The variety of cases of dental health that occur in elementary school children makes it difficult to quickly identify the factors that most influence dental health. To find out the dental health factors, a system that is able to provide a visual representation of the results of statistical analysis is needed based on the factors that affect oral health in elementary school children. Health information system analysis is mostly done through statistical data analysis. A total of 54 students were included in this study to determine the characteristics, factors that affect dental health, and the implementation of the application in the form of a prototype. The results showed that there was a significant relationship between children's perceptions and their knowledge of dental health and children's perceptions of dental health behavior. Furthermore, guidance and examples from parents play an important role in improving children's behavior regarding dental health. The t-test shows that male students get more examples and attention from their parents regarding dental health. Meanwhile, female students are more concerned with practicing matters related to dental health. The mobile application provides information on children's dental health statistics for schools and health centers. In addition, the application can also provide an overview of dental health in children.
{"title":"Sistem Informasi Berbasis Analisis Persepsi, Pengetahuan, dan Praktik Kesehatan Gigi Pada Anak-Anak Sekolah Dasar","authors":"Aditya Ferdiana Arief, Rohmatul Fajriyah, Punik Mumpuni Wijayanti","doi":"10.47701/infokes.v11i2.1293","DOIUrl":"https://doi.org/10.47701/infokes.v11i2.1293","url":null,"abstract":"The variety of cases of dental health that occur in elementary school children makes it difficult to quickly identify the factors that most influence dental health. To find out the dental health factors, a system that is able to provide a visual representation of the results of statistical analysis is needed based on the factors that affect oral health in elementary school children. Health information system analysis is mostly done through statistical data analysis. A total of 54 students were included in this study to determine the characteristics, factors that affect dental health, and the implementation of the application in the form of a prototype. The results showed that there was a significant relationship between children's perceptions and their knowledge of dental health and children's perceptions of dental health behavior. Furthermore, guidance and examples from parents play an important role in improving children's behavior regarding dental health. The t-test shows that male students get more examples and attention from their parents regarding dental health. Meanwhile, female students are more concerned with practicing matters related to dental health. The mobile application provides information on children's dental health statistics for schools and health centers. In addition, the application can also provide an overview of dental health in children.","PeriodicalId":436974,"journal":{"name":"Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920787","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 : 2021-09-29DOI: 10.47701/infokes.v11i2.1294
Wahyu Wijaya Widiyanto, Fitria Rohmatun W, Inka Sasti, Salsa Bila Karin
The workload in one unit is basically a balance between the quantity and quality of work required of employees with the amount of personnel in the unit. The research method used by distributing questionnaires using google from to the Asy Syifa' Sambi General Hospital officers, while the purpose of this study is to determine the proportion of the workload of health workers using WISN. Based on the calculation of the workforce needs using the WISN method in the correspondence and evaluation section of the Asy Syifa' Sambi General Hospital KLPCM in 2019 from the basic quantity per year divided by the standard workload per year, it was found that the workforce needs in the section receiving requests for filling out medical resumes and or Doctor's Certificate for insurance of 0.5 officers, assembling inpatient DRM and other assessments, registering DRM RI 1.3 officers, receiving requests for Visum et Repertum 0.3 officers, Letter of replacement for Birth Certificate and Legalization of death certificates 0.3 officers , prepare a request for an autopsy of death from the health center / other agencies 0.5 officers. So the total need for KLPCM correspondence and evaluation workers is 3 officers. In fact, in the correspondence and evaluation of KLPCM, there is 1 officer. So it is necessary to add 2 officers, so that they can help ease the workload of other officers so that the work is completed quickly and does not pile up again.
{"title":"Proporsi Perencanaan Kebutuhan SDM Di Unit Kerja Rekam Medis Rumah Sakit Umum Asy Syifa' Sambi","authors":"Wahyu Wijaya Widiyanto, Fitria Rohmatun W, Inka Sasti, Salsa Bila Karin","doi":"10.47701/infokes.v11i2.1294","DOIUrl":"https://doi.org/10.47701/infokes.v11i2.1294","url":null,"abstract":"The workload in one unit is basically a balance between the quantity and quality of work required of employees with the amount of personnel in the unit. The research method used by distributing questionnaires using google from to the Asy Syifa' Sambi General Hospital officers, while the purpose of this study is to determine the proportion of the workload of health workers using WISN. Based on the calculation of the workforce needs using the WISN method in the correspondence and evaluation section of the Asy Syifa' Sambi General Hospital KLPCM in 2019 from the basic quantity per year divided by the standard workload per year, it was found that the workforce needs in the section receiving requests for filling out medical resumes and or Doctor's Certificate for insurance of 0.5 officers, assembling inpatient DRM and other assessments, registering DRM RI 1.3 officers, receiving requests for Visum et Repertum 0.3 officers, Letter of replacement for Birth Certificate and Legalization of death certificates 0.3 officers , prepare a request for an autopsy of death from the health center / other agencies 0.5 officers. So the total need for KLPCM correspondence and evaluation workers is 3 officers. In fact, in the correspondence and evaluation of KLPCM, there is 1 officer. So it is necessary to add 2 officers, so that they can help ease the workload of other officers so that the work is completed quickly and does not pile up again.","PeriodicalId":436974,"journal":{"name":"Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123093325","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 : 2021-09-29DOI: 10.47701/infokes.v11i2.1303
Rheni Aprilia Ningrum, Agus Priyanto, Ummi Athiyah
Anemia is caused by a low hemoglobin condition in the human body. Low hemoglobin conditions can cause various symptoms, including fatigue, weakness, dizziness and others. The impact on anemia can reduce concentration, physical endurance and get sick easily. So it is necessary to detect early to diagnose anemia based on the symptoms experienced with maximum accuracy. Users only need to enter the value of symptoms experienced, namely the value of hb, bleeding and weakness, the system will calculate the symptom values using the Tsukamoto fuzzy algorithm. In calculations using the Tsukamoto fuzzy algorithm using the Python programming language, there are 4 stages, namely fuzzification, rule formation, inference engine and defuzzification. At the fuzzification stage, the input symptom value becomes a fuzzy value (0-1), then at the rule formation stage there are 18 rules of 3 symptoms and 3 diagnosis results. After obtaining a rule, it is followed by an inference engine that looks for the α-predicate value in each rule using the min function. After getting the α-predicate value, defuzzification is carried out to get the crisp value or the output value. With the multiple confusion matrix method, the accuracy of the resulting data from the Tsukamoto fuzzy algorithm and prediction data is 85%. This can be used by the community to easily detect anemia early through the website.
