Panduan Definitif Untuk Power Query (M) Jilid 2

Authors

Dr. Phil. Dony Novaliendry, S. Kom., M. Kom
Universitas Negeri Padang

Synopsis

Buku "Panduan Definitif untuk Power Query (M) – Jilid 2" melanjutkan pembahasan dari jilid sebelumnya dengan fokus yang lebih mendalam pada tipe data, nilai terstruktur, dan konsep lanjutan dalam bahasa M. Buku ini dirancang untuk membantu pembaca memahami bagaimana data direpresentasikan, dikelola, dan dimanipulasi secara efisien di dalam Power Query.
Pembahasan dimulai dengan pemahaman tipe data—baik primitif maupun khusus—serta pentingnya konsistensi, validasi, dan konversi tipe dalam proses transformasi data. Selanjutnya, buku menguraikan secara rinci tentang nilai terstruktur seperti list, record, dan tabel, lengkap dengan metode pembuatan, manipulasi, serta penetapan tipe data pada masing-masing struktur.
Pada bagian lanjutan, buku ini membahas konseptualisasi M, termasuk ruang lingkup, lingkungan global, closures, hingga pengelolaan metadata. Topik terakhir memperdalam kemampuan pembaca dalam bekerja dengan struktur bersarang (nested structures), mencakup pengolahan list, record, tabel, hingga kombinasi struktur yang lebih kompleks.
Dengan disertai kasus pemantik berpikir kritis, tes formatif, glosarium, dan lampiran, jilid kedua ini memberikan wawasan praktis sekaligus teoritis, sehingga menjadi panduan berharga bagi mahasiswa, dosen, peneliti, maupun praktisi data yang ingin menguasai Power Query secara komprehensif.

References

Bayazit M. and Önöz B. (2007), To prewhiten or not to prewhiten in trend analysis?, Hydrological Sciences Journal, 52:4, 611-624. https://doi.org/10.1623/hysj.52.4.611.

Box, G. E. P. and Cox, D. R. (1964), An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211-252. http://www.ime.usp.br/~abe/lista/ pdfQWaCMboK68.pdf.

Breiman, L. Random Forests, Machine Learning 45, 5–32 (2001): https://doi. org/10.1023/A:1010933404324.

Chen, Tianqi and Guestrin, Carlos. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘16). Association for Computing Machinery, New York, NY, USA, 785–794: https:// doi.org/10.1145/2939672.2939785.

David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting. 36-3. 1181-1191: https://doi.org/10.1016/j.ijforecast.2019.07.001

David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting. 36-3. 1181-1191: https://doi.org/10.1016/j.ijforecast.2019.07.001

Guerrero, Victor M. (1993), Time-series analysis supported by power transformations. Journal of Forecasting, Volume 12, Issue 1, 37-48. https://onlinelibrary.wiley.com/ doi/10.1002/for.3980120104.

Ke, Guolin et.al. (2017), LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, pages 3149-3157: https://dl.acm.org/doi/ pdf/10.5555/3294996.3295074.

Montero-Manso, P., Hyndman, R.J.. (2020), Principles and algorithms for forecasting groups of time series: Locality and globality. arXiv:2008.00444[cs.LG]: https://arxiv.org/ abs/2008.00444

Montero-Manso, P., Hyndman, R.J.. (2020), Principles and algorithms for forecasting groups of time series: Locality and globality. arXiv:2008.00444[cs.LG]: https://arxiv.org/ abs/2008.00444

Prokhorenkova, Liudmila, Gusev, Gleb et al. (2018), CatBoost: unbiased boosting with categorical features. Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18): https://dl.acm.org/doi/abs/10.5555/3327757.3327770.

Slawek Smyl (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting. 36-1: 75-85 https://doi. org/10.1016/j.ijforecast.2019.03.017

Slawek Smyl (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting. 36-1: 75-85 https://doi. org/10.1016/j.ijforecast.2019.03.017

White, H. (1980), A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica Vol. 48, No. 4 (May 1980), pp. 817-838 (22 pages). https://doi.org/10.2307/1912934.

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Published

September 8, 2025

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