DUKUNGAN KEPUTUSAN DENGAN PENDEKATAN MACHINE LEARNING

Keywords

Machine
Machine Learning

How to Cite

Daniati, E., Firliana, R., & Sari Wardani, A. (2024). DUKUNGAN KEPUTUSAN DENGAN PENDEKATAN MACHINE LEARNING. Universitas Nusantara PGRI Kediri, 1–72. https://doi.org/10.29407/punp.vi.68

Abstract

Buku ini merangkum konsep-konsep kunci dalam dunia analitik bisnis, menyoroti empat aspek utama. Pertama-tama, penulis membahas Bisnis Intelejen dan bagaimana analitik data menjadi elemen kunci dalam mendukung pengambilan keputusan strategis di dunia bisnis. Kemudian, buku mengulas Pemodelan Data, Statistika, dan Visualisasi sebagai alat penting dalam menganalisis informasi bisnis, mengajarkan teknik seperti regresi dan analisis varians. Bagian selanjutnya mengeksplorasi Dukungan Keputusan dengan Machine Learning, memberikan wawasan tentang peran algoritma machine learning dalam meningkatkan prediksi dan analisis data. Akhirnya, buku ini menyajikan Kerangka Evaluasi Pengambilan Keputusan, membantu pembaca memahami cara mengukur keberhasilan keputusan bisnis dan menerapkan proses evaluasi yang efektif. Dengan pendekatan holistik ini, buku ini cocok untuk para profesional bisnis yang ingin mengintegrasikan strategi bisnis modern dan teknologi analitik untuk meningkatkan efisiensi dalam menghadapi tantangan keputusan bisnis. Buku ini dirancang untuk para profesional bisnis, analis data, dan manajer yang ingin mengoptimalkan pengambilan keputusan mereka melalui penerapan strategi bisnis yang cerdas dan modern. Dengan menggabungkan konsep-konsep intelejen bisnis, analitik data, dan machine learning, buku ini memberikan pandangan holistik untuk membantu organisasi menghadapi tantangan bisnis dengan lebih efisien.

https://doi.org/10.29407/punp.vi.68

References

Ameri, A. A., Pourghasemi, H. R., & Cerda, A. (2018). Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of the Total Environment, 613–614, 1385–1400. https://doi.org/10.1016/j.scitotenv.2017.09.210

Daniati, E., Firliana, R., Wardani, A. S., & Zarkasi, A. C. (2021). Evaluation Framework for Decision Making Based On Sentiment Analysis in Social Media. 2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 47–51. https://doi.org/10.1109/ICAMIMIA54022.2021.9807790

Daniati, E., & Utama, H. (2019). Clustering K means for criteria weighting with improvement result of alternative decisions using SAW and TOPSIS. 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2019, 73–78. https://doi.org/10.1109/ICITISEE48480.2019.9003858

Daniati, E., & Utama, H. (2020). Decision Making Framework Based On Sentiment Analysis in Twitter Using SAW and Machine Learning Approach. 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 218–222. https://doi.org/10.1109/ICOIACT50329.2020.9331998

Ducange, P., Fazzolari, M., Petrocchi, M., & Vecchio, M. (2019). An effective Decision Support System for social media listening based on cross-source sentiment analysis models. Engineering Applications of Artificial Intelligence, 78(October 2018), 71–85. https://doi.org/10.1016/j.engappai.2018.10.014

Liu, B. (2012). Sentiment Analysis and Opinion Mining Morgan & Claypool Publishers. Language Arts & Disciplines, May, 167. https://doi.org/10.1007/978-1-4899-7502-7_907-1

Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104. https://doi.org/10.1016/j.future.2020.01.005

Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., Ghorbani, M. A., & Shahbazi, F. (2018). Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma, 310(November 2016), 178–190. https://doi.org/10.1016/j.geoderma.2017.09.012

Sharda, R., Delen, D., & Turban, E. (2022). Analytics, data science, & artificial intelligence. System for decision support (Vol. 11).

Smetanin, S. (2020). The Applications of Sentiment Analysis for Russian Language Texts : Current Challenges and Future Perspectives. IEEE Access, 4, 1–27. https://doi.org/10.1109/ACCESS.2020.3002215

Utama, H. (2019). Sentiment analysis in airline tweets using mutual information for feature selection. 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2019, 295–300. https://doi.org/10.1109/ICITISEE48480.2019.9003903

Yoo, S. Y., Song, J. I., & Jeong, O. R. (2018). Social media contents based sentiment analysis and prediction system. Expert Systems with Applications, 105, 102–111. https://doi.org/10.1016/j.eswa.2018.03.055