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All Journal Jurnal Ekonomi Pembangunan Mobile and Forensics Science in Information Technology Letters Bulletin of Social Informatics Theory and Application Emerging Statistics and Data Science Journal
Tuti Purwaningsih, Tuti
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Mathematics Neuroscience Social Sciences

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EFFECT OF BI RATE, INFLATION, EXCHANGE, AND THE DOW JONES AGAINST COMPOSITE STOCK PRICE INDEX (CSPI CASE STUDY IN 2009-2014) Purwaningsih, Tuti
Jurnal Ekonomi Pembangunan Vol 13, No 2 (2015): Jurnal Ekonomi Pembangunan
Publisher : Pusat Pengkajian Ekonomi dan Kebijakan Publik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.948 KB) | DOI: 10.22219/jep.v13i2.3900

Abstract

The purpose of this study was to determine the influence of the independent variables are indicated by the BI Rate, the rate of inflation, exchange rate and the Dow Jones Against Composite Stock Price Index. The analysis tool used is multiple linear regression using time series data is 2009-2014. In the model equations, Composite Stock Price Index is the dependent variable and the BI Rate, the rate of inflation, exchange rates as well as Dow Jones is the independent variable. Results of regression is that the variable BI Rate (X1) a significant negative effect on the Composite Stock Price Index, inflation (X2) significant negative effect on the Composite Stock Price Index, the exchange rate (X3) significant negative effect on Stock Price Index and Index Dow Jones (X3) positive and significant impact on the Composite Stock Price Index. The coefficient of determination (R2) is 0.970445, or 97%. This indicates that the BI Rate (X1), the rate of inflation (X2), the exchange rate (X3) and Dow Jones (X4) in explaining the dependent variable or dependent Composite Stock Price Index amounted to 97%, while the remaining 3% is explained by other variables outside the model that implicitly reflected in confounding variables.  Suggestions can meet of the results of this study are advised to look at the effect of other macroeconomic variables in detail which can affect and use other variables outside the monetary variables like social and political situation of a country. And also advised to conduct research using other approaches.
Probabilitas Bernoulli Untuk Cluster Status Sekolah Menengah Atas Di Indonesia Purwaningsih, Tuti; Azhari, Ahmad; Purnaramadhan, Riza
Mobile and Forensics Vol 2, No 1 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i1.1953

Abstract

Di pedesaan, biasanya sekolah negeri begitu banyak diminati karena biaya pendidikan yang relatif terjangkau. Namun pada kenyataan, banyak orang tua memilih sekolah swasta untuk mendapatkan fasilitas belajar lebih nyaman baik dari sekolah maupun dari pengajarnya. Unsur-unsur pendidikan menjadi penentu keberhasilan proses peningkatan mutu pendidikan dalam pencapaian tujuan pendidikan di satuan pendidikan. Penelitian ini bertujuan untuk mendeteksi pembagian cluster terkait jumlah sekolah negeri dan swasta tingkat sekolah menengah atas tiap provinsi di Indonesia dengan menerapkan model probabilitas Bernoulli. Berdasarkan analisis yang sudah dilakukan dapat disimpulkan bahwa persebaran cluster menggunakan model probabilitas Bernoulli pada jumlah sekolah negeri tingkat menengah atas di indonesia memiliki 5 cluster dan banyak tersebar di bagian Indonesia wilayah tengah dan timur. Sedangkan pada jumlah sekolah swasta tingkat menengah atas di indonesia memiliki 6 cluster dan banyak tersebar di bagian pulau jawa. Provinsi-provinsi yang masuk ke wilayah cluster tersebut berarti memiliki karakteristik yang mirip yang dimilikinya sehingga mengelompok kedalam satu kelompok yang sama. In rural areas, public schools are usually in great demand because of the relatively affordable costs of education. But in fact, many parents choose private schools to get more comfortable learning facilities from both the school and the teachers. The elements of education are the determinants of the success of the process of improving the quality of education in achieving educational goals in educational units. This study aims to detect the division of clusters related to the number of public and private high school level schools in each province in Indonesia by applying the Bernoulli probability model. Based on the analysis that has been done, it can be concluded that the distribution of clusters using the Bernoulli probability model is that the number of senior high school public schools in Indonesia has 5 clusters and many are scattered in the central and eastern parts of Indonesia. Meanwhile, the number of high school private schools in Indonesia has 6 clusters and many are scattered in parts of the island of Java. Provinces that are included in the cluster area mean that they have similar characteristics so that they are grouped into the same group.
Long Short Term Memory on Bitcoin Price Forecasting Purwaningsih, Tuti; Kusumandari, Gita Evi
Mobile and Forensics Vol 3, No 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v3i1.3857

