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Susiani Susiani

STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

Author-ID : 5368606

Computer Science & IT Engineering

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Application of Artificial Neural Networks in Predicting Salt Imports by Country of Origin Using the Back-propagation Method Sari Marito Tondang; Heru Satria Tambunan; Susiani Susiani
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Salt is a basic consumption material needed by the community and various industries. Indonesia is a country that has many beaches that have great potential as a source of salt production. But Indonesia is still dependent on imports so that industrial imports continue to increase, can directly or indirectly affect the risk of the country’s economic pattern. An increase in salt imports although there was also a decrease but only slightly and did not last long from several countries from 2010-2020 recorded in the Central Statistics Agency (BPS). In this study, the author will predict the import of salt for the next 3 years using the Back-propagation algorithm. Back-propagation is one of the artificial neural network methods that is quite reliable in solving problems where the network tries to achieve stability again to achieve the expected output and there is a learning process by adjusting connection weights. This study uses 6 architectural models : 5-80-1, 5-90-1, 5-100-1, 5-110-1, from the four models the best architectural model is obtained namely 5-90-1 with an accuracy value of 75%, epoch 4265 iterations, and MSE Testing 0,01569.

Classification of Internet Addiction Levels in Students Using the Naïve Bayes Algorithm Fakhriyah Zulfah Parinduri; Rafika Dewi; Susiani Susiani
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

The presence of the internet on students has a big influence on science and technology which makes the internet as an additional insight to find the information needed, apart from being a source of information, students also access the internet as a means of entertainment. So that it makes students last longer in front of gadgets or computers continuously. The purpose of this study is to determine whether students are indicated by internet addiction and provide input to STIKOM Tunas Bangsa to make policies that use the internet as a learning process so that internet addiction does not occur excessively. Because it is very influential in the learning process to add insight about science and technology to students. The subjects carried out by this study were students who were studying at STIKOM Tunas Bangsa. Therefore, the research was conducted using the Naïve Bayes algorithm classification, in which the data was obtained using a questionnaire distributed to students. The subjects carried out by this study were students who were studying at STIKOM Tunas Bangsa. Therefore, the research was conducted using the Naïve Bayes algorithm classification, in which the data was obtained using a questionnaire distributed to students. It is hoped that this research can be information for students to be able to maintain self-control in utilizing various entertainments on the internet.

Data Classification of Marriage Readiness in Young Adults Using the Naïve Bayes Algorithm Rahmi Fauziah; Heru Satria Tambunan; Susiani Susiani
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Readiness to get married usually must be owned by every individual who wants to run a married life in order to become a harmonious family. However, not all young adults prepare for marriage such as financially, emotionally, roles and others. So the classification is carried out to determine the readiness for marriage with ready and not ready classes. Classification is part of data mining that performs the process of building a model based on existing training data, then using the model for classification on new data. The research data used were taken from 103 young adult, male and female. The algorithm used is Naïve Bayes. The conclusion of this research is testing as much as 5 testing data that is processed in RapidMiner 5.3. get test results with an accuracy of 74,33%, namely 3 data that are not ready and 2 data that are ready. So that the research process can be done quickly and efficiently.

Co-Authors Fakhriyah Zulfah Parinduri Heru Satria Tambunan Rafika Dewi Rahmi Fauziah Sari Marito Tondang

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