A Framework for Clustering of Web Users Transaction Based on Soft Set Theory

Sutoyo, Edi; Yanto, Iwan Tri Riyadi; Saadi, Younes; Chiroma, Haruna; Hamid, Sharifah Bee Binti O.A Abd; Herawan, Tutut

doi:10.1007/978-981-13-1799-6_32

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…The most classes from the nearest data number are chosen as the predicted class for new data. Similar to clustering techniques [43,44] on K-Means [45], which is grouping new data based on the distance of the new data to several data/nearest neighbors.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

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Section: K-nearest Neighbormentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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“…While descriptive means that data mining is done to look for patterns that can be understood by humans that explain the characteristics of the data. Some research related to descriptive such as clustering, Mining Frequent Patterns, Associations, Correlations, etc [17]- [19].…”

Section: Data Miningmentioning

confidence: 99%

Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

Khairani

Sutoyo

2020

International Journal of Advances in Data and Information Syste

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Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.

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“…Soft set theory has also been widely applied in knowledge discovery, such as texture classification [53], maximal association rules mining [54], [55], classification of numerical data based on fuzzy soft set theory [56], attribute selection for clustering [57]- [59], sounds classification [60], medical diagnosis [61], as well as tumor classification using attribute selection with information gain ratio in fuzzy rough set theory [62]. The most recent work by [63] proposed the notions of alliance (coalition), neutrality, and conflict (against) among agents in a conflict situation using the model of constraint cooccurrence in multi-soft sets.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Soft Set Approach for Identifying Football Conflict: A Case Study of Indonesian Football Super League

Pratama¹,

Aman²,

Sutoyo³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Conflict situations in football have become a significant issue because they affect the players, supporters, referees, management team, the governing body of football, and the government. As time passes, the discovery of conflicts within the football industry has also become increasingly diverse; both affected in-game or out of the game. In 2015, Indonesia had no activity in football for almost a year when the International Federation of Association Football (FIFA) banned the Football Association of Indonesia (PSSI) from competing in international competitions until the conflict among their internal agents was resolved. The agents involved in this highly controversial ban include the Commission 10 of the Parliament of Indonesia, the National Sports Committee of Indonesia (KONI), the Indonesian President, and the Ministry of Youth and Sport of Indonesia. Conflict resolution strategies outside the football games are delicate and more challenging to overcome due to the involvement of the government and various governing bodies. This opens to higher unpredictability in modeling the conflict situations, hence a lower possibility of a successful conflict resolution model strategy. In addressing this gap, this paper proposes a new Computational Intelligence approach based on the Soft Set Theory, where an alternative algorithm is derived from modeling the conflict situations. We then delineated the proposed algorithm for an instructional example of the Indonesian football conflict situation in 2015 concerning the Indonesia Football Super League. The results showed that the proposed algorithm successfully handled conflict and recommended the Indonesian football agents involved, including PSSI and FIFA.

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A Framework for Clustering of Web Users Transaction Based on Soft Set Theory

Cited by 9 publications

References 7 publications

Twitter sentiment analysis of the relocation of Indonesia's capital city

Twitter sentiment analysis of the relocation of Indonesia's capital city

Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

An Alternative Soft Set Approach for Identifying Football Conflict: A Case Study of Indonesian Football Super League

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