Hotspot Sequence Patterns with an Improvement in Spatial Feature

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Imas Sukaesih Sitanggang 1 Dewi Asiah Shofiana 1,* Boy Sandi Kristian Sihombing 1

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

* Corresponding author.


Received: 25 Mar. 2018 / Revised: 27 Apr. 2018 / Accepted: 8 Jun. 2018 / Published: 8 Nov. 2018

Index Terms

Hotspot, peatland fire, sequential pattern mining, SPADE


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.

Cite This Paper

Imas Sukaesih Sitanggang, Dewi Asiah Shofiana, Boy Sandi Kristian Sihombing,"Hotspot Sequence Patterns with an Improvement in Spatial Feature", International Journal of Engineering and Manufacturing(IJEM), Vol.8, No.6, pp.13-25, 2018. DOI: 10.5815/ijem.2018.06.02


[1]Abriantini G, Sitanggang IS, Trisminingsih R. Hotspot sequential pattern visualization in peatland of Sumatera and Kalimantan using Shiny framework. IOP Conference Series: Earth and Environmental Science 2017; 54:1-7.

[2]Agustina T, Sitanggang IS. Sequential patterns for hotspot occurences based weather data using Clospan algorithm. International Conference on Adaptive and Intellegent Agroindustry (ICAIA) 2015; 3:301-305.

[3]Alnoukari M, Alzoabi Z, Hanna S. Applying adaptive software development (ASD) agile modelling on predictive data mining applications: ASD-DM methodology. IEEE Proceedings of International Symposium on Information Technology 2008; 1083-1087.

[4]Fournier-Viger P, Gomariz A, Gueniche T, Soltani A, Wu CW, Tseng VS. SPMF: A Java Open-Source Pattern Mining Library 2014; available at volume15/fournierviger14a/fournierviger14a.pdf

[5]Nurulhaq NZ, Sitanggang IS. Sequential pattern mining on hotspot data in Riau Province using the PrefixSpan algorithm. International Conference on Adaptive and Intelligent Agro-industry (ICAIA) 2015;3:257-260. 

[6]Pressman RS. Software engineering: A practitioner’s approach. 7th ed. New York: McGraw-Hill; 2010.

[7]Shi Y. A probability model for occurrences of large forest fires. I.J. Engineering and Manufacturing 2012; 2(1):1-7.

[8]Verma M, Mehta D. Sequential pattern mining: A comparison between GSP, SPADE, and PrefixSpan. International Journal of Engineering Development and Research (IJEDR) 2014; 3016-3036.

[9]Zaki MJ. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 2001; 42:31-60.

[10]Zhao Q, Bhowmick SS. Sequential pattern mining: A survey. ITechnical Report CAIS Nayang Technological University Singapore 2003; 1-26.