Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2744
M. Bachtiar, Iwan Kurnianto Wibowo, Rakasiwi Bangun Hamarsudi
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.
{"title":"Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots","authors":"M. Bachtiar, Iwan Kurnianto Wibowo, Rakasiwi Bangun Hamarsudi","doi":"10.25139/inform.v0i1.2744","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2744","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88576675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2715
Mohamad As’ad, S. Sujito, Sigit Setyowibowo
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.
{"title":"Neural Network Autoregressive For Predicting Daily Gold Price","authors":"Mohamad As’ad, S. Sujito, Sigit Setyowibowo","doi":"10.25139/inform.v0i1.2715","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2715","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77952487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2771
Dewi Salma Salsabila, Rinabi Tanamal
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%.
{"title":"Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application","authors":"Dewi Salma Salsabila, Rinabi Tanamal","doi":"10.25139/inform.v0i1.2771","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2771","url":null,"abstract":"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%.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79715670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2720
Akhmad Fahruzi, Ricky Rhamdany
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.
{"title":"Rancang Bangun Prototype Mesin Pengering Gabah Otomatis Menggunakan Metode PID sebagai Kendali Temperatur","authors":"Akhmad Fahruzi, Ricky Rhamdany","doi":"10.25139/inform.v0i1.2720","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2720","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86638170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2740
Andi Sanjaya, E. Setyati, H. Budianto
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.
{"title":"Model Architecture of CNN for Recognition the Pandava Mask","authors":"Andi Sanjaya, E. Setyati, H. Budianto","doi":"10.25139/inform.v0i1.2740","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2740","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80952608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2743
Y. A. Prabowo, W. Pambudi, I. R. Imaduddin
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.
{"title":"Identification of the Flip Folder Folding Machine Using Artificial Neuro Network Method with NARX (Nonlinear Auto Regressive Exogenous) Structure","authors":"Y. A. Prabowo, W. Pambudi, I. R. Imaduddin","doi":"10.25139/inform.v0i1.2743","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2743","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79041583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2707
Mahmud Suyuti, E. Setyati
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%.
{"title":"Pneumonia Classification of Thorax Images using Convolutional Neural Networks","authors":"Mahmud Suyuti, E. Setyati","doi":"10.25139/inform.v0i1.2707","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2707","url":null,"abstract":"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%.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"9 Suppl 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78454800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.25139/inform.v0i1.2691
Aloysius Matz Teguh Utomo
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.
{"title":"Pemodelan Cluster Loyalitas Customer Menggunakan Algoritma K-Means Dengan Parameter LRIFMQ","authors":"Aloysius Matz Teguh Utomo","doi":"10.25139/inform.v0i1.2691","DOIUrl":"https://doi.org/10.25139/inform.v0i1.2691","url":null,"abstract":"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.","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87388182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-20DOI: 10.25139/INFORM.V5I1.2203
Aiz Ahmad Fa’iz Dliya’ul Izz, M. Sholihin, Masruroh Masruroh
Forecasting is the initial part of a decision-making process. In production activities, forecasting is done to determine the amount of demand for a product. Forecasting process requires a certain method and which method is used depends on the data and information to be predicted and the objectives to be achieved. In this study the method used is Trend Moment. In this study using the multimedia instrument rental data CV. Rysma Entertainment Surabaya type of Projector and LED TV from January 2017 to December 2018. Based on the analysis and testing of the system, this system can predict the rental of multimedia equipment in a particular month. The forecasting results of multimedia device rental type Projector 3000 Lumen will be rented as many as 13 units in January 2019 with an error rate of 21%with a total transaction data of 280
{"title":"Trend Moment Method for predicting Multimedia Equipment Rental Needs","authors":"Aiz Ahmad Fa’iz Dliya’ul Izz, M. Sholihin, Masruroh Masruroh","doi":"10.25139/INFORM.V5I1.2203","DOIUrl":"https://doi.org/10.25139/INFORM.V5I1.2203","url":null,"abstract":"Forecasting is the initial part of a decision-making process. In production activities, forecasting is done to determine the amount of demand for a product. Forecasting process requires a certain method and which method is used depends on the data and information to be predicted and the objectives to be achieved. In this study the method used is Trend Moment. In this study using the multimedia instrument rental data CV. Rysma Entertainment Surabaya type of Projector and LED TV from January 2017 to December 2018. Based on the analysis and testing of the system, this system can predict the rental of multimedia equipment in a particular month. The forecasting results of multimedia device rental type Projector 3000 Lumen will be rented as many as 13 units in January 2019 with an error rate of 21%with a total transaction data of 280","PeriodicalId":52760,"journal":{"name":"Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81720819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-20DOI: 10.25139/INFORM.V5I1.2260
Andy Yuono Putra, Achmad Choiron
Anak usia dini adalah anak yang berada dalam tahap pertumbuhan dan perkembangan yang paling pesat, baik fisik maupun mental. Sehingga benar jika dikatakan bahwa usia dini adalah usia emas, karena anak sangat berpotensi mempelajari banyak hal dengan cepat. Pmbelajaran huruf dan alfabet untuk anak usia dini sebagai langkah awal mereka belajar membaca dengan menggabungkan huruf menjadi kata. Pada penelitian ini penulis membuat game edukasi sebagai media pembelajaran yang interaktif yaitu game edukasi pembelajaran alfabet dan nama buah dengan karakter Jawa, yang didalamnya mengangkat konten pendidikan sekaligus kebudayaan. Game yang dibuat ini harus menarik secara audio visual dengan Perancangan antar muka, karakter, warna, dan musik. Sehingga anak-anak akan lebih semangat dalam belajar dan tidak mudah bosan belajar. Aplikasi game alfabet dan buah dengan karakter Jawa dibangun menggunakan metode perancangan waterfall. Adapun hasil dari penelitian ini berupa aplikasi media pembelajaran dan mendapatkan peningkatan nilai presentase 52,6% dari hasil kuesioner yang diisi oleh pengguna.
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