assumed to be responsible for the insecticidal activity of the oil against C. pavonana larvae. LC50 of pure methyleugenol obtained at 0.365% (48 HAT) and 0.363% (72 HAT). The results showed that C. multiflorum was more effective with LC50 value equivalent to methyleugenol content of 0.32% (24 HAT) and 0.24% (48 HAT).
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