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Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi

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Published by Universitas Dr. Soetomo Surabaya

ISSN : 25023470 EISSN : 25810367 DOI : 10.25139

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi is One of the journals published by the Informatics Engineering Department Dr. Soetomo University, was established in January 2016. Inform a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in January and July. Inform with p-ISSN:2502-3470 and e-ISSN:2581-0367 has been accredited by the Ministry of Research and Technology of the National Research and Innovation Agency of the Republic of Indonesia Number 85/M/KPT/2020 dated April 1, 2020. Accreditation is valid for 5 years Vol.3 No.2 2018 to Vol.8 No.1 2023. Focus and Scope that is Scientific research related to information and communication technology fields, including Software Engineering, Information Systems, Human-Computer Interaction, Architecture and Hardware, Computer Vision, Pattern Recognition, Computer Application and Artificial intelligence, Game Technology, and Computer Graphics, but not limited to informatics scope.

Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer

Articles 16 Documents

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Search results for , issue "Vol. 5 No. 2 (2020)" : 16 Documents clear

Pneumonia Classification of Thorax Images using Convolutional Neural Networks 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN). The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

Model Architecture of CNN for Recognition the Pandava Mask 1; 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.

Neural Network Autoregressive For Predicting Daily Gold Price 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Gold is a precious metal that functions as a gem and also an investment. Gold investment is the reason for many people because it is practical, not easily damaged, easy cashed, not taxable, and other purposes. Based on this, many people choose gold as an investment. The problem for people who will invest in gold is related to uncertain gold price predictions so that the accuracy of forecasting methods are needed. The purpose of this paper is to forecast accurately daily gold prices using the Neural Network Autoregressive (NNAR) method. Training Data to find out the value of accuracy in the NNAR method uses secondary data obtained from Yahoo Finance in the form of daily gold prices. Test results on the NNAR method produce a better and more accurate level using the NNAR (25,13) model with a MAPE value of 0.370707, a MASE of 0.5851083, and an RMSE of 6.939331. The conclusion of the results of this paper is the daily price of gold is influenced by the daily price of gold a day ago to 24 periods ago with the NNAR (25,13) model.

Identification of the Flip Folder Folding Machine Using Artificial Neuro Network Method with NARX (Nonlinear Auto Regressive Exogenous) Structure 1; 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Folding machine is a tool that is needed in the small and medium scale laundry industry that has a goal for the efficiency of production time. The flip folder is the main component of this tool, which functions to fold the clothes by moving to form a certain deflection angle where the movement process is controlled by the controller. The system modeling process is the first step to study the characteristics of the system. In a dynamic system, the form of linear modeling is approved difficult to obtain a model that represents the actual physical model. Selecting the structure of the NARX (Nonlinear Autoregressive eXogenous) model was chosen to obtain the dynamic nature of the system. An estimation method to obtain parameter values from the system used Artificial Neural Networks (ANN), which is a trading scheme to be able to predict the output of a system that uses input data and output. Based on the offline assessment process using measurement data obtained by the NARX ANN model on the variation of the number of layers in 30 with a value of MSE 0,38641.

Rancang Bangun Prototype Mesin Pengering Gabah Otomatis Menggunakan Metode PID sebagai Kendali Temperatur 1; 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

