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All Journal Jurnal Dakwah Risalah Analisa Analisa: Journal of Social Science and Religion
Fitri Fitri
Universitas Islam Negeri Walisongo, Semarang
Religion Education Languange, Linguistic, Communication & Media Social Sciences

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Pemetaan untuk Strategi Dakwah di Kota Semarang Menggunakan Pendekatan Data Mining (Mapping for Da'wah Strategy in Semarang City Using Data Mining Approach) Abdul Karim; Adeni Adeni; Fitri Fitri; Alifa Nur Fitri; Mustofa Hilmi; Silvia Riskha Fabriar; Farida Rachmawati
Jurnal Dakwah Risalah Vol 32, No 1 (2021): June 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jdr.v32i1.12549

Abstract

This paper aims to explore the potential of da'wah in the city of Semarang with a data mining approach. The data mining approach is carried out by implementing the fuzzy c-means (FCM) algorithm in order to obtain the optimum number of clusters in the potential clustering of da'wah in the city of Semarang. The data used in this study from the Ministry of Religion of the Republic of Indonesia and the Central Statistics Agency (BPS) of Semarang City. The results of the FCM analysis show that the optimum number of clusters is two clusters, where the sub-district in the second cluster is an area with a high potential for da'wah. This study provides information that in effective da'wah activities, certainty and clarity is needed regarding the targets of da'wah through mapping of da'wah in the form of clustering potential da'wah. This can be a consideration of dakwah strategies for the successful implementation of da'wah studies so that an increase in the target behavior of da'wah can be achieved. The application of FCM to get the optimum cluster of potential da'wah in order to produce da'wah mapping is novelty in the field of Islamic studies, especially the science of da'wah.
MITIGATING POVERTY: THE CLUSTERING OF POTENTIAL ZAKAT IN INDONESIA Abdul Karim; Ayuf Mufakhidin; Hamdan Hadi Kusuma; Adeni Adeni; Fitri Fitri
Analisa: Journal of Social Science and Religion Vol 7, No 1 (2022): Analisa Journal of Social Science and Religion
Publisher : Balai Penelitian dan Pengembangan Agama Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18784/analisa.v7i1.1641

Abstract

The objective of this study was to examine the fuzzy c-means clustering (FCM) method to establish the optimum cluster accuracy of zakat potential in Indonesia. A spatial mapping approach is also suggested and can be considered as the first step in knowing the distribution of zakat potential in Indonesia. Furthermore, strategies that can be implemented are formulated to increase zakat collection in Indonesia. Potential zakat data from the National Amil Zakat Agency (Baznas) in 2020 consisting of bank deposits, salaries, agricultural products, plantation products, and staple foods. Each province in Indonesia is used as the proposed variable. In this paper, firstly collecting data on indicators of potential zakat. Second, the FCM clustering algorithm. Third, the results of the FCM grouping are visualized in the form of a mapping. This novel mapping study with FCM was applied in order to analyze clustering accuracy. The FCM results confirm 2 optimum clusters for zakat potential in Indonesia where cluster 2 has more members than cluster 1. Besides, the second cluster only has one variable that has a high value, namely agricultural products, while the rest is in the first cluster. This indicates that the first cluster has a higher potential for zakat. The application of fuzzy c-means (FCM) to obtain the optimum cluster on zakat potential to produce a mapping of zakat potential is a novelty in the field of Islamic economic studies. Finally, the results of the analysis with this approach provide optimum results to strengthen the zakat collection strategy in Indonesia.
MITIGATING POVERTY: THE CLUSTERING OF POTENTIAL ZAKAT IN INDONESIA Abdul Karim; Ayuf Mufakhidin; Hamdan Hadi Kusuma; Adeni Adeni; Fitri Fitri
Analisa: Journal of Social Science and Religion Vol 7, No 1 (2022): Analisa Journal of Social Science and Religion
Publisher : Balai Penelitian dan Pengembangan Agama Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (788.401 KB) | DOI: 10.18784/analisa.v7i1.1641

Abstract

The objective of this study was to examine the fuzzy c-means clustering (FCM) method to establish the optimum cluster accuracy of zakat potential in Indonesia. A spatial mapping approach is also suggested and can be considered as the first step in knowing the distribution of zakat potential in Indonesia. Furthermore, strategies that can be implemented are formulated to increase zakat collection in Indonesia. Potential zakat data from the National Amil Zakat Agency (Baznas) in 2020 consisting of bank deposits, salaries, agricultural products, plantation products, and staple foods. Each province in Indonesia is used as the proposed variable. In this paper, firstly collecting data on indicators of potential zakat. Second, the FCM clustering algorithm. Third, the results of the FCM grouping are visualized in the form of a mapping. This novel mapping study with FCM was applied in order to analyze clustering accuracy. The FCM results confirm 2 optimum clusters for zakat potential in Indonesia where cluster 2 has more members than cluster 1. Besides, the second cluster only has one variable that has a high value, namely agricultural products, while the rest is in the first cluster. This indicates that the first cluster has a higher potential for zakat. The application of fuzzy c-means (FCM) to obtain the optimum cluster on zakat potential to produce a mapping of zakat potential is a novelty in the field of Islamic economic studies. Finally, the results of the analysis with this approach provide optimum results to strengthen the zakat collection strategy in Indonesia.
Co-Authors Adeni Adeni Alifa Nur Fitri Ayuf Mufakhidin Farida Rachmawati Hamdan Hadi Kusuma Karim, Abdul Mustofa Hilmi Silvia Riskha Fabriar