Pub Date : 2023-05-26DOI: 10.14421/jiska.2023.8.2.125-139
Mohammad Faisal Fajar Fadilah, Ajib Hanani, Totok Chamidy
Piles of waste increase in line with population growth and consumption patterns. The concept of bioconversion using black soldier fly larvae can solve the problem of organic waste management. From these problems, an application of Internet of Things technology is needed. The system implemented aims to allow the system to find out how much accuracy, precision, and recall are in making decisions on media quality values using the Naive Bayes method. The main feature of this Naive Bayes Classifier is the very strong assumption of the independence of each condition or event. From the research results, the system has been successfully built according to the research design, as well as the goals that have been fulfilled in completing the development of the smart maggot. Several sensors used in this study were tested so that sensor performance could be determined by finding the average error value. Three parameters are measured; namely, the temperature obtained an average error of 1.6%, air humidity obtained an average error of 2.03%, and soil moisture obtained an average error of 2.7%. By measuring using Python, the Confusion Matrix is obtained so that the test results from the calculation of the Naive Bayes method can find the data in the form of accuracy, precision, and recall. Accuracy percentage results obtained 92%, precision percentage average results obtained 93%, and recall percentage average results obtained 92%. The conclusion shows the results of the system's accuracy obtained have worked well.
{"title":"Sistem Pengukuran Kualitas Media pada Larva BSF (Black Soldier Fly) Berbasis Internet of Things Menggunakan Metode Naive Bayes","authors":"Mohammad Faisal Fajar Fadilah, Ajib Hanani, Totok Chamidy","doi":"10.14421/jiska.2023.8.2.125-139","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.2.125-139","url":null,"abstract":"Piles of waste increase in line with population growth and consumption patterns. The concept of bioconversion using black soldier fly larvae can solve the problem of organic waste management. From these problems, an application of Internet of Things technology is needed. The system implemented aims to allow the system to find out how much accuracy, precision, and recall are in making decisions on media quality values using the Naive Bayes method. The main feature of this Naive Bayes Classifier is the very strong assumption of the independence of each condition or event. From the research results, the system has been successfully built according to the research design, as well as the goals that have been fulfilled in completing the development of the smart maggot. Several sensors used in this study were tested so that sensor performance could be determined by finding the average error value. Three parameters are measured; namely, the temperature obtained an average error of 1.6%, air humidity obtained an average error of 2.03%, and soil moisture obtained an average error of 2.7%. By measuring using Python, the Confusion Matrix is obtained so that the test results from the calculation of the Naive Bayes method can find the data in the form of accuracy, precision, and recall. Accuracy percentage results obtained 92%, precision percentage average results obtained 93%, and recall percentage average results obtained 92%. The conclusion shows the results of the system's accuracy obtained have worked well.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42935021","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 : 2023-05-26DOI: 10.14421/jiska.2023.8.2.90-101
Supiyandi Supiyandi, Almanna Hussein, I. Gunawan, William Lutfi Rahman Harjo
Broken home is a term that defines a situation in a family where most people handle no harmony, happiness, or peace. The impact of a broken home on a depressed family on children who can experience mental, emotional, and behavioral changes that are uncontrolled and undirected. Therefore, a classification is needed to categorize a child in a family as a broken home or not. The classification process will apply the Naïve Bayes Classifier classification method by taking into account the factors that refer to the statement that a child is called a broken home. With this classification, it is hoped that it can help know what and how a broken home child can be called a broken home and with this classification, it is expected that parents can minimize broken homes in children in the future by paying attention to the determining factors.
{"title":"Analisis Klasifikasi Broken Home pada Anak Menggunakan Metode Naïve Bayes Classifier","authors":"Supiyandi Supiyandi, Almanna Hussein, I. Gunawan, William Lutfi Rahman Harjo","doi":"10.14421/jiska.2023.8.2.90-101","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.2.90-101","url":null,"abstract":"Broken home is a term that defines a situation in a family where most people handle no harmony, happiness, or peace. The impact of a broken home on a depressed family on children who can experience mental, emotional, and behavioral changes that are uncontrolled and undirected. Therefore, a classification is needed to categorize a child in a family as a broken home or not. The classification process will apply the Naïve Bayes Classifier classification method by taking into account the factors that refer to the statement that a child is called a broken home. With this classification, it is hoped that it can help know what and how a broken home child can be called a broken home and with this classification, it is expected that parents can minimize broken homes in children in the future by paying attention to the determining factors.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46975382","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}
Government builds public facilities to support the needs of the community. The use of these public facilities needs to be re-evaluated, and one way to do it is through community response. Google Maps is one platform that receives the most responses from the community about location. Google Maps Reviews allow us to see how the public reacts to a location. Naïve Bayes method is used for classification in this study because it is one of the simple methods in machine learning that can be easily applied to several experiments conducted by the author. In the classification process, reviews produce many features that will be calculated based on their class. More features generated, more features processed too in the system. Chi-Square feature selection will be used to reduce features that have low dependence on the system. In this study, performance values will be calculated based on the experimental use of feature ratios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. The results show that the use of 10% Chi-Square features produces the best performance, with an accuracy rate of 86.94%, precision of 80.42%, recall of 80.42%, and f-measure of 80.42%.
