Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries

Polak-Ribiere updates analysis with binary and linear function in determining coffee exports in Indonesia

https://iopscience.iop.org/article/10.1088/1742-6596/1255/1/012023/pdf

Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth

Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN

Abstrak — Beras merupakan makanan pokok yang paling banyak dikonsumsi oleh masyarakat Indonesia . Tingginya kebutuhan pangan terutama beras memaksa pemerintah dan penduduk untuk menerima tingginya harga eceran beras. Tujuan penulis melakukan penelitian ini untuk memprediksi harga eceran beras terhitung 2017-2020 dengan menggunakan algoritm backpropagation. Dalam penggunaannya algoritma backpropagation ini sangat baik untuk melakukan tugas predi k si dengan tingkat kesalahan yang cukup rendah. Data pada penelitian ini diperoleh dari Badan Pusat Statistik (BPS ) Indonesia . Penelitia n ini menggunakan 5 arsitektur antara lain : 4-25-1 , 4-35-1, 4-40-1, 4-42-1, 4-45-1 dengan learning rate 0,09 . Dari ke 5 Arsitektur ini, arsitektur terbaiknya adalah 4-45-1 dengan tingkat akurasi sebesar 88 % , epoch 12718 iterasi dan waktu 1 menit 14 detik . Kata kunci— Prediksi, Harga, Beras, Indonesia, Backpropagation. Abstract — Rice is the staple food most consumed by the people of Indonesia. The high demand for food, especially rice, forced the government and residents to accept the high retail price of rice. The purpose of the authors to do this research to predict the retail price of rice counted 2017-2020 using backpropagation algorithm. In its use the backpropagation algorithm is excellent for performing prediction tasks with a fairly low error rate. The data in this study were obtained from the Indonesian Central Bureau of Statistics (BPS). This study uses 5 architectures, among others: 4-25-1, 4-35-1, 4-40-1, 4-42-1, 4-45-1. From the 5th Architecture, the best architecture is 4-45-1 with an accuracy of 88%, epoch 12718 iteration and 1 minute 14 seconds . Keywords — Prediction, Price, Rice, Indonesia, Backpropagation .

/pdf/prediksi-harga-eceran-beras-di-pasar-tradisional-di-33-kota-2acdune23n.pdf

Prediksi Harga Eceran Beras Di Pasar Tradisional Di 33 Kota Di Indonesia Menggunakan Algoritma Backpropagation

Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

Classification is a method of data analysis that is used to create models that describe data classes that are considered important. For the classification of the classification process, the data used is THPS Visitor data which consists of 4 classes including Education, Gender, Age and Visit. The classification used as a comparison of results is the Naive Bayes Classifier. By classifying the number of visitors who are most dominant visiting by age category consisting of adults, adolescents and children. This study aims to classify the highest number of values for visitors by age category. This study was reviewed using the Naive Bayes algorithm. The results of this study indicate that the visitor data that is the most dominant visiting by age category is children who have 77% accuracy is the age of children. This accuracy value is the age that most often visits THPS.

Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category

Pada saat ini penempatan ilmu teknologi komputer dalam mengelola informasi sangat diperlukan. Penggunaan sistem informasi pengolahan data berbasis komputer akan mempermudah dalam penyajian data. Sistem Informasi Data Rehabilitasi Narkoba berbasis web, ini dikembangkan dengan menggunakan beberapa software yaitu Adobe Dreamwaver CS6 dan Mysql sebagai Database. Ada pun tabel-tabel yang dipakai dalam pembuatan Sistem Informasi Data Rehabilitasi Narkoba ini adalah tabel regist, tabel pasien, tabel keluarga, tabel admin, tabel konselor, tabel jenis rehab dan tabel rehab. Model yang digunakan dalam pembuatan Sistem Informasi Data Rehabilitasi Narkoba adalah Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), Flowchart. Kesimpulan yang dapat penulis ambil yaitu penggunaan program Sistem Informasi Data Rehabilitasi Narkoba ini dapat memudahkan dalam pengelolaan data-data rehabilitasi narkoba dan pembuatan laporan data rehabilitasi narkoba pada Badan Narkotika Nasional Kota (BNNK) Pematangsiantar. Kata Kunci : Sistem Informasi, Data Rehabilitasi, Adobe Dreamwaver CS6 dan Mysql

/pdf/sistem-informasi-data-rehabilitasi-narkoba-pada-badan-1qb27r101c.pdf

Sistem Informasi Data Rehabilitasi Narkoba Pada Badan Narkotika Nasional Kota (BNNK) Pematangsiantar

Keroncong, Pop, Dangdut, and Hip Pop. Music is basically to entertain and express themselves from feelings of sadness or happiness. Along with the increasing popularity of music, a lot of music lessons were made especially in Pematangsiantar. This makes consumers confused and needs information to decide which music lessons are suitable for their needs. So the author uses a Decision Support System with the Multi-Factor Evaluation Process (MFEP) method to facilitate decision making. MFEP is a method with emphasis on various factors and criteria that perform the calculation of weighting system where the calculation will be valuable for each factor that affects the decision making of data data to be processed. The MFEP method is also called a scaled score that requires a comparative norm to be interpreted qualitatively and this makes the advantages of the MFEP method. The previous criteria were Criteria Place C1 (C1), Music Facilities Criteria (C2), Price Criteria (C3), Criteria Schedule / time guidance (C4), Employee Service Criteria (C5). By using 4 Alternative Place of Music Les existed memematangsiantar include: Legato Art Center, Era Music Siantar, Grace Music Studio, C and C Music Education. Results of recommendation of Decision Support System in Recommending Place Les Music dipematangsiantar using method MFEP (Multifactor Evaluation Process). Which we write with first rank that is Legato Art Center .  It is expected that this research can help the consumer to get the right recommendation in choosing the place of music lesson in accordance with the cost of music lesson and music quality. Keywords : Decision Support System, Multi-Factor Evaluation Process, ranking, Music

https://www.ejurnal.stmik-budidarma.ac.id/index.php/komik/article/download/933/808

Sistem pendukung keputusan dalam merekomendasikan tempat les musik dipematangsiantar menggunakan metode multifactor evaluation process (mfep)

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

/pdf/model-jaringan-syaraf-tiruan-memprediksi-produksi-ekspor-1nd8gy6a2s.pdf

Model jaringan syaraf tiruan memprediksi produksi ekspor batu bara menurut negara tujuan utama dalam mendorong laju pertumbuhan ekonomi

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