PENELUSURAN POLA ASOSIASI PENALARAN ADAPTIF DENGAN ALGORITMA APRIORI

Abstract

Penalaran adaptif adalah salah satu komponen kunci dari kemahiran matematika. Dalam perkembangannya diketahui penalaran adaptif didukung oleh aspek non-kognitif individu yakni affective dan behavioral. Sejumlah teori mengungkapkan peluang optimalisasi aspek non-kognitif untuk pengembangan kemampuan penalaran adaptif. Tujuan penelitian ini adalah menelusuri faktor non-kognitif yang berpeluang memberi dampak pada optimalisasi kemampuan penalaran adaptif melalui pola asosiasi yang muncul pada basis data. Pendekatan penelitian data mining dengan teknik penemuan pola asosiasi (Association Rule Mining) atau Algoritma Apriori digunakan sebagai metode penelusuran faktor. Langkah penelitian yang dilakukan yakni persiapan data (fiksasi instrumen dan penentuan sampel penelitian), pengolahan data (preprocess dan transformation), dan mining dan analisa (menentukan nilai support dan nilai confidence serta interpretasi hasil). Subjek penelitian yakni siswa Sekolah Menengah Atas di lingkungan Kota Serang yang memiliki rentang usia 16-17 tahun. Adapun untuk sampel penelitian diambil sebanyak 100 orang siswa yang mengisi tes kognitif dan non-kognitif. Berdasarkan hasil pengujian dan analisis data diketahui bahwa seorang siswa memiliki kemungkinan 100% berada di level tinggi kemampuan penalaran adaptif jika memiliki dominasi penggunaan otak kiri, self-efficacy di level yang minimal sedang, kecenderungan kepribadian Melankolis dan ­self-directed learning berada di level tinggi. Adaptive reasoning is one of the key components of mathematical proficiency. In its development, it is known that adaptive reasoning is supported by individual non-cognitive aspects, namely affective and behavioral. A number of theories reveal opportunities for optimizing non-cognitive aspects for the development of adaptive reasoning skills. The purpose of this study is to explore non-cognitive factors that have the possibility to impact on optimizing adaptive reasoning skills through association rule appeared in the database. Data mining research approach with association rule mining technique or Apriori Algorithm is used as a factor tracing method. The research steps carried out were data preparation (fixation of instruments and determination of research samples), data processing (preprocess and transformation), and mining and analysis (determining support values and confidence values and interpretation of results). The research subjects were high school students in Kota Serang who have an age range of 16-17 years. As for the research sample, 100 students were taken who filled out cognitive and non-cognitive tests. Based on the results of testing and data analysis, it is identified that a student has a 100% probability of being at a high level of adaptive reasoning skills if he has left brain dominance, self-efficacy is at a minimum level of moderate, Melancholic personality tendencies and self-directed learning are at a high level.

Keywords

• algoritma apriori
• aspek non-kognitif
• penalaran adaptif
• pola asosiasi
• adaptive reasoning
• apriori algorithm
• association rule
• non-cognitive aspect