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Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications 利用聚类绘制血吸虫病病例与村庄人口分布图大型应用软件

Jurnal Varian Pub Date : 2024-07-01 DOI: 10.30812/varian.v7i2.3423

Mohammad Fajri, Rais Rais, Nurul Fiskia Gamayanti, Siti Natazha Dg Mabaji, Shalsa Yunita Rahman Jati, Rizwan Arisandi

{"title":"Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications","authors":"Mohammad Fajri, Rais Rais, Nurul Fiskia Gamayanti, Siti Natazha Dg Mabaji, Shalsa Yunita Rahman Jati, Rizwan Arisandi","doi":"10.30812/varian.v7i2.3423","DOIUrl":"https://doi.org/10.30812/varian.v7i2.3423","url":null,"abstract":"Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases. \u0000 ","PeriodicalId":474744,"journal":{"name":"Jurnal Varian","volume":"3 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693928","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}

引用次数: 0

Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation 无核密度估计和有核密度估计的 Naive Bayes 分类方法比较

Jurnal Varian Pub Date : 2024-07-01 DOI: 10.30812/varian.v7i2.3199

Agus Hermawan, Siswanto Siswanto, Andi Kresna Jaya

{"title":"Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation","authors":"Agus Hermawan, Siswanto Siswanto, Andi Kresna Jaya","doi":"10.30812/varian.v7i2.3199","DOIUrl":"https://doi.org/10.30812/varian.v7i2.3199","url":null,"abstract":"Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% Fmeasure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% Fmeasure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.","PeriodicalId":474744,"journal":{"name":"Jurnal Varian","volume":"27 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702945","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}

引用次数: 0

PLS Analysis: How Family Support Affect Students' Self-Confidence in Completing Thesis PLS分析:家庭支持如何影响学生完成论文的自信心

Jurnal Varian Pub Date : 2023-05-17 DOI: 10.30812/varian.v6i2.2442

Arya Fendha Ibnu Shina

{"title":"PLS Analysis: How Family Support Affect Students' Self-Confidence in Completing Thesis","authors":"Arya Fendha Ibnu Shina","doi":"10.30812/varian.v6i2.2442","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2442","url":null,"abstract":"A student's success can not be separated from a final project called a thesis as a determinant of student graduation in college. However, in the process, students often face various obstacles and challenges. To overcome this, students need to have high self-confidence. In increasing self-confidence, the role of the family is required as the main factor that encourages students to succeed. The purpose of this paper is to determine the influence of forms of family support in the form of informational support, instrumental support, appraisal and reward support, and emotional support on the self-confidence of 58 students of the Islamic Guidance and Counseling Study Program class of 2018 UIN Sunan Kalijaga Yogyakarta who are working on a thesis for students. The data analysis method used is Partial Least Square (PLS). Partial Least Square (PLS) is a component or variant-based Structural Equation Model (SEM) model. Partial Least Square (PLS) is a variant-based Structural Equation Model (SEM) model.The results of this study state that 46.7% of students' self-confidence variations in completing a thesis are influenced by family support while the rest is influenced by other factors. In addition, emotional support has a significant effect on student self-confidence in doing a thesis with a p-value of 0.000. Thus, it can be concluded that emotional support becoming the form of family support that played the most important role in increasing student self-confidence. The results of this study will later be used as suggestion for students’s family and also for counselors in modifying behavior to increase students’s self-confidence.","PeriodicalId":474744,"journal":{"name":"Jurnal Varian","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135916717","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}

引用次数: 0