Pub Date : 2022-08-31DOI: 10.33096/ilkom.v14i2.1012.142-149
Suherman Suherman, A. Nursikuwagus, A. Sugiyarta, Indah Komala
{"title":"Classification of Early Childhood Reading with C4.5 Algorithm","authors":"Suherman Suherman, A. Nursikuwagus, A. Sugiyarta, Indah Komala","doi":"10.33096/ilkom.v14i2.1012.142-149","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1012.142-149","url":null,"abstract":"","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44114227","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1219.99-111
Rodiah Rodiah, Eka Patriya, Diana Tri Susetianingtias, Ety Sutanty
One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.
{"title":"Implementation of the Prophet Model in COVID-19 Cases Forecast","authors":"Rodiah Rodiah, Eka Patriya, Diana Tri Susetianingtias, Ety Sutanty","doi":"10.33096/ilkom.v14i2.1219.99-111","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1219.99-111","url":null,"abstract":"One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47954045","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1128.134-141
Warnia Nengsih, Yuli Fitrisia, Mardhiah Fadhli
The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each used classification method. The object that became simulation in this research was the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have high accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%.
{"title":"Comparative Analysis to Determine the Best Accuracy of Classification Methods","authors":"Warnia Nengsih, Yuli Fitrisia, Mardhiah Fadhli","doi":"10.33096/ilkom.v14i2.1128.134-141","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1128.134-141","url":null,"abstract":"The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each used classification method. The object that became simulation in this research was the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have high accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47828261","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}
In order to solve the problems that exist in the economic aspect due to the COVID-19 pandemic in Indonesia, the government has implemented various programs related to economic recovery. One of these programs is cash social assistance (BST). During the implementation of the social assistance program in various regions, it was reported that the recipients of the program were not properly targeted. Based on the results of a survey from one of the leading universities in Indonesia, it is known that many social assistance programs related to the impact of the COVID-19 pandemic are suspected to have not been in accordance with their designation. Based on this, the research was conducted in Bandar Lampung City. The purpose of this study is to conduct an analysis for recommendations for prospective BST recipients, namely people affected by Covid-19. The method used is profile matching by taking samples in the Jagabaya village, Bandar Lampung City. The criteria used include the work of the head of the family, wife's work, home status, number of dependents and ID cards. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City.
{"title":"Analysis of Recommendations for Recipients of Covid-19 Cash Social Assistance Financing the Ministry of Social Affairs","authors":"Erliyan Redy Susanto, Rusliyawati Rusliyawati, A. Wantoro, Citra Andini Purnama, Itce Diasari","doi":"10.33096/ilkom.v14i2.1138.126-133","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1138.126-133","url":null,"abstract":"In order to solve the problems that exist in the economic aspect due to the COVID-19 pandemic in Indonesia, the government has implemented various programs related to economic recovery. One of these programs is cash social assistance (BST). During the implementation of the social assistance program in various regions, it was reported that the recipients of the program were not properly targeted. Based on the results of a survey from one of the leading universities in Indonesia, it is known that many social assistance programs related to the impact of the COVID-19 pandemic are suspected to have not been in accordance with their designation. Based on this, the research was conducted in Bandar Lampung City. The purpose of this study is to conduct an analysis for recommendations for prospective BST recipients, namely people affected by Covid-19. The method used is profile matching by taking samples in the Jagabaya village, Bandar Lampung City. The criteria used include the work of the head of the family, wife's work, home status, number of dependents and ID cards. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47745833","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1226.112-119
Basri Basri, Muhammad Assidiq, H. A. Karim, A. Nuraisyah
This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.
{"title":"Extreme Learning Machine with Feature Extraction Using GLCM for Phosphorus Deficiency Identification of Cocoa Plants","authors":"Basri Basri, Muhammad Assidiq, H. A. Karim, A. Nuraisyah","doi":"10.33096/ilkom.v14i2.1226.112-119","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1226.112-119","url":null,"abstract":"This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492534","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1127.160-168
A. S. Laswi, M. Yusuf, Ulvah Ulvah, Bungawati Bungawati
This study aims to analyze various public opinion on the Covid-19 vaccines that appear on social media pages, especially on Facebook and Twitter via # (hastag). The death rate caused by COVID-19 was so high which reached 144,227 people until 2022. The Indonesian government required vaccines for the community starting from children aged 6 years as an effort to prevent the spread of the Covid-19 virus. Unfortunately, the implementation of complete vaccines in Indonesia has only reached 51.3% of the mandatory vaccine population, which is 140 million out of 339 million people. The non-achievement of the target set by the government causes the need to conduct a sentiment analysis on vaccines in Indonesia through social media. Based on the sample data, from 1000 words obtained from 320 opinions there are positive and negative opinions. This data is then analyzed and processed to find out how many positive and negative responses occurred. The data was then processed into several stages to test the level of truth through training data and test data. The results of the data processing were tested using the Naïve Bayes algorithm which resulted in an accuracy value with a precession of 77.08% taken from 90 samples test data, recall with a percentage of 97.87% based on positive data which was predicted to be true with a positive opinion status from 47 samples of test data and 1 positive data status which is still predicted to be negative. Furthermore, the specific percentage value obtained was 65.30% of the 132 test data that are predicted
{"title":"Analysis of Public Opinion on Covid-19 Vaccine through Social Media Using Naïve Bayes Theory Algorithm","authors":"A. S. Laswi, M. Yusuf, Ulvah Ulvah, Bungawati Bungawati","doi":"10.33096/ilkom.v14i2.1127.160-168","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1127.160-168","url":null,"abstract":"This study aims to analyze various public opinion on the Covid-19 vaccines that appear on social media pages, especially on Facebook and Twitter via # (hastag). The death rate caused by COVID-19 was so high which reached 144,227 people until 2022. The Indonesian government required vaccines for the community starting from children aged 6 years as an effort to prevent the spread of the Covid-19 virus. Unfortunately, the implementation of complete vaccines in Indonesia has only reached 51.3% of the mandatory vaccine population, which is 140 million out of 339 million people. The non-achievement of the target set by the government causes the need to conduct a sentiment analysis on vaccines in Indonesia through social media. Based on the sample data, from 1000 words obtained from 320 opinions there are positive and negative opinions. This data is then analyzed and processed to find out how many positive and negative responses occurred. The data was then processed into several stages to test the level of truth through training data and test data. The results of the data processing were tested using the Naïve Bayes algorithm which resulted in an accuracy value with a precession of 77.08% taken from 90 samples test data, recall with a percentage of 97.87% based on positive data which was predicted to be true with a positive opinion status from 47 samples of test data and 1 positive data status which is still predicted to be negative. Furthermore, the specific percentage value obtained was 65.30% of the 132 test data that are predicted","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492523","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1153.150-159
Siska Anraeni, Erika Riski Melani, H. Herman
This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.
