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Isran K. Hasan
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Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
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INDONESIA
JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.37905/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
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Search results for , issue "Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics" : 5 Documents clear
Analisis Hasil Ujian Nasional Sekolah Menengah Kejuruan di Kota Makassar Menggunakan Linear Mixed Model Regression Iman Setiawan; Abdullah Pannu
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.21287

Abstract

Graduates of Vocational High Schools (SMK) in South Sulawesi Province, especially Makassar City, have a higher Open Unemployment Rate (TPT) than other levels of education. In fact, during the 2016-2020 period, various education management policies such as the revitalization of SMKs, the arrangement of study groups and the accreditation of SMK Education Units were held to encourage increased competence of graduates and the quality of SMK Education. The purpose of this study is to see how the pattern of National Examination (UN) results for SMKs in Makassar City in 2018-2020 and whether there are differences in the pattern of UN results for SMK accreditation. Because the analysis was carried out in multi years, the analysis was carried out using the Linear Mixed Model (LMM). Modeling is carried out in stages using 4 model schemes. This research shows that there has been a decrease in the SMK National Exam results in Makassar City in 2018-2020. In 2018, 2019 and 2020 each has an average of 52.26, 50.33 and 46.50. When viewed based on SMK accreditation, it can be seen that the decline in SMK National Examination results only occurs in SMKs that are not accredited A. This shows that policies related to increasing the competence of graduates and the quality of SMK education, especially in Makassar City, have no impact or even cannot be implemented properly for SMKs with accreditation other than A. 
Implementasi Regresi Logistik Biner Stratifikasi Pada Pemodelan Stunting Untuk Anak Balita Di Kabupaten Gorontalo Setia Ningsih; Muhammad Rifai Madonsa; Sri Lestari Mahmud; Ismail Djakaria; Salmun K Nasib
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.19793

Abstract

Stunting is a condition where toddlers fail to grow due to chronic malnutrition in the first 1000 days of life (HPK). Therefore, stunting cases in Gorontalo Province, especially in Gorontalo Regency, are among the cases that need to be addressed as soon as possible. The data used is secondary data from each Puskesmas in Gorontalo Regency, to see the factors that have a significant effect on the incidence of stunting in Gorontalo Regency in urban and rural areas using the stratified binary logistic regression method. In this study, the independent variables used were Gender, Birth Weight, Birth Height, Toddler Age and Nutritional Status. The test results using the stratified binary logistic regression method show that for urban strata there are 3 significant variables, namely Birth Weight, Age of Toddlers and Nutritional Status, then for rural strata there are 2 significant variables, namely Age of Toddlers and Nutritional Status. Wald test results show that there are differences between urban and rural areas.
K-Means Clustering dan Mean Variance Efficient Portfolio dalam Portofolio Saham Yogi Pratama; Evy Sulistianingsih; Naomi Nessyana Debataraja; Nurfitri Imro’ah
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.20298

Abstract

K-means clustering is one of the non-hierarchical clustering algorithms that partitions n objects into k clusters. K-means clustering is used to determine which cluster an object belongs to by calculating the proximity distance between the object and the cluster center (centroid). This research aims to form a portfolio using K-means clustering and determine the weights of the portfolio using the Mean Variance Efficient Portfolio (MVEP) method. The data analyzed in this research is the closing price data of 11 stocks in the LQ45 index from January 3, 2022, to January 3, 2023. The analysis results obtained using K-means clustering reveal the formation of two portfolios. The first portfolio consists of the stocks BMRI, INCO, INDF, INTP, and SMGR. The second portfolio consists of the stocks ADRO, ANTM, BBRI, ERAA, and UNVR. Based on the MVEP method calculation, the weights of each stock in the first portfolio are 22.74\% (BMRI), 10.11\% (INCO), 49.76\% (INDF), 18.75\% (INTP), and -1.36\% (SMGR). The calculation results of stock weights show that there is a stock weight with a negative value, which is -1.36\% for SMGR, indicating a short sale in the investment. Furthermore, the weighting results for the second portfolio are 7.08\% (ADRO), 9.62\% (ANTM), 34.05\% (BBRI), 24.80\% (ERAA), and 24.45\% (UNVR).The variance values of stock portfolio 1 and stock portfolio 2 are 0.000080 and 0.000137, respectively. From the portfolio variance results, it is known that the risk of portfolio 1 is 0.008953 and the risk of portfolio 2 is 0.011706.
Model Markov Switching Autoregressive pada Data Covid-19 di Indonesia Setyo Wira Rizki; Shantika Martha; Bartolomius Bartolomius; Rita Apriliyanti
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.19429

Abstract

The Covid-19 pandemic has had a very influential impact on socio-economic conditions in Indonesia. Forecasting the number of Covid-19 cases is needed to support taking preventive action. The method that can be used to determine the number of Covid-19 cases is a forecasting method using the Markov Switching Autoregressive (MSAR) time series data model as an alternative for analyzing structural change data. This research uses Covid-19 confirmation data in Indonesia for the period March 2020-June 2021, with the aim of designing an MSAR model and calculating the magnitude of the transition opportunity in each state in the Covid-19 confirmation data in Indonesia. The MSAR model begins by describing the data and checking the stationarity of the data. After that, Box-Jenkins modeling was carried out to test heteroskedasticity and structural changes. Next, the MSAR model parameters were estimated and the transition matrix was formed. This research shows that the best MSAR model formed is the MS (2)-AR (5) model, with a static transition probability value in state 1 of 0.981330. However, it appears that there is a chance of 0.018670 for the Covid-19 confirmation condition to move to state 2. Testing in the case of state 2 produces a transition chance of 0.980991 in state 2, with a transition chance of Covid-19 confirmation changing to state 1 of 0.019009.
Penerapan Principal Component Analysis untuk Reduksi Variabel pada Algoritma K-Means Clustering Istina Alya Rosyada; Dina Tri Utari
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.18733

Abstract

K-Means clustering is a widely used clustering algorithm. However, it has the disadvantage that the performance of clustering data decreases if the variables of the processed data are immense. The complex variables problem in K-Means can be overcome by combining the Principal Component Analysis (PCA) variable reduction method. This study uses seven indicator variables for the welfare of the people of West Java Province in 2021 to measure the welfare level of districts/cities. The results of the analysis obtained two principal components based on eigenvalues. Clustering from cluster analysis with the K-Means with variable reduction using PCA formed the three best clusters where the number of members of each cluster consisted of 12, 8, and 7 districts/cities.

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