Teknologi Opinion Mining untuk Mendukung Strategic Planning
DOI:
https://doi.org/10.25126/jtiik.2020721685Abstrak
Banjir data di era Big Data sudah tidak bisa terelakkan lagi. Termasuk di dalamnya data yang sangat melimpah di media sosial daring. Peluang inilah yang ditangkap sebagai alasan utama pada penelitian ini. Opinion mining sebagai salah satu teknologi dalam mengolah data teks untuk memperoleh arah informasi dari komentar/opini masyarakat. Mengambil obyek penelitian UIN Sunan Ampel Surabaya, penelitian ini bertujuan untuk menganalisis opini masyarakat tentang kampus Islam terbesar di Surabaya. Sehingga bisa menjadi pendukung keputusan bagi pihak manajemen untuk merumuskan perencanaan strategis terwujudnya visi World Class University. Penelitian ini menggunakan 4009 data sampel berbahasa Indonesia yang diambil dari opini masyarakat di media sosial Twitter dalam kurun waktu dua tahun terakhir (2017 – 2018). Dari 4009 data dihasilkan 31837 jenis kata setelah melalui proses stop-word removal. Berdasarkan analisis sentiment menggunakan pendekatan Vader dan Liu yang divisualisasikan melalui grafik K-Means, dihasilkan bahwa opini publik terhadap UIN Sunan Ampel mengarah pada sentimen ’netral’ sebesar 97,54%, sedangkan sentiment positif =2,16%, dan sentiment negatif = 0,34%. Hasil tersebut membuktikan bahwa Information Capital tentang UIN Sunan Ampel perlu diperkuat menuju nilai “positif”. Sehingga diperlukan upaya maksimal untuk membangun innovation and commercially supremacy, perception (public relation) dan scalability strategies supaya internal operation bisa handal untuk ketercapaian visi misi UIN Sunan Ampel Surabaya.
Abstract
Data deluge in Big Data era is inevitable, this including a very abundant data in online social media. This phenomenon was chosen as the main background reason in this research. Opinion mining is as one of the technologies in processing text data to obtain information direction from public comments/opinions. Taking the object of research at Sunan Ampel Islamic State University Surabaya, this study aims to analyze public community opinion toward the biggest Islamic campus in Surabaya. Hopefully, it would be beneficial as decisional support for management in formulating strategic planning to manifest the World Class University vision. This study uses 4009 Indonesian language sample data taken from public opinion on Twitter social media in the past two years (2017 - 2018). Out from 4009 data, 31837 types of words are obtained after going through a stop-word removal process. Based on sentiment analysis by Vader and Liu’s approach which was visualized by K-Means graphs, the finding was that 97,54% of public opinion toward Sunan Ampel Islamic State University Surabaya led to a 'neutral' sentiment, while positive = 2,16% and negative=0,34%. These results prove that Information Capital about Sunan Ampel UIN needs to be strengthened towards "positive" image. For this reason, maximum effort is needed to build innovation and commercialization of supremacy, perception (public relations) and scalability strategies so that internal operations can be reliable in achieving the vision of Sunan Ampel Islamic State University Surabaya.
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ARMITAGE, H. M. and SCHOLEY, C. (2006) Management Accounting Guideline, Using Strategy Maps to Drive Performance. Available at: http://www.cimaglobal.com/Documents/ImportedDocuments/Tech_MAG_Strategy_Mapping_March07.pdf.
AUINGER, A. and FISCHER, M. (2008) ‘Mining consumers ’ opinions on the web’, FH Science Day, (Linz, Österreich), pp. 410–419.
DEVIKA, M. D., SUNITHA, C. and GANESH, A. (2016) ‘Sentiment Analysis: A Comparative Study on Different Approaches’, Procedia Computer Science. The Author(s), 87, pp. 44–49. doi: 10.1016/j.procs.2016.05.124.
FANG, X. and ZHAN, J. (2015) ‘Sentiment analysis using product review data’, Journal of Big Data. Journal of Big Data, 2(1). doi: 10.1186/s40537-015-0015-2.
HADDI, E., LIU, X. and SHI, Y. (2013) ‘The role of text pre-processing in sentiment analysis’, Procedia Computer Science. Elsevier B.V., 17, pp. 26–32. doi: 10.1016/j.procs.2013.05.005.
HAFNIKA, F. et al. (2016) ‘Strategy Map Formulation For Designing Strategic Plan’, Advance Business and Social Studies, 2(2), pp. 103–112. Available at: https://apiar.org.au/wp-
content/uploads/2016/08/8_APJABSS_ICABSS_BRR732_BIZ-103-112.pdf.
HINTON, K. E. (2012) A Practical Guide to Strategic Planning in Higher Education, Society for College and University Planning. doi: 10.1073/pnas.1719902115.
