Imas Sukaesih Sitanggang

Work place: Department of Computer Science, Bogor Agricultural University, Jl. Meranti Wing 20 Level V Dramaga, Bogor, 16680, Indonesia



Research Interests: Data Mining


Imas Sukaesih Sitanggang is a lecturer in Computer Science Department, Bogor Agricultural University, Indonesia. Her main research interests include spatial data mining and data warehousing.

Author Articles
Hotspot Sequence Patterns with an Improvement in Spatial Feature

By Imas Sukaesih Sitanggang Dewi Asiah Shofiana Boy Sandi Kristian Sihombing

DOI:, Pub. Date: 8 Nov. 2018

Forest fires in Sumatra and Kalimantan resulted in degradation of peatlands significantly. The strong indicator of forest and land fires including in peatland can be identified using hotspots which occurred consecutively in 2 to 5 days. The previous studies have been conducted in mining sequence patterns on hotspot datasets in Sumatra and Kalimantan. However, those studies applied the sequential pattern algorithms on the datasets containing temporal and rough spatial features. This study aims to generate sequence pattern of hotspot datasets using the SPADE algorithm with the improvement of the spatial feature. The study results in 892 1-frequent sequences and 28 2-frequent sequence patterns at the minimum support of 0.02%. A total of 484 hotspots were found from the 28 2-frequents sequence patterns, most of which were occurred in September to November 2014 and 2015. Central Kalimantan, Riau, and South Sumatra are the area where hotspots mostly occurred in 2014 and 2015. The visualization module for hotspot sequences was successfully developed in two iterations using the JavaScript.

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Clustering Undergraduate Computer Science Student Final Project Based on Frequent Itemset

By Lusi Maulina Erman Imas Sukaesih Sitanggang

DOI:, Pub. Date: 8 Nov. 2016

Abstract is a part of document has an important role in explaining the whole document. Words that frequently appear can be used as a reference in grouping the final project document into categories. Text mining method can be used to group the abstracts. The purpose of this study is to apply the method of association rule mining namely ECLAT algorithm to find most common terms combination and to group a collection of abstracts. The data used in this study is documents of final project abstract in English of undergraduate computer science student of IPB from 2012 to 2014. This research used stopwords about common computer science terminology, applied association rule mining with support of 0.1, 0.15, 0.2, 0.25, 0.3, and 0.35, and used k-Means clustering with number of cluster (k) of 10 because it gives the lowest SSE. This research compared the value of support, SSE, the number of cluster members, and purity value in each cluster. The best clustering result is data with additional stopwords and without applying association rule mining, and with k is 10. The SSE result is 23 485.03, and with purity of 0.512

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