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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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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 67 Documents
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Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing Lutfi Sivana Ihzaniah; Adi Setiawan; Rachel Wulan N. Wijaya
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.18948

Abstract

This research was made in order to see which method  performance is better between the KNN (K-Nearest Neighbor) regression method and the multiple linear regression method on Boston Housing data. The method performace referred here is MAE, RMSE, MAPE, and R2. The KNN method is a method to predict something based on the closest training examples of an object. Meanwhile, multiple linear regression is a forecasting technique involving more than one independent variable. The comparison of the two methods is based on the results of the Mean Absolute Percent Error (MAPE). In this research the definitions of distance used are Euclidean distance and Minkowski distance. The K value in the KNN method defines the number of nearest neighbors to be examined to determine the value of a dependent variable, in this research we use K values from 1 to 10 for each test data and definition of distance. In this research, the percentage of test data used was 20%, 30%, and 40% for both methods. The best MAPE value obtained by the KNN regression method was 12,89% at K = 3 for Euclidean distance and 13,22% at K = 3 for Minkowski distance. Meanwhile the best MAPE value for the multiple linear regression method is 17,17%. The best method between the two methods is the KNN regression method as seen from the MAPE value of the KNN regression method which is smaller than the MAPE value of the multiple linear regression method.
Implementasi Metode Runtun Waktu dalam Pemodelan Total Harga Alat Kedokteran dan Kesehatan Daniar Wahyu Laraswati; Achmad Fauzan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.17873

Abstract

Hospital is an institution or health service that provides total individual health care by providing outpatient, inpatient, and emergency services. The health services that will be provided are promotive, preventive, and rehabilitative services. One of the efforts to improve the quality of hospital services is to provide good health services. In terms of supporting the health services provided, a health management is needed. The high price of medical supplies and equipment is due to several other factors causing hospitals to be able to make plans in the procurement of medical equipment and hospital medicine. Therefore, the author uses the Autoregressive Moving Average (ARMA) method in this study to predict the Total Price of Medical and Health Equipment Needed at the Sleman Regional General Hospital in the coming period. Based on the analysis that has been found, one significant and best ARMA model is obtained with the AIC value of 223.92 with equation  and the MAPE value of 18.78%, which means the accuracy of the forecasting is 81.22%.
Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo Alvitha Habibie; Lailany Yahya; Isran K. Hasan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.17453

Abstract

Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method. 
Prediksi Jumlah Wisatawan Menggunakan Metode Random Forest, Single Exponential Smoothing dan Double Exponential Smoothing di Provinsi NTB Ristu Haiban Hirzi; Umam Hidayaturrohman; Kertanah Kertanah; M. Hadiyan Amaly; Rody Satriawan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.17088

Abstract

The aim of study is to forecast global tourist visits and compare the forecasting methods to determine the best method using random forest, single exponential smoothing and double exponential smoothing, respectively. These methods are applied in global tourist visit data in West Nusa Tenggara Province. Random forest, single exponential smoothing and double exponential smoothing are familiar methods and are frequently utilized in forecasting. In addition, the three methods have great accuracy for time series data, such as data of global tourist visits. The data used in this study is data of global tourist visits from 2014 to 2021 in West Nusa Tenggara province. Applying the random forest, single exponential smoothing and double exponential smoothing methods in forecasting, the result shows that double exponential smoothing method is the best, based on the smallest value of Mean Absolute Percentage Error (MAPE) of 325.759. The forecasting result found out that tourist visits will increase from previous time, starting from August, 2021 to July, 2021 with an estimated 847 to 1045 lives
Analisis Hasil Ujian Nasional Sekolah Menengah Kejuruan di Kota Makassar Menggunakan Linear Mixed Model Regression Setiawan, Iman; Pannu, Abdullah
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. 
Peramalan Jumlah Sampah di Kabupaten Lombok Timur dengan Metode ARIMA dan Dekomposisi Nurmayanti, Wiwit Pura; Kertanah, Kertanah; Hasanah, Siti Hadijah; Rahim, Abdul; hendrayani, hendrayani
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): 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.v4i2.19954

