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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 12 Documents
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Search results for , issue "Vol 3 No 3 (2019)" : 12 Documents clear
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM Lingga Aji Andika; Hasih Pratiwi; Sri Sulistijowati Handajani
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.560

Abstract

Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.

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