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All Journal BERKALA FISIKA Jurnal Kedokteran Diponegoro Jurnal Ilmiah Arena Tekstil Journal of Physics and Its Applications
Pandji Triadyaksa
Department Of Physics, Faculty Of Science And Mathematics, Diponegoro University, Semarang
Astronomy Earth & Planetary Sciences Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Physics

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Improving water absorption time and the natural silk strength (Bombyx Mori) using atmospheric dielectric barrier discharge plasma Zaenul Muhlisin; Muhammad Adrian Lathif; Fajar Arianto; Pandji Triadyaksa
Journal of Physics and Its Applications Vol 3, No 2 (2021): May 2021
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v3i2.10658

Abstract

This researchaimed to obtain Dielectric Barrier Discharge plasma discharge characteristics with and without the placement of natural silkBombyx Mori on one of the electrodes. Furthermore, the strength and the water absorption time of the irradiated silk samples will be analyzed.  Plasma discharge is generated by connecting electrodes of point-to-plane configuration with a sheet of glass inserted on the plane electrode at atmospheric conditions. The characterization of plasma discharge, either with or without the natural silk samples' placement on the plane electrode, was performed by increasing A.C.'s high voltage power source to reach arch discharge. Theelectrode spacing varied from 0.7 cm to 2.5 cm with a 0.3 cm increment. Sample irradiation was performed using cold plasma for 5, 15, and 30 minutes respectively. Placing or not placing the natural silk samples on the plane electrode will increase the plasma's discharge current and increase the high voltage. Moreover, increasing the distance between the electrodes and placing the sample on the plane electrode decreases the discharge current. Using Scanning Electron Microscopy, it was found that increasing plasma irradiation time on samples decreases the silk thread'sdiameterand shortening its water absorption time. The strength of irradiated fabric was reduceduntil 15 minutes of irradiation. However, at 30 minutes of irradiation, there was an increase in sample thickness compared to control samples.
Support Vector Machine, Naive Bayes, and Artificial Neural Network Back Propagation Comparison in Detecting Brain Tumor Triadyaksa, Pandji; Ahmad, Harisma Zaini; Marhaendrajaya, Indras
Jurnal Kedokteran Diponegoro (Diponegoro Medical Journal) Vol 13, No 4 (2024): JURNAL KEDOKTERAN DIPONEGORO (DIPONEGORO MEDICAL JOURNAL)
Publisher : Faculty of Medicine, Universitas Diponegoro, Semarang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/dmj.v13i4.45462

Abstract

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.
Dose Distribution of Pencil Beam Proton Therapy using Geant4 Simulation for Breast Cancer Treatment Budiman, Rizki; Sutanto, Heri; Tursinah, Rasito; Triadyaksa, Pandji
Journal of Physics and Its Applications Vol 7, No 2 (2025): May 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i2.25067

Abstract

This study aims to obtain a Spread-Out Bragg Peak (SOBP) for breast cancer treatment using proton pencil beams Monte Carlo simulation. Proton beams with 2 MeV energy steps from 70 to 110 MeV were simulated using Geant 4 software to generate the SOBP. The optimization tool Linear Least Squares (lsqlin) was used to configure the proper proton beam weighting fraction. This tool successfully produced SOBPs within a depth range of 4-8 cm, 4-6 cm, and 5-7 cm. Comparison against a trial-and-error approach to creating SOBP by a different study shows that Linear Least Squares (lsqlin) approximation leads to a better SOBP.
Co-Authors Achmad Sjaifudin Tayibnapis Ahmad, Harisma Zaini Ali Khumaeni Asep Yoyo Wardaya Binu Soesanto, Qidir Maulana Budiman, Rizki Dhyan Prastiwi Evi Setiawati Fajar Arianto Fajar Ariyanto Hasan, Siti Nurjannah Heri Sutanto I Gusti Bagus Wiksuana Indras Marhaendrajaya J. Wasiq Jatmiko Endro Suseno Mahendra Kusuma Nugraha Muchammad Azam, Muchammad Muhammad Adrian Lathif Muhammad Nur Muhammad Nur Ningrum, Febri Sintia Priyono, Andi Rasito Tursinah, Rasito SRI RAHAYU Sumariyah Sumariyah Susilo Hadi Wienda Intan Permatasari Yusup Hidayat Zaenul Muhlisin