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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) International Journal of Artificial Intelligence Research Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JURNAL ILMIAH INFORMATIKA Jurnal Teknoinfo KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Bumigora Information Technology (BITe) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Solikhun Solikhun
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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RETRACTED : Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP) Method Solikhun Solikhun; Agus Perdana Windarto; Amri Amri
International Journal of Artificial Intelligence Research Vol 1, No 1 (2017): June 2017
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (124.218 KB) | DOI: 10.29099/ijair.v1i1.1

Abstract

Following a rigorous, carefully concerns and considered review of the article published in International Journal of Artificial Intelligence Research to article entitled “Decision support system in Predicting the Best teacher with Multi-Attribute Decision Making Weighted Product (MADMWP) Method” Vol 1, No 1, pp. 47-53, June 2017, DOI: https://doi.org/10.29099/ijair.v1i1.1This paper has been found to be in violation of the International Journal of Artificial Intelligence Research Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in JURASIK(Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol. 1, No. 1, pp. 56-63, 2016,  http://ejurnal.tunasbangsa.ac.id/index.php/jurasik/article/view/9The document and its content have been removed from International Journal of Artificial Intelligence Research, and reasonable effort should be made to remove all references to this article.
MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI PRODUKSI EKSPOR BATU BARA MENURUT NEGARA TUJUAN UTAMA DALAM MENDORONG LAJU PERTUMBUHAN EKONOMI Rafiqa Dewi; Sundari Retno Andani; Solikhun Solikhun
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 6, No 2 (2019)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v6i2.227

Abstract

Prediction is a process for estimating how many needs in the future. This study aims to predict the amount of coal exports according to the country the main goal in driving the pace of economic growth. The role of the agricultural sector in the national economy is very important and strategic. Coal is one of the fossil fuels. The general definition is a sedimentary rock that can burn, formed from organic deposits, mainly the remains of plants and formed through the process of pembatubaraan. The main elements consist of carbon, hydrogen and oxygen. Domestic production makes the government continue to implement coal export policies according to the state's main goal in driving the pace of economic growth in Indonesia. By using Artificial Neural Networks and backpropagation algorithms, architectural models will be sought to predict the amount of coal exports according to the state's main goal in driving the pace of economic growth to determine steps to assist the government in exporting coal based on the main destination country. This study uses 12 input variables with 1 target. Using 4 architectural models to test the data to be used for prediction, namely models 12-8-1, 12-16-1, 12-32-1 and 12-64-1. The best architectural model results obtained are 12-16-1 architectural models with 100% truth accuracy, the number of epoch 2602 and MSE is 0.0032. By using this model, predictions of coal exports are in accordance with the main destination countries with 100% accuracy.Keywords: Coal, Exports, predictions, backpropagation, Artificial Neural Networks Prediksi adalah proses untuk memperkirakan berapa banyak kebutuhan di masa depan. Studi ini bertujuan untuk memprediksi jumlah ekspor batubara menurut negara tujuan utama dalam mendorong laju pertumbuhan ekonomi. Peran sektor pertanian dalam ekonomi nasional sangat penting dan strategis. Batubara adalah salah satu bahan bakar fosil. Definisi umum adalah batuan sedimen yang dapat terbakar, terbentuk dari endapan organik, terutama sisa-sisa tanaman dan terbentuk melalui proses pembatubaraan. Unsur utama terdiri dari karbon, hidrogen, dan oksigen. Produksi dalam negeri membuat pemerintah terus menerapkan kebijakan ekspor batubara sesuai dengan tujuan utama negara dalam mendorong laju pertumbuhan ekonomi di Indonesia. Dengan menggunakan Jaringan Saraf Tiruan dan algoritma backpropagation, model arsitektur akan dicari untuk memprediksi jumlah ekspor batubara sesuai dengan tujuan utama negara dalam mendorong laju pertumbuhan ekonomi untuk menentukan langkah-langkah untuk membantu pemerintah dalam mengekspor batubara berdasarkan negara tujuan utama . Penelitian ini menggunakan 12 variabel input dengan 1 target. Menggunakan 4 model arsitektur untuk menguji data yang akan digunakan untuk prediksi, yaitu model 12-8-1, 12-16-1, 12-32-1 dan 12-64-1. Hasil model arsitektur terbaik yang diperoleh adalah model arsitektur 12-16-1 dengan akurasi 100%, jumlah zaman 2602 dan MSE adalah 0,0032. Dengan menggunakan model ini, prediksi ekspor batubara sesuai dengan negara tujuan utama dengan akurasi 100%.Kata kunci: Batubara, Ekspor, prediksi, backpropagation, Jaringan Syaraf Tiruan
Model Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Produksi Telur Ayam Petelur Berdasarkan Provinsi Di Indonesia Pipit Mutiara Putri; Devi Monika; Lulu Apriliani; Solikhun Solikhun
Jurnal Teknoinfo Vol 13, No 2 (2019): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v13i2.273

