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All Journal Journal of Applied Health Management and Technology Jurnal Ners Asian Journal of Social and Humanities
Donny Kristanto Mulyantoro
Poltekkes Kemenkes Semarang
Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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BENEFITS OF STEEPING BLACK TEA AS A NEGATIVE CONTRAST MEDIUM ON CT UROGRAPHY EXAMINATION Sagita Yudha; Suharyo Hadisaputro Hadisaputro; Jeffri Ardiyanto; Donny Kristanto Mulyantoro; Siti Masrochah
Journal of Applied Health Management and Technology Vol 2, No 2 (2020): April 2020
Publisher : Politeknik Kesehatan Kementerian Kesehatan Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.425 KB) | DOI: 10.31983/jahmt.v2i2.5697

Abstract

The use of water as a contrast medium requires large amounts of water to fill the lumen of the Urinary Tractus and more water is reabsorbed by the body than is secreted into urine. Steeping Black tea contains Caffeine which is able to increase blood flow in the kidneys thus inhibiting the process of absorption of Na, Ca and Mg causing stimulation of the kidneys to increase the amount of urine production. The purpose of this study is to prove that drinking black tea can increase urine production as a negative contrast medium to see differences in the distension and density of the Urinary Tract on CT Urography examination. This type of research uses True Experimental with Pretest-Posttest Control Group Design research design. Patients selected by Simple Random Sampling. Analysis: Paired t test and Independent t test. The results of the study of the use of 600 ml steeping Black Tea as a negative contrast medium on CT Urography examination did not show the difference in mean difference between the left renal Pelvis p value 0.956, Left UVJ 0.640, Right UVJ 0.935 while on the right renal Pelvis p value 0.001 showed differences in mean difference between the left renal Pelvis p value 0.956, Left UVJ 0.640, Right UVJ 0.935 while on the right renal Pelvis p value 0.001 intervention and control group. Hasil pengukuran p value  densitas Vesika urinaria sebesar 0,678. Conclusion: Black tea can be used as a negative contrast medium on CT Urographic examination but when compared with mineral water it does not show a significant difference.
Application of Fusion Technique with ImageJ Stacks Feature for Brain Tumor MRI Image Optimization Nur Wahyu Tajuddin; Bambang Satoto; Rini Indrati; Donny Kristanto Mulyantoro; Darmini Darmini; Gatot Murti Wibowo
Asian Journal of Social and Humanities Vol. 2 No. 11 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i11.359

Abstract

Fusion techniques on MRI for brain tumors can provide comprehensive visualization by combining Axial T2-Flair and Axial T1-GD (T1-weighted post-contrast) sequence images. Fusion MRI in brain tumors is able to clearly display the location, size and characteristics of the tumor. However, not all institutions can install such additional fusion software due to significant additional costs. Therefore, this study aims to prove that the Stacks feature on ImageJ as an alternative can be optimal in visualizing brain tumor image information through MRI fusion techniques. This study used 17 image samples with a quasi experimental design post test only without control group design to compare three analysis methods, namely fusion maximum intensity, minimum intensity and average intensity so that the most suitable projection can be determined. The evaluation of image quality was carried out through a histogram which was then analyzed with a crucal-wallis and the Mann Whitney u test, while the analysis of pathological information used a crucal-wallis, followed by a post hoc test and continued with Mann Whitney u for further analysis. The results show that the stacks feature on ImageJ can be used in the application of fusion techniques so that it will improve the contrast and sharpness of MRI images, especially in areas with high tumor activity. MRI images of brain tumors with maximum fusion intensity produced images with the highest average gray level and the best pathological information. This projection is more optimal than the minimum intensity and average intensity because it provides a more detailed and clear visualization of brain tumors.
Application of Fusion Technique with ImageJ Stacks Feature for Brain Tumor MRI Image Optimization Nur Wahyu Tajuddin; Bambang Satoto; Rini Indrati; Donny Kristanto Mulyantoro; Darmini Darmini; Gatot Murti Wibowo
Asian Journal of Social and Humanities Vol. 2 No. 11 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i11.359

