Judul: Development of Mobile Application for Visualization Sequence Pattern and Cluster in Hotspot
Abstrak: Menurut Kementerian Lingkungan Hidup dan Kehutanan (KLHK), luas hutan Indonesia pada tahun 2020 yaitu 95,6 juta hektar. Namun luas hutan tersebut semakin berkurang seiring berjalannya waktu disebabkan oleh sering terjadinya kebakaran hutan. Pemerintah telah melakukan upaya pencegahan kebakaran hutan di Indonesia dengan mendeteksi munculnya titik panas lebih awal. Pemerintah juga telah menetapkan prioritas pengecekan titik panas yaitu titik panas yang membentuk cluster, dan/atau bila titik panas ditumpangsusunkan dengan data citra satelit terindikasi disertai asap, dan/atau titik panas yang terjadi berulang paling singkat 3 (tiga) hari berturut-turut. Penelitian ini bertujuan mengembangkan aplikasi mobile untuk memvisualisasikan titik panas yang terjadi secara sekuens dan titik panas yang membentuk cluster menggunakan algoritme SPADE dan DBSCAN. Metode yang digunakan adalah Adaptive Software Development (ASD) yang dilakukan sebanyak 2 iterasi. Hasil pengujian pada iterasi 1 menunjukkan pembangkitan pola sekuens dan visualisasinya pada peta berhasil dilakukan. Hasil pengujian pada iterasi 2 menunjukkan pembentukan cluster data titik panas dan visualisasinya pada peta juga berhasil dilakukan., According to the Ministry of Environment and Forestry (KLHK), Indonesia's forest area in 2020 was 95.6 million hectares. However, the forest area is decreasing over time due to frequent forest fires. The government has made efforts to prevent forest fires in Indonesia by detecting hotspots early. The government has also set priorities for checking hotspots, namely hotspots that form clusters, and/or when hotspots are overlaid with satellite image data indicated to be accompanied by smoke, and/or hotspots that occur repeatedly for at least 3 (three) consecutive days. This research aims to develop a mobile application to visualize hotspots that occur in sequence and hotspots that form clusters using the SPADE and DBSCAN algorithms. The method used is Adaptive Software Development (ASD) which is carried out as many as 2 iterations. The test results in iteration 1 showed that the generation of sequence patterns and their visualization on the map were successfully carried out. The test results on iteration 2 showed that the formation of hotspot data clusters and their visualization on the map were also successfully carried out.
Keyword: clustering, DBSCAN, hotspots, sequential pattern mining, SPADE, SPMF library

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: Buprenorphine and Fischer-344 Rats Post-operative Stress Levels: Impact on Serum Corticosterone Levels.
Abstrak: The use of laboratory animals in experiment often requires surgical operations. Post-operative condition often associated with pain and stress. Preemptive analgesia and post operative treatment with buprenorphine can reduce post operative pain and stress and prevent prolonged pain. Therefore, the aim of the present study was to test the hypothesis that pre operative treatment of buprenorphine reduces stress and suppresses the pre-operative serum corticosterone levels in Fischer 344 rats. A total of 14 adult male Fischer 344 (F344/Sca) rats permanently catheterized in artery carotis communis were used. During 94 hour post-operatively the blood sample was collected at specific time point using Accusampler®, an automated blood sampling. Corticosterone levels were quantified with ELISA method using sheep polyclonal antibody to corticosterone. Buprenorphine is effective as pre-emptive analgesia in F344 strain. However the dose is higher than what has been reported in Sprague Dawley strain. Buprenorphine 1.0 mg/kg significantly suppressed serum corticosterone levels immediately after rats regain consciousness. Rats in buprenorphine 1.0 mg/kg group were protected from post operative body weight loss and reduced water intake compare to control, nutella, and buprenorphine 0.4 mg/kg group. Quantification of fecal corticosteroid metabolites failed to detect stress in present study.
Keyword: