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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Sri Hartati
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Knowledge-Based Systems Selection of Contraceptive Equipment for The Handling of Uncertainty Achmad Siddik Fathoni; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.58305

Abstract

 Contraceptives is one of the products of the government program for controlling the population. The government has established the Department of Population Control and family planning and empowerment of women and child protection that specifically manages the dissemination and socialization of the apparatus. But to choose the appropriate contraceptives for himself The Community of people still feel trouble. Not only prospective of common people who feel difficulties, sometimes the KB officers also feel uncertain in giving advice of tool contraceptives. That is because, sometimes the condition of the user does not comply with the existing rules, the latest knowledge about the development of contraception has not been owned by the officer, thus resulting in uncertainty in the suggestion of selection of contraceptives. In this study proposed a knowledge-based system to assist the public in providing an overview of the type of contraceptive equipment suitable for theyself and can be used by the KB officers the as interactive media and in the handling of the uncertainty problem that mentioned before. Then for the handling of uncertatinty problems will use dempster shafer method. dempster shafer method is Chosen because this method can provide an estimate of the value of confidence against a result of the diagnosis, by conducting the calculation of the combination of the same symptoms will be obtained the highest confidence value, or the most dominant. In the testing process, there will be 40 cases compared to the results. This research aims to solve the uncertainty problems of the suggestion the selection of contraceptives tools. The results of this research can provide a consulting medium that is able to provide selection of contraceptives that solve the problem of uncertainty and confidence level of the system to the tool. The test showed an accuracy rate of 95%
Face Image Generation and Enhancement Using Conditional Generative Adversarial Network Ainil Mardiah; Sri Hartati; Agus Sihabuddin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.58327

Abstract

The accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value.
Co-Authors Achmad Siddik Fathoni Agus Sihabuddin Ainil Mardiah