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p-Index From 2019 - 2024
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This Author published in this journals
All Journal International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Amri Muhaimin
UPN "Veteran" Jawa Timur
Computer Science & IT Decision Sciences, Operations Research & Management

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Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association) Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Kartika Maulida Hindrayani; Amri Muhaimin; Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1292.381 KB) | DOI: 10.33005/ijdasea.v1i2.12

Abstract

Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.
Urban Village Clustering in Surabaya City based on Live Birth Rate using K-Means with Principle Component Analysis Regita Putri Permata; Rifdatun Ni’mah; Amri Muhaimin
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.41

Abstract

Pregnancy and childbirth are important times in a mother's life. Mothers and children are vulnerable so their health efforts should be prioritized. The health level is a useful indicator to see the health efforts achievement or success of an area. The Surabaya City Government is very concerned about the health and safety of mothers and babies problem. Therefore, this study aims to map and classify urban villages in Surabaya based on the number of live births and pregnant women using the K-Means algorithm and feature reduction techniques using Principal Component Analysis. Two main components can be formed as the result of the variable reduction. The most optimal grouping of urban villages in the city of Surabaya is 3 groups/clusters. Based on the number of live births and pregnant women, those consisted of 3 clusters, in which cluster 0 consisted of 99 villages, cluster 1 consisted of 42 villages, and cluster 2 consisted of 12 villages
Co-Authors Kartika Maulida Hindrayani Prismahardi Aji Riyantoko Regita Putri Permata Rifdatun Ni’mah Tresna Maulana Fahrudin Trimono