Indonesian Journal of Applied Statistics最新文献

{"title":"Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor","authors":"Dian Tri Wilujeng, Mohamat Fatekurohman, I. M. Tirta, J. Matematika, Fakultas Matematika, Dan Ilmu, Pengetahuan Alam, U. Jember","doi":"10.13057/ijas.v5i2.58426","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.58426","url":null,"abstract":"<p dir=\"ltr\"><span>Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms.  Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning.  One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN).  The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data.  The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit.  The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN.</span><span> </span></p><span id=\"docs-internal-guid-3225d0b5-7fff-da27-d883-e71a565d51af\"><span><strong>Keywords</strong>: </span><span>Machine learning, KNN, NWKNN</span></span>","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316572","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}

{"title":"Pemodelan Tingkat Kerawanan Pangan Rumah Tangga di Indonesia Tahun 2021 dengan Pendekatan Regresi Logistik Ordinal","authors":"Tasya Aguilera, Yogo Aryo Jatmiko","doi":"10.13057/ijas.v5i2.65141","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.65141","url":null,"abstract":"Until 2021, Indonesia has succeeded in reducing the prevalence rate of the population with moderate or severe food insecurity. But on the other hand, Indonesia's Global Food Security Index (GFSI) score which has declined in the last three years shows that Indonesia's food security is getting weaker in various aspects. The condition of food security that begins to weaken can trigger food insecurity. Food insecurity that can have an impact on health, nutrition and health system problems is a national health problem that needs attention. Therefore, this study aims to examine the level of household food insecurity and the variables that influence it. This study uses The National Socioeconomic Survey (Susenas) March 2021 data which was analyzed using partial proportional odds model (PPOM) ordinal logistics regression method. In general, the results show that variables area of residence, gender, age, education, business field, number of household members, residence ownership status, and per capita expenditure affect the level of household food insecurity in Indonesia in 2021.Keywords: food insecurity; ordinal logistic regression; PPOM","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316945","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}

{"title":"Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids","authors":"Dela Gustiara, Anisa Dwi Mulyaningsih, Rahmi Anadra, Edy Widodo","doi":"10.13057/ijas.v5i2.65668","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.65668","url":null,"abstract":"The territory of Indonesia is located in geographical, geological, hydrological, and demographic conditions that allow Indonesia to be prone to disasters. The most common natural disaster in Indonesia is flooding. If accumulated, there have been 682 flood events in the country since the beginning of 2022. In Indonesia, especially West Java Province, flooding is the most common disaster, especially during the rainy season. So a study will be conducted that aims to determine the grouping of districts / cities in West Java Province based on the occurrence of flood disasters. The data used in this study were obtained from the publication of the National Disaster Management Agency. In this study, there are 4 variables of the impact of flood disasters, namely total deaths, total submerged houses, total damaged houses and total injured. The clustering method used in this research is K-Medoids. K-Medoids is one of the clustering methods that uses the partition clustering method in grouping a set of n objects into a number of k clusters. From the results of the K-Medoids analysis, three clusters were obtained. The first cluster consists of 3 districts/cities with high impact of flood disasters, the second cluster consists of 23 districts/cities with moderate impact of flood disasters, and the third cluster consists of 1 district/city with low impact of flood disasters. Based on the results of the analysis, efforts can be made by the government to focus more on designing steps that must be taken in preventing or overcoming the impact of flood disasters.Keywords: Cluster; K-Medoids; Floods West Java","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316911","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}

