Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Math Educa Journal Jurnal Nasional Pengelolaan Energi MigasZoom Edukasia: Jurnal Pendidikan dan Pembelajaran
Umi Yuliatin
PEM Akamigas
Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Education Energy Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Performance Analysis of 120 kWp Grid-Connected Rooftop Solar Photovoltaic System in Central Java Mukhamad Faeshol Umam; Ferry Purwo Saputro; Muhammad Rizqi Al Asy’ari; Sesi Selia; Amrullah Farad Sunaryo; Umi Yuliatin
Jurnal Nasional Pengelolaan Energi MigasZoom Vol. 3 No. 2 (2021): Jurnal Nasional Pengelolaan Energi MigasZoom
Publisher : Pusat Pengembangan Sumber Daya Manusia Minyak dan Gas Bumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37525/mz/2021-2/298

Abstract

This study examines the performance of a solar power plant with a total capacity of 125 KWp, which operates for one year in Blora, Central Java. The ability of this power plant is a total of PV modules with 20 kWp, ten kWp, and five kWp capacities spread across eight locations. The annual performance of the rooftop solar power plant is measured automatically by the converter installed in each module. The resulting data from inverters are compared to the meteorological conditions from the meteorological agency. This research will investigate the influence of climate on the power generated, the efficiency of the equipment in a power plant, and the effect of pseudo motion of the sun. It was found that there were variations in energy output throughout the year, and it was concluded that the maximum annual energy was produced in July-August. In addition to weather, other factors need to be investigated further to determine the causes of variations in solar PV output.
ESTIMASI PARAMETER REGRESI LOGISTIK DATA PANEL EFEK TETAP UNTUK T=2 Umi Yuliatin; Dedi Rosadi; Ezhari Asfa’ani
Math Educa Journal Vol 6, No 2 (2022)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/mej.v6i2.4569

Abstract

Logistic regression is a dichotomous classification method that uses several mathematical concepts in the estimating of variables parameters. In the estimation of parameter using the MLE (Maximum Likelihood Estimation) estimation method are obtained by Newton Raphson's numerical method. Unfortunately, this estimation doesn’t work in binary panel data with fixed effects for time T=2 because the present of fixed effec .  Thus, Conditional MLE is used to provide consistent estimator of . This estimation shows by sample data  N=1.151 obtained  while the discussion shows the parameter values are at .
Pemodelan Energi Listrik yang Dihasilkan oleh PV Menggunakan Metode Time Series dan Neural Network untuk Komparasi Umi Yuliatin; Asepta Surya Wardhana; Astrie Kusuma Dewi; Chalidia Nurin Hamdani
EDUKASIA: Jurnal Pendidikan dan Pembelajaran Vol. 4 No. 2 (2023): Edukasia: Jurnal Pendidikan dan Pembelajaran
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v4i2.541

Abstract

Renewable energy sourced from the sun has become one of the focal points of alternative renewable energy as fossil energy reserves diminish. Solar energy, which is converted into electricity using photovoltaic technology, is influenced by several variables, particularly weather variables such as temperature, humidity, and solar radiation. This study involves modeling and forecasting the power output of a 100 Watt PV Solar system using Time Series Analysis and Neural Network techniques. The PV solar system is connected to various weather variable measurement sensors, such as a pyranometer, temperature sensor, and humidity sensor. The data collected from these sensors serve as input for calculating the power output of the installed 100 Watt PV system. The power output is observed on an hourly and daily basis. The modeling results indicate that the best model obtained using ARIMA with variables is ARIMA (0,0,2), incorporating all weather variables (Radiation, Humidity, Temperature*, Wind, and Light*) with a MAPE (Mean Absolute Percentage Error) of 2.91%. Meanwhile, for the best Neural Network (LSTM) model, the input variables of radiation, temperature, and intensity achieved a MAPE of 3.41%
Co-Authors Amrullah Farad Sunaryo Asepta Surya Wardhana Astrie Kusuma Dewi Chalidia Nurin Hamdani Dedi Rosadi Ezhari Asfa’ani Ferry Purwo Saputro Muhammad Rizqi Al Asy’ari Mukhamad Faeshol Umam Sesi Selia