Sistem Rekomendasi Psikotes untuk Penjurusan Siswa SMA menggunakan Metode Modified K-Nearest Neighbor - Muchlas Mughniy; Randy Cahya Wihandika; Barlian Henryranu Prasetio (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Major selection for high school student intended to facilitate students to focusing on specific field for higher education. However, assigning academic potential for each student through school counselor needs plenty of time. A recommendation system can generate a recommendation for major selection based on cognitive ability by Intelligenz Struktur Test (IST). Modified k-Nearest Neighbor is applied to system which classifying academic potential based on neighborhood by training data. Training data consist of nine cognitive intelligences and two majors. So, system will provide a recommendation for major. From the testing process that has been done, has obtain highest averaged accuracy on 90% dataset is 67,95%, averaged accuracy on 4-fold Cross Validation is 63,58%, averaged Sensitivity and Specificity is 23,64% and 92,34%, accuracy comparison between MKNN and KNN is 63,58% and 57,11%, and then highest accuracy for feature reduction using PCA is 55,26% which is reduced to 6 features. According to test result indicate that Modified k-Nearest Neighbor recommendation system not optimal yet to generate a recommendation for major selection.