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: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4