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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Digital Zone: Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Informasi dan Pendidikan Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Computer Scine and Information Technology Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat
Yuhandri Yuhandri, Yuhandri
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids dan Backpropagation pada kasus Pandemi Covid-19 di Indonesia Windarto, Agus Perdana; Na`am, Jufriadif; Yuhandri, Yuhandri; Wanto, Anjar; Mesran, Mesran
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2505

Abstract

The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.
Peramalan Penjualan Pada Toko Retail Menggunakan Algoritma Backpropagation Neural Network Musli Yanto; Eka Praja Wiyata Mandala; Dewi Eka Putri; Yuhandri Yuhandri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 3 (2018): Juli 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i3.811

Abstract

Retail is one or more activities that add value to the product to the consumer either for family needs or for personal use. Retail can sell products depending on current market needs. The goods we enjoy today are not apart from retail services, retail helps producers / distributors and consumers so that every need will be fulfilled. In this problem the author tries to do retail store research in the city of Padang. This research aims to help retail stores to forecast procurement of goods. Artificial Neural Network Backpropagation can make the forecasting process for procurement of goods for the next period of time on each item on the retail and will ultimately be useful for retail store managers. The forecasting process begins with determining the variables that will be required in the network pattern, then the pattern of established network will be continued on the network training process by using backpropagation algorithm. After doing the network training process the researchers will do a comparison with some pattern of network that has been formed. The last process undertaken in this research is the process of determining the best network pattern of the average value of errors obtained from each training network pattern. In the final result of the forecasting process, the results of the calculation have a total error of = 3.57%. Judging from the forecasting process that will be done not only used to predict the procurement of goods but also can predict sales figures in retail stores. In principle, this research can help to determine the procurement of goods in the sales process that will minimize the losses that occur in every sales activity.
Analisis Penurunan Gradien dengan Kombinasi Fungsi Aktivasi pada Algoritma JST untuk Pencarian Akurasi Terbaik Anjar Wanto; Jufriadif Na`am; Yuhandri Yuhandri; Agus Perdana Windarto; Mesran Mesran
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2509

Abstract

There are many training function methods for gradient descent (gradient descent) and activation functions (transfer functions) that can be used in the ANN algorithm, especially the backpropagation algorithm. Therefore the aim of this paper is to analyze the best gradient descent that can be used as a reference for use in the ANN algorithm, especially the backpropagation algorithm in data prediction, classification and pattern management problems. The gradient descent methods to be analyzed include; Gradient descent backpropagation (traingd), Gradient descent with momentum backpropagation (traingdm), Gradient descent with adaptive learning rate backpropagation (traingda), and Gradient descent with momentum and adaptive learning rate backpropagation (traingdx). The training function will be combined with the activation function (transfer function) of bipolar sigmoid (tansig), linear transfer (purelin) and binary sigmoid (logsig). The sample data used for the analysis process is the time-series data for the Human Development Index in Indonesia, which is obtained from the Central Bureau of Statistics (BPS). Architectural models used for gradient descent analysis include: 6-10-15-1, 6-15-20-1, 6-20-25-1 and 6-25-30-1. Based on the analysis results, the best training function is traingda with an architectural model of 6-15-20-1 which produces an accuracy rate of 91% and MSE testing is 0.000731529 (smaller than other methods)
Co-Authors Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Andrean, Fajri Ilhami Anita Sindar Arif Budiman Aulia, Allans Prima Budayawan, Khairi Chandra, Mrs Montesna Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Efori Buulolo Eka Praja Wiyata Mandala Fauzan, Yuniko Febri Hadi Feri Irawan Firzada, Fahmi Fuad El Khair Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartomi, Zupri Henra Hendrick, H Heri Nurdiyanto Iftitah, Hasanatul Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak Liga Mayola M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nopi Purnomo Nuning Kurniasih Petti Indrayati Sijabat Pohan, Yosua Ade Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putra, Yendi Putri, Stefani Rahmad Dian Rakhmad Kuswandhie Rio Andika Malik Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Sarjon Defit Sarjon Defit Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Tessa Y M Sihite Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yeni, Nasma Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif