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: Karakteristik Carboxymethyl Chitosan Dengan Variasi Konsentrasi NaOH
Abstrak: Carboxymethyl chitosan is chitosan derivative product obtained from chemical modification. CMCh were made by alkalization process and carboxymethylation process. Factors that affect CMCh characteristics were excess sodium hydroxide in alkalization process, ratio between chitosan and monochloroacetic acid, and carboxymethylation temperature. The objectives of this research were to study the NaOH effect to CMCh characteristics, to learn process of making CMCh, to set the proper concentration of NaOH, and to analyze CMCh characteristics. CMCh yields were 110.72%-114.22%. CMCh pH value were 3.74-4.31. The optimum NaOH concentration to obtain the best CHCh was 10 M. Percentage of CMCh moisture content was 10.70%-11.42%. Ash content and nitrogen content of CMCh were 0.49%-0.50% and 4.52%-4.81%. CMCh viscousity was 8.25 cps. Percentage of CMCh solubility was 61.61%-91.50%. The different functional groups from chitosan which observed in CMCh were C=O group, CH group from alkene functional group, C−O group, and C−O−C group.
Keyword: solubility., functional group, CMCh characteristics, chitosan, carboxymethyl chitosan