International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(4): 373-381
Published online December 25, 2022
https://doi.org/10.5391/IJFIS.2022.22.4.373
© The Korean Institute of Intelligent Systems
Yudi Priyadi1 , Krishna Kusumahadi2, and Pramoedya Syachrizalhaq Lyanda1
1Department of Software Engineering, Telkom University, Bandung, Indonesia
2Department of Informatics Business, Telkom University, Bandung, Indonesia
Correspondence to :
Yudi Priyadi (whyphi@telkomuniversity.ac.id)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Systems thinking is a discipline for understanding wholeness and frameworks based on the changing patterns of the interconnectedness of the whole system. The storytelling of a system is a description of the mental model of an individual in describing the state of the environment. There are differences in the interpretation of the system description. This difference occurs because each individual has a different level of systems thinking in terms of experience, learning process, insight, intuition, and assumption in understanding system interactions. This study aims to extract data in the description of the storytelling of a systems thinking case by performing text mining and similarity to identify and find a variable to form causal loop diagrams. Based on the results of this study, there are results in the data extraction from the description of storytelling for the systems thinking case. The conclusions of this study are as follows: First, processing the five documents has successfully identified two documents with the highest similarity value, such as d1 and d3. Second, based on the cosine similarity calculation results and the results of the similarity value, there is a value closest to 1, such as 0.0913166. This value is at the d1 and d3 positions. Third, it produces a variable approach in the form of a group of words used in modeling thinking systems based on a connectedness value greater than 0.50.
Keywords: Systems thinking, Storytelling, Text mining, Similarity, Causal loop diagrams
No potential conflict of interest relevant to this article was reported.
E-mail: whyphi@telkomuniversity.ac.id
International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(4): 373-381
Published online December 25, 2022 https://doi.org/10.5391/IJFIS.2022.22.4.373
Copyright © The Korean Institute of Intelligent Systems.
Yudi Priyadi1 , Krishna Kusumahadi2, and Pramoedya Syachrizalhaq Lyanda1
1Department of Software Engineering, Telkom University, Bandung, Indonesia
2Department of Informatics Business, Telkom University, Bandung, Indonesia
Correspondence to:Yudi Priyadi (whyphi@telkomuniversity.ac.id)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Systems thinking is a discipline for understanding wholeness and frameworks based on the changing patterns of the interconnectedness of the whole system. The storytelling of a system is a description of the mental model of an individual in describing the state of the environment. There are differences in the interpretation of the system description. This difference occurs because each individual has a different level of systems thinking in terms of experience, learning process, insight, intuition, and assumption in understanding system interactions. This study aims to extract data in the description of the storytelling of a systems thinking case by performing text mining and similarity to identify and find a variable to form causal loop diagrams. Based on the results of this study, there are results in the data extraction from the description of storytelling for the systems thinking case. The conclusions of this study are as follows: First, processing the five documents has successfully identified two documents with the highest similarity value, such as d1 and d3. Second, based on the cosine similarity calculation results and the results of the similarity value, there is a value closest to 1, such as 0.0913166. This value is at the d1 and d3 positions. Third, it produces a variable approach in the form of a group of words used in modeling thinking systems based on a connectedness value greater than 0.50.
Keywords: Systems thinking, Storytelling, Text mining, Similarity, Causal loop diagrams
Fundamental ideas for the IdVar4CL method.
Paragraphs as dataset. Source:
Illustration of causal loop variable identification.
Case folding.
Dataset documents.
Normalization and index.
Term frequency (TF) matrix.
Inverse document frequency (IDF).
Preview of TF-IDF matrix results.
Cosine similarity.
Stopword removal process.
Measures semantic similarity/relatedness between words.
Table 1 . Documents.
Document labeling | Document identification |
---|---|
d1 | The death toll from severe flooding in and around the Indonesian capital of Jakarta has risen to 66 as parts of the country continue to reel from heavy rain that began on New Year’s Eve. |
d2 | Landslides and flash floods have displaced more than 36,000 in Jakarta and the nearby provinces of West Java and Banten, according to the ASEAN Coordinating Center for Humanitarian Assistance (AHA). |
d3 | These are the worst floods Indonesia has seen since 2013, when at least 29 people edited in the aftermath of torrential rains. |
d4 | The disaster, experts say, underscores the impacts of climate change in a country with a capital city that is sinking so quickly that officials are working to move it to another island. |
d5 | The floods are also threatening to exacerbate the already severe wealth inequality that plagues the Southeast Asian nation. |
Table 2 . Document similarity value..
d1 | d2 | d3 | d4 | d5 | |
---|---|---|---|---|---|
d1 | 1 | 0.036 | 0.091 | 0.079 | 0.049 |
d2 | 0.036 | 1 | 0.033 | 0 | 0.035 |
d3 | 0.091 | 0.033 | 1 | 0 | 0.045 |
d4 | 0.079 | 0 | 0 | 1 | 0 |
d5 | 0.049 | 0.035 | 0.045 | 0 | 1 |
Table 3 . Identification of variables for causal loops.
Variable identification results | Value of connectedness | |
---|---|---|
Death | Floods | 0.7500 |
Death | People | 0.5455 |
Death | Aftermath | 0.7059 |
Death | Rains | 0.6316 |
Toll | Floods | 0.7059 |
Toll | Aftermath | 0.7059 |
Capital | Floods | 0.5217 |
Capital | People | 0.5333 |
Risen | Seen | 0.5714 |
Risen | Died | 0.6667 |
Parts | Floods | 0.6000 |
Parts | People | 0.6000 |
Country | People | 0.9091 |
Continue | Seen | 0.5714 |
Rain | Floods | 0.6316 |
Began | Seen | 0.6667 |
Year | Floods | 0.5333 |
Year | People | 0.6667 |
Eve | Floods | 0.5333 |
Mohamedou Cheikh Tourad, and Abdelmounaim Abdali
International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(4): 303-315 https://doi.org/10.5391/IJFIS.2018.18.4.303Ishara Sandun, Sagara Sumathipala, and Gamage Upeksha Ganegoda
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(4): 307-314 https://doi.org/10.5391/IJFIS.2017.17.4.307Minyoung Kim
Int. J. Fuzzy Log. Intell. Syst. 2016; 16(4): 293-298 https://doi.org/10.5391/IJFIS.2016.16.4.293Fundamental ideas for the IdVar4CL method.
|@|~(^,^)~|@|Paragraphs as dataset. Source:
Illustration of causal loop variable identification.
|@|~(^,^)~|@|Case folding.
|@|~(^,^)~|@|Dataset documents.
|@|~(^,^)~|@|Normalization and index.
|@|~(^,^)~|@|Term frequency (TF) matrix.
|@|~(^,^)~|@|Inverse document frequency (IDF).
|@|~(^,^)~|@|Preview of TF-IDF matrix results.
|@|~(^,^)~|@|Cosine similarity.
|@|~(^,^)~|@|Stopword removal process.
|@|~(^,^)~|@|Measures semantic similarity/relatedness between words.