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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Journal of Applied Data Sciences Bulletin of Social Informatics Theory and Application
Izdihar, Zahra Nabila
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Social effects of digital pornography Saputra, Muhammad Firman Aji; Siregar, Sherly Allsa; Izdihar, Zahra Nabila
Bulletin of Social Informatics Theory and Application Vol. 1 No. 2 (2017)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v1i2.42

Abstract

Emerging technology will provide benefits for users. Especially with the presence of the internet will make technology more perfect. On the one hand, technology and the internet provide benefits, on the other hand can also give a negative impact for users, one of which is the rise of pornographic content on the internet or often called cyberporn. With the internet, pornography can be obtained easily. That's because the producers of pornographic content think that the internet is the most optimal marketing medium. The presence of pornography has occurred pros and cons in various countries. In Indonesia, it is strictly prohibited because it can damage the morale of the next generation of the nation. In addition, pornography is something very addictive to humans that will eventually happen in various things. In fact, porn addiction is more severe addictive substance addiction than psychotropic. Some efforts that can be done to minimize the pornography deal either by the government of Indonesia, family, or other related parties are making laws that regulate pornography, blocking pornographic sites, providing early childhood sex education, and the role of parents in child supervision.
Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Lestari, Widya; Saputra, Irzan Tri; Izdihar, Zahra Nabila; Pujianto, Utomo; Haviluddin, Haviluddin; Nafalski, Andrew
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.97

Abstract

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.
Optimizing Image Classification Performance with MnasNet Model on Blurred Images Puspita, Rani; Izdihar, Zahra Nabila
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29571

Abstract

In this era, the development of fashion in clothing is increasing. Over the last 30 years, the fashion industry has experienced significant improvements, causing its growth and development to increase. Fashion has many types and variants, but blurry images can also make it difficult for people to classify whether this is a shirt, t-shirt, or something else. Because of that, we proposed image classification. By classifying images, we can help the fashion industry to separate categories and types of various fashion. The approach uses MnasNet which is included in the deep learning approach. The data used is 70,000 which is divided into 60,000 training data and 10,000 testing data. The MnasNet architectural model produces an accuracy of 89% and a loss of 0.4426. It can be seen that MnasNet is the right method for image classification so that the problems described in the background have been successfully solved.
Co-Authors Aji Prasetya Wibawa Haviluddin Haviluddin Hery Widijanto Nafalski, Andrew Rani Puspita Saputra, Irzan Tri Saputra, Muhammad Firman Aji Siregar, Sherly Allsa Utama, Agung Bella Putra Utomo Pujianto