SENTIMENT ANALYSIS OF ISLAMIC WAQF: EVIDENCE IN INDONESIA

AAM SLAMET RUSYDIANA

Abstract


It is important to do research on public sentiment towards waqf presence in a country in order to know public response to its existence. This study aimed to determine public sentiment towards waqf in Indonesia. Data were collected from 80 articles, journals and other writings. Data were analyzed using the software Semantria as an analytical tool in the form of text. The results showed that the assessment of existence of waqf in Indonesia amounted to 66% of the community showed positive and high positive sentiment, 11% indicate negative sentiment and 23% indicates a neutral sentiment. Therefore, stakeholders need to take advantage of the awakening momentum of waqf in Indonesia so that in the future they can be a solution to the problems of social economy and the benefit of society.

Keywords


Islamic Waqf, Sentiment Analysis, Social Finance

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References


Barber, I. (2010). Bayesian Reasoning and Machine Learning. USA: Cambridge University Press.

Dehaff, M. (2010). Sentiment Analysis, Hard But Worth It!. USA: Cambridge University Press.

Faishol, M.A. (2011). Implementasi Text Mining Untuk Mendukung Pencarian Topik Pada e-Library Menggunakan Mobile Device. Malang: University of Islam Negeri Maulana Malik Ibrahim.

Fathurrohman, T. (2012). Wakaf dan Penanggulangan Kemiskinan (Studi Kasus Pengelolaan Wakaf di Kabupaten Bandung Jawa Barat). Dissertation. Unpublished. Jakarta: University of Indonesia.

Hasanah, U. (1997). Peranan Wakaf dalam Mewujudkan Kesejahteraan Sosial (Studi Kasus Pengelolaan Wakaf di Jakarta Selatan). Dissertation. Unpublished. Jakarta: IAIN Jakarta.

Jiawei, H., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Massachusetts: Morgan Kaufmann.

Kahf, M, Mahamood, S.M. (2011). Essential Readings in Contemporary Waqf Issues. Kuala Lumpur : CERT Publications.

Mihalcea, R. Banea, C. & Wiebe, J. (2007). Learning Multilingual Subjective Language via Cross-Lingual Projections. Proceedings of the Association for Computational Linguistics (ACL), 976–983.

Miner, G., Delen, D., Elder, J., Fast, A., Hill, T., & Nisbet, R. (2012). Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. Oxford: Elsevier.

Mumtaz, H., Asghar, S., & Rima, T. (2015). An Overview of Islamic Finance. International Monetary Fund Working Paper June, 15(120).

Nurfalah, I., Rusydiana, A.S., Laila, N., & Cahyono, E.F. (2018). Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach. JKAU: Islamic Economics, 31(2): 133-156.

Pang, B. & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Proceedings of the Association for Computational Linguistics (ACL), 271–278.

Pang, B. & Lee, L. 2005. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales. Proceedings of the Association for Computational Linguistics (ACL), 115–124.

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(2): 1-135.

Rozi, I. F., Pramono, S. H., & Dahlan, E. A. (2013). Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. Jurnal EECCIS, 6(1): 37-43.

Rusydiana, A. S., Firmansyah, I., & Marlina, L. (2018). Sentiment Analysis of Micro Takaful Industry: Comparison Between Indonesia and Malaysia. International Journal of Nusantara Islam, 6(1).

Rusydiana, A. S., & Devi, A. (2018). Elaborating Cash Waqf Development in Indonesia Using Analytic Network Process. International Journal of Islamic Business and Economics, 2(1): 1-13.

Rusydiana, A. S., & Devi, A. (2017). Analisis Pengelolaan Dana Wakaf Uang di Indonesia: Pendekatan Metode Analytic Network Process (ANP). Al-Awqaf: Jurnal Wakaf dan Ekonomi Islam, 10(2): 115-133.

Rusydiana, A. S., & Al Parisi, S. (2016). How Far Has Our Wakaf Been Researched?. Etikonomi, 15(1): 31-42.

Saraswati, N.W.S. (2011). Text Mining Dengan Metode Naïve Bayes Classifier dan Support Vector Machines Untuk Sentiment Analysis. Denpasar: Program Pasca Sarjana Universitas Udayana.

Snyder, B. & Barzilay, R. (2007). Multiple Aspect Ranking using the Good Grief Algorithm. Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL), 300–307.

Su, F., & Markert, K. (2008, August). From Words to Senses: A Case Study of Subjectivity Recognition. Proceedings of the 22nd International Conference on Computational Linguistics, 1: 825-832.

Sunni, I, & Widyantoro, D.H. (2012). Analisis Sentimen dan Ekstraksi Topik Penentu Sentimen pada Opini terhadap Tokoh Publik. Jurnal Sarjana Institut Teknologi Bandung Bidang Teknik Elektro dan Informatika, 1(2).

Wulandini, F. & Nugroho, A. N. (2009). Text Classification Using Support Vector Machine for Web Mining Based Spat Ion Temporal Analysis of the Spread of Tropical Diseases. International Conference on Rural Information and Communication Technology, 189-192.


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