HUTCHISON, C. S. (2018) ‘Essential Insight Into Internet, Social Media, Mobile, and Ecommerce Use Around the World’, In: the Ocean basins and margins: The Indian Ocean (Nairn, A.E.M. & Stehli, F.G., Eds) Plenum Press, New York, 6, pp. 451–512.
HUTTO, C. J. and GILBERT, E. (2014) ‘VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text’, Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, pp. 216–225.
JANSSEN, D. et al. (2015) ‘Sentiment Analysis of Social Media for Evaluating Universities’, pp. 49–62.
LEE, M. and PARK, H. W. (2012) ‘Exploring the web visibility of world-class universities’, Scientometrics, 90(1), pp. 201–218. doi: 10.1007/s11192-011-0515-6.
LI, Q. et al. (2018) ‘Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective’, IEEE Transactions on Knowledge and Data Engineering, 30(2), pp. 381–399. doi: 10.1109/TKDE.2017.2763144.
LV, X. and EL-GOHARY, N. (2016) ‘Text Analytics for Supporting Stakeholder Opinion Mining for Large-scale Highway Projects’, Procedia Engineering. Elsevier B.V., 145, pp. 518–524. doi: 10.1016/j.proeng.2016.04.039.
MCCOY, C. G., NELSON, M. L. and WEIGLE, M. C. (2017) ‘University Twitter Engagement: Using Twitter Followers to Rank Universities’. Available at: http://arxiv.org/abs/1708.05790.
MEDHAT, W., HASSAN, A. AND KORASHY, H. (2014) ‘Sentiment analysis algorithms and applications: A survey’, Ain Shams Engineering Journal. Faculty of Engineering, Ain Shams University, 5(4), pp. 1093–1113. doi: 10.1016/j.asej.2014.04.011.
MISHRA, N. (2012) ‘Classification of Opinion Mining Techniques’, 56(13), pp. 1–6.
‘Orange3 Text Mining Documentation’ (2018).
PANG, B. and LEE, L. (2006) ‘Opinion Mining and Sentiment Analysis’, Foundations and Trends® in InformatioPang, B., & Lee, L. (2006). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 1(2), 91–231. doi:10.1561/1500000001n Retrieval, 1(2), pp. 91–231. doi: 10.1561/1500000001.
PUTRANTI, N. D. and WINARKO, E. (2014) ‘Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine’, IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 8(1), pp. 91–100. doi: 10.22146/ijccs.3499.
RITSON, N. (2011) The Levels and Formulation of Strategy, Strategic Management.
ROZI, I. F., PRAMONO, S. H. and DAHLAN, E. A. (2012) ‘Implementasi Opinion Mining ( Analisis Sentimen ) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi’, Electrical Power, Electronics, Communications, Controls, and Informatics Seminar (EECCIS), 6(1), pp. 37–43.
SCIENCE, C. and ENGINEERING, S. (2016) ‘Algorithms for Opinion Mining and Sentiment Analysis : An Overview’, 6(2), pp. 455–459.
SINGH, P. K. and HUSAIN, M. S. (2014) ‘Methodological Study of Opinion Mining and Sentiment Analysis Techniques’, 5(1), pp. 11–21. doi: 10.5121/ijsc.2014.5102.
SINGLA, Z., RANDHAWA, S. and JAIN, S. (2017) ‘Sentiment Analysis of Customer Product Reviews Using Machine Learning’.
STEFANOVSKA, L. and SOKLEVSKI, T. (2014) ‘Benefits of Using Balanced Scorecard in Strategic and Operational Planning’, Universal Journal of Management, 2(4), pp. 165–171. doi: 10.13189/.
STIEGLITZ, S. et al. (2014) ‘Socialmedia analytics’, Business and Information Systems Engineering, 6(2), pp. 89–96. doi: 10.1007/s12599-014-0315-7.
STIEGLITZ, S. et al. (2018) ‘Social media analytics – Challenges in topic discovery, data collection, and data preparation’, International Journal of Information Management. Elsevier, 39(October 2017), pp. 156–168. doi: 10.1016/j.ijinfomgt.2017.12.002.
TARTIR, S. and ABDUL-NABI, I. (2017) ‘Semantic Sentiment Analysis in Arabic Social Media’, Journal of King Saud University - Computer and Information Sciences. King Saud University, 29(2), pp. 229–233. doi: 10.1016/j.jksuci.2016.11.011.
THAMRIN, H. and PAMUNGKAS, E. W. (2017) ‘A Rule Based SWOT Analysis Application: A Case Study for Indonesian Higher Education Institution’, Procedia Computer Science. Elsevier B.V., 116(December), pp. 144–150. doi: 10.1016/j.procs.2017.10.056.
WEBOMETRIC (2018) The Ranking Web or Webometrics Methodology. Available at: http://www.webometrics.info/en/Methodology.
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