Abstract

In modern times, many people rely on sophisticated technology to meet their needs. Already many technologies today can replace the role and function of society in the field of investment. There are many ways to fulfill the lives of these people, such as Bitcoin investment. Bitcoin is a digital asset that only exists in digital form by means of peer-to-peer work. To maximize profits, it is necessary to forecast Bitcoin prices when it will go up or down. This study tries to address the changes in Bitcoin prices whether to go up or down the next day with an artificial neural network model. The editor used in this study is the LSTM method. The data used is the Bitcoin blockchain data, namely time-series data in a one-day period from 1 January 2018 to 31 May 2019. Obtained forecasting results in June 2019 for Bitcoin to rise slowly and an accuracy value of 97.5% based on MAPE with the first day worth $8901.50.
Probabilitas Bernoulli Untuk Cluster Status Sekolah Menengah Atas Di Indonesia Purwaningsih, Tuti; Azhari, Ahmad; Purnaramadhan, Riza
Mobile and Forensics Vol. 2 No. 1 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i1.1953

Abstract

Di pedesaan, biasanya sekolah negeri begitu banyak diminati karena biaya pendidikan yang relatif terjangkau. Namun pada kenyataan, banyak orang tua memilih sekolah swasta untuk mendapatkan fasilitas belajar lebih nyaman baik dari sekolah maupun dari pengajarnya. Unsur-unsur pendidikan menjadi penentu keberhasilan proses peningkatan mutu pendidikan dalam pencapaian tujuan pendidikan di satuan pendidikan. Penelitian ini bertujuan untuk mendeteksi pembagian cluster terkait jumlah sekolah negeri dan swasta tingkat sekolah menengah atas tiap provinsi di Indonesia dengan menerapkan model probabilitas Bernoulli. Berdasarkan analisis yang sudah dilakukan dapat disimpulkan bahwa persebaran cluster menggunakan model probabilitas Bernoulli pada jumlah sekolah negeri tingkat menengah atas di indonesia memiliki 5 cluster dan banyak tersebar di bagian Indonesia wilayah tengah dan timur. Sedangkan pada jumlah sekolah swasta tingkat menengah atas di indonesia memiliki 6 cluster dan banyak tersebar di bagian pulau jawa. Provinsi-provinsi yang masuk ke wilayah cluster tersebut berarti memiliki karakteristik yang mirip yang dimilikinya sehingga mengelompok kedalam satu kelompok yang sama. In rural areas, public schools are usually in great demand because of the relatively affordable costs of education. But in fact, many parents choose private schools to get more comfortable learning facilities from both the school and the teachers. The elements of education are the determinants of the success of the process of improving the quality of education in achieving educational goals in educational units. This study aims to detect the division of clusters related to the number of public and private high school level schools in each province in Indonesia by applying the Bernoulli probability model. Based on the analysis that has been done, it can be concluded that the distribution of clusters using the Bernoulli probability model is that the number of senior high school public schools in Indonesia has 5 clusters and many are scattered in the central and eastern parts of Indonesia. Meanwhile, the number of high school private schools in Indonesia has 6 clusters and many are scattered in parts of the island of Java. Provinces that are included in the cluster area mean that they have similar characteristics so that they are grouped into the same group.
Long Short-Term Memory on Bitcoin Price Forecasting Purwaningsih, Tuti; Kusumandari, Gita Evi
Mobile and Forensics Vol. 3 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v3i1.3857

Abstract

In modern times, many people rely on sophisticated technology to meet their needs. Already many technologies today can replace the role and function of society in the field of investment. There are many ways to fulfill the lives of these people, such as Bitcoin investment. Bitcoin is a digital asset that only exists in digital form by means of peer-to-peer work. To maximize profits, it is necessary to forecast Bitcoin prices when it will go up or down. This study tries to address the changes in Bitcoin prices whether to go up or down the next day with an artificial neural network model. The editor used in this study is the LSTM method. The data used is the Bitcoin blockchain data, namely time-series data in a one-day period from 1 January 2018 to 31 May 2019. Obtained forecasting results in June 2019 for Bitcoin to rise slowly and an accuracy value of 97.5% based on MAPE with the first day worth $8901.50.
Spatial regression analysis for discovering quality living index (QLI) in Asia Sumardi, Devina Gilar Fitri Ayu; Purwaningsih, Tuti
Bulletin of Social Informatics Theory and Application Vol. 2 No. 1 (2018)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v2i1.100

