Indonesian Journal of Applied Statistics最新文献

{"title":"K-Medoids Clustering dan Mean-Value at Risk untuk Optimasi Portofolio Saham Jakarta Islamic Index","authors":"Eka Sri Puspaningsih, Di Asih I Maruddani, Tarno Tarno","doi":"10.13057/ijas.v6i1.79231","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.79231","url":null,"abstract":"The problem of the portfolio is how to choose stocks and determine their weights in order to generate maximum returns with minimal risk. Portfolios are formed by selecting stocks that have different characteristics. K-Medoids Clustering can be used to group data sets that contain outliers. Validate cluster results using the Davies Bouldin Index to determine the best number of clusters. Portfolio weighting is determined using the Mean-VaR method by taking into account the expected return value and minimizing the VaR risk value. Stocks are grouped based on Return on Assets, Return on Equity, Debt to Asset Ratio, and Debt to Equity Ratio. The results of cluster formation on the Jakarta Islamic Index stocks obtained six portfolio constituent stocks based on the highest expected return value from each cluster, consisting of PTBA, ADRO, AKRA, EXCL, PTPP, and UNVR. The results of calculating the weight of the optimal portfolio with Mean-VaR obtained a weight for PTBA of 0.46536; AKRA of 0.24018; EXCL of 0.25421; and UNVR of 0.25392. ADRO and PTPP stocks have a negative weight value of -0,07775 and -0,13593 this indicates the occurrence of short selling in the weighting. At the 95% confidence level, the VaR portfolio value is 5.06%.Keywords: Clustering; K-Medoids; Daveis Bouldin Index; Portfolio; Mean-VaR","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"184 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503790","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 Spasial Angka Kematian Balita di Pulau Papua Menggunakan Mixed Geographically Weighted Regression","authors":"Muhammad Fathu Rahman, Hamada Syafia, Sya'adatul Maf Ula, Nur Azizah Amini, Arief Priambudi, Tiodora Hadumaon Siagia","doi":"10.13057/ijas.v6i1.75064","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.75064","url":null,"abstract":"One of the goals of the Sustainable Development Goals is to end under five mortality which can be prevented by at least 25 per 1000 live births by 2030. Based on Badan Pusat Statistik (BPS) data, in 2020 the Under Five Mortality Rate (U5MR) in Papua Province is 49.04, while in West Papua Province of 47.23. This figure makes the island of Papua the island with the highest U5MR compared to other islands in Indonesia. The problem of U5MR has different influencing factors for each region, so it is important to include spatial effects in the analysis. The Mixed GWR model can be used to overcome spatial linkages between regions, accommodate variations in the form of spatial heterogeneity, and handle variations in parameters that are global and local in nature. Therefore, this study aims to analyze the variables that affect U5MR in Papua Island using Mixed GWR. This study uses secondary data sourced from BPS. The unit of analysis for this research is the districts/cities in Papua Island. The dependent variable in this study is U5MR, while the independent variables include the percentage of women aged at first pregnancy less than 21 years, Gross Regional Domestic Product per capita, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water. The results showed that the percentage of women aged at first pregnancy less than 21 years, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water had a significant effect on U5MR in several districts/cities on the island of Papua. Therefore, it is hoped that district/city governments on the island of Papua in developing programs/policies to reduce U5MR can adjust to the conditions of each region.Keywords: Under Five Mortality Rate; Papua island; Mixed GWR","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503950","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":"Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP)","authors":"Deby Fakhriyana, Indira Ihnu Brilliant","doi":"10.13057/ijas.v6i1.79907","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.79907","url":null,"abstract":"In Indonesia, crude oil plays a significant role in the country’s economy as it serves as a source of income and meets the country's energy needs. Therefore, fluctuations in crude oil prices have a significant impact on the economic activities of the society. Forecasting the price of Indonesian crude oil is thus crucial. The international price of crude oil in Indonesia is known as the Indonesian Crude Oil Price (ICP). One commonly used statistical method for forecasting is the ARIMA method. However, the ARIMA method has certain assumptions that need to be fulfil, and many real-world data cannot meet these assumptions. Hence, forecasting using the Fuzzy Time Series (FTS) method, which does not rely on assumptions, is employed. Some popular FTS methods include the Cheng FTS method and the Markov Chain FTS method. This study implements the Cheng FTS and Markov Chain FTS methods on the ICP data from May 2018 to June 2023 to determine the most appropriate method for forecasting. The analysis results using the Cheng FTS method on the testing data yield a Mean Absolute Percentage Error (MAPE) value of 4,083%, while the Markov Chain FTS method has MAPE value of 4,585%. The Cheng FTS method selected as the appropriate model for forecasting the ICP data since it has a smaller MAPE value. Using the Cheng FTS method, the predicted ICP value for July 2023 is US$72,907 per barrel.Keywords: ICP; FTS Cheng; FTS Markov Chain; MAPE","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"27 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503981","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 Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA","authors":"Suparti Suparti, R. Santoso","doi":"10.13057/ijas.v6i1.79385","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.79385","url":null,"abstract":"Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R2 or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R2 is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.Keywords: 3 time series; kernel regression; GCV; MDKA stock price.","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504034","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":"Machine Learning Predictive Modeling of Agricultural Sustainability Indicators","authors":"Raden Roro Shafira Meisy Sudarsono, H. Wandebori","doi":"10.13057/ijas.v6i1.64245","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.64245","url":null,"abstract":"Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.Keywords: Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"36 4-5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504584","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":"Penentuan