The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.
{"title":"Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying","authors":"S. L. Wungo, F. Aziz","doi":"10.22146/ijccs.73179","DOIUrl":"https://doi.org/10.22146/ijccs.73179","url":null,"abstract":"The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43003942","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}
Yuliana Astiti Fonga Wea Tae, Rambu Yetti Kalaway, Pingky Alfa Ray Leo Lede
Intelligence cannot solely be measured in terms of intellectual intelligence. There are various types of intelligence in children, which cause teachers and parents require time to determine the type of intelligence of children. Quick and easy decision-making can be achieved using a decision support system. One method that can be adopted in the decision-making process of a decision support system is the weighted product method. This study aims to measure the level of accuracy of the weighted product method in determining the type of multiple intelligence of children. The decision support system determines the type of intelligence of children in early childhood (ages 4-6 years) using Garner's [1] eight types of multiple intelligences as decision-making criteria. The data was collected using interviews and questionnaires to the teachers of Mutiara State Kindergarten. The study found that a decision support system using the weighted product method can determine the type of children's multiple intelligences with an accuracy rate of 96%. Based on the result of analysis and calculation using the weighted product method from test questionnaire data of 55 children, compared to the results of identification by the teacher, it was found that the compatibility of 53 children.
{"title":"Application of the Weighted Product Method in a Decision Support System to Determine Children's Multiple Intelligence","authors":"Yuliana Astiti Fonga Wea Tae, Rambu Yetti Kalaway, Pingky Alfa Ray Leo Lede","doi":"10.22146/ijccs.70810","DOIUrl":"https://doi.org/10.22146/ijccs.70810","url":null,"abstract":"Intelligence cannot solely be measured in terms of intellectual intelligence. There are various types of intelligence in children, which cause teachers and parents require time to determine the type of intelligence of children. Quick and easy decision-making can be achieved using a decision support system. One method that can be adopted in the decision-making process of a decision support system is the weighted product method. This study aims to measure the level of accuracy of the weighted product method in determining the type of multiple intelligence of children. The decision support system determines the type of intelligence of children in early childhood (ages 4-6 years) using Garner's [1] eight types of multiple intelligences as decision-making criteria. The data was collected using interviews and questionnaires to the teachers of Mutiara State Kindergarten. The study found that a decision support system using the weighted product method can determine the type of children's multiple intelligences with an accuracy rate of 96%. Based on the result of analysis and calculation using the weighted product method from test questionnaire data of 55 children, compared to the results of identification by the teacher, it was found that the compatibility of 53 children.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48239371","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}
Astrid Noviana Paradhita, A. Sari, Agus Sihabuddin
Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5.
{"title":"Ontology-based Complementary Breastfeeding Search Model","authors":"Astrid Noviana Paradhita, A. Sari, Agus Sihabuddin","doi":"10.22146/ijccs.71963","DOIUrl":"https://doi.org/10.22146/ijccs.71963","url":null,"abstract":"Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46245163","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}
Maniah Maniah, B. Soewito, F. Gaol, E. Abdurachman
The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration.
{"title":"Risk Assessment for Logistics Applications in Cloud Migration","authors":"Maniah Maniah, B. Soewito, F. Gaol, E. Abdurachman","doi":"10.22146/ijccs.74567","DOIUrl":"https://doi.org/10.22146/ijccs.74567","url":null,"abstract":"The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45258845","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}
Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems.
