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Adinugroho, I., 2016, Pengujian Properti Psikometrik Intelligenz Struktur Test Subtes Kemampuan Spasial Dua Dimensi (Form Auswahl), Jurnal Ilmiah Psikologi MANASA 5 (2), 165 – 180.

Alias, M., dan Abu Bakar, M. N. F., 2010, Factors Contributing to Programme Choice and Subsequent Career Selection amon Engineering Students, The 3rd Regional Conference on Engineering Education and Research in Higher Education, Sarawak, June 7-9, 6-9.

Barrett, J., 2009, Aptitude, Personality and Motivation Test, Kogan Page Limited, London.

Carson, A.D., Bizot, E.B., Hendershot, P.E., Barton, M.G., Garvin, M.K., dan Kraemer, B., 1999, Modelling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map, Journal of Vocational Behavior 54 (1), 196-213.

Chen, G., Ye, D., Xing, Z., Chen, J., dan Cambria, E., 2017, Ensemble application of convolutional and recurrent neural networks for multi-label text categorization, Proceedings of 2017 International Joint Conference on Neural Networks, Anchorage, 2377-2383

Conard, M. A., 2006, Aptitude is not Enough: How Personality and Behavior Predict Academic Performance, Journal of Research in Personality, 339 – 346.

Dai, L., Zhang, J., Li, C., Zhou, C., dan Li, S., 2018, Multi-label feature selection with application to TCM state identification, Concurrency and Computation:

Practice and Experience

Furnham, A., dan Crump, J., 2015, The Myers-Briggs Type Indicator (MBTI) and Promotion at Work. Psychology 6, 1510 – 1515.

(2)

74 Gibaja, E., dan Ventura, S., 2010, A Tutorial on Multi-Label Learning, ACM

Computing Survey 9 (4).

Guan, R., Wang, X., Yang, M.Q., Zhang, Y., Zhou, F., Yang, C., dan Liang, Y., 2018, Multi-Label Deep Learning for Gene Function Annotation in Cancer Pathways, Scientific Reports 8 (267).

Kong, X., Ng, M. K., dan Zhou, Z., 2013, Transductive Multilabel Learning via Label Set Propagation, IEEE Transactions on Knowledge and Data Engineering 25 (3), 704 – 719

Li, X., Xie, H., Rao Y., Chen, Y., Liu, X., Huang, H., dan Wang, F.L., 2016, Weighted Multi-Label Classification Model for Sentiment Analysis of Online News, 2016 International Conference on Big Data and Smart Computing (BigComp), Hong Kong, January 8-12, 215 – 222.

Liu, J., Chang, W.C., Wu, Y., dan Yang, Y., 2017, Deep learning for extereme multi-label text classification, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, 115-124

Liu, Y., Wen, K., Gao, Q., Gao, X., dan Nie, F., 2018, SVM based multi-label learning with missing labels for image annotation, Pattern Recognition (78), 307-317

Loza, E., dan Furnkranz, J., 2010, An Evaluation of Efficient Multilabel Classification for Large-Scale Problems in the Legal Domain, Semantic Processing of Legal Texts, vol. 6036, Berlin, 192-215.

Nam, J., Kim, J., Mencia, E.L., Gurevych, I., dan Furnkranz J., 2014, Large-Scale Multi-Label Text Classification – Revisiting Neural Networks, Proceedings of the 2014th European Conference on Machine Learning and Knowledge Discovery in Databases, vol. 2, Nancy, September 15, 437 – 452.

Nugraha, D.A., dan Retnowati, W., 2015, Sistem Pendukung Keputusan Penjurusan di SMA Menggunakan Metode Neural Network Backpropagation (Studi

(3)

75 Kasus SMA Islam Kepanjen Malang), Universitas Kanjuruhan Malang, Malang.

Strickland, J.S., 2014, Predictive Modeling and Analytics, Simulation Educators, Colorado.

Suo, Q., Ma, F., Yuan, Y., Huai, M., Zhong, W., Zhang, A., Gao, J., 2017, Personalized disease prediction using a CNN-based similarity learning method, Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, 811-816.

Turban, E., Aronson, J.E., dan Liang, T., 2004, Decision Support Systems and Intelligent Systems, Prentice-Hall, New Jersey.

Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., dan Xu, W., 2016, CNN-RNN:

A Unified Framework for Multi-label Image Classification, IEEE Conference on Computer Vision and Pattern Recognition, 2285 – 2294.

Xie, T., Yu, H., dan Wilamowski, B., 2011, Comparison Between Tradional Neural Networks and Radial Basis Function Networks, IEEE International Symposium on Electronics, Gdansk, June 27 – 30, 1194 – 1199.

Zhang, M.L., dan Zhou Z.H., 2006, Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization, IEEE Transactions on Knowledge and Data Engineering 18 (10), 1338 – 1351.

Zhou, D., Yang, Y., dan He, Y., 2018, Relevant Emotion Ranking from Text Constrained with Emotion Relationship, Proceedings of NAACL-HLT 2018, New Orleans, June 1 – 6, 561 – 571.

Zhu, F., Li, H., Ouyang, W., Yu, N., dan Wang, X., 2017, Learning spatial regularization with image-level supervisions for multi-label image classification, Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 5513-5522

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