Pub Date : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.86-99
Veladita Apriyanti, Epha Diana Supandi
In stock investments, every investor wants to get a high level of return and low risk. The stock price is very volatile and unpredictable, this makes investors have to find solutions in order to get a benefit from this investment. One way is to form a portfolio. A portfolio is a collection of several shares. There are several models for calculating stock portfolios such as CAPM (Capital Asset Pricing Model) and LCAPM (Liquidity Adjusted Capital Asset Pricing Model). The CAPM is a model that describes the relationship between the expected return and risk of investing in a security. The LCAPM is an extension of CAPM by taking into account the liquidity of assets. Data from Jakarta Islamic Index is used to verify the two models. In this case, the empirical results show that the performance of CAPM is better than the LCAPM.
{"title":"PERBANDINGAN MODEL CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL SAHAM SYARIAH","authors":"Veladita Apriyanti, Epha Diana Supandi","doi":"10.14710/MEDSTAT.12.1.86-99","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.86-99","url":null,"abstract":"In stock investments, every investor wants to get a high level of return and low risk. The stock price is very volatile and unpredictable, this makes investors have to find solutions in order to get a benefit from this investment. One way is to form a portfolio. A portfolio is a collection of several shares. There are several models for calculating stock portfolios such as CAPM (Capital Asset Pricing Model) and LCAPM (Liquidity Adjusted Capital Asset Pricing Model). The CAPM is a model that describes the relationship between the expected return and risk of investing in a security. The LCAPM is an extension of CAPM by taking into account the liquidity of assets. Data from Jakarta Islamic Index is used to verify the two models. In this case, the empirical results show that the performance of CAPM is better than the LCAPM.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.86-99","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43529068","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.63-72
Dwi Ispriyanti, Alan Prahutama, M. Mustafid, Tarno Tarno
Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.
{"title":"KLASIFIKASI PENERIMAAN BERAS MISKIN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART)","authors":"Dwi Ispriyanti, Alan Prahutama, M. Mustafid, Tarno Tarno","doi":"10.14710/MEDSTAT.12.1.63-72","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.63-72","url":null,"abstract":"Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.63-72","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41267236","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.39-49
Hasbi Yasin, Budi Warsito, Arief Rachman Hakim
This research covers Spatial Extreme Value method application with Max-Stable Process (MSP) approach that will be used to analysis Extreme Rainfall in Semarang city. Extreme value sample are selected by Block Maxima methods, it will be estimated into Spatial Extreme Value form by including location factors. Then it transform to Frechet distribution because it has a heavy tail pattern. Max Stable Process (MSP) is an extension of the multivariate extreme value distribution into infinite dimension of the Extreme Value Theory. After the best model of extreme rainfall data in Semarang is obtained, then calculated the prediction of extreme rainfall with a certain time period. Predictions are calculated using a return level, predictions of extreme rainfall using the return period of the next two years, at the Semarang City Climatology Station predicted to be a maximum of 100.7539 mm. At the Tanjung Mas Rain Monitoring Station it is predicted that a maximum of 100.1052 mm, Ahmad Yani Rain Monitoring Station is predicted to be a maximum of 109.9379 mm. Maximum prediction of extreme rainfall can also be calculated for future t (time) periods.
{"title":"PREDIKSI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS (MSP)","authors":"Hasbi Yasin, Budi Warsito, Arief Rachman Hakim","doi":"10.14710/MEDSTAT.12.1.39-49","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.39-49","url":null,"abstract":"This research covers Spatial Extreme Value method application with Max-Stable Process (MSP) approach that will be used to analysis Extreme Rainfall in Semarang city. Extreme value sample are selected by Block Maxima methods, it will be estimated into Spatial Extreme Value form by including location factors. Then it transform to Frechet distribution because it has a heavy tail pattern. Max Stable Process (MSP) is an extension of the multivariate extreme value distribution into infinite dimension of the Extreme Value Theory. After the best model of extreme rainfall data in Semarang is obtained, then calculated the prediction of extreme rainfall with a certain time period. Predictions are calculated using a return level, predictions of extreme rainfall using the return period of the next two years, at the Semarang City Climatology Station predicted to be a maximum of 100.7539 mm. At the Tanjung Mas Rain Monitoring Station it is predicted that a maximum of 100.1052 mm, Ahmad Yani Rain Monitoring Station is predicted to be a maximum of 109.9379 mm. Maximum prediction of extreme rainfall can also be calculated for future t (time) periods.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.39-49","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42398114","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.73-85
Mutik Dian Prabaning Tyas, D. A. I. Maruddani, R. Rahmawati
Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.