Abstract

Improving human resources cannot be achieved without adequate nutrition. To educate, strengthen and improve the achievements of Indonesian people, much depends on fulfilling good nutrition, especially animal protein such as meat, milk and eggs (Anonymous, 1990).Eggs are one product that can meet some of the nutritional needs of the community. These livestock products also have the potential to be developed optimally, because in addition to the price that is relatively cheap compared to other animal proteins, the business is also relatively easy and even though it is cultivated in small-scale businesses it can increase income and expand employment opportunities (Anonymous, 1994). The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is data on the number of egg production of laying hens based on the provinces in 2010 to 2017. The algorithms used in this study are Artificial Neural Networks with the Backpropogation method. The input variables used are data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5) data for 2015 (X6) and data in 2016 (X7) with a training and testing architecture model of 4 architectures namely 7-4-1, 7-8-1, 7-16-1dan 7-32-1. Target data is taken from 2017 data. The output produced is the best pattern of ANN architecture. The best architectural model is 32-1 with MSE 0.0082336 and an accuracy rate of 96.88%. From this model, the prediction of egg production of laying hens is based on the province of each province in Indonesia.
Model Jaringan Syaraf Tiruan Dalam Memprediksi Produksi Susu Segar Di Indonesia Berdasarkan Propinsi Rika Asma Dewi; Rusmansyah Rusmansyah; Syahrul Ramadan; Sundari Retno Andani; Solikhun Solikhun
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 6, No 1 (2019): Teknovasi April 2019
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v6i1.281

Abstract

The problem of drinking fresh milk in Indonesia looks minimal and rarely from children to adults who drink fresh milk, both milk and milk in canned milk. In an effort to realize the provisions as stipulated in the Minister of Health Regulation No. 75 of 2013 informing one's nutritional needs based on age and sex, the Indonesian Government has increased nutritional needs in Indonesia. This research contributes to the government to be able to predict the Production of Fresh Milk in Indonesia by Province. The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is predictive data of fresh milk from 2010 to 2017. The algorithm used in this study is Artificial Neural Networks with the Backpropogation method. The input variables used were data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5), data for 2015 (X6) and 2016 data (X7) with 4 training and testing architectural models, namely 7-2-1, 7-4-1, 7-16-1 and 7-32-1. Target data is taken from 2017 data. The resulting output is the best pattern of Artificial Neural Network architecture. The best architectural model is 7-4-1 with EPOCH 1042, MSE 0.0095979 and 100% accuracy rate. From this model, the prediction of Fresh Milk Production is based on provinces in Indonesia.
ANALISIS ALGORITMA AES DALAM MENGAMANKAN DATA PADA KANTOR WALIKOTA PEMATANGSIANTAR eko hartato; Indra Gunawan; Iin Parlina; Solikhun Solikhun; Anjar Wanto
JURNAL ILMIAH INFORMATIKA Vol 8 No 01 (2020): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.308 KB) | DOI: 10.33884/jif.v8i01.1799

Abstract

Data is information that is kept very confidential because it contains important information about the company or agency. Computers are currently the main component in the company that is able to store data, speed up work, improve the quality and quantity of services, simplify the transaction process, and others. But in terms of computer security still has several loopholes that allow a person or group to easily retrieve data or information on the computer. To avoid theft and manipulation of data, it is necessary to implement a security system. Cryptography is the study of how to change information from normal conditions / forms (can be understood) into a form that cannot be understood. One method that can be used to secure messages / information is the Advanced Encryption Standard (AES). The application of the AES cryptographic algorithm in securing data at the Pematangsiantar Mayor's Office shows that this algorithm can generate encryption that cannot be understood by humans and produces the exact decryption with the initial plaintext input.
PENGELOMPOKKAN JUMLAH DESA/KELURAHAN YANG MEMILIKI SARANA KESEHATAN MENURUT PROVINSI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTER Dwi Retno Sekar Mayangsari; Solikhun Solikhun; Irawan Irawan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1615

Abstract

Health problems that exist in the community, especially in developing countries such as Indonesia are influenced by two factors, namely physical aspects and non-physical aspects. Physical aspects such as health facilities and treatment of diseases, while the second is non-physical aspects that involve health problems. The construction of health facilities is an effort to fulfill one of the basic rights of the people carried out by the government to provide health facilities that will be used in helping the community to be healthy. So the purpose of this study is to group villages / kelurahan that have health facilities quickly and effectively. Describe the number of villages / kelurahan that have health facilities using the K-Means method, in order to find out whether the constraints faced by the government in grouping villages / kelurahan that have inadequate health facilities based on the provinces in Indonesia, require a long time to group them. It is expected that using this method can produce clustering which is proven to be accurate in the case of the number of villages / kelurahan that have inadequate health facilities based on the provinces in Indonesia.Keywords: Health Facilities, Datamining, K-Means
SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PEMILIHAN SMARTPHONE TERBAIK MENGGUNAKAN METODE TOPSIS Anggi Eryzha; Solikhun Solikhun; Eka Irawan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1668

