ANALISIS TIME SERIES TEMPERATUR DI KOTA KUPANG MENGGUNAKAN METODE K-MEANS CLUSTERING

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Published: 2021-10-21
Keywords:
Forecasting, Time Series, Weather, K-Means, Clustering
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Frengky Fernandez

Abstract

Weather is an important part of human daily activities. Therefore, many people need faster, more complete, and more accurate information about atmospheric conditions (weather). Weather forecasts can be used to solve problems caused by weather, such as drought detection, bad weather, harvesting and production, energy planning, aviation, communications, and others. The Neural Network method is more efficient in fast calculations and is able to handle unstable data in the case of ordinary weather forecast data. For Weather Prediction with synoptic data input, the data is used. Several experiments were conducted to obtain the optimal architecture and produce accurate predictions.

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Vol. 16 No. 2 (2021): Jurnal Komputer dan Informatika

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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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References

[1] Agusta, Y. 2007. K-Means-Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika Vol.3 , 47- 60.

[2] E. Rendón, I. Abundez, A. Arizmendi, and E. M. Quiroz, “Internal versus External cluster validation indexes,” vol. 5, no. 1, 2011.

[3] Pramesti, D. F., Furqon, M. T., & Dewi, C. (2017). Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Hotspot). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X.Y..

[4] Wira, B., Budianto, A. E., & Wiguna, A. S. (2019). Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang. Rainstek: Jurnal Terapan Sains & Teknologi, 1(3), 53-68.

[5] Daniel Riano Kaparang dan Eko Sediyono. (2013). “Penentuan Alih Fungsi Lahan Marginal Menjadi Lahan Pangan Berbasis Algoritma K-means di Wilayah Kabupaten Boyolali.” JdC. 2. 20.