{"title":"Upaya Penegakan Emansipasi Wanita melalui Optimalisasi Pembangunan Gender dengan Metode Regresi Panel","authors":"Inu Alifiyah Phalufi, Raden Nabila Alya Hartarie, Ellena Novitriani, Edy Widodo","doi":"10.13057/ijas.v5i2.58034","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.58034","url":null,"abstract":"<p dir=\"ltr\"><span>The role of women nowadays is no different from the men, only to a reasonable extent. The role of women's emancipation itself has been upheld in Indonesia, as those who will be the spearheads in family education for their children that must have broad skills and insights. The Human Development Index (HDI) is mostly becoming an important index as a measurement of the success level in quality of human life (community) building efforts. By conducting an analysis using the panel regression method in the Regency / City of West Sulawesi (as a province in Indonesia that has the 4th lowest HDI score) to find out how much women's participation can affect the level of quality of life in Indonesia and as an evaluation of which components must be improved by government for the next period in the welfare of its people. This analysis concludes that the Mamuju regency is known as the region that contributes the largest weight to the increase in GDI while the Pasangkayu regency contributes the lowest weight to the increase in GDI so that the government should make the development of supporting facilities for community welfare more equitable.</span></p><span id=\"docs-internal-guid-22ffeaad-7fff-fdb7-57db-cb880984fbb2\"><span><strong>Keywords</strong> : </span><span>GDI, Woman Emancipation, Panel Regression</span></span>","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316982","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}

{"title":"Perbandingan K-Nearest Neighbor dan Random Forest dengan Seleksi Fitur Information Gain untuk Klasifikasi Lama Studi Mahasiswa","authors":"Isran K. Hasan, Resmawan Resmawan, J. Ibrahim","doi":"10.13057/ijas.v5i1.58056","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.58056","url":null,"abstract":"Accreditation is a quality and feasibility assessment form in carrying out higher education. One of the factors that affect accreditation is the length of student study. In this study, the length of student study is classified by using the best attributes resulting from selecting information gain features. In optimizing the classification algorithm, we process the data by converting the original data into data that is ready to be mined. The next step is dividing the data into training and testing data so that the classification algorithm can be applied. This study gives the best four attributes, with K-nearest neighbor (K-NN) classification of 86.67% and random forest classification of 100%.Keywords: length of study; information gain; K-nearest neighbor; random forest","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019144","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}

{"title":"New Mathematical Properties of the Kumaraswamy Lindley Distribution","authors":"Samy Abd Elmoez Mahommed, Salah M. Mohamed","doi":"10.13057/ijas.v5i1.56206","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.56206","url":null,"abstract":"The Kumaraswamy Lindley distribution is a generalized distribution that has many applications in various fields, including physics, engineering, and chemistry. This paper introduces new mathematical properties for Kumaraswamy Lindley distribution such as probability weighted moments, moments of residual life, mean of residual life, reversed residual life, cumulative hazard rate function, and mean deviation. Keywords: Kumaraswamy Lindley distribution; probability weighted moments; residual  life; hazard rate; mean deviation","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123918528","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}

{"title":"Implementation of Transfer Learning for Covid-19 and Pneumonia Disease Detection Through Chest X-Rays Based on Web","authors":"Nindya Eka Apsari, S. Sugiyanto, S. Handajani","doi":"10.13057/ijas.v5i1.59442","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.59442","url":null,"abstract":"Coronavirus disease 2019, known as COVID-19, attacks the human respiratory system caused by severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2). COVID-19 disease and pneumonia show similar symptoms such as fever, cough, even headache. Diagnosis of pneumonia can be tested through diagnostic tests, including blood tests, chest X-rays, and pulse oximetry, while the diagnosis of COVID-19 recommended by WHO is with swab test (RT-PCR). But in fact, the swab test method takes a relatively long time, for about one to seven days, for the result, and is not cheap. For that, there needs to be a development that can be one of the options in diagnosing COVID-19 and pneumonia at once, especially since both diseases have similar symptoms. One option that can be done is the diagnosis using a chest X-ray. This research aims to detect COVID-19 disease and pneumonia through chest X-rays using transfer learning to increase the accuracy of disease diagnosis with a more efficient time. The architecture used is EfficientNet B0 with variations in optimization parameters, learning rates, and epochs. EfficientNet B0 Adam optimization with a learning rate of 0.001 in the 6th epochs is a great model that we obtained. Furthermore, the evaluation of the model got accuracy, precision, recall, and f1-score of 92%. Then the model visualization is done using Grad-CAM. To implement the best model, web application development is done to make it easier to detect COVID-19 disease and pneumonia.Keywords: COVID-19; pneumonia; EfficientNet; transfer learning; web","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124351243","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}

