Claim Missing Document
Check
Articles

Found 5 Documents
Search

DOSIMETRI BORON NEUTRON CAPTURE THERAPY DENGAN VARIASI PROYEKSI PADA TRANSVERSE COLON CANCER (TCC) MENGGUNAKAN PHITS Putri, Sarika Setya; Sardjono, Yohanes; Irwan, Mochammad Rafli
Jurnal Fisika : Fisika Sains dan Aplikasinya Vol 10 No 1 (2025): Jurnal Fisika : Fisika Sains dan Aplikasinya
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/fisa.v10i1.20743

Abstract

Transverse Colon Cancer (TCC) is a type of cancer that attacks one of the pars of Colon, patients with TCC clinically have a low quality of life due to the risks caused. So that optimal treatment is needed that is able to stop the metastasis of cancer cells, but has minimal side effects on other tissues. BNCT (Boron Neutron Capture Therapy) treatment with the selective targeting method is claimed to be able to provide high patient justification, but the most optimal projection is needed to ensure an effective dose distribution in cancer cells. In this case the collimator should be kept as close as possible, since the thermal neutron fluxs greatly decreases as the distance from the collimator increases. The evaluation of BNCT planning treatment was carried out computationally using the Particle and Heavy Ion Code Transport System (PHITS) version 3.30, to determine the dosymmetry of the abdominal phantom voxel, which was varied into four projections, namely LLD AP (Left Lateral Decubitus Antero-Posterior), LLD PA (Left Lateral Decubitus Postero-Anterior), Lateral Dextra and Lateral Sinistra. Based on the results of simulation and evaluation, it is confirmed that the dose and irradiation time examined in the transverse colon are significantly reduced in the projection of LLD AP is more effectively applied to TCC.
Selection Dominant Features Using Principal Component Analysis for Predictive Maintenance of Heave Engines Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Sardjono, Yohanes; Lea, Lea
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.22854

Abstract

This article aims to identify the dominant features that have a significant impact on the health of a heavy machine that relates to the digital infrastructure of a company. The importance of this research is that the authors define predictive maintenance based on Principal Component Analysis (PCA), which is the novelty of this article. The novel contribution of this research lies in the application of Principal Component Analysis (PCA) for predictive maintenance of heavy machinery, which has not been integrated into the Scheduled Oil Sampling (SOS) procedures. The recorded data are called Scheduled Oil Sampling (SOS) and historical data from an equipment called CoreDataQ, which works for recording many features from heavy machine activities. The data contain two sets data. The method is Principal Component Analysis (PCA). This method leads to obtain a maximum of 20 significant features on data based on SOS. The results have been confirmed and agreed upon by the manager who owned CoreDataQ to consider the selected dominant features for further related maintenance. 
Comparison Study of Beryllium and Lithium Target System for Beam Shaping Assembly Used in Boron Neutron Capture Therapy Harendza, David; Trihandaru, Suryasatriya; Santosa, Slamet; Sardjono, Yohanes
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2348

Abstract

Boron neutron capture therapy (BNCT) is a selective targeting cancer therapy method which is by radiating epithermal neutron beams to a tumor which has been injected by boron-10 compound. The implementation of this method requires 2 (two) key elements, namely the boron-10 compound and the irradiating neutron beam of epithermal energy level. The neutron source used in this research was 13 MeV 1mA cyclotrons which have been developed by Indonesia National Nuclear Energy Agency. This paper was preoccupied in beam shaping assembly (BSA) target system. BSA target system is used to convert the proton produced by cyclotron into neutron. IAEA generated a recommendation for BNCT neutron beam quality, one of them was epithermal neutron flux which is higher than 109
Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Susanto, Bambang; Sardjono, Yohanes
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v5i1.3468

Abstract

The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period  for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas.  It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro)  i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers  are computed as discrete values depending on time. The used research method is  finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.
Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohanes
SJME (Supremum Journal of Mathematics Education) Vol 4 No 2 (2020): Supremum Journal of Mahematics Education
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v4i2.3515

Abstract

Tujuan penelitian ini adalah penyusunan hyperplane untukmemisahkan data yang mempunyai 2 kelas dan bersifat linear padabidang datar sebagai pembelajaran vektor untuk klasifikasi data.Adapun metode yang digunakan adalah pre-Support Vector Machine(SVM). Metode ini mencari garis (hyperplane) terbaik yangmemisahkan data dan memberi ruang antar 2 kelas data dimana ruangpemisah tersebut tidak boleh memuat data serta ruang tersebutmerupakan margin maksimal. Langkah awal adalah menduga garispemisah (hyperplane) awal melalui titik O. Dengan mengambil salahsatu titik data yang menjadi titik referensi, disusun vektor dari Oterhadap titik referensi dan garis melalui titik referensi sebagai bataspertama margin. Kemudian dibentuk vektor arah dari titik O yangtegak lulus terhadap garis awal (hyperplane). Selanjutnya vektorproyeksi dibentuk dari titik referensi terhadap vektor arah sehinggavektor arah dan vektor proyeksi berhimpit (searah). Penyusunanmargin diperoleh dengan menyusun garis yang pararel terhadap garisawal sebagai hyperplane serta berjarak 2 kali dengan panjang vektorproyeksi tersebut. Hyperplane terbaik diperoleh secara manual denganmengatur batas kedua dari margin yang diperoleh dengan menggambargaris melalui suatu titik data pada kelas ke-2 dengan jarak terdekat danpararel terhadap garis yang melalui titik referensi dari data kelas ke-1.