PANDUAN DEFINITIF UNTUK POWER QUERY (M) JILID 3

Authors

Dony Novaliendry

Universitas Negeri Padang

Synopsis

Buku ini merupakan kelanjutan dari seri Power Query yang mendalami kemampuan lanjutan dalam mengelola dan mentransformasi data menggunakan bahasa M. Disusun secara sistematis dan praktis, buku ini membahas cara membuat parameter dan custom function, serta penerapannya dalam pembuatan solusi dinamis. Anda juga akan mempelajari teknik lengkap dalam menangani tanggal, waktu, durasi, serta manipulasi data menggunakan fungsi pembanding, pengganti, penggabung, dan pemisah—semuanya disertai contoh kasus yang aplikatif.Bagian penting lainnya adalah pembahasan mendalam tentang penanganan kesalahan (error handling) dan debugging, yang akan membantu Anda membangun transformasi data yang kokoh dan bebas error. Ditujukan bagi pengguna Excel dan Power BI tingkat menengah hingga mahir, buku ini memperkuat pemahaman teknis dengan tes formatif, daftar fungsi, dan glosarium lengkap—menjadikannya referensi ideal untuk praktisi data yang ingin menguasai Power Query secara profesional.

References

Neco, R. P., and Forcada, M. L. (1997), Asynchronous translations with recurrent neural nets. Neural Networks, 1997., International Conference on (Vol. 4, pp. 2535–2540). IEEE: https:// ieeexplore.ieee.org/document/614693.

Kalchbrenner, N., and Blunsom, P. (2013), Recurrent Continuous Translation Models. EMNLP (Vol. 3, No. 39, p. 413): https://aclanthology.org/D13-1176/.

Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. (2014), Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724–1734, Doha, Qatar. Association for Computational Linguistics: https://aclanthology.org/D14-1179/.

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. (2014), Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems – Volume 2: https://dl.acm.org/doi/10.5555/2969033.2969173.

Rumelhart, D., Hinton, G., and Williams, R (1986). Learning representations by back-propagating errors. Nature 323, 533–536: https://doi.org/10.1038/323533a0.

Schuster, M., and Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681: https://doi.org/10.1109/78.650093.

Sepp Hochreiter (1991) Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, TU Munich: https://people.idsia.ch/~juergen/ SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf.

Y. Bengio, P. Frasconi, and P. Simard (1993), The problem of learning long-term dependencies in recurrent networks. IEEE International Conference on Neural Networks, pp. 1183-1188 vol.3: 10.1109/ICNN.1993.298725.

Y. Bengio, P. Simard, and P. Frasconi (1994) Learning long-term dependencies with gradient descent is difficult in IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157–166, March 1994: 10.1109/72.279181.

Hochreiter, S., and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780: https://doi.org/10.1162/neco.1997.9.8.1735.

Cho, K., Merrienboer, B.V., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP: https://www.aclweb.org/anthology/D14-1179.pdf.

Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybernetics 36, 193–202 (1980): https:// doi.org/10.1007/BF00344251.

Y. Le Cun, B. Boser, J. S. Denker, R. E. Howard, W. Habbard, L. D. Jackel, and D. Henderson. 1990. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 396–404: https://proceedings.neurips.cc/paper/1989/file/53c3bce6 6e43be4f209556518c2fcb54-Paper.pdf.