{"title":"Structural Equation Model (SEM) dalam Pemodelan Kemiskinan di Pulau Sumatera","authors":"Hasrat Ifolala Zebua, Geni Andalria Harefa","doi":"10.13057/ijas.v5i1.50493","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.50493","url":null,"abstract":"Poverty is a serious issue that must be addressed immediately by countries in the world, including Indonesia. The Indonesian government has implemented a variety of poverty reduction projects, such as providing education and health insurance. The rising poverty rate is due to the poor quality of education and health care. On Sumatra, there are 5,83 million poor people or 22,06 percent of the total number of poor people in Indonesia. This statistic appears to be quite large, and the government should be concerned about it. Factors causing poverty such as education and health are latent variables that cannot be measured directly. The suitable statistical method used is Structural Equation Model (SEM). In SEM analysis, there are three types of model fit tests: measurement model fit with Confirmatory Factor Analysis (CFA), overall model fit, and structural model fit. The results indicated that the model was fit or suitable for the model's tests. From the SEM model that was formed, it was found that health had a negative and significant effect on poverty and education did not have a significant effect on poverty and 77 percent of the variation in poverty could be explained by the SEM model that was formed.Keywords: poverty; education; health; SEM; CFA","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116322073","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}

{"title":"Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network","authors":"H. Utami","doi":"10.13057/ijas.v5i1.56825","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.56825","url":null,"abstract":"Sentiment analysis on unbalanced data will cause classification errors where the classification results tend to be in the majority class. Therefore, it is necessary to handle unbalanced data. In this study, a combination of synthetic minority oversampling technique (SMOTE) and Tomek link methods will be used to handle unbalanced data. In this study, we use the Recurrent Neural Network (RNN) method to analyze the sentiment of Shopee application users based on review data. Shopee Indonesia application review data shows that around 80% of Shopee application users have positive sentiments and 20% have negative sentiments, which means the data is not balance. In this study, preprocessing process with combination of synthetic minority oversampling technique (SMOTE) and Tomek link method used to handle the condition. The performance of the result is quite good, namely 80% accuracy, 84.1% precision, 92.5% sensitivity, 30% specificity, and 88.1% F1-score. It is better than performance of sentiment analysis that without preprocessing to handle imbalanced data.Keywords: sentiment analysis; imbalanced data; Tomek link; SMOTE; RNN","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126772713","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}

{"title":"Pemilihan Metode Predictive Analytics dengan Machine Learning untuk Analisis dan Strategi Peningkatan Kualitas Kredit Perbankan","authors":"Aznovri Kurniawan, Ahmad Rifa’i, Moch Abdillah Nafis, Nimas Sefrida, Harry Patria","doi":"10.13057/ijas.v5i1.55483","DOIUrl":"https://doi.org/10.13057/ijas.v5i1.55483","url":null,"abstract":"As a factor that determines bank’s profitability, loan quality, that is categorized based on debtor’s collectability classification, always gets attention and become main analysis topic in banking industry. Through recent development of statistics and data science, especially in predictive analytics using machine learning techniques, more comprehensive analysis and prediction in loan quality can be conducted. This research is intended to give example on application of predictive analytics using machine learning technique for analysis and strategy recommendation in increasing bank’s loan quality improvement. In this research, some machine learning classification methods are compared to conduct predictive analytics in loan quality with big data size (big data analytics). Computation result of different methods are compared and summarized, resulted in recommendation on most appropriate method to achieve this research objective. This research concluded that for imbalanced big data size such as bank’s loan collectability, Tree Ensemble method, further development of Decision Tree method that is commonly used in machine learning, is one of appropriate methods to get satisfactory result in this research. Imbalanced data that can result in false positive may be overcame by oversampling Synthetic Minority Oversampling Technique (SMOTE). This research scope is limited to analysis and prediction of debtor’s collectability for the next several months, combined with analysis and strategy recommendations based on product type, gender, and debtor’s occupation. Further predictive analytics for the next several years by including external factors, such as economic growth, is not covered in this research and possible to be conducted. As machine learning application in Indonesian banking industry analysis is still in early phase, this research is expected to become one of reference in application of predictive analytics using machine learning in banking industry. Keywords: predictive analytics; machine learning; loan collectability; loan quality","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133367280","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}