e-Journal UIR (Journal Universitas Islam Riau)
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Kajian Semiotika dalam Kumpulan Puisi Gazal Hamzah Karya Marhalim Zaini
Get PDFLiterary works describe various phenomena that occur in society. In which there are elements of semiotics namely Icons, Indexes and Symbols. In the Collection of Poetry Gazal Hamzah by Marhalim Zaini contains many semiotic elements such as icons, indexes, and symbols and the works produced have a lot of relationships between signifiers (text) and signifieds (context) related to icons, indexes and symbols. The research approach used is a qualitative approach. The method used is descriptive method. The results of this study are that in the Collection of Poetry of Gazal Hamzah by Marhalim Zaini there are still many icons, indexes and symbols. In the entire collection of Gazal Hamzah's poems by Marhalim Zaini, semiotics is used to convey meaning through the use of icons, indexes, and symbols. This research helps understand how poets use semiotic elements to create effects and explore the themes raised in the literary work. Semiotics opens the door to understanding language and the meaning hidden behind words, thereby enriching the experience of reading and enjoying Gazal Hamzah's poetry
Machine Learning Application of Two-Dimensional Fracture Properties Estimation
Get PDFFractures are substantial contributors to solute transport sedimentary systems that form pathways. The pathway formed in a fracture has two physical parameters, there are mean aperture and surface roughness. Mean aperture is the thickness of the pathway that the fluid will pass through, and surface roughness is the roughness of the fracture pathway. The two physical parameters of the fracture are important to determine since they affect the permeability value in petroleum reservoir analysis. We developed a machine learning algorithm based on the Convolutional Neural Network (CNN) to predict those two parameters. Furthermore, image processing analysis is performed to generate the datasets. The results show that the CNN algorithm shows good agreement with the reference results. In addition, the algorithms showed efficient performance in terms of computational time. CNN is a type of deep neural designed to perform analysis on multi-channel images that can classify fracture geometry. The best model was determined using a benchmark dataset with a CNN model provided by Keras. The results of experiments conducted on fracture geometry images show that the machine learning model created is able to predict the mean aperture and surface roughness values
Analysis of Petrophysical Parameter on Shaly Sand Reservoir by Comparing Conventional Method and Shaly Sand Method in Vulcan Subbasin, Northwest Australia
Get PDFVulcan Subbasin is an area with a lot of oil and gas exploration where is located in the Bonaparte Basin, Northwest Australia. There is some formation identified as sandstone reservoir with clay content which is usually called shaly sand based on the screening between resistivity log and density log. Clay content caused lower resistivity log readings so the shaly sand reservoir is considered as non-reservoir. To overcome this, a method besides the conventional method was applied to analyze the petrophysical parameters of shaly sand reservoir, it was shaly sand method. Petrophysical analysis is an analysis of rock physical parameters such as shale volume, porosity, and water saturation based on well log data. In this study, petrophysical analysis was carried out in the Vulcan Subbasin using 35 well log data, including gamma ray log, resistivity log, neutron log, and density log for the conventional method and shaly sand method involved Stieber equation and Thomas Stieber plot. The results obtained from this study are the comparison of petrophysical parameter values and pay summary between the conventional method and the shaly sand method, also its relation to the shale distribution type. By applying the shaly sand method, the average shale volume has decreased, the average porosity has increased, the average water saturation has increased, the average net to gross has increased, the average net thickness has increased, and the average net pay has increased. Changes in the average value were caused by laminated-dispersed shale distribution type which is influenced by diagenesis and the depositional environment of the formation
Stress Analysis of Existing Underground Gas Pipeline due to New Road Crossing with ODOL Transportation