{"title":"Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square","authors":"Adhitya Prayoga Permana, Totok Chamidy, Cahyo Crysdian","doi":"10.14421/jiska.2023.8.2.112-124","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.2.112-124","url":null,"abstract":"Government builds public facilities to support the needs of the community. The use of these public facilities needs to be re-evaluated, and one way to do it is through community response. Google Maps is one platform that receives the most responses from the community about location. Google Maps Reviews allow us to see how the public reacts to a location. Naïve Bayes method is used for classification in this study because it is one of the simple methods in machine learning that can be easily applied to several experiments conducted by the author. In the classification process, reviews produce many features that will be calculated based on their class. More features generated, more features processed too in the system. Chi-Square feature selection will be used to reduce features that have low dependence on the system. In this study, performance values will be calculated based on the experimental use of feature ratios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. The results show that the use of 10% Chi-Square features produces the best performance, with an accuracy rate of 86.94%, precision of 80.42%, recall of 80.42%, and f-measure of 80.42%.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42350299","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 : 2023-05-26DOI: 10.14421/jiska.2023.8.2.154-163
N. Kadek, Winda Patrianingsih, I. Kadek, Arya Sugianta
Student potential cannot only be measured based on the result of academic scores, and many things influence student academic determination. The purpose of this research is to prove that students' potential is influenced by many things, such as character, academic activity, socioeconomic status, and distance of residence. By using the naïve Bayes method and testing with the confusion matrix, it will give results for this research. The data is from V-grade students at SD Negeri 5 Singakerta, with 120 students assisted by the homeroom teacher. Based on the results of the tests that have been carried out using a data sample of 10 students and 1 data using the Naïve Bayes, it is obtained that students have academic potential, and the results with the confusion matrix are accuracy of 75%, precision of 81%, and recall of 89%. In this case, it can be concluded that the academic potential of students can not only be measured based on the results of the final grade, but many other factors have an effect, the application of the Naïve Bayes in students' academic potential is appropriate to use.
学生的潜力不能仅仅根据学业成绩来衡量，很多事情都会影响学生的学业决心。本研究的目的是为了证明学生的潜能受到许多因素的影响，如性格、学术活动、社会经济地位和居住距离。通过naïve贝叶斯方法和混淆矩阵的检验，给出本研究的结果。数据来自SD Negeri 5 Singakerta的v年级学生，其中120名学生由班主任协助。基于使用Naïve贝叶斯方法对10名学生和1个数据样本进行的测试结果，得出学生具有学术潜力，混淆矩阵的结果准确率为75%，精密度为81%，召回率为89%。在这种情况下，可以得出结论，学生的学术潜力不仅可以根据最终成绩的结果来衡量，而且许多其他因素都有影响，Naïve贝叶斯在学生学术潜力中的应用是合适的。
{"title":"Penerapan Naïve Bayes pada Potensi Akademik Siswa SD Negeri 5 Singakerta","authors":"N. Kadek, Winda Patrianingsih, I. Kadek, Arya Sugianta","doi":"10.14421/jiska.2023.8.2.154-163","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.2.154-163","url":null,"abstract":"Student potential cannot only be measured based on the result of academic scores, and many things influence student academic determination. The purpose of this research is to prove that students' potential is influenced by many things, such as character, academic activity, socioeconomic status, and distance of residence. By using the naïve Bayes method and testing with the confusion matrix, it will give results for this research. The data is from V-grade students at SD Negeri 5 Singakerta, with 120 students assisted by the homeroom teacher. Based on the results of the tests that have been carried out using a data sample of 10 students and 1 data using the Naïve Bayes, it is obtained that students have academic potential, and the results with the confusion matrix are accuracy of 75%, precision of 81%, and recall of 89%. In this case, it can be concluded that the academic potential of students can not only be measured based on the results of the final grade, but many other factors have an effect, the application of the Naïve Bayes in students' academic potential is appropriate to use.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42037607","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.50-65
Noerul Hanin, David Jordy Dhandio, Della Zaria
The existence of boarding houses in public spaces is highly expected by the community, especially migrants such as students who need a temporary house in oversea areas. In Pontianak, especially around Tanjungpura University, there are many boarding houses that offer various facilities with various rental prices. Thus, decision support analysis is needed to choose a good boarding house for students around Tanjungpura University. In this study, two decision support system methods were selected, those are SAW and TOPSIS. These two methods were chosen because they have uncomplicated calculations, but are capable to produce good decisions. A comparison of the two methods was carried out to find out differences in results and calculation concepts to choose boarding houses for students in Pontianak. Data that was used for the trial were 10 alternative boarding houses located around the university. Based on trial results, the best boarding house obtained using SAW and TOPSIS methods is Yoga Kost.
