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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/10.34312/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 38 Documents
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ANALISIS STATISTICAL QUALITY CONTROL DALAM UPAYA MENGURANGI JUMLAH PRODUK CACAT DI PABRIK ROTI THE LI NO’U BAKERY RAHMAWATY AHMAD; RESMAWAN RESMAWAN; DEWI RAHMAWATY ISA
Jambura Journal of Probability and Statistics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i1.4578

Abstract

Quality control is a technical and management activity which measures the quality characteristics of a product or service. Statistical quality control can be used to find production errors that result in defective products so that further corrective action can be taken to overcome them. The objective to be achieved in this research is to determine the Statistical Quality Control (SQC) method with pareto diagrams, control charts, cause and effect diagrams and 5W+1H analysis applied to The Li No'u Bakery in controlling quality to minimize failed products. The data in this study were obtained through direct observation and field interviews. Data analysis tools used are control charts, pareto diagrams, cause and effect diagrams and 5W + 1H analysis. Through a cause and effect diagram, the main factors causing the failure of bakery products at The Li No'u Bakery are manufacturers/employees. This is because the operator fails in making bakery products both the preparation of raw materials, the production process and packaging. So training is needed on making the dough, how to put bread and how to covenant and employee order according to the standard of The Li No'u Bakery.
PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA YULINAR I. AJUNU; NOVIANITA ACHMAD; MUHAMMAD REZKY FRIESTA PAYU
Jambura Journal of Probability and Statistics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i1.5393

Abstract

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.
PENDEKATAN MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK MERAMALKAN FAKTOR-FAKTOR YANG MEMPENGARUHI INFLASI DI PROVINSI GORONTALO HARIYATI H. USMAN; ISMAIL DJAKARIA; MUHAMMAD REZKY FRIESTA PAYU
Jambura Journal of Probability and Statistics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i1.5408

Abstract

The vector autoregressive (VAR) model is a simultaneous equation modeling used to construct forecasting systems from interrelated time-series data. This study intends to predict factors that significantly influence inflation in the province of Gorontalo. Moreover, the data used in this study involved inflation data and factors that influence inflation every month in the province in the period of January 2009 - December 2018. The results of inflation forecasting in Gorontalo in 2019 show that at the beginning of 2019, the inflation was considered to be very low at around -0.48% to -0.40%. However, the inflation surged in March with -0.25% (the highest inflation rate). The percentage decreased to -0.30% and -0.33% in April and May. After the decline in April and May, in the middle of the year (June) inflation returned to -0.31% and did not experience a significant change until the end of the year, which was still in the range of -0.32%. The accuracy of the prediction results seen in the MAPE value from out sample data of variables Y1 to Y8 is on the average below 10%, indicating that VAR is a significant forecasting model.
PENGGUNAAN RESAMPLING DALAM PENGGAMBARAN QUICK COUNT ADI SETIAWAN
Jambura Journal of Probability and Statistics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i1.5643

Abstract

In this paper a descriptive statistical analysis of the results of the 2019 presidential election was presented related to the quick count result. Descriptive statistical analysis was also conducted on the results of the 2019 presidential election in Salatiga City (Central Java province), Solok City (West Sumatra province) and Rejang Lebong Regency (Bengkulu province). The resampling method is used to illustrate how the quick count method can be explained for finite populations in Salatiga City, Solok City and Rejang Lebong Regency. By using resampling, the percentage obtained by the Jokowi-Amin pair in Salatiga, Solok and Rejang Lebong are 78.05%, 87.86% and 56.85%, whereas the reality for the three cities in a row is 78.03%; 87,79% and 56.36%.
REGRESI GENERALIZED POISSON UNTUK MEMODELKAN JUMLAH PENDERITA GIZI BURUK PADA BALITA DI SURABAYA MAHFUDHOTIN MAHFUDHOTIN
Jambura Journal of Probability and Statistics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i1.6876

Abstract

The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP  distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population .  The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP  distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population .  The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.
PENERAPAN METODE EXPONENTIAL MOVING AVERAGE PADA PERAMALAN PENGGUNAAN AIR DI PDAM KOTA GORONTALO WA SALMI; ISMAIL DJAKARIA; RESMAWAN RESMAWAN
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7152

Abstract

Facing the dry season, it is probable that there is a lack of water or excess distribution at one point during distribution to every house that uses PDAM water every day. This will result in community instability in using water and inaccurate users. Therefore, forecasting of the amount of water used in PDAM Kota Gorontalo for the next period. The method used to forecast is the Exponential Moving Average method. Criteria in determining the best method is based on the value of Mean Absolute Deviation and Mean Absolute Percentage Error. After forecasting each smoothing constant is compared, the best model. in predicting the amount of water use in PDAM Kota Gorontalo is an Exponential Moving Average with a smoothing constant of 0.15 because it has the smallest MAD and MAPE values.
PENGGUNAAN SELF ORGANIZING MAP DALAM PENGELOMPOKAN TINGKAT KESEJAHTERAAN MASYARAKAT IRWAN IRWAN; ASTRI YUNI HASHARI; HISYAM IHSAN; AHMAD ZAKI
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7266

