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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pekommas Water Circulation Monitoring System in The Vanname Shrimp Cultivation based on Internet of Thing
Nurdin Nurdin
Universitas Dipa Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Social Sciences

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Performance Analysis of Weather Forecasting using Machine Learning Algorithms (Analisis Performansi Prakiraan Cuaca Menggunakan Algoritma Machine Learning) Indo Intan; Rismayani Rismayani; St. Aminah Dinayati Ghani; Nurdin Nurdin; Aswar TC. Koswara
Jurnal Pekommas Vol 6, No 2 (2021): Oktober 2021
Publisher : BBPSDMP KOMINFO MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jpkm.2021.2060221

Abstract

Weather forecasting are very important in various fields of human life, including in big cities. The need for accurate weather forecasts will be effective and efficient in managing the quality of civilization flexibly. In many cases it is found that the results of weather forecasts in the same city differ depending on the radius. This of course requires a precise and accurate algorithm to determine it. The algorithm used is based on machine learning type of artificial neural network which compares backpropagation and bayessian regularization. The results obtained show that bayessian regularization outperforms backpropagation with the smallest MSE and the highest accuracy and the shortest computation time to determine sunny, cloudy, light rain and heavy rain forecasts. The unbalanced distribution of data causes fluctuations in the MSE calculation and accuracy. The addition of training will improve system performance which is indicated by a significant increase in accuracy. Likewise, decreasing the MSE can increase the accuracy of the system to reach the point of convergence. This is an indicator that the performance of Bayessian regularization is the recommended algorithm for forecasting weather in cities and their surroundings, even between provinces or between countries.
Performance Analysis of Weather Forecasting using Machine Learning Algorithms (Analisis Performansi Prakiraan Cuaca Menggunakan Algoritma Machine Learning) Indo Intan; Rismayani Rismayani; St. Aminah Dinayati Ghani; Nurdin Nurdin; Aswar TC. Koswara
Jurnal Pekommas Vol 6, No 2 (2021): Oktober 2021
Publisher : BBPSDMP KOMINFO MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jpkm.2021.2060221

Abstract

Weather forecasting are very important in various fields of human life, including in big cities. The need for accurate weather forecasts will be effective and efficient in managing the quality of civilization flexibly. In many cases it is found that the results of weather forecasts in the same city differ depending on the radius. This of course requires a precise and accurate algorithm to determine it. The algorithm used is based on machine learning type of artificial neural network which compares backpropagation and bayessian regularization. The results obtained show that bayessian regularization outperforms backpropagation with the smallest MSE and the highest accuracy and the shortest computation time to determine sunny, cloudy, light rain and heavy rain forecasts. The unbalanced distribution of data causes fluctuations in the MSE calculation and accuracy. The addition of training will improve system performance which is indicated by a significant increase in accuracy. Likewise, decreasing the MSE can increase the accuracy of the system to reach the point of convergence. This is an indicator that the performance of Bayessian regularization is the recommended algorithm for forecasting weather in cities and their surroundings, even between provinces or between countries.
Facial recognition using multi edge detection and distance measure Indo Intan; Nurdin Nurdin; Fitriaty Pangerang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1330-1342

Abstract

Face recognition provides broad access to several public devices, so it is essential in cutting-edge technology. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.
Co-Authors Aswar TC. Koswara Fitriaty Pangerang Indo Intan Indo Intan Rismayani Rismayani ST. Aminah Dinayati Ghani