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All Journal Progressive Physics Journal Geosains Kutai Basin
Sutaji Putri, Devina Rayzy Perwitasari
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Astronomy Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Health Professions Materials Science & Nanotechnology Physics

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Identifikasi Keberadaan Hidrokarbon Menggunakan Inversi Impedansi Akustik dengan Algoritma Artificial Neural Network (ANN) Maulana, Ansar; Lepong, Piter; Sutaji Putri, Devina Rayzy Perwitasari; Munir, Rahmiati
GEOSAINS KUTAI BASIN Vol. 7 No. 1 (2024)
Publisher : Geophysics Study Program, Faculty of Mathematics and Natural Sciences, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/geofisunmul.v7i1.1127

Abstract

Hydrocarbons are the main energy source in the world, especially in Indonesia, this is what makes hydrocarbons a natural resource that has been extensively explored to determine the presence of hydrocarbons. The exploration used is the Geophysics method, namely the seismic method and the well logging method, both methods are processed to provide an overview of the subsurface. The data processing technique used is acoustic impedance inversion modeling which aims to determine the characteristics of the reservoir based on changes in the acoustic impedance value in each layer. In this study using model-based acoustic impedance inversion using an artificial neural network (ANN) algorithm and the results obtained in the inversion analysis obtained an error of 0.002, so that the model can be used on seismic trace data to produce an acoustic impedance model. modeling section with a value of less than 5000 which may mean that there are hydrocarbons in the research location. Keywords: Artificial Neural Network, Hydrocarbons, Acoustic Impedance, Inversion.
Pengaruh Iklim Kerja Panas Terhadap Respon Fisiologis Pekerja dalam Ruang Preparasi di PT-X fitriani, nur shabrina ulima; Natalisanto, Adrianus Inu; Sutaji Putri, Devina Rayzy Perwitasari; Mislan, Mislan; Putri, Erlinda Ratnasari
Progressive Physics Journal Vol 4 No 1 (2023): Progressive Physics Journal
Publisher : Program Studi Fisika, Jurusan Fisika, FMIPA, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/ppj.v4i1.1025

Abstract

Work climate is one of the factors whose gave big influence on the performance of human resources, for both in hot work climate and cold work climate, even the effect is not limited to performance but can go further, specifically on the safety and health of workers. For this reason, it is necessary to measure and evaluate the working climate standard. This study was conducted to determine the effect of hot working climate on the physiological response of workers in the preparation room at PT-X and to find a temperature control strategy in the preparation room at PT-X. The research were carried out in 4 steps, namely: first was collecting data on the work climate in the Preparation Room at PT-X, second was collecting data on the workload of workers in the Preparation Room at PT-X, third was collecting data on the physiological response of the worker in the form of measuring body temperature, measuring blood pressure, measuring pulse rate and measuring the weight of workers. The last step was analysis. Based on the results of the research, the hot working climate affects the physiological response of the workers before and after work. The strategy for controlling the hot working climate in the Preparation Room at PT-X is by increasing the rest time for workers, maximizing the use of PPE (Personal Protection Equipment) for each worker, and adding health signs at the worker's location.
Klasifikasi Sel Tumor Payudara Menggunakan Algoritma Support Vector Machine (SVM) Rismawati, Puspa; Natalisanto, Adrianus Inu; Sutaji Putri, Devina Rayzy Perwitasari
Progressive Physics Journal Vol. 5 No. 1 (2024): Progressive Physics Journal
Publisher : Program Studi Fisika, Jurusan Fisika, FMIPA, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/ppj.v5i1.1092

Abstract

Cancer is a large group of diseases that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably, beyond their normal limits to invade adjacent parts of the body and/or spread to other organs. There is a lot of information about breast cancer that can be accessed easily. Information about breast cancer can be processed with machine learning. Machine learning can discover new meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technology and statistical and mathematical techniques. The purpose of this research is to determine the value of the accuracy of the SVM model on training data and testing data; and to determine the precision value of the SVM model on training data and testing data. Wisconsin Breast Cancer (WBC) data available in the UCI Machine Learning Repository. The data have been processed using the Python programming language with a support vector machine (SVM) modeling algorithm. The results of this research indicate that the value of accuracy in training data was equal to , the value of accuracy in testing data was equal to , and the value of precision in the SVM model algorithm was obtained as large as for training data and as large as for data testing.
Co-Authors Adrianus Inu Natalisanto fitriani, nur shabrina ulima Lepong, Piter Maulana, Ansar Mislan Munir, Rahmiati Rismawati, Puspa