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All Journal International Journal of Electrical and Computer Engineering Proceeding of the Electrical Engineering Computer Science and Informatics IJITEE (International Journal of Information Technology and Electrical Engineering)
Pranowo Pranowo
Universitas Atma Jaya Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Galvanic Skin Response Data Classification for Emotion Detection Djoko Budiyanto Setyohadi; Sri Kusrohmaniah; Sebastian Bagya Gunawan; Pranowo Pranowo; Anton Satria Prabuwono
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.148 KB) | DOI: 10.11591/ijece.v8i5.pp4004-4014

Abstract

Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.
Numerical simulation of electromagnetic radiation using high-order discontinuous galerkin time domain method Pranowo Pranowo; Djoko Budiyanto Setyohadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (809.191 KB) | DOI: 10.11591/ijece.v9i2.pp1267-1274

Abstract

In this paper, we propose the simulation of 2-dimensional electromagnetic wave radiation using high-order discontinuous Galerkin time domain method to solve Maxwell's equations. The domains are discretized into unstructured straight-sided triangle elements that allow enhanced flexibility when dealing with complex geometries. The electric and magnetic fields are expanded into a high-order polynomial spectral approximation over each triangle element. The field conservation between the elements is enforced using central difference flux calculation at element interfaces. Perfectly matched layer (PML) boundary condition is used to absorb the waves that leave the domain. The comparison of numerical calculations is performed by the graphical displays and numerical data of radiation phenomenon and presented particularly with the results of the FDTD method. Finally, our simulations show that the proposed method can handle simulation of electromagnetic radiation with complex geometries easily.
Honey Yield Prediction Using Tsukamoto Fuzzy Inference System Tri Hastono; Albertus Joko Santoso; Pranowo Pranowo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.026 KB) | DOI: 10.11591/eecsi.v4.1084

Abstract

Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides  a  solution  for  prediction  of  honey  yield  type  Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield   using FIS Tsukamoto  closed  the  real  honey  yield  with  RMSE  value  of 9.44933860119277.
Prediction of Peat Forest Fires Using Wavelet and Backpropagation Novera Kristianti; Albertus Joko Santoso; Pranowo Pranowo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 2, No 2 (2018): June 2018
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1289.705 KB) | DOI: 10.22146/ijitee.42156

Abstract

One of the causes of smog as well as climate damage, particularly in Palangka Raya, Center Kalimantan, are peat forest fires. There are a lot of losses inflicted by the smog including the increasing number of people who suffer respiratory infection (ARI) due to polluted air and any other related aspects. Peat fires are problematic to overcome because the locations of fires are difficult to be accessed. This paper focuses on building the system to predict the distribution of peat forest fire hotspots by utilizing satellite imagery. In designing the system for predicting the fire hotspots distribution, wavelet orthogonal was used as the initial processing of mapping the distribution of peat forest fire hotspots. Meanwhile, backpropagation method was used to identify the fire hotspot distribution patterns of peat forest fire in this system. From the result of the data tested which had been done for predicting the peat forest fire hotspots, the decomposition image obtained using Haar wavelet had the highest percentage of accuracy to recognize the fire hotspots, which is 90%. The recency of this system was its ability to predict the peat forest fire hotspots distribution which can be used as peat forest fires prevention, especially in Palangka Raya, Central Kalimantan.
Co-Authors Albertus Joko Santoso Anton Satria Prabuwono Djoko Budiyanto Setyohadi Kristianti, Novera Sebastian Bagya Gunawan Sri Kusrohmaniah, Sri Tri Hastono