Abstract

The quality of life that is synonymous with welfare lately much discussed. Survival or quality of life is an important issue in the economy and political knowledge. Quality of life describes the achievement of an ideal human life or in accordance with the desired. The quality of life index (IKH) provides a comprehensive ranking of the quality of life of a society in a country around the world. According to katadata news and research in 2016, the quality of life in 5 Southeast Asian countries is ranked lowest. The research finds that there are regional imbalances based on the indices of quality of life in 2018. Therefore, countries in Southeast Asia need to be genjotic in all areas to boost contributions to the quality of life in the country, as Dominic Volek, Southeast Asia chief in Henley & Partners Singapore. In this study we use the data from numbeo.com to see the value of quality indices in Asia in 2018 with various supporting indicators such as purchasing power index, security index, health care index, cost of living index, property price to income ratio, time index travel traffic, pollution index, and climate index. Where this study aims to determine how much influence the indicators that have been determined to calculate the CPI, knowing the best spatial regression model that can be used and determine whether there is asi gap in the region of Asia. The results of this study hope can be useful information for the community and a reference to make policy by the state officials in the field, but it can be a research material that can be developed again to see the difference and development of quality of life index in each country in Asia next year.
Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia Fithriyyah, Anisahtul; Purwaningsih, Tuti; Konate, Siaka; Abdalla, Modawy Adam Ali
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1203

Abstract

In Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Humairoh, Nanda Lailatul; Purwaningsih, Tuti; Saifullah, Shoffan; Dwiyanto, Felix Andika; Rabbimov, Ilyos
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
Gender inequality in HDI and per capita expenditure: A probabilistic distribution and spatial data analysis Fadilah, Zainal; Purwaningsih, Tuti; Inderanata, Rochmad Novian; Konate, Siaka; P, Cicin Hardiyanti
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1214

Abstract

Men and women have different habits or lifestyles, which inevitably leads to variances in other areas. As a result, gender statistics emerged. In this example, researchers seek to discover if there are discrepancies in HDI and per capita expenditure in Indonesia between men and women. To determine this, data from reliable sources is required; thus, researchers use data from the official BPS website, bps.go.id. The data comes from many tables, so the researcher will join them so that they may be studied. The data used in this scenario are HDI data by gender in 2020 and Per Capita Expenditure data by gender in 2020. Researchers employed graphical tools, such as boxplots and thematic charts, to examine whether there are differences in HDI and per capita expenditure between men and women in Indonesia. Aside from that, researchers used the two-sample t-test approach to see if there were variations in HDI and per capita expenditure between men and women. Researchers will utilize Python software to run this hypothesis test. According to the findings of the investigation, there is still gender imbalance in Indonesia in terms of HDI and per capita expenditure. As a result, it is intended that this research can be utilized as a reference in analyzing existing policies to ensure that there is no gender discrepancy in terms of HDI and per capita expenditure between men and women. It is also envisaged that this research would be beneficial to many people.
Factors Influencing open unemployment rates: a spatial regression analysis Purwaningsih, Tuti; Inderanata, Rochmad Novian; Pradana, Sendhyka Cakra; Snani, Aissa; Sulaiman, Sarina
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1202

Abstract

The present study employed spatial regression analysis as a methodological approach to get insights into the unemployment rates across Indonesian provinces in the year 2016. The official website of the Bureau of Labor Statistics (BPS) offers secondary data pertaining to several socio-economic indicators, including the Total Open Unemployment Rate, Economic Growth Rate, Human Development Index, Severity of Poverty Index, and School Participation Rates. The investigation employed the Geoda software package and encompassed Ordinary Least Squares (OLS) regression, Dependency/Correlation investigation, and Spatial Autoregressive Model. The data presented in the study revealed the existence of three distinct provincial groupings characterized by varying levels of unemployment rates. In the context of unemployment variance, the traditional regression model accounted for 30 percent of the observed variation. However, the spatial regression model used spatial dependencies to enhance accuracy in capturing the phenomenon. The aforementioned findings have the potential to assist policymakers in formulating strategies to address unemployment in regions characterized by distinct spatial attributes, hence offering a potential blueprint for other nations.
Co-Authors Abdalla, Modawy Adam Ali Ahmad Azhari As Shidiqi, Anis Fitriana Dwiyanto, Felix Andika Fadilah, Zainal Fithriyyah, Anisahtul Humairoh, Nanda Lailatul Inderanata, Rochmad Novian Konate, Siaka Kusumandari, Gita Evi P, Cicin Hardiyanti Pradana, Sendhyka Cakra Purnaramadhan, Riza Rabbimov, Ilyos Saifullah, Shoffan Sarina Sulaiman Snani, Aissa Sumardi, Devina Gilar Fitri Ayu