The value of rice grain content after harvest is quite high, around 20-23% in the dry season, and around 24-27% in the wet season. It was drying grain after harvest was processed by the conventional or manual method that carried out the grain drying in the sun. This method has several disadvantages, such as the dependence on the weather, requires a large area, and 54 hours for drying so that the grain becomes dry with a moisture content of 14.12%. From this problem, the researchers made a grain drying machine that could work automatically. The drying machine is made to solve the issues of conventional grain drying so that the machine was completed with a K type thermocouple temperature sensor and grain moisture content. Whereas the heating media uses a fire that is fueled with LPG gas, and then the heat from the fire has flowed into the furnace or grain drying chamber. The heating arrangement was made by regulating of flowing LPG gas to the nozzle through the opened and closed variable valve where the valve shaft was connected to the DC motor shaft. The application of the PID method also used in this drying machine, which has a purpose while controlling the drying temperature to match the Set Value (SV) or the desired temperature at 38oC. The grain moisture content value is considered to have dried up when the grain moisture content value is 14%. The PID method that is implanted into the ATmega16 microcontroller will give a signal to the motor driver circuit to regulate the direction of rotation of the DC motor connected to the opened and closed valve variable. PID method testing was done by trial error and has produced a steady-state error of 5.2% at S0056=38oC with constant values Kp=2, Ki=2, and Kd=10. Whereas for drying grain testing on harvested is done by selecting Ciherang grain with a moisture content of 20% and a weight of 3 kg. The grain drying process takes 30 minutes so that the value of the water content becomes 14% with a drying temperature of 38oC, so the grain drying rate on this machine is 0.17% per minute.

Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots 1; 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

The ERSOW robot is a soccer robot developed by Politeknik Elektronika Negeri Surabaya, Indonesia. One important ability of a soccer robot is the ability to find the goal in the field. Goal Post is often used as a sign by soccer robots in a match. The mark is a reference robot in the field to be used in determining the strategy. By knowing the location of the goal in a field, the soccer robot can decide to maneuver in the match to get the right goal kick. There are various methods of detecting goals. One of them is to detect goal posts using vision. In this study, the radial search lines method is used to detect the goalposts as markers. Image input is generated from an omnidirectional camera. The goal area is detected on the front side of the goal area. With experiments from 10 robot position points in the field, only 1 position point cannot detect the goal. The robot cannot detect the goal because what is seen from the camera is the side of the goal, so the front side of the goal area is not visible.

Pemodelan Cluster Loyalitas Customer Menggunakan Algoritma K-Means Dengan Parameter LRIFMQ 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Loyal customers are one of the factors that determine the development of a business. Therefore, businesses need a strategy to keep customers loyal, even making customers who were previously less loyal to become more loyal. The strategy used must be right on target according to customer segmentation. The purpose of this paper is to model a cluster of customer loyalty to help businesses in making the right decisions of marketing strategy. Segmentation is done using the k-means algorithm with LRIFMQ (length, recency, interval, frequency, monetary, quantity) as parameters, and the CLV (customer lifetime value) of each cluster is calculated. Data obtained from PT. XYZ (a company engaged in food processing) for one year (1 January 2019 - 31 December 2019), with 337.739 transactions, and 26.683 customers. AHP (analytical hierarchy process) method is used for LRIFMQ weighting because this method has a consistency index calculation. The silhouette coefficient is used to calculate the cluster quality and determine the optimal number of clusters. The best results are obtained with the silhouette coefficient value of 0,632904 with the number of clusters 6.

Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Shown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.

Pemodelan Cluster Loyalitas Customer Menggunakan Algoritma K-Means Dengan Parameter LRIFMQ Utomo, Aloysius Matz Teguh
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Pneumonia Classification of Thorax Images using Convolutional Neural Networks Suyuti, Mahmud; Setyati, Endang
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN). Â The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing.Â Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study usedÂ Convolutional Neural NetworkÂ (CNN) method with architecture ResNet-50.Â In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network.Â The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Achmad Choiron

Contact Email journal.inform@unitomo.ac.id

Phone +6281332765765

Journal Mail Official journal.inform@unitomo.ac.id

Editorial Address Jl. Semolowaru no 84, Surabaya, 60118

Location Kota surabaya, Jawa timur INDONESIA

Abstract

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Abstract

Contact Name
Achmad Choiron

Contact Email
journal.inform@unitomo.ac.id

Phone
+6281332765765

Journal Mail Official
journal.inform@unitomo.ac.id

Editorial Address
Jl. Semolowaru no 84, Surabaya, 60118

Location
Kota surabaya,

Jawa timur

INDONESIA