Abstract

Smartphones are a primary need for all upper class and lower class people. As these needs are many smartphone vendors that offer different prices, features, systems and technologies at competitive prices. Many people want specifications that are capable but limited in financial terms. This causes smartphone users not to be able to make the right choice according to their needs because the frequent selection of smartphones is based on prestige and consumer consumptive behavior. The TOPSIS method is a multicriteria method used to identify solutions from alternative sets based on simultaneous minimization of the ideal point distance and maximizing the distance from the lowest point. The expected results can be input to potential smartphone buyers in accordance with their finances and qualified specifications.Keywords: Decision Support System, Topsis Method, Smartphone.
PENERAPAN METODE PROFILE MATCHING DALAM MEREKOMENDASIKAN BIBIT KELAPA SAWIT Ika Septi Mahdia; Solikhun Solikhun; M. Fauzan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1674

Abstract

Oil palm plants are plantation crops that are cultivated and require intensive care, oil palm seeds are a major factor in the success of oil palm plantation cultivation. Quality palm oil seedlings will provide satisfactory yields, oil palm seedlings should be obtained with superior seed selection and good maintenance, in nurseries carried out by following the existing stages, oil palm plantations are long-term business and maintenance of oil palm seeds must be be considered and managed with incentives to ensure optimal production and business results in the future. This study aims to determine quality oil palm seeds in PTPN IV Bah Jambi Plantation. In the nursery is carried out by following the existing stage, this research uses Decision Support System (SPK) Profile Matching method. This method can be used to solve semi-structured problems by calculating consistency using the Profile Matching method, if consistent values are generated consistently can be used as a reference for ranking quality oil palm seeds in PTPN IV Bahjambi Plantation. For the results of the research it can be produced that it is easier to recommend oil palm seedlings and be an input to the plantation in recommending the best oil palm seeds.Keywords: Palm oil seeds, Decision Support System, Profile Matching
Komparasi K-Means Clustering dan K-Medoids Clustering dalam Mengelompokkan Produksi Susu Segar di Indonesia Berdasarkan Nilai DBI Mochamad Wahyudi; Solikhun Solikhun; Lise Pujiastuti
Jurnal Bumigora Information Technology (BITe) Vol 4 No 2 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i2.2104

Abstract

The purpose of this study was to find the optimal grouping from the comparison of the two methods in grouping fresh milk production using the K-Means algorithm and the K-Medoids algorithm. To find optimal grouping, the authors compare the grouping results by looking for the smallest DBI (Davies Bouldin Index) value. The data used in this study is data on fresh milk production in Indonesia which is sourced from the Indonesian Central Bureau of Statistics for 2018-2020. Evaluation of the DBI value for the K-Means Clustering algorithm is 0.094 and the DBI value for K-Medoids Clustering is 0.072. Therefore, grouping fresh milk production using the K-Medoids algorithm has better results than using the K-Means Clustering algorithm, because the K-Medoids Clustering algorithm has a smaller DBI value of 0.072. The benefit of this study is to obtain optimal clusters in classifying fresh milk in Indonesia to provide information to the government in increasing fresh production in Indonesia in the future.
The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province Astri Rismauli Simbolon; Solikhun Solikhun
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 1 (2023): March 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i1.2426

Abstract

Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the same as the probability of the population attending school per total population for the current age. Length of School is also a benchmark for evaluating government programs in improving Human Resources that excel in the competition of technological advances. The purpose of this study is to apply the Conjugate Gradient Algorithm with the Best Performance for Predicting School Life Expectancy in Indonesia. Research data on the Expectation of Schooling in Indonesia consists of 10 Provinces obtained from the Central Statistics Agency from 2016 to 2021. This study uses 5 architectural models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1 and 2-30-1. Of the five architectural models used, the best architectural model is 2-3-1 with an MSE of 0.000000002 in two seconds. Based on this best architectural model, it will be used to predict the Expectation of Old Schools in Indonesia for the next five years, namely from 2022 to 2026.
Co-Authors Achmad Noerkhaerin Putra Agus Perdana Windarto Amri Amri Anggi Eryzha Astri Rismauli Simbolon Devi Monika Dewi, Rafiqa Dwi Retno Sekar Mayangsari Eka Irawan eko hartato Iin Parlina Ika Septi Mahdia Irawan Irawan Lise Pujiastuti Lulu Apriliani M. Fauzan Mochamad Wahyudi Pipit Mutiara Putri Rika Asma Dewi Rusmansyah Rusmansyah Sundari Retno Andani Syahrul Ramadan Wanto, Anjar