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Journal : JAMBURA JOURNAL OF PROBABILITY AND STATISTICS

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.
DISTRIBUTED LAG MODEL PENGARUH JUMLAH UANG BEREDAR TERHADAP NILAI TUKAR RUPIAH MENGGUNAKAN METODE KOYCK DAN ALMON LIHAWA, SRIRAPI H; RESMAWAN, RESMAWAN; ISA, DEWI RAHMAWATY; NASHAR, LA ODE
Jambura Journal of Probability and Statistics Vol 3, No 1 (2022): 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.v3i1.11805

Abstract

A regression model that contains the dependent variable which is influenced by the current independent variable, and is also influenced by the independent variable at the previous time is called a distributed lag model. Distributed lag model is a dynamic model in econometrics that is useful in empirical econometrics because it makes a static economic theory dynamic by taking into account the role of time explicitly. There are two distributed lag models, namely the infinite lag model and the finite lag model using the Koyck method and the Almon method in determining the estimated Distributed lag model. This study aims to determine the Distributed lag model for the effect of the money supply on the rupiah exchange rate and determine the best model based on the Koyck method and the Almon method. From the results of selecting the best model based on the SIC value and judging by the more precise R2 of the Koyck method, the resulting model ist  = 7958 + 0.0002Xt + 0.000177Xt-1+ 0.000157Xt-2+ 0.000139Xt-3 + 0.0000123Xt-4
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.