{"title":"Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network","authors":"D. Fudholi, Muhammad Auzan, Novia Arum Sari","doi":"10.22146/ijccs.75697","DOIUrl":"https://doi.org/10.22146/ijccs.75697","url":null,"abstract":" Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46220686","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}
Rahmadathul Wisdawati, Rani Nooraeni, Bagaskoro Cahyo Laksono, Bintang Izzatul Fatah
Under-five poverty is a condition where the needs of toodlers are not met, resulting in undernourished children and unable to reach their full potential in the social sphere. East Nusa Tenggara is a province that still faces the biggest nutritional problems in Indonesia in 2019. This study aims to explain the variables that form toodlers multidimensional poverty in East Nusa Tenggara (ENT), form the Multidimensional Under-Five Poverty Index (MUPI), and compare the results of index formed with the results of bicluster. Data source used in this study is SUSENAS KOR 2019. The analytical method used is a factor and bicluster analysis. The results shows that 11 multidimensional poverty indicators form three dimensions, namely the Adequate Food and Beverage Facility Factor, Health Protection Factor, and Housing and Nutrition Factor, which is used to form the index. Based on regional grouping, there are five areas with low MUPI scores, fourteen areas with medium MUPI scores, and three areas with high MUPI scores. However, biclustering results show that there are two areas with low poverty category, thirteen regions with moderate poverty category, and seven regions with high poverty category. The result of the comparison of MUPI grouping with the biclustering method obtained different results based on the composition of the resulting area.
五岁以下儿童的贫困是指儿童的需求得不到满足，导致儿童营养不良，无法充分发挥其在社会领域的潜力。2019年，东努沙登加拉省仍然面临着印尼最大的营养问题。本研究旨在解释构成东努沙登加拉（ENT）儿童多维贫困的变量，形成多维五岁以下贫困指数（MUPI），并将指数形成的结果与双聚类结果进行比较。本研究使用的数据来源为SUSENAS KOR 2019。所使用的分析方法是因子和双聚类分析。结果表明，11个多维贫困指标构成了三个维度，即充足的食品和饮料设施系数、健康保护系数和住房和营养系数，用于构成该指数。根据地区分组，有5个地区的MUPI得分较低，14个地区的MUPI得分中等，3个地区的MUPI得分较高。然而，双聚类结果显示，有两个地区属于低贫困类别，13个地区属于中等贫困类别，7个地区属于高贫困类别。MUPI分组与双聚类方法的比较结果根据所得区域的组成获得了不同的结果。
{"title":"Implementation of Factor Analysis and BiClustering in Classifying Multidimensional Under-Five Poverty in East Nusa Tenggara","authors":"Rahmadathul Wisdawati, Rani Nooraeni, Bagaskoro Cahyo Laksono, Bintang Izzatul Fatah","doi":"10.22146/ijccs.70433","DOIUrl":"https://doi.org/10.22146/ijccs.70433","url":null,"abstract":"Under-five poverty is a condition where the needs of toodlers are not met, resulting in undernourished children and unable to reach their full potential in the social sphere. East Nusa Tenggara is a province that still faces the biggest nutritional problems in Indonesia in 2019. This study aims to explain the variables that form toodlers multidimensional poverty in East Nusa Tenggara (ENT), form the Multidimensional Under-Five Poverty Index (MUPI), and compare the results of index formed with the results of bicluster. Data source used in this study is SUSENAS KOR 2019. The analytical method used is a factor and bicluster analysis. The results shows that 11 multidimensional poverty indicators form three dimensions, namely the Adequate Food and Beverage Facility Factor, Health Protection Factor, and Housing and Nutrition Factor, which is used to form the index. Based on regional grouping, there are five areas with low MUPI scores, fourteen areas with medium MUPI scores, and three areas with high MUPI scores. However, biclustering results show that there are two areas with low poverty category, thirteen regions with moderate poverty category, and seven regions with high poverty category. The result of the comparison of MUPI grouping with the biclustering method obtained different results based on the composition of the resulting area.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41706502","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}
T. Suratno, Edi Saputra, Z. Abidin, Daniel Arsa, Norman Syarief
Comfort room temperature is determined by indoor air quality, such as temperature and CO2 gas. This study aims to determine the comfort of a class by reviewing the number of students, CO2 gas, and temperature in an Arduino-based classroom using an automatic IoT system with a Completely Randomized Design (CRD) method. Research proves that there is a significant effect between the number of students on the concentration of CO2, but it does not directly affect the air temperature in the room. The lecture hall is still relatively safe but not ideal and requires a temperature reduction of -7oC.