Rate Asuransi Kendaraan Bermotor Menggunakan Kredibilitas Bayesian","authors":"Rahmila Dapa, I. T. Utami","doi":"10.13057/ijas.v6i1.79813","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.79813","url":null,"abstract":"This paper uses Credibility to determine new rate based on data of historical claim in a motor vehicle insurance in Bandung, Indonesia. Rate is formed based on past loss through experience rating. Credibility is one of the examples of experience rating that considers group historical claims. One of the credibility methods is Bayesian credibility that considers rate as a random variable. Bayesian credibility is used based on claim frequency and claim severity from a group of policy holders in order to create new rates. In this paper, claim frequency followed the Poisson distribution while claim severity followed the Lognormal distribution. Result of analysis showed that rate values based on claim frequency and severity are higher than the rate values that were used back in 2010.Keywords: bayesian credibility; rate; claim frequency; claim severity","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"25 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504123","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 Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment","authors":"Pikata Aselnino, Ariek Wijayanto","doi":"10.13057/ijas.v6i1.68876","DOIUrl":"https://doi.org/10.13057/ijas.v6i1.68876","url":null,"abstract":"The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.Keywords: clustering; fuzzy; k-means; hierarchical; women empowerment","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504366","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":"Implementasi Clustering K-Medoids dalam Pengelompokan Kabupaten di Provinsi Aceh Berdasarkan Faktor yang Mempengaruhi Kemiskinan","authors":"Freditasari Purwa Hidayat, Royhan Pina Putra, Dendi Alfitrah, Edy Widodo","doi":"10.13057/ijas.v5i2.55080","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.55080","url":null,"abstract":"The economy is one of the parameters to see how the development of a country. Ending poverty anywhere and in any form is goal 01 of the Sustainable Development Goals (SDGs) program. Until now, poverty has become one of the main problems in Indonesia, so poverty must be a concern of the government. Based on data from the Central Statistics Agency (BPS) shows that as of September 2020 the percentage of poor people in Aceh Province is still the highest on the island of Sumatra, which is 15.43%. The purpose of this study is to classify districts based on factors that affect poverty in Aceh Province. The method used in this study is the K-Medoids Cluster Analysis algorithm. The optimal number of clusters is 2 clusters with cluster 1 consisting of 11 districts and cluster 2 consisting of 12 districts. Cluster 1 has a higher percentage of poor population and poverty depth index than cluster 2, while cluster 2 has higher Gini Ratio, AHH, and RLS values than cluster 1.Keywords : Clusters, Economy, Poverty, SDGs","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316696","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 Dan Perbandingan Pembangunan Sosial Provinsi Di Indonesia","authors":"Anne Indiarti Banjar Nahor","doi":"10.13057/ijas.v5i2.61224","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.61224","url":null,"abstract":"Social development still not become priority in policy formulation in Indonesia. The reality of development without social aspects will not be able to be enjoyed evenly by the community. This study examines social development in Indonesia by grouping social development issues and comparing social development achievements in 2016-2017 to find out which areas should be a priority. Eleven social development indicators was used to present social development in Indonesia. Biplot analysis as an initial indication of regional grouping based on social development indicators, and cluster analysis to facilitate interpretation of grouping results. The percentage of diversity data that can be worked on by biplot analysis are 65 percent for 2016 and 61,3 percent for 2017. The results of biplot analysis produce character variables from each province based on the quadrant. It can be seen that in quadrant II the members of the Province of Bangka Belitung Islands, East Java, Central Java, North Sulawesi, West Nusa Tenggara, Central Sulawesi are characterized by high scores on health dimension variables, literacy rates and the percentage of households with adequate access. Based on the cluster analysis produce the group of provinces according to three levels of social development namely low, medium, high. Papua Province is the only province that does not change and still exists at a low level of social development.Keywords: social development, biplot analysis, cluster analysis","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"35 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316490","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":"Keterkaitan Indeks Harga Konsumen (IHK) Kelompok Bahan Makanan dengan Kelompok Makanan Jadi, Minuman, Rokok, dan Tembakau di Indonesia Tahun 2014-2019 (Pendekatan Vector Error Correction Model)","authors":"Lira Azima, Erni Tri Astuti","doi":"10.13057/ijas.v5i2.54988","DOIUrl":"https://doi.org/10.13057/ijas.v5i2.54988","url":null,"abstract":"Pricing a commodity depends on the price of other commodities. As the largest contributor to inflation, the pattern of price movements in CPI of prepared food, beverages, cigarette, and tobacco group is inseparable from CPI of foodstuff group as the raw material for that group. This condition indicates that in analyzing the pattern of price movements of a commodity, it cannot be separated from the influence of other commodities. The aims of the study is to examine the linkages between CPI of foodstuff group and CPI of prepared food, beverages, cigarette, and tobacco group, also its response and contribution when there is shock during January 2014 until December 2019 in Indonesia using Vector Error Correction Model (VECM). The results suggest that in long-term CPI of prepared food, beverages, cigarette, and tobacco group has positive effect on CPI of foodstuff group. Impulse Response Function (IRF) shows that shocks to CPI of foodstuff group is positively responded by CPI of prepared food, beverages, cigarette, and tobacco group, and vice versa. In addition, Forecast Error Variance Decomposition (FEVD) show that the variation of CPI of prepared food, beverages, cigarette, and tobacco group are dominated by contribution of CPI of foodstuff group.Keywords : consumer price index, VECM, impulse response function, forecast error variance decomposition","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316668","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}