Judul: Pembangunan Sistem Spatial Online Analytical Processing untuk  Analisis Data Titik Panas
Abstrak: Kebakaran hutan dan lahan (karhutla) kerap melanda dan menjadi krisis endemi yang harus diperhatikan oleh pemerintah dan juga masyarakat. Kementerian Lingkungan Hidup dan Kehutanan (KLHK) melalui Direktorat Jenderal Pengendalian Perubahan Iklim (PPI) mengembangkan sebuah sistem pemantau titik panas (hotspot) harian yang disebut dengan SiPongi. Sistem dapat diakses secara publik melalui https://sipongi.menlhk.go.id/ (per 27 Oktober 2023). Namun, sistem belum mampu menampilkan ringkasan data dan tren titik panas hingga tingkat desa dan belum terdapat visualisasi dalam bentuk grafik. Penelitian ini bertujuan untuk membangun sistem OLAP untuk data titik panas berbasis web. Pembangunan sistem berbasis web ini menggunakan metode prototyping. Penelitian ini berhasil membangun sistem OLAP untuk data titik panas menggunakan framework Go Fiber dengan library xdbsoft/olap untuk backend dan Angular untuk frontend. Hasil pengujian menggunakan blackbox testing menunjukkan bahwa sistem mampu berjalan sesuai dengan kebutuhan pengguna., Forest and land fires (karhutla) often occur and have become an endemic crisis that needs attention from both the government and the public. The Ministry of Environment and Forestry (KLHK) through the Directorate General of Climate Change Control (PPI) has developed a daily hotspot monitoring system called SiPongi. This system is accessible to the public at https://sipongi.menlhk.go.id/ (as of October 27 2023). However, the system is not yet capable of providing a summary of hotspot data and trends down to the village level, and there is no visualization in the form of graphs. This study aims to build a web-based OLAP system for hotspot data. The development of this web-based system uses a prototyping method. The study successfully constructed an OLAP system for hotspot data using the Go Fiber framework with the xdbsoft/olap library for the backend and Angular for the frontend. Testing results using blackbox testing show that the system operates according to user requirements.
Keyword: backend, frontend, hotspot, OLAP, SiPongi

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