Abstract

AbstractForecasting is the science of predicting events that will occur using historical data and projecting them into the future with some form of mathematical model that aims to handle and policy in the future. In forecasting there are several methods, two of which are Autoregeressive Integrated Moving Average (ARIMA) and Decomposition. ARIMA is a method developed by George Box and Gwilym Jenkins in 1970. The Decomposition Method is a method that decomposes (breaks) time series data into several patterns, namely trend, cyclical and seasonal, and identifies each of these components separately. Both of these methods can be applied in various fields, one of which is in the field of environmental health, especially data on the amount of waste. Problems related to the amount of waste in East Lombok are still a concern of the government because as the population increases and the needs of the community each year have the potential to cause waste problems. The final disposal site (TPA) in East Lombok is located in Ijo Balit, this TPA is the only one in East Lombok. The purpose of this research is to see which method is the best between ARIMA and Decomposition, and to see the forecasting results of the amount of waste entering TPA Ijo Balit from the best method. Based on the results of the analysis carried out by the Decomposition model, it gives the best performance in terms of the smallest error value so that it can be used for Forecasting and produces an RMSE value of 5201.694, a MAPE of 0.955827 and a MASE of 0.0129691. The results of forecasting using the Decomposition method are that the highest forecast occurs in December, while the lowest occurs in January with a total of 1,439,439 (tons) and 1,117,000 (tons). Keywords:  Forecasting, ARIMA, Decomposition, Waste
Implementasi Regresi Logistik Biner Stratifikasi Pada Pemodelan Stunting Untuk Anak Balita Di Kabupaten Gorontalo Ningsih, Setia; Madonsa, Muhammad Rifai; Mahmud, Sri Lestari; Djakaria, Ismail; Nasib, Salmun K
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.
Implementasi Algoritma Random Forest dengan Forward Selection untuk Klasifikasi Indeks Pembangunan Manusia Posangi, Tiara; Yahya, Lailany; Wungguli, Djihad
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): 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.v4i2.18460

Abstract

Development is essentially a process of continuous change carried out to achieve better living condition. So that the benchmark for the success of a development is seen in its human development. 3 The basic dimensions that form human development are long and healthy life, knowledge, and a decent life. The indicators that represent the three dimensions are summarized in a single value, namely the Human Development Index (IPM). In 2021 the HDI figure in Indonesia is 72.29, which means it is high. However, due to the diverse geographical location of regions in Indonesia, this also influences the HDI rate in each region in Indonesia, so this study uses the Random Forest Algorithm to obtain accurate results from the HDI classification and uses Forward Selection to determine features that influence the classification. The results of the study show that the features that influence the classification are per capita spending, expected length of schooling, life expectancy, and average length of schooling, and get a final accuracy of 80%.
K-Means Clustering dan Mean Variance Efficient Portfolio dalam Portofolio Saham Pratama, Yogi; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Imro’ah, Nurfitri
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.
Implementasi Deep Learning dalam Pengklasifikasian Wajah Menggunakan Library Tensorflow pada Algoritma Convolutional Neural Network (CNN) Usman, Rahmat Setiawan; Hasan, Isran K.; Isa, Dewi Rahmawaty
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): 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.v4i2.18264

Abstract

The convolutional neural network is a deep learning method that functions to recognize and classify objects in an image. An example of its application is a facial recognition system which consists of a detection and classification process. Facial recognition by computers can be influenced by many things such as lighting, expressions, and the amount of dataset provided. This study aims to find out how to implement CNN to identify faces using Tensorflow with the Python programming language. The number of datasets used is 120 data and 10 respondents in total with different lighting conditions and shooting angles. Apart from the dataset, this study also uses several different scenarios in the training process, namely the difference in the number of epochs and the difference in the number of learning rates. Based on the results of the discussion, two models were obtained. In the first model, the results obtained an accuracy of 100% in the training process and 65% in the testing process. In the second model, the results obtained are 100% accuracy in the training process and 75% in the testing process. performance of the model made in this study can be said to be optimal in recognizing objects in several lighting conditions and image angles.

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