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All Journal RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JOMLAI: Journal of Machine Learning and Artificial Intelligence
Zulia Almaida Siregar
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

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Journal : RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI

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Penerapan Jaringan Syaraf Tiruan Backpropagation Dalam Memprediksi Jumlah Pertumbuhan Kendaraan Di Provinsi Sumatera Utara Bagus Supranda; S Solikhun; Zulia Almaida Siregar
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 2 No. 4 (2022): RESOLUSI Maret 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v2i4.333

Abstract

Motorized vehicles are part of the need for transportation of vehicles that are derivatives due to economic, social, and other activities. The growth of vehicles is not proportional to the population in the province of North Sumatra. This causes various negative impacts, one of which is an increase in traffic congestion, air pollution from motorized vehicles which causes an increase in greenhouse gas emissions. Based on this problem, it is necessary to predict the number of vehicles in North Sumatra Province using the backpropagation algorithm artificial neural network. The results of trials carried out with MATLAB R2011b software, the best architectural model is the 2-2-1 model with an accuracy rate of 94% with MSE number 0.000208514, epoch value 789. It can be concluded that the Backpropagation method can be used as one of the predictive methods that make it easier to find predictions. Whatever.
Penerapan Metode K-Means Dalam Mengelompokkan Persebaran Lahan Kritis Di Indonesia Berdasarkan Provinsi Putra Pratama Siregar; S Solikhun; Zulia Almaida Siregar
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 2 No. 4 (2022): RESOLUSI Maret 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v2i4.335

Abstract

The study aims to group the distribution of critical land in Indonesia by province. To solve this problem, researchers applied the K-Means Algorithm method. Where the source of research data is collected based on documents - documents of Information on The Extent and Dissemination of Critical Land By Province produced by the Central Statistics Agency (BPS). The data used in the study was data from 2011, 2013 and 2018 consisting of 34 provinces. Data will be processed by clustering in 2 clusters, namely clusters of high critical land distribution rates and clusters of low critical land distribution rates. The high cluster amounted to 4 data, namely the provinces of North Sumatra, Jambi, East Java, and Central Kalimantan. With the conduct of research can contribute in improving the performance of Balai Pengelolaan Daerah Aliran Sungai dan Hutan Lindung (BPDASHL) on the process of fixing and tackling critical land in the provinces in Indonesia.
Co-Authors Bagus Supranda Dedy Hartama Dewi, Rafiqa Elvri Rahayu Iin Parlina Ika Purnama Sari Irfan Sudahri Damanik Mira Ariffiani Muhammad Fahrur Rozi Putra Pratama Siregar S Solikhun S Sumarno Sri Rahmayani