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

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.

Pengaruh Seleksi Fitur Pada Tingkat Akurasi Metode Random Forest Untuk Identifikasi Akun Buzzer Tweet Tokoh Politik Indonesia

“…Korelasi ini dihitung menggunakan algoritma perhitungan Spearman's Rank Correlation Coefficient [15] :…”