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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pendidikan dan Profesi Keguruan
Putra, Muhammad Reza
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Computer Science & IT Education Other

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EFEKTIVITAS PENGGUNAAN SOFTWARE EAGLE TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN MENGGAMBAR TEKNIK DI JURUSAN TEKNIK INSTALASI TENAGA LISTRIK SMKN 2 MAKASSAR Putra, Muhammad Reza; Sidin, Udin Sidik; Miru, Alimuddin Sa'ban
Jurnal Pendidikan dan Profesi Keguruan Vol 3, No 2 (2024): Jurnal Pendidikan dan Profesi Keguruan
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/progresif.v3i2.35279

Abstract

Penelitian ini bertujuan mengetahui Efektivitas Penggunaan Software Eagle Terhadap Hasil Belajar Siswa pada Mata Pelajaran Menggambar Teknik di Jurusan Teknik Instalasi Tenaga Listrik SMKN 2 Makassar. Metode penelitian yang digunakan adalah metode kuasi eksperimen dengan desain non equivalent control design, yang sampelnya 37 siswa untuk kelas kontrol dan 37 siswa untuk kelas eksperimen. Instrumen penelitian yang digunakan adalah dokumentasi dan tes pretest, posttest berupa soal-soal pilihan ganda. Hasil analisis statistik deskriptif menunjukkan bahwa terdapat peningkatan hasil belajar pada kelas eksperimen dengan nilai rata-rata pretest sebesar 75 dan dari hasil posttest diperoleh nilai rata-rata sebesar 85, sedangkan untuk nilai rata- rata pretest kelas kontrol sebesar 73 dan hasil dari posttest nilai rata-rata sebesar 80. Hasil uji normalitas dengan analisis Kolmogrov Smirnov menunjukkan bahwa data pretest dan posttest kelas ekperimen dan kelas kontrol dalam penelitian ini berdistribusi normal karena nilai p > α (0,05). Hasil uji t dengan analisis independent sampel t test menunjukkan bahwa nilai Sig. (2-tailed) sebesar 0,000 < alpha 0,05 sehingga dapat disimpulkan bahwa Ho ditolak dan Ha diterima, yang berarti bahwa ada perbedaan yang signifikan antara hasil belajar kelas eksperimen dengan kelas kontrol. Berdasarkan hasil analisis data yang diperoleh bahwa terdapat perbedaan yang signifikan antara hasil belajar siswa kelas eksperimen dan kelas kontrol yang berarti pelaksanaan pembelajaran menggunakan Software Eagle efektif terhadap hasil belajar siswa pada mata pelajaran menggambar teknik di jurusan teknik instalasi tenaga listrik SMKN 2 Makassar.
Effectiveness of VGG19 in deep learning for brain tumor detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1210-1218

Abstract

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.
Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3619

Abstract

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.
Co-Authors Bakar, Azuraliza Abu Miru, Alimuddin Sa'ban Syafri Arlis Udin Sidik Sidin Yanto, Musli