Klasifikasi Kecerdasan Majemuk pada Anak Berdasarkan Posting Aktivitas di Media Sosial Menggunakan SentiStrength dan Spearman's Rank Correlation Coefficient

Authors

  • Baihaqi Siregar Universitas Sumatera Utara
  • Cindy Aprilia Universitas Sumatera Utara
  • Filia D Anggaraeni Universitas Sumatera Utara
  • Ivan Jaya Universitas Sumatera Utara

DOI:

https://doi.org/10.30865/mib.v3i4.1500

Abstract

Intelligence are things related to intelligence, intellectual action, and perfect development of the mind. Every human being has the right to develop himself based on intelligence. A child who is good at playing violin shouldn't let himself feel like he is stupid because he is unable to complete his mathematical tasks. Therefore, the author wants to create a system for determining children's talent based on multiple intelligence as measured by the tendency of posts about their daily activities on social media by using the Sentistrength and Spearman's Correlation Coefficient methods. The purpose of this research is to create a social media application called Juicer, which is able to determine one's talent according to multiple intelligence theory based on data in the form of user posts about their daily activities. Users input their daily activities and opinions about the activities that he is currently undergoing. The system will analyze the data entered a negative or positive opinion. Then determine whether the data entered are musical-rhythmic, visual-spatial, verbal-linguistic, logical-mathematical, bodily-kinesthetic, interpersonal, intrapersonal, naturalistic, and / or spiritual-type using lexicon-based sentiment analysis with the Sentistrength method. After a long period of time, the system will sort the data that has been stored. After the data is classified, user intelligence types will be found based on multiple intelligence theory, as a result of this study. The result of the correlation between intelligence type data obtained through the system compared to intelligence type data obtained through the manual method is 80%, and the deviation value is 0.09.

Author Biographies

Baihaqi Siregar, Universitas Sumatera Utara

Fakultas Ilmu Komputer dan Teknologi dan Informasi, Prodi Teknologi Informasi

Cindy Aprilia, Universitas Sumatera Utara

Fakultas Ilmu Komputer dan Teknologi dan Informasi, Prodi Teknologi Informasi

Filia D Anggaraeni, Universitas Sumatera Utara

Fakultas Psikologi, Prodi Psikologi

Ivan Jaya, Universitas Sumatera Utara

Fakultas Ilmu Komputer dan Teknologi dan Informasi, Prodi Teknologi Informasi

References

Buntoro, Ghulam Asrofi. 2017. Analisis Sentimen Calon Gubernur DKI Jakarta 2017 di Twitter. INTEGER: Journal of Information Technology 2579-566X 1:32-41.

Gardner H. 2011. Frames of Mind: The Theory of Multiple Intelligences. Ed ke-2011. New York (US): Basic Books.

Ghiassi M, Skinner J, Zimbra D. 2013. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications. 40(16):6266-6282.

Glasser, G.J. & Winter, R.F. 1961. Critical values of the coefficient of rank correlation for testing the hypothesis of independance. Biometrika 48, pp. 444–448.

Marshall DZ&I. 2000. SQ: Connecting with Our Spiritual Intelligence. Ed ke-1. New York (US): Bloomsbury.

McKenzie, Walter. 1999. Multiple intelligences inventory. http://surfaquarium.com/MI/inventory.htm (diakses 15 Desember 2017)

Minsih. 2013. Pengembangan Kecerdasan Majemuk pada Implementasi Kurikulum 2013. The 1st Summit Meeting on Education the End of the Year 2013 Seminar Nasional: Refleksi dan Realisasi Kurikulum 2013; pp. 278-286.

Musfiroh, Tadkiroatun. 2014. Pengembangan Kecerdasan Majemuk. Hakikat Kecerdasan Majemuk (Multiple Intelligences), pp. 1-60.

Newsround survey reveals majority of 10 to 12 year-olds are on social media. 2016. http://www.bbc.co.uk/mediacentre/latestnews/2016/newsround-survey-social-media, 9 Februari 2016 (diakses 2 Mei 2017).

Pratama EE, Trilaksono BR. 2015. Klasifikasi topik keluhan pelanggan berdasarkan tweet dengan menggunakan penggabungan feature hasil ekstraksi pada metode support vector machine (SVM). Jurnal Edukasi dan Penelitian Informatika (JEPIN). 1(2):53-59.

Putranti ND, 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. 8.1:91-100.

Wahid DH, SN A. 2016. Peringkasan sentimen esktraktif di twitter menggunakan hybrid TF-IDF dan cosine similarity. IJCCS (Indonesian Journal of Computing and Cybernetics Systems). 10(2):207.

Winarto P. 2010. Maximizing Your Talent (Menemukan & Memaksimalkan Potensi Diri Anda). Medan (ID): PT BPK Gunung Mulia.

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Published

2019-10-22