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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Dede Dirgahayu Domiri, Dede Dirgahayu
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Identification of Peatland Burned Area based on Multiple Spectral Indices and Adaptive Thresholding in Central Kalimantan Pratikasiwi, Hilda Ayu; Taufik, Muh.; Santikayasa, I Putu; Domiri, Dede Dirgahayu
Agromet Vol. 38 No. 2 (2024): DECEMBER 2024
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.38.2.68-77

Abstract

Nowadays, spectral index has become popular as a tool to identify fire-burned areas. However, the use of a single index may not be universally applicable to region with diverse landscape and vegetation as peatlands. Here, we propose to develop a procedure that integrates multiple spectral indices with an adaptive thresholding method to enhance the performance of burned area detection. We combined the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) using MODIS imagery from 2002 to 2022 to calculate (Confirmed Burned Pixel) by filtering dNDVI and dNBR. The mean and standard deviation of serve as inputs for image thresholding. We tested our approach in Sebangau peatland, Central Kalimantan, where fires occur annually. The results showed that the model performed well with overall accuracy > of 91%, indicating that the model is effective and reliable for identifying burned areas. The findings also revealed that the frequency of fire is below 2 times/year, with the southeastern is the most fire prone regions. Further, our findings provide an alternative approach for identifying burned areas in locations with diverse vegetation cover and different geographical regions.
PENGEMBANGAN MODEL PENDUGAAN KELENGASAN LAHAN MENGGUNAKAN DATA MODIS Domiri, Dede Dirgahayu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 3 No. 1 (2006)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v3i1.3172

Abstract

This research aims to estimate the land moisture condition of the agricultural land, especially for paddy fields based on MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data with 250 m and 500 m spatial resolution and daily temporal resolution. An index called Land Moisture Index (LMI) is created from 1st principle component result of NDSI (Normalize Difference Soil Index), and NDVI (Normalize Difference Vegetation Index), NDWI (Normalize Difference Water Index). There is a high correlation between the LMI and the soil moisture (LM) for the agricultural land with the soil moisture then more 75%, as the increasing of LM is followed by the increasing of the LMI. Based on the above method, the land moisture can be derived spatially from the agricultural land, and especially on the paddy fields for drought prediction.
APLIKASI SIMULASI MODEL DINAMIS PERTUMBUHAN TANAMAN UNTUK MENDUGA PRODUKSI TANAMAN PADI Domiri, Dede Dirgahayu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 8 No. 1 (2011)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v8i1.3249

Abstract

The study was conducted to explore the physical processes and weather and its influence on the development of rice plants, and to analyze the simulation results that can be applied to predict rice crop production. Methods which used in this research are water balance model, growth and development model trough Dynamic Modeling Simulation. Results of a study showed that the optimal planting time can be predicted from the simulation model; rice yield potential can be estimated based on the maximum leaf area index, and a decrease in rice yield can be predicted from changes in ratio value of Actual and Maximum Evapotranspiration (ETa/ETm) which generated by the model.
ANALISIS POTENSI BANJIR DI SAWAH MENGGUNAKAN DATA MODIS DAN TRMM (STUDI KASUS KABUPATEN INDRAMAYU) Febrianti, Nur; Domiri, Dede Dirgahayu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 1 (2012)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v9i1.3258

Abstract

The occurrence of flooding in paddy field may cause the decrease of total production. To increase the food sufficiency within the country, the monitoring of flood affected paddy field is very important to be implemented. The satellite imagery is one of tools for monitoring the flooding area. In this study; we used remotely sensed data from MODIS (Moderate Resolution Imaging Spectroradiometer) and TRMM (Tropical Rainfall Measuring Mission) for January 2011 and January 2012, respectively. The district of Indramayu was selected as the study site due to one of the center of the rice production. The flood frequency method was utilized to estimate the flood duration. Some assumption used in this study, i.e.: (i) the assumed to be wetland rained rice. (ii) Rice fields are assumed in the flat. (iii) The rainfall exceeds the crop water demand will be potentially because the floods, (iv) The rainfall have large impact causing flooding when compare to index vegetation greenness. The calculation of the flood potential did known that the equation used compelling enough because it has been in accordance with actual flood events. The class of potential flooding were identifying as a class of height severe flooding. The calculation of flood frequency in January 2011 showed that there had been flooding up to 4 times a month. Besides, there is 18,400 ha that has four times frequency of flooding, respectively and requires to be aware crop failures occurred in both conditions. The condition on January 2012 was in a safe because floods occurred only one time. The extensive flooding of rice fields in Indramayu district January 2012.
Support vector machine performance: simulation and rice phenology application Muradi, Hengki; Saefuddin, Asep; Sumertajaya, I Made; Soleh, Agus Mohamad; Domiri, Dede Dirgahayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4878-4890

Abstract

In the case of classification, model accuracy is expected to result in correct predictions. This study aims to analyze the performance of two kinds of support vector machine (SVM) methods: the support vector machine one versus one (SVM OvO) method and the generalized multiclass support vector machine (GenSVM) method. This method will compare to the generalized linear model, namely the multinomial logistic regression (MLR) method. Simulations were conducted using SVM OvO and GenSVM methods to get an overview of the parameters affecting both methods' performance. Furthermore, the three classification methods are implemented in the case of modelling the rice phenology and tested for performance. Simulation results show that, however, the SVM OvO and GenSVM machine learning methods are sensitive to the choice of model parameters. The empirical study results show that the SVM OvO and GenSVM methods can produce satisfactory model accuracy and are comparable to the MLR method. The best rice phenology model accuracy was obtained from the SVM OvO model, where 79.20 ± 0.21 overall accuracy and 70.69 ± 0.29 kappa were obtained. This research can be continued by handling samples, especially when class members are a minority, and can also add random effects to the SVM model.
Co-Authors Agus Mohamad Soleh ASEP SAEFUDDIN Febrianti, Nur I Made Sumertajaya Muh. Taufik Muradi, Hengki Pratikasiwi, Hilda Ayu Santikayasa, I Putu