Model Estimasi Data Intensitas Radiasi Matahari Untuk Wilayah Banten

Munawar, Munawar; Mulsandi, Adi; Hidayat, Anistia Malinda

doi:10.29122/jstmc.v21i2.4171

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…The three main weather variables used as model data input are rainfall (mm), maximum temperature (°C), and minimum temperature (°C). The results of testing have an R 2 value of 0.72 [11]. Similar research related to solar radiation data is to estimate the potential intensity of solar radiation as a renewable energy.…”

Section: Figure 1 Monthly Data Of Global Horizontalmentioning

confidence: 91%

Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

Syahab

Hermawan

Avianto

2023

JISA

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To reduce the discrepancy between the results of the expected data and the actual data, prediction is a procedure that is calculated systematically based on owned historical and present information. For the creation of solar energy projects and for decision-making in other connected domains, solar radiation intensity prediction is essential. This study aims to create a predictive model on monthly global horizontal irradiance data. The method used is the Simple Moving Average algorithm, Exponentially Weighted Moving Average and Single Exponential Smoothing. The stages carried out in this study include data collection, data preprocessing, testing of predictive models, interpretation of data visualization, and performance evaluation. The results of calculating the error value and correlation produce an evaluation of the performance of the prediction model. The SES method, which obtained an MAE value of 7.13, a MAPE of 0.02%, an MSE of 88.07, an RMSE of 9.38, and an R2 of 0.94, was determined to be the best prediction model by the calculation of the prediction model performance evaluation. A MAE value of 9.45, a MAPE of 0.02%, an MSE of 150.16, an RMSE of 12.25, and an R2 of 0.91 were obtained by the EWMA method, which is also the method that produced the second-best result. A MAE value of 14.38, a MAPE of 0.04%, an MSE of 367.59, an RMSE of 19.17, and an R2 of 0.77 were obtained by the SMA method, which is the third-best result.

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Section: Figure 1 Monthly Data Of Global Horizontalmentioning

confidence: 91%

Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

Syahab

Hermawan

Avianto

2023

JISA

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“…Statistik uji yang digunakan adalah: RMSE digunakan untuk menilai seberapa besar kesalahan nilai estimasi dibandingkan dengan data aktual [17]. Jika nilai RMSE mendekati nol, maka data hasil estimasi memiliki akurasi yang tinggi karena memiliki nilai error yang rendah relatif terhadap data aktual.…”

Section: Uji Signifikasi Parameterunclassified

Geographically Weighted Panel Regression (Gwpr) Model for Poverty Data in West Java Province 2019-2021

Nasri,

Gusriani,

Anggriani

2023

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The problem of poverty in West Java shows a pattern that tends to be concentrated in adjacent areas, indicating spatial heterogeneity in the problem. On the other hand, poverty in West Java also shows an increasing trend from year to year so that dynamic changes occur in various regions. From this situation, it is necessary to know the factors that affect poverty spatially using panel data. One way is to model the poverty problem with the Geographically Weighted Panel Regression (GWPR) model. The GWPR model is the development of a regression model that combines Geographically Weighted Regression (GWR) with panel data regression assuming a Fixed Effect Model (FEM). The data used in this study are secondary data in the 2019-2021 range from the Central Bureau of Statistics and Open Data Jabar which consists of the dependent variable (Y), namely the percentage of poor people and the independent variable (X), namely the factors that influence the percentage of poverty. The purpose of this study is to produce a GWPR model using the Weighted Least Square (WLS) method with the Tricube adaptive kernel weighting function. By conducting overall and partial testing through the F test and t test, the results show that the model for each location and the factors that influence the percentage of poor people in West Java are different for each location due to spatial variations in the relationship between the independent variable and the dependent variable.

