Pub Date : 2022-12-19DOI: 10.33096/ilkom.v14i3.1316.329-338
Hendri Ramdan, Moh. Arief Soeleman, Purwanto Purwanto, Bahtiar Imran, R. A. Pramunendar
As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
{"title":"Semantic segmentation of pendet dance images using multires U-Net architecture","authors":"Hendri Ramdan, Moh. Arief Soeleman, Purwanto Purwanto, Bahtiar Imran, R. A. Pramunendar","doi":"10.33096/ilkom.v14i3.1316.329-338","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1316.329-338","url":null,"abstract":"As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47956544","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1505.348-354
H. Jayadianti, Wilis Kaswidjanti, Agung Tri Utomo, S. Saifullah, F. Dwiyanto, Rafał Dreżewski
Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
{"title":"Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN","authors":"H. Jayadianti, Wilis Kaswidjanti, Agung Tri Utomo, S. Saifullah, F. Dwiyanto, Rafał Dreżewski","doi":"10.33096/ilkom.v14i3.1505.348-354","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1505.348-354","url":null,"abstract":"Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46815254","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1432.303-313
Anang Kukuh Adisusilo, E. Wahyuningtyas, N. Saurina, Radivoje Radi
The primary goal of Serious Games is not only for fun but also for lesson. In learning the first stage of soil tillage which using the mouldboard plow, a proper understanding is needed so that the soil tillage process will follow the needs of plant growth. The use of serious games as a study instrument for soil tillage is under the concept of digital game-based learning (DGBL). The problem of players when playing serious games is less motivated to play because the serious game system and scenario are less challenging. That challenges accelerate the shape of knowledge and experience when playing the games (user experience). By referring to the Learning Mechanics Gaming Mechanics (LM-GM) model, which is based on multiplayer in serious games, hopefully the learning process of land management using the mouldboard plow can be optimized. This process can increase learning motivation and elevate the user experience. This research results a design concept of a learning mechanism and a game mechanism for a serious multiplayer game of soil tillage with a mouldboard plow. There are three types of learning mechanisms in conceptual and concrete components, also six types of game mechanisms that can be used as a reference for the formation of multiplayer serious games and the increase player motivation.
{"title":"Multiplayer mechanism design for soil tillage serious game","authors":"Anang Kukuh Adisusilo, E. Wahyuningtyas, N. Saurina, Radivoje Radi","doi":"10.33096/ilkom.v14i3.1432.303-313","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1432.303-313","url":null,"abstract":"The primary goal of Serious Games is not only for fun but also for lesson. In learning the first stage of soil tillage which using the mouldboard plow, a proper understanding is needed so that the soil tillage process will follow the needs of plant growth. The use of serious games as a study instrument for soil tillage is under the concept of digital game-based learning (DGBL). The problem of players when playing serious games is less motivated to play because the serious game system and scenario are less challenging. That challenges accelerate the shape of knowledge and experience when playing the games (user experience). By referring to the Learning Mechanics Gaming Mechanics (LM-GM) model, which is based on multiplayer in serious games, hopefully the learning process of land management using the mouldboard plow can be optimized. This process can increase learning motivation and elevate the user experience. This research results a design concept of a learning mechanism and a game mechanism for a serious multiplayer game of soil tillage with a mouldboard plow. There are three types of learning mechanisms in conceptual and concrete components, also six types of game mechanisms that can be used as a reference for the formation of multiplayer serious games and the increase player motivation.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45064066","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1221.229-236
Syarifah Fitrah Ramadhani, Pujianti Wahyuningsih, Abdul Jalil, Syarifah Suryana
This study aims to design a digital kWh-meter prototype to top-up the electricity pulse by automatically using relay modules based on Short Message Service (SMS) and Arduino Uno. The utilization of 12 relay modules to substitute the keypad input function in the digital kWh meter is our basic idea in this study. The method we used to replace the keypad input function with the relay module is based on the integration between the circuit path in the keypad board and the relay module as an electric switch that can activate when the relay gets a trigger from the Arduino Uno. In this study, when the user wants to charge the electric pulse, the user will send the voucher number to the GSM SIM900A module via SMS, then it will be processed to the Arduino Uno. Then Arduino Uno will trigger the relay to be activated so that it can automatically fill the voucher number to the digital kWh-meter. This study result is the success of relay modules can substitute the function of keypad input to fill the voucher pulse number to the digital kWh-meter through SMS with the successful voucher number filling up to 98%. The usefulness of the relay module to change the keypad input function on the digital kWh meter is our original idea for this study.
