Judul: Pengelompokan Data Indeks Pembangunan Manusia di Pulau Jawa dengan Algoritme ST-DBSCAN dan Bahasa Pemrograman Python
Abstrak: Human Development Index (HDI) is an indicator commonly used by the United Nations Development Programme (UNDP) to assess the success of development. Three elements forming the HDI value are the level of health, education, and economics. The HDI data per district used in this research only cover Java region in 2012, obtained from the Central Bureau of Statistics including spatial data (area coordinates), temporal data (years), and non-spatial data (HDI). Data mining technique applied in this research is clustering with ST-DBSCAN using Python programming language. Clustering can be used to found similar grouping based on HDI in each region. Visualization of HDI data objects produces a map of the cluster spread. By using spatial distance parameter (Eps1)=0.4, temporal distance (Eps2)=0, size of the tolerated difference in value of non-spatial atributes (Δ)=2, and minimum number of members in each cluster (MinPts)=5 resulted in 4 clusters and 18 point of noise with silhouette coefficient worth 0.020.
Keyword: Bogor Agricultural University (IPB), ST-DBSCAN, Python, human development index, clustering

Judul: Clustering Data Indeks Pembangunan Manusia (IPM) Pulau Jawa Menggunakan Algoritme ST-DBSCAN dan Bahasa Pemrograman R
Abstrak: Human development index (HDI) is an important indicator for regional quality evaluation. HDI data consist of a polygon-shaped spatial and temporal data. ST-DBSCAN can process both spatial and temporal data. The purpose of this research is to apply ST-DBSCAN algorithm on HDI data on Java. Nine clusters are found on Eps1 = 0.4, Eps2 = 0, MinPts = 4 and delta-e equal to 2 local z-score. The highest HDI value is located at Jabodetabek, 77.67. It is caused by its location at capital of Indonesia. This is in contrast with the eastern of Java that has the lowest HDI value, 72.24. The precision of a cluster is calculated using silhouette index. In this research, the cluster evaluation is 0.1599514. Based on this result, it is still not close to the best value. ST-DBSCAN algorithm is implemented using R language, using maptools, cluster and rgeos packages.
Keyword: Bogor Agricultural University (IPB), ST-DBSCAN, Silhouette Index, clustering

Judul: Sintesis 1-Kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai Prekursor Asam (S)-(+)-(6-Metoksi-2-naftil)propanoat
Abstrak: Senyawa 1-kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai prekursor asam (S)-(+)-(6-metoksi-2-naftil)propanoat disintesis dari bahan awal 2-metoksinaftalena. Tahapan sintesis diawali dengan klorinasi menggunakan 2 jenis pereaksi klorinasi, yakni isosianurat terklorinasi dan NaOCl. Klorinasi dengan isosianurat terklorinasi berhasil menyintesis 1-kloro-2-metoksinaftalena sebagai produk monoklorinasi melalui pencirian dengan kromatograf gas-spektrometer massa (GC-MS). Nisbah konversi produk paling efisien diperoleh pada laju penambahan 2.5 mmol/jam. Sebaliknya, klorinasi dengan NaOCl menghasilkan produk triklorinasi. 1-Kloro-2-metoksinaftalena selanjutnya diasilasi dengan propanoil klorida, tetapi analisis dengan GC menunjukkan bahwa produk asilasi tidak terbentuk. Sistem bebas-air diperlukan untuk keberhasilan proses asilasi tersebut.
Keyword: isosianurat terklorinasi, NaOCl, nisbah konversi