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All Journal Serupa : The Journal of Art Education Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Surya Afriza
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Arts Computer Science & IT Education

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PERBEDAAN HASIL BELAJAR APRESIASI SENI RUPA ANTARA METODE ARTIKULASI DENGAN METODE KONVENSIONAL Surya Afriza; Eswendi .; Efrizal .
Serupa The Journal of Art Education Vol 1, No 1 (2012): Seri B
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (255.679 KB) | DOI: 10.24036/sr.v1i1.382

Abstract

Abstract The purposes of  this were to test different of the students’ result of study of visual art appreciation between articulation method with convensional method in SMP Negeriy 2 Sintuk Toboh Gadang, Pariaman. This research is experiment quacy through Design One Group Pretest - Postest. The Population is all of the student of  7 th of  SMP Negeri 2 Sintuk Toboh Gadang, Pariaman. The technique of sampling was Purposive Sampling. Experimenth class was VII.4 and control  class was VII.3. The result of study can be seen trought the result of study of visual art appreciation. The mean of students’ result of study the experiment class taller than the mean of result of study control class. After  T-tes done, the researcher got count- T  2.520 and table – T 1.669 (count – T > table- T) Katakunci: Apresiasi, Artikulasi, Konvensional, Eksperimen, Kontrol.
Implementation of CNN Method with Otsu Thresholding Preprocessing for Pneumonia Detection Surya Afriza; Noval Aditya Candra Pratama; Muhammad Abdul Aziz; Faisal Muttaqin
JITTER: Jurnal Ilmiah Teknologi dan Komputer Vol. 7 No. 1 (2026): JITTER, Vol.7, No.1, April 2026
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

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Abstract

Pneumonia is a lung infection requiring rapid diagnosis to prevent fatal complications, yet X-ray image quality often hinders manual detection accuracy. This study proposes a hybrid approach using a Convolutional Neural Network (CNN) optimized with Otsu Thresholding for lung area (Region of Interest) segmentation. Experiments were conducted on 1,840 images from a secondary dataset. Evaluation results demonstrate a highly balanced and superior model performance, achieving 96% Recall, 91% F1-Score, and 96% Accuracy. The alignment between accuracy and recall values indicates that the model possesses equally good sensitivity and specificity in detecting both positive and negative cases. These findings prove that Otsu pre-processing effectively assists the CNN in focusing on pathological features, making this method a promising automated diagnostic solution.
Co-Authors Efrizal . Eswendi . Faisal Muttaqin Muhammad Abdul Aziz Noval Aditya Candra Pratama