{"title":"Analisis Perbandingan Metode Pendukung Keputusan Pemilihan Kos Mahasiswa di Pontianak","authors":"Noerul Hanin, David Jordy Dhandio, Della Zaria","doi":"10.14421/jiska.2023.8.1.50-65","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.1.50-65","url":null,"abstract":"The existence of boarding houses in public spaces is highly expected by the community, especially migrants such as students who need a temporary house in oversea areas. In Pontianak, especially around Tanjungpura University, there are many boarding houses that offer various facilities with various rental prices. Thus, decision support analysis is needed to choose a good boarding house for students around Tanjungpura University. In this study, two decision support system methods were selected, those are SAW and TOPSIS. These two methods were chosen because they have uncomplicated calculations, but are capable to produce good decisions. A comparison of the two methods was carried out to find out differences in results and calculation concepts to choose boarding houses for students in Pontianak. Data that was used for the trial were 10 alternative boarding houses located around the university. Based on trial results, the best boarding house obtained using SAW and TOPSIS methods is Yoga Kost.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44037906","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.66-77
Febriansyah Febriansyah, Siti Muntari
The purpose of this study was to obtain a poverty data cluster in Pagar Alam City. The data collection of beneficiaries of the Program Keluarga Harapan (PKH) is not correct, the provision of assistance only pays attention to the criteria for poverty in general, so there are still many poor people who feel more deserving of PKH assistance. To overcome the problem of PKH recipients, it is necessary to cluster the community into various levels, so that the government can know the level of poverty of the community and can provide PKH assistance appropriately. The methods used in this study are CRISP-DM and the K-Means clustering algorithm. The attributes used are Identity Number, Name, Family Family Card Number, Poverty Rate, Pregnant Women, Early Childhood, Elementary School, Junior High School, Senior High School, Elderly, and Family Hope Program Recipient Group. This clustering process produced three clusters, namely cluster_0 as many as 156 people, cluster_1 as many as 82 people, and cluster_2 as many as 233 people. Furthermore, it was developed into a system with the Rapid Application Development (RAD) system development method. Thus producing a K-Means algorithm system to classify the poor in Pagar Alam City. The system test method uses black box testing with the alpha method and obtained database test results with a value of 4, interfaces with a value of 4, functionality of 4.42, and algorithms with a value of 4. In the testing process with UAT, in the system aspect got 87% of users agreed, in the user aspect 86% agreed, and in the interaction aspect 87% of users agreed. So it can be concluded that this system is worth using.
