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All Journal Indonesian Journal of Educational Research (IJER) JEDMA Jurnal Edukasi Matematika Mathematical Sciences and Applications Journal
gusmi kholijah
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Decision Sciences, Operations Research & Management Education Mathematics

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PENDUGAAN MODEL PARTISIPASI EKONOMI MASYARAKAT PROVINSI JAMBI DENGAN METODE ANALISIS REGRESI LOGISTIK BINER gusmi kholijah
JEDMA Jurnal Edukasi Matematika Vol 1 No 1 (2020): JEDMA Jurnal Edukasi Matematika
Publisher : Program Studi Pendidikan Matematika STKIP PGRI Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.577 KB) | DOI: 10.51836/jedma.v1i1.123

Abstract

Economic well-being is still uneven in Indonesia. This is still a problem in overcoming poverty. Many efforts have been made in poverty alleviation, one of which is increasing economic participation. Economic participation can be categorized as working and not working. Economic participation is a response variable that is influenced by several predictor variables. The relationship between predictor variables and response variables in the form of binary categories can be analyzed by binary logistic regression. The model generated from this study conveys that people who live in rural areas have more economic participation than those in cities. Then marital status in the category of marriage, divorced life has more economic participation than divorce, and divorced people die more than those who have not yet married. Furthermore, men participate more economically than women. Then when viewed from the age, shows the productive age of work has more economic participation, then the higher the level of education then contributes to the higher economic participation.
Project Based Learning Model in Computer Programming Courses at Mathematics Student Gusmi Kholijah; Niken Rarasati; Corry Sormin; Fazar Aryanto
IJER (Indonesian Journal of Educational Research) Vol. 8 No. 1 (2023): IJER Vol 8 No 1 (2023)
Publisher : Universitas Islam Negeri (UIN) Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/ijer.v8i1.251

Abstract

Project-based learning is carried out for computer programming courses. In this lesson, students are expected to have skills in recognizing, constructing, and creating programs from a problem using a programming language. This is a research and development (R&D) that consist of team based project scheme stage, development stage, implementation stage, and final or evaluation stage. When making a computer program on a problem, students must be able to identify the required input, process, and output. Then the information is constructed in an algorithm which is then made into a computer program. Its implementation there are schemes that have been implemented, namely: the preparation stage, and the project work activity process, which includes; forming groups, case studies, gathering information on project-based activities, and producing documentation and portfolios of project implementation and evaluation. This research also produces products in the form of graduate learning outcomes (CPL), Syllabus and Lecture Contracts, Semester Learning Plans (RPS), Student Project Task Plans (RTM) or Student Project Worksheets (LKM), Learning Outcomes Assessment Sheets (LPHB) in accordance with project-based learning model (Project Based Learning). The results of the evaluation through a questionnaire obtained that student perceptions of computer programming courses were very high.
CLUSTERING ANALYSIS WITH AVERAGE LINKAGE METHOD FOR GROUPING PROVINCE IN INDONESIA BASED ON WELFARE INDICATORS dwiki Prasetia; sufri; gusmi kholijah
Mathematical Sciences and Applications Journal Vol. 1 No. 1 (2020): Mathematical Sciences and Applications Journal
Publisher : Department of Mathematics, Faculty of Science and Technology Universitas Jambi

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Abstract

The high level of social inequality in Indonesia is a problem that must be resolved immediately. High social inequality will result in an increase in social tension which also impacts on the high level of conflict and crime in society. The problem of social inequality can be solved by accelerating the welfare distribution program by the government. The provision of this program must be fair and adapted to the conditions needed by each region. This is because each region has different causes of welfare problems. Therefore, in providing the program, the government must have a priority scale on welfare issues in an area that can be done using a mathematical method in the field of statistics, namely cluster analysis. This study aims to obtain, analyze and interpret the results of grouping provinces in Indonesia based on indicators of people's welfare. As many as 34 provinces in Indonesia as objects will be grouped based on 20 variables related to people's welfare. The grouping is done using the Hierarchy Method, the agglomeration grouping procedure with the Average Linkage technique and the size of the Euclidean Distance. From the clustering algorithm, it was found that from 34 provinces in Indonesia grouped into 5 clusters namely Cluster 1 consisting of 24 members namely Aceh Province; North Sumatra; West Sumatra; Riau; Jambi; South Sumatra; Bengkulu; Lampung; Head of Pacific Islands; Riau Islands; West Java; Central Java; East Java; Banten; West Nusa Tenggara; Central Kalimantan; South Borneo; East Kalimantan; North Kalimantan; North Sulawesi; Central Sulawesi; South Sulawesi; Southeast Sulawesi; and Gorontalo. Cluster 2 consists of 1 member, DKI Jakarta Province. Cluster 3 consists of 2 members including DI Yogyakarta and Bali Provinces. Cluster 4 consists of 6 members including East Nusa Tenggara Province; West Kalimantan; West Sulawesi; Maluku; North Maluku; and West Papua. Cluster 5 consists of 1 member, Papua Province. Based on the comparison of the average value of each cluster, the five clusters are sorted based on their level of welfare, namely: Cluster 3 as a very good cluster, Cluster 2 as a better cluster, Cluster 1 as a good cluster, Cluster 4 as a pretty good cluster and cluster 5 as a less good claser. Keywords: Cluster Analysis, Average Linkage, People's Welfare
PANEL DATA REGRESSION ANALYSIS OF PORT SERVICES SERVICES TOWARDS RATE ACCEPTANCE NOT TAXES (PNBP) (Case Study: At the Class IV Harbor Authority and Kuala Tungkal Port Authority) iga mawarni; Kamid; gusmi kholijah
Mathematical Sciences and Applications Journal Vol. 1 No. 1 (2020): Mathematical Sciences and Applications Journal
Publisher : Department of Mathematics, Faculty of Science and Technology Universitas Jambi

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Abstract

on-Tax State Revenue (PNBP) is the receipt of the central government that is not derived from taxation. One source of PNBP is direct services by the state, such as the use of services at the port. This type of service is used in the related sector activities of the shipping company. The more port activities are carried out, the service at the port is increasing, so that PNBP received will increase. PNBP data in this study is a combination of time series data and cross section data called panel data. This study aims to determine the type of service that significantly influences PNBP using the Panel Data Regression Analysis method. From the analysis it is concluded that the best regression model estimation is the Fixed Effect Model (FEM) with dummy variables. FEM model states that the regression coefficient for navigation variables is 0.090824, PUJK variable is 0.267160 and the number of ships (JK) variable is 0.472592. These three variables are positive which means they have a significant effect on the level of PNBP. The PUP variable has a negative value of -0.061411, which means that the PUP variable does not significantly influence the level of PNBP. The variability of PNBP level in FEM model can be explained by the Navigation Services, Shipping Services (PUP), Port of Services (PUJK) and Number of Vessels (JK) variables of 88.75%. Keywords: Fixed Effect, Panel Data Regression, Port Services
Co-Authors Corry Sormin dwiki Prasetia Fazar Aryanto Iga Mawarni Kamid Kamid Niken Rarasati Sufri Sufri