Abstract

Fusion techniques on MRI for brain tumors can provide comprehensive visualization by combining Axial T2-Flair and Axial T1-GD (T1-weighted post-contrast) sequence images. Fusion MRI in brain tumors is able to clearly display the location, size and characteristics of the tumor. However, not all institutions can install such additional fusion software due to significant additional costs. Therefore, this study aims to prove that the Stacks feature on ImageJ as an alternative can be optimal in visualizing brain tumor image information through MRI fusion techniques. This study used 17 image samples with a quasi experimental design post test only without control group design to compare three analysis methods, namely fusion maximum intensity, minimum intensity and average intensity so that the most suitable projection can be determined. The evaluation of image quality was carried out through a histogram which was then analyzed with a crucal-wallis and the Mann Whitney u test, while the analysis of pathological information used a crucal-wallis, followed by a post hoc test and continued with Mann Whitney u for further analysis. The results show that the stacks feature on ImageJ can be used in the application of fusion techniques so that it will improve the contrast and sharpness of MRI images, especially in areas with high tumor activity. MRI images of brain tumors with maximum fusion intensity produced images with the highest average gray level and the best pathological information. This projection is more optimal than the minimum intensity and average intensity because it provides a more detailed and clear visualization of brain tumors.
Deteksi Tumor Otak pada Citra Magnetic Resonance Imaging (MRI) Brain dengan Metode Support Vector Machine (SVM) Hervina BR Tarigan; Donny Kristanto Mulyantoro; Dwi Rochmayanti
Jurnal Ners Vol. 9 No. 3 (2025): JULI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i3.46148

Abstract

Latar Belakang: Melihat jumlah kejadian tumor ganas Brain yang terus meningkat, selain itu kelemahan metode manual memerlukan keterampilan secara akurat dengan memilih daerah abnormal, yang akan memakan waktu. Oleh karena itu perlu adanya peningkatan metode pegembangan software deteksi otomatis, sebagai pelengkap dalam modalitas MRI Brain di Radiologi. Maka pada penelitian ini memberikan solusi suatu metode algoritma machine learning yang diusulkan adalah deteksi otomatis jinak dan ganas dengan ekstraksi fitur akan diklasifikasi dengan baik oleh Support Vector Machine. Tujuan: Menganalisis perbedaan hasil bacaan citra MRI Brain software Support Vector Machine dengan hasil Ekspertise Radiolog dalam mendeteksi tumor jinak dan ganas. Metode: Penelitian quasi eksperimen dengan citra radiografi MRI Brain. Membangun Machine learning Support Vector Machine melalui program matlab. Pengujian Support Vector Machine dilakukan dengan mengukur akurasi, sensitivitas, spesifisitas, Nilai prediksi positif dan Negatif. Sampel digunakan berjumlah 180 citra mammogram. Analisis data menggunakan uji diagnostik dengan uji statistik Wilcoxon. Hasil: Penelitian membuktikan dari 180 sampel diperoleh kinerja model Support Vector Machine baik dalam mendeteksi tumor Brain pada citra MRI Brain dengan nilai akurasi sebesar 97,77%, sensitivitas sebesar 95,00%, spesifisitas sebesar 99,16%, NPP sebesar 98,27% dan nilai NPN sebesar 95,00% serta terdapat kesamaan hasil Machine learning dengan hasil Ekspertise Radiolog. Kesimpulan: Terdapat kesamaan hasil bacaan citra MRI Brain dalam mendeteksi tumor Brain antara Support Vector Machine (SVM) dengan hasil Ekspertise Radiolog dengan nilai p-value (p>0,05) sebesar 0,898, dengan makna ketika machine learning diterapkan dipopulasi, maka machine learning memberikan angka ketepatan yang tinggi dalam memprediksi.
Co-Authors Bambang Satoto Darmini Darmini Dwi Rochmayanti Gatot Murti Wibowo Hervina BR Tarigan Jeffri Ardiyanto Nur Wahyu Tajuddin Rini Indrati Sagita Yudha Siti Masrochah Suharyo Hadisaputro Hadisaputro