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All Journal Faktor Exacta Seminar Nasional Teknologi Informasi Komunikasi dan Industri
Suryarini Widodo
Universitas Gunadarma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics

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ALGORITMA NEURAL NETWORK BACKPROPAGATION UNTUK PREDIKSI HARGA SAHAM PADA TIGA GOLONGAN PERUSAHAAN BERDASARKAN KAPITALISASINYA Nopri Santi; Suryarini Widodo
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.9365

Abstract

Stock is one type of investment where investors can gain profits in the form of capital gains and dividends. Types of shares based on the level of capitalization are divided into 3 types, namely the first layer (blue chips), the second layer, and the third layer. One of the techniques that investors use in order to make a profit is technical analysis, which is using data of past stock prices and volumes based on the assumption that trends can recur following historical data patterns. Based on the assumptions of technical analysis, it is possible to use data mining to predict stock prices. In this study, stock price predictions will be carried out by comparing three types of companies based on their capitalization, for first layer stocks using PT. Bank Central Asia Tbk (BBCA), the second layer using PT. XL Axiata Tbk (EXCL), and third layer using PT Pembangunan Graha Lestari Indah Tbk. The data mining algorithm that will be used is the Neural Network Backpropagation method. The attributes used as predictors are open, high, low, and volume, while the objective attribute is close. This study aims to determine whether daily stock historical data can be used to predict stock prices using the Neural Network Backpropagation method and how to compare the results of predictions between 3 companies with different capitalization levels. The result of RMSE for BBCA by using the most optimal combination of parameters and 3 hidden layer is 123.84. The result of RMSE for EXCL by using the most optimal combination of parameters and hidden layer 2 is 37.36. The result of RMSE for PGLI by using the most optimal combination of parameters and hidden layer 6 is 6.16. So that the backpropagation neural network algorithm is most optimally applied to third layer companies, PT. Pembangunan Graha Lestari Indah Tbk because the RMSE value is the smallest.
Representasi Kode IRMA pada Basis Data Mammografi MIAS Karmilasari Karmilasari; Suryarini Widodo; Lussiana ETP
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2012: SNTIKI 4
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.977 KB)

Abstract

Limitations of mammography database with image coding and the identification of a variety ofcharacteristics, such as pathology, and abnormal breast tissue types, is an issue in the development ofcomputer systems for the diagnosis of breast cancer. IRMA coding system was developed to facilitatecontent-based image retrieval identify (CBIR) as a prototype application in medical diagnostic radiologyimagery. IRMA Code was developed following the network code American College of Radiology (ACR)and data system (BI-RAD). Through IRMA code, obtained standardized code for the type of tissue, thelevel of tumor and lesion description. The results of the code in the form of a character string of no morethan 13 characters (IRMA: YYYY - DDD - AAA - BBB). The code can be extended by introducingcharacters in certain positions code if there is a new modality is introduced. IRMA coding system can beapplied to mammographic Digital Mammogram Image Analysis Society (MIAS). Complete initialinformation from mammography is the basis for the study of medical image breast cancer, while the finalinformation obtained from IRMA coding system can be input for clinicians in decision-making for patientaction.Keywords : Mammography, IRMA coding system, MIAS database
Co-Authors Karmilasari, - Lussiana ETP Nopri Santi