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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science The Indonesian Journal of Computer Science
Azani, Nilam Wahdiaz
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Performance Comparison of ARIMA, LSTM and SVM Models for Electric Energy Consumption Analysis Azani, Nilam Wahdiaz; Trisya, Cintia Putri; Sari, Laras Mayangda; Handayani, Hani; Alhamid, Muhammad Rizki Miftha
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.869

Abstract

The changing needs of electrical energy result in the electrical power needed for everyday life being unstable, so planning and predicting how much electrical load is needed so that the electricity generated is always of good quality. So it is necessary to predict the consumption of electrical energy by using forecasting on the machine learning method. Support Vector Machine (SVM), Autoregressive Integrated Motion Average (ARIMA), and Long Short-Term Memory (LSTM) are models that are often used to overcome patterns in predictions. To find out the best models how to predict electricity consumption in the future and how the SVM, LSTM, and ARIMA algorithms perform in predicting electricity consumption. This research will look for the RMSE value and prediction time, then compare it with the best average value. The results of the study show that the ARIMA model is able to predict electricity usage for the next 1 year period, in the evaluation using the RMSE metric, where SVM shows a much lower value than ARIMA and LSTM. In this case, SVM achieved RMSE of 0.020, while ARIMA and LSTM achieved RMSE of 7.659 and 11.4183, respectively. Even though SVM has a lower RMSE, it is still unable to predict electricity usage for the next 1 year with sufficient accuracy.
Implementation Of Naïve Bayes Classifier And Support Vector Machine For Stunting Classification Azani, Nilam Wahdiaz; M. Afdal
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4040

Abstract

Stunting is a condition when a child's physical growth and development are stunted or delayed due to a lack of adequate nutritional intake over a long period of time, especially during the early years of life. Indonesia still has a stunting prevalence rate above the WHO standard, which is at 21.6%. 2020 UN statistics recorded more than 149 million (22%) toddlers worldwide were stunted, of which 6.3 million were early childhood or stunted toddlers were Indonesian toddlers. This study aims to create a classification model using Data Mining Algorithms NBC and SVM to analyze and describe the class of a total of 2018 toddler nutritional status data in Lima Puluh Kota Regency. The results of this study are expected to be an evaluation of whether the stunting prevention program implemented has been successful, and can be the basis for creating the next program.
Co-Authors Alhamid, Muhammad Rizki Miftha Hani Handayani, Hani M. Afdal Sari, Laras Mayangda Trisya, Cintia Putri