{"title":"Ripeness Identification of Chayote Fruits using HSI and LBP Feature Extraction with KNN Classification","authors":"Siska Anraeni, Erika Riski Melani, H. Herman","doi":"10.33096/ilkom.v14i2.1153.150-159","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1153.150-159","url":null,"abstract":"This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44666682","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 : 2022-08-31DOI: 10.33096/ilkom.v14i2.1114.91-98
L. Wahid, Ahmad R. Pratama
Smartphones are the world's most widely used personal computing devices. PINs and passcodes have long been the most popular authentication methods in smartphones and even in the pre-smartphone era. Due to the inconvenient nature of PINs and passcodes, a new biometric authentication method for smartphones was developed and has been gaining traction in terms of adoption, beginning with flagship devices and progressing to some mid-range devices. This article aims to investigate the factors influencing smartphone owners' acceptance of biometric authentication methods by developing a new model based on the Technology Acceptance Model (TAM). It also validates the data with survey data from 233 Indonesian smartphone owners via an online survey and analyzed it using Structural Equation Modeling (SEM). The results from the SEM analysis show that all nine hypotheses in the proposed model are supported. In other words, all six factors in the proposed model (i.e., attitude toward the use, perceived usefulness, perceived the ease of use, perceived enjoyment, perceived security, and social influence) have significant effects on the behavioral intention of adopting biometric authentication methods among smartphone owners. More specifically, the findings indicate that most Indonesian smartphone users have a favorable attitude toward biometric authentication, which is why they are willing to adopt it. Furthermore, it is discovered that the perceived usefulness of a biometric authentication method on smartphones outweighs its perceived ease of use. It reveals that the user's belief in the intrinsic value of biometric authentication methods in the form of perceived security outweighs both the internal user motivation of perceived enjoyment and the external user motivation of social influence in terms of their acceptance of biometric authentication methods.
{"title":"Factors Influencing Smartphone Owners' Acceptance of Biometric Authentication Methods","authors":"L. Wahid, Ahmad R. Pratama","doi":"10.33096/ilkom.v14i2.1114.91-98","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1114.91-98","url":null,"abstract":"Smartphones are the world's most widely used personal computing devices. PINs and passcodes have long been the most popular authentication methods in smartphones and even in the pre-smartphone era. Due to the inconvenient nature of PINs and passcodes, a new biometric authentication method for smartphones was developed and has been gaining traction in terms of adoption, beginning with flagship devices and progressing to some mid-range devices. This article aims to investigate the factors influencing smartphone owners' acceptance of biometric authentication methods by developing a new model based on the Technology Acceptance Model (TAM). It also validates the data with survey data from 233 Indonesian smartphone owners via an online survey and analyzed it using Structural Equation Modeling (SEM). The results from the SEM analysis show that all nine hypotheses in the proposed model are supported. In other words, all six factors in the proposed model (i.e., attitude toward the use, perceived usefulness, perceived the ease of use, perceived enjoyment, perceived security, and social influence) have significant effects on the behavioral intention of adopting biometric authentication methods among smartphone owners. More specifically, the findings indicate that most Indonesian smartphone users have a favorable attitude toward biometric authentication, which is why they are willing to adopt it. Furthermore, it is discovered that the perceived usefulness of a biometric authentication method on smartphones outweighs its perceived ease of use. It reveals that the user's belief in the intrinsic value of biometric authentication methods in the form of perceived security outweighs both the internal user motivation of perceived enjoyment and the external user motivation of social influence in terms of their acceptance of biometric authentication methods.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41970609","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}
Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.
{"title":"Comparison of Correlated Algorithm Accuracy Naive Bayes Classifier and Naive Bayes Classifier for Classification of heart failure","authors":"Pungkas Subarkah, Wenti Risma Damayanti, Reza Aditya Permana","doi":"10.33096/ilkom.v14i2.1148.120-125","DOIUrl":"https://doi.org/10.33096/ilkom.v14i2.1148.120-125","url":null,"abstract":"Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44192992","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 : 2022-04-30DOI: 10.33096/ilkom.v14i1.910.1-9
Mila Jumarlis, M. Mirfan, Abdul Rachman Manga’
Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.
{"title":"Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor","authors":"Mila Jumarlis, M. Mirfan, Abdul Rachman Manga’","doi":"10.33096/ilkom.v14i1.910.1-9","DOIUrl":"https://doi.org/10.33096/ilkom.v14i1.910.1-9","url":null,"abstract":"Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42589532","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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