{"title":"Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam","authors":"Febriansyah Febriansyah, Siti Muntari","doi":"10.14421/jiska.2023.8.1.66-77","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.1.66-77","url":null,"abstract":"The purpose of this study was to obtain a poverty data cluster in Pagar Alam City. The data collection of beneficiaries of the Program Keluarga Harapan (PKH) is not correct, the provision of assistance only pays attention to the criteria for poverty in general, so there are still many poor people who feel more deserving of PKH assistance. To overcome the problem of PKH recipients, it is necessary to cluster the community into various levels, so that the government can know the level of poverty of the community and can provide PKH assistance appropriately. The methods used in this study are CRISP-DM and the K-Means clustering algorithm. The attributes used are Identity Number, Name, Family Family Card Number, Poverty Rate, Pregnant Women, Early Childhood, Elementary School, Junior High School, Senior High School, Elderly, and Family Hope Program Recipient Group. This clustering process produced three clusters, namely cluster_0 as many as 156 people, cluster_1 as many as 82 people, and cluster_2 as many as 233 people. Furthermore, it was developed into a system with the Rapid Application Development (RAD) system development method. Thus producing a K-Means algorithm system to classify the poor in Pagar Alam City. The system test method uses black box testing with the alpha method and obtained database test results with a value of 4, interfaces with a value of 4, functionality of 4.42, and algorithms with a value of 4. In the testing process with UAT, in the system aspect got 87% of users agreed, in the user aspect 86% agreed, and in the interaction aspect 87% of users agreed. So it can be concluded that this system is worth using.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42556846","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.78-89
Verry Noval Kristanto, Imam Riadi, Yudi Prayudi
Facial recognition is a significant part of criminal investigations because it may be used to identify the offender when the criminal's face is consciously or accidentally recorded on camera or video. However, a majority of these digital photos have poor picture quality, which complicates and lengthens the process of identifying a face image. The purpose of this study is to discover and identify faces in these low-quality digital photographs using the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) face identification method and the Viola-Jones face recognition method. The success percentage for the labeled face in the wild (LFW) dataset is 63.33%, whereas the success rate for face94 is 46.66%, while LDA is only a maximum of 20% on noise and brightness. One of the names and faces from the dataset is displayed by the facial recognition system. The brightness of the image, where the facial item is located, and any new objects that have entered the scene have an impact on the success rate.
{"title":"Analisa Deteksi dan Pengenalan Wajah pada Citra dengan Permasalahan Visual","authors":"Verry Noval Kristanto, Imam Riadi, Yudi Prayudi","doi":"10.14421/jiska.2023.8.1.78-89","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.1.78-89","url":null,"abstract":"Facial recognition is a significant part of criminal investigations because it may be used to identify the offender when the criminal's face is consciously or accidentally recorded on camera or video. However, a majority of these digital photos have poor picture quality, which complicates and lengthens the process of identifying a face image. The purpose of this study is to discover and identify faces in these low-quality digital photographs using the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) face identification method and the Viola-Jones face recognition method. The success percentage for the labeled face in the wild (LFW) dataset is 63.33%, whereas the success rate for face94 is 46.66%, while LDA is only a maximum of 20% on noise and brightness. One of the names and faces from the dataset is displayed by the facial recognition system. The brightness of the image, where the facial item is located, and any new objects that have entered the scene have an impact on the success rate.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49008442","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.1-9
I. Wijaya, Muhammad Afif Hendrawan, Nurcahya Nania Anabela
Probolinggo Regency is an area in East Java that has tourism potential. The condition is seen from the many tourists visiting various attractions in Probolinggo Regency. To increase the number of tourist visits, it is necessary to develop tourism objects. However, not all attractions in Probolinggo Regency can be developed at the same time. This is due to budget limitations for tourism development. Therefore, it is necessary to have a grouping of attractions according to the priority level of development. In this study, researchers utilized Self Organizing Maps (SOM) and Sum Additive Weighing (SAW) methods to group attractions based on their development priority levels. SOM is used to determine groups of tourist objects based on the parameters of the number of domestic tourists, the number of foreign tourists, infrastructure, and the number of attractions. Furthermore, SAW is used to find out which group has the highest priority among other groups based on these parameters. To measure the quality of the resulting group, researchers used the value of the silhouette coefficient. Results from the grouping process resulted in three groups. Group C1 consists of 4 attractions, group C2 consists of 20 attractions, and group C3 consists of 10 attractions. The value of the silhouette coefficient also holds a good value, especially in group 1, which is 0.75006. Furthermore, based on the ranking of groups by the SAW method, the C1 group is the group of tourist attractions with the highest priority for development.