{"title":"Design of digital kWh-Meter to top-up the electric pulse by automatically using Relay Module Based on SMS and Arduino Uno","authors":"Syarifah Fitrah Ramadhani, Pujianti Wahyuningsih, Abdul Jalil, Syarifah Suryana","doi":"10.33096/ilkom.v14i3.1221.229-236","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1221.229-236","url":null,"abstract":"This study aims to design a digital kWh-meter prototype to top-up the electricity pulse by automatically using relay modules based on Short Message Service (SMS) and Arduino Uno. The utilization of 12 relay modules to substitute the keypad input function in the digital kWh meter is our basic idea in this study. The method we used to replace the keypad input function with the relay module is based on the integration between the circuit path in the keypad board and the relay module as an electric switch that can activate when the relay gets a trigger from the Arduino Uno. In this study, when the user wants to charge the electric pulse, the user will send the voucher number to the GSM SIM900A module via SMS, then it will be processed to the Arduino Uno. Then Arduino Uno will trigger the relay to be activated so that it can automatically fill the voucher number to the digital kWh-meter. This study result is the success of relay modules can substitute the function of keypad input to fill the voucher pulse number to the digital kWh-meter through SMS with the successful voucher number filling up to 98%. The usefulness of the relay module to change the keypad input function on the digital kWh meter is our original idea for this study.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45713558","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1280.245-254
Randa Gustiawan, Ulung Pribadi
This study aimed to prove the researcher's hypothesis regarding users’ factor analysis satisfaction of the Mobile JKN application in the Covid-19 era in Sungai Penuh City using the PIECES framework. The measurement variables of the PIECES framework were performance, information, economy, control, efficiency, and service. In this study, researchers used quantitative descriptive methods with data sources from questionnaires via google form with 101 respondents, and data processing was carried out using SEM-pls. The results of this study indicated the value of R square was 0.732. It can be concluded that the interpretation of the users’ satisfaction level of the application was 73.2%, which R-square identifies in the Strong/Good category. Several PIECES variables that has a significant effect on people's satisfaction with the JKN mobile application were efficiency and performance variables with P values of 0.004 and 0.033 while variables that did not have significant effect were control, economy, information and services.
{"title":"Factor analysis satisfaction levels of users toward the JKN mobile application in the COVID-19 Era using the PIECES framework","authors":"Randa Gustiawan, Ulung Pribadi","doi":"10.33096/ilkom.v14i3.1280.245-254","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1280.245-254","url":null,"abstract":"This study aimed to prove the researcher's hypothesis regarding users’ factor analysis satisfaction of the Mobile JKN application in the Covid-19 era in Sungai Penuh City using the PIECES framework. The measurement variables of the PIECES framework were performance, information, economy, control, efficiency, and service. In this study, researchers used quantitative descriptive methods with data sources from questionnaires via google form with 101 respondents, and data processing was carried out using SEM-pls. The results of this study indicated the value of R square was 0.732. It can be concluded that the interpretation of the users’ satisfaction level of the application was 73.2%, which R-square identifies in the Strong/Good category. Several PIECES variables that has a significant effect on people's satisfaction with the JKN mobile application were efficiency and performance variables with P values of 0.004 and 0.033 while variables that did not have significant effect were control, economy, information and services.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49589763","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1317.209-217
Muh Nasirudin Karim, R. A. Pramunendar, M. Soeleman, Purwanto Purwanto, Bahtiar Imran
This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
{"title":"Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)","authors":"Muh Nasirudin Karim, R. A. Pramunendar, M. Soeleman, Purwanto Purwanto, Bahtiar Imran","doi":"10.33096/ilkom.v14i3.1317.209-217","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1317.209-217","url":null,"abstract":"This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43850503","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1116.203-208
Teguh Adriyanto, Risky Aswi Ramadhani, R. Helilintar, Aidina Ristyawan
Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.
