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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI
S Solikhun
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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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.
Prediksi Ekspor Minyak Bumi Mentah di Indonesia dengan Algoritma Resilient Backpropagation S Solikhun; Isti Rahayu
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 1A (2022): Edisi Desember (Spesial Issue)
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i1A.157

Abstract

Indonesia is the largest producer of crude oil by country and main destination. The need for world crude oil currently tends to increase significantly, this is also the basis for import activities in Indonesia in line with the increase, of course all of this is due to the increase in the amount of oil consumption from within the country, this makes crude oil exports in Indonesia by country the main goal is to experience instability and even tend to decrease. Therefore a prediction is needed to find out the amount of crude oil exports in Indonesia in the future and later this prediction is useful for the government to increase the amount of crude oil exports in Indonesia. The data used in this study is data on the amount of crude oil exports in Indonesia taken from the Central Bureau of Statistics from 2014 to 2020. The algorithm used to make predictions is a Resilient Backpropagation neural network. There are five architectural modeling used in this prediction, namely, 5-9-1 has an accuracy rate of 43%, 5-10-1 has an accuracy rate of 71%, 5-11-1=56%, 5-12-1=86% , and 5-13-1 at 71%. The best architecture of the five models is 5-12-1 where the architecture has an accuracy of up to 86% and an MSE of 0.000995. So this architectural model is good enough to be used to predict the amount of crude oil exports in Indonesia.
A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023) Agus Perdana Windarto; Anjar Wanto; S Solikhun; Ronal Watrianthos
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5274

Abstract

The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study.
Co-Authors Agus Perdana Windarto Bagus Supranda Isti Rahayu Putra Pratama Siregar Ronal Watrianthos Wanto, Anjar Zulia Almaida Siregar