Imas S. Sitanggang

Work place: Department of Computer Science, Bogor Agricultural University, Bogor, 16680, Indonesia



Research Interests: Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Imas Sukaesih Sitanggang received the PhD Degree in Computer Science from Faculty of Computer Science and Information Technology, Universiti Putra Malaysia in 2013. She is a lecturer in Computer Science Department, Bogor Agricultural University, Indonesia.  Her main research interests include spatial data mining and data warehousing.

Author Articles
ICT Training Recommendation using Web Usage Mining

By Susi Maulidiah Imas S. Sitanggang Heru Sukoco

DOI:, Pub. Date: 8 Dec. 2018

The sustainability of a course and training institute depends on the availability of students. There are many ways to promote the courses and training programs including promoting it through the institution's website. The visitor behavior of a website have hidden information that can be found using web usage mining approach. This study aims to discover the hidden information from the visitor patterns of course website. The data used are web access log data of August 2016. Web usage mining process was done using the Co-Occurence Map Sequential Pattern Mining using Bitmap Representation (CM-SPAM) algorithm which is available in the SPMF tool. Based on sequential pattern mining on the access log data, this study recommends improvements regarding the website structure and information that should be displayed on certain web pages. This study also found that the visitors of course website interested in three page types: one day seminar, tutorial and the training program.

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Spatial Co-Location Patterns on Weather and Forest Fire Data

By Imas S. Sitanggang Sergi Roseli Lailan Syaufina

DOI:, Pub. Date: 8 Sep. 2018

One of problems that can increase the risk of forest fire occurrences in Indonesia is drought which is affected by weather conditions. Therefore, weather conditions and forest fire are strongly related. Spatial co-location pattern can be applied to identify the weather conditions that are vulnerable to fires based on the distance between weather observation points and hotspot occurrences. The purpose of this study is to apply the co-location miner algorithm on the weather and hotspot data in Rokan Hilir Riau Indonesia and to analyze the generated co-location patterns. Experimental results show that precipitation which co-located with hotspot occurrences are 0.08–6.69 mm/day. In addition, the temperature which co-located with hotspot occurrences are 22°C–29.17°C. Inside the intervals, hotspots will occur in the radius of 9.724 km from the precipitation and temperature observation points. In 2008, many hotspots were found on the three areas in the study area with the average of precipitation around 3.65–3.71 mm/day and temperature around 24.44°C–25.23°C.

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