{"title":"Pengelompokan Obyek Wisata Potensial dengan Self Organizing Maps (SOM) dan Sum Additive Weighting (SAW)","authors":"I. Wijaya, Muhammad Afif Hendrawan, Nurcahya Nania Anabela","doi":"10.14421/jiska.2023.8.1.1-9","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.1.1-9","url":null,"abstract":"Probolinggo Regency is an area in East Java that has tourism potential. The condition is seen from the many tourists visiting various attractions in Probolinggo Regency. To increase the number of tourist visits, it is necessary to develop tourism objects. However, not all attractions in Probolinggo Regency can be developed at the same time. This is due to budget limitations for tourism development. Therefore, it is necessary to have a grouping of attractions according to the priority level of development. In this study, researchers utilized Self Organizing Maps (SOM) and Sum Additive Weighing (SAW) methods to group attractions based on their development priority levels. SOM is used to determine groups of tourist objects based on the parameters of the number of domestic tourists, the number of foreign tourists, infrastructure, and the number of attractions. Furthermore, SAW is used to find out which group has the highest priority among other groups based on these parameters. To measure the quality of the resulting group, researchers used the value of the silhouette coefficient. Results from the grouping process resulted in three groups. Group C1 consists of 4 attractions, group C2 consists of 20 attractions, and group C3 consists of 10 attractions. The value of the silhouette coefficient also holds a good value, especially in group 1, which is 0.75006. Furthermore, based on the ranking of groups by the SAW method, the C1 group is the group of tourist attractions with the highest priority for development.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44420160","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.36-49
Nur Madia, Anindita Septiarini, H. Hatta, Hamdani Hamdani, Masna Wati
Contents Poverty is the inability to meet the necessities of life, such as food, clothing, and shelter. The poor have an average monthly per capita expenditure below the poverty line. The case of poverty in Indonesia is still unresolved; the Government continues to try to give the best to the entire community so that the problem of poverty can at least continue to decrease. One form of government concern for the poor is the assistance program provided to the poor. This study will classify based on data from the North Penajam Paser (PPU) community obtained from the results of the National Socio-Economic Survey (Susenas) to know how the Naïve Bayes method is in determining the eligibility of the poor recipients of assistance. Based on the research that has been carried out, a system for determining the poor recipients of assistance is produced, where the test results get the highest accuracy in the third scenario, namely 60% or 328 training data and 40% or 218 test data, where the accuracy obtained is 77.98%.
{"title":"Penentuan Kelayakan Masyarakat Miskin Penerima Bantuan Menggunakan Metode Naïve Bayes (Studi Kasus: Kabupaten Penajam Paser Utara)","authors":"Nur Madia, Anindita Septiarini, H. Hatta, Hamdani Hamdani, Masna Wati","doi":"10.14421/jiska.2023.8.1.36-49","DOIUrl":"https://doi.org/10.14421/jiska.2023.8.1.36-49","url":null,"abstract":"Contents Poverty is the inability to meet the necessities of life, such as food, clothing, and shelter. The poor have an average monthly per capita expenditure below the poverty line. The case of poverty in Indonesia is still unresolved; the Government continues to try to give the best to the entire community so that the problem of poverty can at least continue to decrease. One form of government concern for the poor is the assistance program provided to the poor. This study will classify based on data from the North Penajam Paser (PPU) community obtained from the results of the National Socio-Economic Survey (Susenas) to know how the Naïve Bayes method is in determining the eligibility of the poor recipients of assistance. Based on the research that has been carried out, a system for determining the poor recipients of assistance is produced, where the test results get the highest accuracy in the third scenario, namely 60% or 328 training data and 40% or 218 test data, where the accuracy obtained is 77.98%.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44839982","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 : 2023-01-30DOI: 10.14421/jiska.2023.8.1.22-35
Theresia Liana Sinaga, Novrido Charibaldi, N. Cahyana
Currently, humans live in an era of data oceans, where the amount of data production is increasing from time to time, which is followed by severe challenges in terms of processing, storing, and analyzing data, especially big data. The increase in the number of large data production can affect the speed of access to the database, effectiveness, and speed of response time in the data processing. Relational databases have been the leading model for data storage, analysis, processing, and retrieval for more than forty years. However, due to the increasing need for large-scale data storage, the scalability and performance of a data processing system, as well as the constant growth of the amount of data, another alternative to databases emerged, namely NoSQL technology. Based on previous studies regarding the comparison of response time and database performance, the average concludes that NoSQL performance is more effective and efficient than relational databases. Based on the implementation and testing, it can be concluded that the NoSQL database application MongoDB is proven to be superior in every command of CRUD tested compared to the Elasticsearch NoSQL database application, where in testing the create data command with a JSON file, the MongoDB database application is 42.5 times faster than the Elasticsearch database application. In testing the command to create data into a database containing different amounts of data, the MongoDB database application is 333.9 times faster than the average response time of the Elasticsearch database application. In testing the read command for data in a database containing different amounts of data, the MongoDB database application is 35.5 times faster than the Elasticsearch database application. In testing the update operation of data in a database containing different amounts of data, the MongoDB database application is 9.8 times faster than the Elasticsearch database application. in testing the delete operation of data in a database containing different amounts of data, the MongoDB database application is 58.9 times faster than the Elasticsearch database application.
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