{"title":"Implementasi Algoritma Fuzzy Tsukamoto Untuk Diagnosis Penyakit Anemia (Studi Data: Rekam Medis Pasien Ibu RSIA Bunda Arif Purwokerto)","authors":"Rheni Aprilia Ningrum, Agus Priyanto, Ummi Athiyah","doi":"10.47701/infokes.v11i2.1303","DOIUrl":"https://doi.org/10.47701/infokes.v11i2.1303","url":null,"abstract":"Anemia is caused by a low hemoglobin condition in the human body. Low hemoglobin conditions can cause various symptoms, including fatigue, weakness, dizziness and others. The impact on anemia can reduce concentration, physical endurance and get sick easily. So it is necessary to detect early to diagnose anemia based on the symptoms experienced with maximum accuracy. Users only need to enter the value of symptoms experienced, namely the value of hb, bleeding and weakness, the system will calculate the symptom values using the Tsukamoto fuzzy algorithm. In calculations using the Tsukamoto fuzzy algorithm using the Python programming language, there are 4 stages, namely fuzzification, rule formation, inference engine and defuzzification. At the fuzzification stage, the input symptom value becomes a fuzzy value (0-1), then at the rule formation stage there are 18 rules of 3 symptoms and 3 diagnosis results. After obtaining a rule, it is followed by an inference engine that looks for the α-predicate value in each rule using the min function. After getting the α-predicate value, defuzzification is carried out to get the crisp value or the output value. With the multiple confusion matrix method, the accuracy of the resulting data from the Tsukamoto fuzzy algorithm and prediction data is 85%. This can be used by the community to easily detect anemia early through the website.","PeriodicalId":436974,"journal":{"name":"Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133551896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-28DOI: 10.47701/INFOKES.V10I2.1031
Ana Yuliana, Tri Wahyuni
Background: Maternal unpreparedness in facing childbirth is one of the factors causing the high MMR,which is 306 per 100,000 live births in 2019. 90% of maternal mortality occurred around delivery and 95%of the causes of death were obstetric complications that were often not predicted beforehand. At the time ofdelivery, if obstetric complications are found and the mother does not understand the preparations neededfor delivery, the mother does not get appropriate and timely services, resulting in three delays in referrals.This study aims to describe the knowledge of primigravida pregnant women about preparation for childbirthin Wonorejo Village, Mojolaban District, Sukoharjo Regency.Methods: This type of research is quantitative descriptive, the research location is in Wonorejo Village,Mojolaban District, Sukoharjo Regency, the total sample is 30 people, with the sampling technique usingsaturated sampling. The data collection tool used was a questionnaire. The data analysis used was univariateanalysis.Results: The knowledge of primigravida pregnant women about preparation for delivery was in the moderatecategory as many as 21 respondents (70%).Conclusion: Most of the primigravida pregnant women had sufficient knowledge, namely 21 respondents(70%).
{"title":"Pengetahuan Ibu Hamil Primigravida Tentang Persiapan Persalinan Di Desa Wonorejo Kecamatan Mojolaban Kabupaten Sukoharjo","authors":"Ana Yuliana, Tri Wahyuni","doi":"10.47701/INFOKES.V10I2.1031","DOIUrl":"https://doi.org/10.47701/INFOKES.V10I2.1031","url":null,"abstract":"Background: Maternal unpreparedness in facing childbirth is one of the factors causing the high MMR,which is 306 per 100,000 live births in 2019. 90% of maternal mortality occurred around delivery and 95%of the causes of death were obstetric complications that were often not predicted beforehand. At the time ofdelivery, if obstetric complications are found and the mother does not understand the preparations neededfor delivery, the mother does not get appropriate and timely services, resulting in three delays in referrals.This study aims to describe the knowledge of primigravida pregnant women about preparation for childbirthin Wonorejo Village, Mojolaban District, Sukoharjo Regency.Methods: This type of research is quantitative descriptive, the research location is in Wonorejo Village,Mojolaban District, Sukoharjo Regency, the total sample is 30 people, with the sampling technique usingsaturated sampling. The data collection tool used was a questionnaire. The data analysis used was univariateanalysis.Results: The knowledge of primigravida pregnant women about preparation for delivery was in the moderatecategory as many as 21 respondents (70%).Conclusion: Most of the primigravida pregnant women had sufficient knowledge, namely 21 respondents(70%).","PeriodicalId":436974,"journal":{"name":"Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124688500","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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