{"title":"Internet of Things (IoT) Arduino-Based Classroom Monitoring Utilizes Temperature Sensors And CO2 Sensors","authors":"T. Suratno, Edi Saputra, Z. Abidin, Daniel Arsa, Norman Syarief","doi":"10.22146/ijccs.76241","DOIUrl":"https://doi.org/10.22146/ijccs.76241","url":null,"abstract":"Comfort room temperature is determined by indoor air quality, such as temperature and CO2 gas. This study aims to determine the comfort of a class by reviewing the number of students, CO2 gas, and temperature in an Arduino-based classroom using an automatic IoT system with a Completely Randomized Design (CRD) method. Research proves that there is a significant effect between the number of students on the concentration of CO2, but it does not directly affect the air temperature in the room. The lecture hall is still relatively safe but not ideal and requires a temperature reduction of -7oC.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44974206","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}
Muhammad Fadhlil Hadi, De-Ron Liang, T. K. Priyambodo, Azhari Sn
Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.
{"title":"Financial Distress Prediction with Stacking Ensemble Learning","authors":"Muhammad Fadhlil Hadi, De-Ron Liang, T. K. Priyambodo, Azhari Sn","doi":"10.22146/ijccs.76575","DOIUrl":"https://doi.org/10.22146/ijccs.76575","url":null,"abstract":"Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43247096","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}
Development of Bali tourist destinations using the concept of local wisdom Tri Hita Karana (THK). THK is a concept that contains the philosophy of community life in Bali which means three causes of welfare. This concept is needed to realize tourism, culture and nature. In determining a decision to develop an object in a tourist destination using the THK concept, knowledge from several stakeholders is needed. To combine decisions from several stakeholders is needed. GDSS is a computer-based system that can support the Bali Provincial Government Tourism Office and several components involved in THK to take a decision in developing an object in a tourist destination. To determine the decision of each individual used the AHP model. The AHP model is a model that can solve complex multi-criteria problems into a hierarchy. This AHP model will produce alternative individual decisions from the results of parameter weight processing for each individual. Based on the final result of the GDSS, the development of Bali tourism destinations based on THK is in the form of ranking of the six parameters used (Promotion of tourist destinations, Improvement of facilities, Human Resources, Synergy, Environmental preservation, Setting of holy places). The alternative that has the highest value is used as a reference in developing a THK-based tourist destination,
{"title":"GDSS Development of Bali Tourism Destinations With AHP and Borda Algorithms Based on Tri Hita Karana","authors":"P. Sugiartawan, I. Sudipa, I. A. Wiguna","doi":"10.22146/ijccs.76605","DOIUrl":"https://doi.org/10.22146/ijccs.76605","url":null,"abstract":"Development of Bali tourist destinations using the concept of local wisdom Tri Hita Karana (THK). THK is a concept that contains the philosophy of community life in Bali which means three causes of welfare. This concept is needed to realize tourism, culture and nature. In determining a decision to develop an object in a tourist destination using the THK concept, knowledge from several stakeholders is needed. To combine decisions from several stakeholders is needed. GDSS is a computer-based system that can support the Bali Provincial Government Tourism Office and several components involved in THK to take a decision in developing an object in a tourist destination. To determine the decision of each individual used the AHP model. The AHP model is a model that can solve complex multi-criteria problems into a hierarchy. This AHP model will produce alternative individual decisions from the results of parameter weight processing for each individual. Based on the final result of the GDSS, the development of Bali tourism destinations based on THK is in the form of ranking of the six parameters used (Promotion of tourist destinations, Improvement of facilities, Human Resources, Synergy, Environmental preservation, Setting of holy places). The alternative that has the highest value is used as a reference in developing a THK-based tourist destination,","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49256943","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}
Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.
{"title":"Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space","authors":"Bob subhan Riza, Jufriadif Na’am, S. Sumijan","doi":"10.22146/ijccs.74531","DOIUrl":"https://doi.org/10.22146/ijccs.74531","url":null,"abstract":"Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43638235","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：