{"title":"Classification of Dog and Cat Images using the CNN Method","authors":"Teguh Adriyanto, Risky Aswi Ramadhani, R. Helilintar, Aidina Ristyawan","doi":"10.33096/ilkom.v14i3.1116.203-208","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1116.203-208","url":null,"abstract":"Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44943009","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1377.339-347
Muhammad Ma’ruf, Adam Prayogo Kuncoro, Pungkas Subarkah, Faridatun Nida
Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.
{"title":"Sentiment analysis of customer satisfaction levels on smartphone products using Ensemble Learning","authors":"Muhammad Ma’ruf, Adam Prayogo Kuncoro, Pungkas Subarkah, Faridatun Nida","doi":"10.33096/ilkom.v14i3.1377.339-347","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1377.339-347","url":null,"abstract":"Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42395892","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1156.255-263
Ummi Syafiqoh, A. Yudhana, S. Sunardi
The representation of the fuzzy set membership curve consisting of trapezoidal, triangular, and linear shapes, has an important role in the fuzzy logic system. The selection of the curve's shapes determines the useable membership function and affects the fuzzy output value. Previous studies generally used curves that had been employed in predecessors or other studies that did not explain the reason for choosing a fuzzy member curve. This condition became problem because there was not a guide in selecting the appropriate membership function model for the parameters used in the fuzzy process so that most researchers only use membership functions that are commonly used in previous studies or in the same case as their research. The purpose of this study was to determine the effect of selecting trapezoidal and triangular curves on the performance of Tsukamoto's fuzzy logic for determining the rice-fields suitability status. The research methodology comprised 3 main stages. The first stage was data collecting, to collect soil pH values, soil moisture, and air temperature in rice fields. The second stage was the implementation of the Tsukamoto fuzzy. At this stage, two membership function curves were used. The third stage was a comparative analysis of Tsukamoto's fuzzy's output of trapezoidal and triangular curves. The results obtained indicate that there is no significant performance difference between the two different membership functions. The results of the research with the trapezoidal membership function have a better accuracy rate of 93% while the triangular membership function has an accuracy rate of 90%.
{"title":"Comparative analysis of Fuzzy Tsukamoto's membership functions for determining irrigated rice field feasibility status","authors":"Ummi Syafiqoh, A. Yudhana, S. Sunardi","doi":"10.33096/ilkom.v14i3.1156.255-263","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1156.255-263","url":null,"abstract":"The representation of the fuzzy set membership curve consisting of trapezoidal, triangular, and linear shapes, has an important role in the fuzzy logic system. The selection of the curve's shapes determines the useable membership function and affects the fuzzy output value. Previous studies generally used curves that had been employed in predecessors or other studies that did not explain the reason for choosing a fuzzy member curve. This condition became problem because there was not a guide in selecting the appropriate membership function model for the parameters used in the fuzzy process so that most researchers only use membership functions that are commonly used in previous studies or in the same case as their research. The purpose of this study was to determine the effect of selecting trapezoidal and triangular curves on the performance of Tsukamoto's fuzzy logic for determining the rice-fields suitability status. The research methodology comprised 3 main stages. The first stage was data collecting, to collect soil pH values, soil moisture, and air temperature in rice fields. The second stage was the implementation of the Tsukamoto fuzzy. At this stage, two membership function curves were used. The third stage was a comparative analysis of Tsukamoto's fuzzy's output of trapezoidal and triangular curves. The results obtained indicate that there is no significant performance difference between the two different membership functions. The results of the research with the trapezoidal membership function have a better accuracy rate of 93% while the triangular membership function has an accuracy rate of 90%.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43698066","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 : 2022-12-19DOI: 10.33096/ilkom.v14i3.1149.323-328
Ruhmi Sulaehani, M. H. Botutihe
.
{"title":"Predicting the success of the government’s program of lomaya (Regional PKH) in reducing poverty","authors":"Ruhmi Sulaehani, M. H. Botutihe","doi":"10.33096/ilkom.v14i3.1149.323-328","DOIUrl":"https://doi.org/10.33096/ilkom.v14i3.1149.323-328","url":null,"abstract":".","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43212023","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}
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