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“…Ditambah lagi, perbedaan posisi lintang juga turut mempengaruhi adanya perbedaan mekanisme dinamika atmosfer yang berlaku dan membentuk cuaca dan iklim di kedua wilayah. Wilayah Indonesia berada di lintang rendah dimana mendapatkan pemanasan matahari maksimum sepanjang tahun (Munawar et al, 2020) yang berbeda baik dengan wilayah Pasifik barat bagian utara maupun dengan Australia yang berada di wilayah subtropis.…”

Section: Hasil Dan Pembahasanunclassified

Evaluasi Performa Indeks Monsun Ausmi Dan Wnpmi Di Wilayah Indonesia

Mulsandi

Sopaheluwakan

Faqih

et al. 2021

JSTMC

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Intisari Iklim di wilayah Indonesia sangat dipengaruhi oleh aktivitas monsun Asia-Australia. Variabilitas kedua sistem monsun tersebut dapat direpresentasikan dengan baik masing-masing oleh indeks monsun Australian Summer Monsoon Index (AUSMI) dan Western North Pacific Monsoon Index (WNPMI). Saat ini, BMKG secara operasional menggunakan indeks AUSMI dan WNPMI untuk memonitor aktivitas monsun di wilayah Indonesia sebagai bahan prakiraan musim. Meskipun banyak literatur menyatakan bahwa wilayah Indonesia merupakan bagian dari sistem monsun Asia-Australia, namun kondisi topografi lokal yang kompleks berpotensi memodifikasi sirkulasi monsun sehingga perlu dikaji performa kedua indeks tersebut sebelum digunakan secara operasional. Penelitian ini dilakukan untuk menguji performa indeks monsun AUSMI dan WNPMI dalam menggambarkan variasi antartahunan (interannual), variasi dalam musim (intraseasonal), dan siklus tahunan (annual cycle) hujan monsun Indonesia. Hasil penelitian mengungkapkan bahwa kedua indeks memiliki performa yang sangat baik hanya di wilayah dimana indeks tersebut didefinisikan namun kurang baik untuk wilayah Indonesia seperti yang ditunjukan oleh nilai koefisien korelasi yang tidak signifikan dari hasil uji statistik antara kedua indeks dengan curah hujan dari Global Precipitation Climatology Project (GPCP) pada periode 1981-2010. Selain itu, kedua indeks juga memperlihatkan karakteristik siklus tahunan yang berbeda dengan karakteristik siklus tahunan hujan wilayah Jawa sebagai wilayah kunci monsun Indonesia. Hasil ini mengindikasikan perlunya pendefinisian indeks sendiri untuk memonitor aktivitas monsun di wilayah Indonesia. Abstract The climate of Indonesia is strongly affected by the Asian-Australian monsoon system. The variability of the two monsoon systems can be well represented by the Western North Pacific Monsoon Index (WNPMI) and the Australian Summer Monsoon Index (AUSMI) respectively. For producing seasonal forecast, BMKG uses the WNPMI and AUSMI monsoon index to monitor monsoon activity in Indonesia. Although most literature states that the Indonesian region is part of the Asian-Australian monsoon system, the complex local topography may modify the monsoon circulation. Hence, it is necessary to assess the performance of the two indices before they are operationally used. This study was conducted to evaluate the performance of the AUSMI and WNPMI monsoon indices in describing the annual cycle, intraseasonal and interannual variability of the Indonesian monsoon rainfall. The results revealed that the two indices only performed very well in the areas where the index was defined but lack of skill for the Indonesian region because of insignificant linear correlation based on a statistical significance test between the two indices and the Global Precipitation Climatology Project (GPCP) rainfall in the 1981-2010 period. In addition, both monsoon indices and Java rainfall showed different characteristics of the annual cycle. These results indicate that it is necessary to define a specific index for monitoring monsoon activity in Indonesia.

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Model Estimasi Data Intensitas Radiasi Matahari Untuk Wilayah Banten

Cited by 4 publications

References 13 publications

Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

Geographically Weighted Panel Regression (Gwpr) Model for Poverty Data in West Java Province 2019-2021

Evaluasi Performa Indeks Monsun Ausmi Dan Wnpmi Di Wilayah Indonesia

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