股票是最受欢迎的金融资产投资类型。在购买股票之前，投资者必须估计将要承受的风险。风险价值(VaR)是衡量风险水平的一种方法。在投资股票时，如果投资者想获得高回报，那么他必须准备好面对更高的风险。大多数股票收益数据具有波动性聚类特征或存在异方差，股票收益分布具有重尾特征。可以用来克服异方差问题的时间序列模型之一是ARCH/GARCH模型，而分析重尾数据的方法是极值理论(EVT)。本文采用非对称ARCH模型，结合阈值ARCH (TARCH)和广义极值EVT (GEV)方法，计算了PT Bumi Serpong Damai Tbk公司2012年9月至2018年10月期间股票收益的VaR。最佳选择模型为AR([3]) -TARCH(1)。在95%的置信水平下，使用TARCH-GEV计算，投资者在第二天内将收到的最大损失为0.18%。
{"title":"PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE","authors":"Mutik Dian Prabaning Tyas, D. A. I. Maruddani, R. Rahmawati","doi":"10.14710/MEDSTAT.12.1.73-85","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.73-85","url":null,"abstract":"Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.73-85","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48710963","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.1-12
Yogo Aryo Jatmiko, S. Padmadisastra, Anna Chadidjah
The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in Bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
{"title":"ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI","authors":"Yogo Aryo Jatmiko, S. Padmadisastra, Anna Chadidjah","doi":"10.14710/MEDSTAT.12.1.1-12","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.1-12","url":null,"abstract":"The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in Bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.1-12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43791125","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 : 2019-07-24DOI: 10.14710/medstat.12.1.100-116
Isnayanti Isnayanti, Abdurakhman Abdurakhman
{"title":"MODEL PERSAMAAN STRUKTURAL DENGAN METODE DIAGONALLY WEIGHTED LEAST SQUARE (DWLS) UNTUK DATA ORDINAL","authors":"Isnayanti Isnayanti, Abdurakhman Abdurakhman","doi":"10.14710/medstat.12.1.100-116","DOIUrl":"https://doi.org/10.14710/medstat.12.1.100-116","url":null,"abstract":"","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/medstat.12.1.100-116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67038965","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.13-25
Michael Andre, N. Nasrudin
Indonesian Crude Oil Price (ICP) often fluctuates by the shock of world oil prices. Because of its important role, the fluctuations or shocks in ICP will affect Indonesia's macro economy. To overcome this problem, this study analyzes the impact of the crude oil price shocks on Indonesia's macro economy which includes economic growth and the money supply (M2) during 2010-2016 using Vector Error Correction Mechanism (VECM). The results show that short-term fluctuations of ICP have a significant and positive effect on economic growth but have a non-significant effect on the money supply. In the long term equilibrium, ICP have a positive and significant effect to both economic growth and money supply which in line with Impulse Response Function (IRF) and Decomposition of Variance (FEDV) analysis. Given its positive impact, the recent decline in oil prices will harm the Indonesian economy. Therefore, the government needs to reduce its dependence on crude oil exports and accurately predict the crude oil price in the future.
{"title":"ANALISIS DAMPAK GUNCANGAN HARGA MINYAK MENTAH TERHADAP MAKROEKONOMI INDONESIA: APLIKASI VECTOR ERROR CORRECTION MECHANISM","authors":"Michael Andre, N. Nasrudin","doi":"10.14710/MEDSTAT.12.1.13-25","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.13-25","url":null,"abstract":"Indonesian Crude Oil Price (ICP) often fluctuates by the shock of world oil prices. Because of its important role, the fluctuations or shocks in ICP will affect Indonesia's macro economy. To overcome this problem, this study analyzes the impact of the crude oil price shocks on Indonesia's macro economy which includes economic growth and the money supply (M2) during 2010-2016 using Vector Error Correction Mechanism (VECM). The results show that short-term fluctuations of ICP have a significant and positive effect on economic growth but have a non-significant effect on the money supply. In the long term equilibrium, ICP have a positive and significant effect to both economic growth and money supply which in line with Impulse Response Function (IRF) and Decomposition of Variance (FEDV) analysis. Given its positive impact, the recent decline in oil prices will harm the Indonesian economy. Therefore, the government needs to reduce its dependence on crude oil exports and accurately predict the crude oil price in the future.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.13-25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43370822","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 : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.50-62
A. Djuraidah, Cici Suheni, Banan Nabila
Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.
{"title":"PERAMALAN CURAH HUJAN EKSTRIM DI PROVINSI BANTEN DENGAN MODEL EKSTRIM SPASIAL","authors":"A. Djuraidah, Cici Suheni, Banan Nabila","doi":"10.14710/MEDSTAT.12.1.50-62","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.50-62","url":null,"abstract":"Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.50-62","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46673904","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 : 2019-01-01DOI: 10.14710/medstat.12.1.117-128
Rita Rahmawati, Agus Rusgiyono, Abdul Hoyyi, D. I. Maruddani
{"title":"EXPECTED SHORTFALL UNTUK MENGUKUR RISIKO KERUGIAN PETANI JAGUNG","authors":"Rita Rahmawati, Agus Rusgiyono, Abdul Hoyyi, D. I. Maruddani","doi":"10.14710/medstat.12.1.117-128","DOIUrl":"https://doi.org/10.14710/medstat.12.1.117-128","url":null,"abstract":"","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/medstat.12.1.117-128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67038974","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 : 2018-12-30DOI: 10.14710/MEDSTAT.11.2.107-117
T. Widiharih, M. Mukid
Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method. Keywords : sampling design, all possible samples, statistical efficiency , cost efficienc y
{"title":"CREDIT SCORING MENGGUNAKAN METODE LOCAL MEANS BASED K HARMONIC NEAREST NEIGHBOR (MLMKHNN)","authors":"T. Widiharih, M. Mukid","doi":"10.14710/MEDSTAT.11.2.107-117","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.2.107-117","url":null,"abstract":"Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method. Keywords : sampling design, all possible samples, statistical efficiency , cost efficienc y","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.2.107-117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49307644","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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