Abstract

Self Organizing Map (SOM) is one of the topology forms of Unsupervised Neural Network where in the learning process does not require output target. Clusters in this research consist of one or more regency/city areas that have certain characteristics based on the variables. Each cluster had to be validated by using the Davies Bouldin Index value to get the best cluster formation from the SOM algorithm learning process. The best cluster model is the cluster model that has the smallest Davies Bouldin Index value. This research used 30 variables that refer to the key statistics of South Sulawesi Province People's Prosperity in 2018 by BPS of South Sulawesi Province. In this research, four cluster formation models were formed which began by forming 2 cluster model to form 5 cluster. Based on the Davies Bouldin Index value, it was found that the  5 cluster model have minimum value of 0.17.
PEMODELAN JUMLAH TITIK PANAS DI PROVINSI KALIMANTAN TIMUR DENGAN METODE SINGULAR SPECTRUM ANALYSIS KUKUH WAHYU HIDAYAT; SRI WAHYUNINGSIH; YUKI NOVIA NASUTION
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7287

Abstract

Singular Spectrum Analysis (SSA) merupakan metode analisis runtun waktu dengan pendekatan nonparametrik, di mana metode ini tidak memerlukan beberapa asumsi. SSA cukup powerfull terutama untuk menangani data runtun waktu berpola musiman. Jumlah titik panas di Provinsi Kalimantan Timur memiliki unsur pola musiman berdasarkan beberapa penelitian yang telah dilakukan. Tujuan penelitian ini adalah menentukan model SSA terbaik yang digunakan untuk meramalkan data jumlah titik panas di Provinsi Kalimantan Timur. SSA terbagi menjadi dua tahap dasar yang saling berkaitan, yaitu tahap dekomposisi dan rekonstruksi. Pola musiman pada data dapat diketahui menggunakan analisis periodogram. Berdasarkan hasil analisis diperoleh model SSA, yaitu terdiri dari model peramalan untuk komponen tren dan model peramalan untuk komponen musiman. Tahap berikutnya dilakukan peramalan berdasarkan model yang diperoleh untuk bulan Februari 2020 hingga Januari 2021. Jumlah titik panas pada rentang bulan Februari 2020 hingga bulan Januari 2021 akan terjadi peningkatan dan penurunan titik panas secara drastis. Jumlah titik panas tertinggi akan terjadi pada bulan April tahun 2020, yaitu sebesar 1.840 titik panas.
PERBANDINGAN REGRESI NONPARAMETRIK KERNEL DAN B-SPLINES PADA PEMODELAN RATA-RATA LAMA SEKOLAH DAN PENGELUARAN PERKAPITA DI INDONESIA SEPTIE WULANDARY; DRAJAT INDRA PURNAMA
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7501

Abstract

Analisis regresi merupakan salah satu alat statistik yang banyak digunakan untuk mengetahui hubungan antara dua variabel acak atau lebih. Metode penaksiran model regresi terbagi atas regresi parametrik dan nonparametrik. Penelitian ini bertujuan menganalisis pola hubungan pengeluaran perkapita terhadap rata-rata lama sekolah di Indonesia tahun 2018 melalui perbandingan regresi nonparametrik, yaitu regresi kernel dan spline. Regresi kernel yang digunakan adalah regresi kernel dengan metode penaksir Nadaraya-Watson (NWE), sedangkan regresi spline yang digunakan adalah B-Splines. Berdasarkan nilai Generalized Cross Validation (GCV) yang minimum dari model regresi B-Splines, digunakan model dengan degree 2. Perbandingan model terbaik antara model NWE dan B-Splines dilakukan berdasarkan nilai RMSE terkecil dan kurva yang dihasilkan. Pada penelitian ini, model yang terbaik adalah model B-Splines karena memiliki RMSE 0,705, lebih kecil dibandingkan NWE dengan RMSE 1,854. Selain itu, regresi B-Splines memiliki kurva yang halus dan mengikuti sebaran data dibandingkan kurva NWE.
PENGUJIAN HIPOTESIS SIMULTAN MODEL REGRESI NONPARAMETRIK SPLINE TRUNCATED DALAM PEMODELAN KASUS EKONOMI ANDREA TRI RIAN DANI; NARITA YURI ADRIANINGSIH; ALIFTA AINURROCHMAH
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7755

Abstract

The pattern in a relationship between the response variable and the predictor variable can be known and some cannot be known. In determining the unknown pattern of relationships, nonparametric regression approaches can be used. The nonparametric regression approach is very flexible. One of the most frequently used nonparametric regression approaches is the truncated spline. Truncated splines are polynomial pieces that are segmented and continuous. The purpose of this study is to obtain the best estimator model in the Gini Ratio case against the variables suspected of influencing it, then perform simultaneous hypothesis testing on the nonparametric regression model. The criteria for the goodness of the model use the GCV and R2 values. In the case modeling of the District / City Gini Ratio in East Java Province using a nonparametric regression approach, it was found that the truncated spline estimator with 3 knots points gave quite good results. This is indicated by the coefficient of determination of the truncated spline estimator, which is 84.76%. Based on the results of simultaneous testing, it was found that the open unemployment rate, the percentage of poor people and the rate of economic growth simultaneously had an influence on the Gini Ratio.

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