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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan JOMLAI: Journal of Machine Learning and Artificial Intelligence

Heru Satria Tambunan

STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

Author-ID : 3642645

Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Articles

Penerapan Clustering Pada Laju Inflasi Kota Di Indonesia Dengan Algoritma K-Means Yudi Prayoga; Heru Satria Tambunan; Iin Parlina
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 1, No 1 (2019): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Inflation is a process of increasing prices in general and continuously, related to market mechanisms that can be caused by various factors, among others, increased public consumption, excess liquidity in the market which triggers consumption or even speculation, to include the consequences of inability to distribute goods. Inflation is an indicator to see the level of change, and is considered to occur if the process of price increases takes place continuously and influences each other. Inflation stability is a prerequisite for sustainable economic growth which ultimately benefits the improvement of people's welfare. With the large amount of data generated from the inflation rate of cities in Indonesia it is difficult for the government to classify the inflation rate. The author took the initiative to conduct research on classifying the inflation rate of cities in Indonesia by using the K-Means Clustering Data Mining algorithm, with the number of clusters being 3. The high value group is in cluster 1 (above average), the value group is in cluster 2 (around the average based on the distance used from the centroid), and the low value group is in cluster 3 (below average). flat). By grouping the rate of inflation of cities in Indonesia, it will be known which cities in Indonesia have high, medium and low inflation rates.

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.

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.

Application of the FP-Growth Algorithm in Analyzing Patterns and Layout of Foodstuffs Ayu Padillah; Heru Satria Tambunan; Rizki Alfadillah Nasution
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

The purpose of this study was to determine the pattern and layout of the appropriate goods in Gono Stores using the FP-Growth Algorithm. Gono Store is a store that is engaged in the sale of food ingredients located in Nagori Dolok Kataran, Kec. Dolok Batu Nanggar. The arrangement of the layout of the goods greatly affects the volume of sales. However, in setting the layout at the Gono Store, there are some problems, namely the lack of knowledge of the shop owner in setting the layout . The FP-Growth algorithm is one of the alternative data mining algorithms that can be used to determine groups of data that often appear (Frequent item sets) in a set of data.The source of the research data used is by conducting observations and interviews at Gono Stores. From the overall results of the sales data 10 rules are formed with the minimum value limit of Support = 0.3and Confidence = 0,8. Its hoped that the result of this study will provide benefits in the form of information that can help shop owners in analyzing the pattern and layout of foodstuffs.

Co-Authors Ayu Padillah Iin Parlina Nasution, Rizki Alfadillah Rahmi Fauziah Sari Marito Tondang Susiani Susiani Yudi Prayoga

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