Get PDFPipelines are the main choice for transport oil and gas due to its resilience, reliability, safety, and lower cost. Most road crossing pipelines are located underground where protections from the loads can be used such as additional pavement. Underground road crossing pipelines withstand stresses caused by the internal load, earth load, and live load. These loads are affected by the pipe and fluid specifications, soil and environment data, and also the vehicle data. Over dimension and over loading (ODOL) vehicles are a very common problem found in Indonesia. Hence, a stress analysis towards the underground road crossing pipeline being crossed by ODOL vehicles are relevant. A manual calculation of the stress analysis can be done by using API RP 1102: “Steel Pipelines Crossing Railroads and Highways”. A stress analysis using the finite element method (FEM) is conducted using a computer software, namely Abaqus, which also shows the displacement of the pipeline. The case study is an underground road crossing pipeline with depth of 8 feet and uses rigid pavement. The use of rigid pavements over the soil decreases the stress experienced by the pipeline. The results of the total effective stress show a value of 4,785 psi which is still within the allowable range. The stress is found to be directly proportional to the displacement value obtained using FEA. By conducting parametric studies, it is also found that the total effective stress decreases as the burial depth of the pipe is larger
4-dimensional seismic interpretation to monitor CO2 injection in carbon capture & storage project of Sleipner field, North Sea, Norway using inversion method
Get PDFSleipner is the world's first commercial Carbon Capture and Storage (CCS) project, located off the coast of Norway, with the goal of reducing carbon emissions by capturing CO2 and storing it in a utsira saline aquifer sandstone reservoir capable of storing up to 600 billion tonnes of CO2. The CO2 injection in these projects increases year after year, so the CO2 development must be monitored to see the distribution pattern and its implications for the reservoir zone. The purpose of this research is to calculate and model the CO2 distribution resulting from acoustic impedance inversion using 4-dimensional inversion, to calculate the repeatability from seismic data between baseline and monitor using the Normalized Root Mean Square attribute. In the processing, baseline and monitor data must be matched in the overburden zone using a cross-equalization process so that the inversion process. The results revealed a correlation between the two seismic data sets (baseline and monitor) with the classification of Reasonable Repeatability, and CO2 distribution in a securely stored reservoir that spreads laterally and does not leak
Tutur Kata Penonton Piala Dunia 2022
Get PDF2022 World Cup international event. Furthermore, the context of the audience's speech while watching together provides an overview of how Indonesian people express themselves through speech when watching World Cup matches. The purpose of this study is to explore the forms of speech acts by the public during collective World Cup viewing. The research was conducted in the city of Kolaka, Southeast Sulawesi, using qualitative research methods. Data collection techniques included observation, recording, note-taking, and interviews. Based on the described research results, the identified forms of speech acts tend to be expressive and emotional. Declarative speech acts accounted for 6 instances, imperative speech acts for 4 instances, and interrogative speech acts for 4 instances. The audience's speech patterns were influenced by factors such as the atmosphere, gender, and age. The study findings illustrate the dynamic nature of the audience's response, as the unpredictable World Cup matches often exceeded the audience's expectations. Consequently, people's words when watching the 2022 World Cup together could change depending on the ongoing match situation and conditions. Emotions such as joy, disappointment, sadness, annoyance, and disbelief were common expressions witnessed during these matches
Pengaruh Lingkungan Keluarga dan Motivasi Belajar Terhadap Hasil Belajar Pada Mata Pelajaran Ekonomi Siswa Kelas XI IPS SMA Negeri 2 Siak Hulu
Get PDFAbstrak
Penelitian ini dilakukan di SMA Negeri 2 Siak Hulu dengan tujuan untuk mengetahui pengaruh lingkungan keluarga dan motivasi belajar terhadap hasil belajar pada mata pelajaran ekonomi siswa kelas XI IPS di SMA Negeri 2 Siak Hulu. Adapun jumlah populasi pada penelitian ini 209 orang siswa dengan jumlah sampel 137 orang siswa kelas XI IPS di SMA Negeri 2 Siak Hulu. Terknik pengumpulan data pada penelitian ini menggunakan angket yang diolah dengan menggunakan IBM SPSS for Windows. Teknik menentukan sampel menggunakan teknik Sampel Random. Uji isntrumen yang dilakukan yaitu: Uji Validitas, Uji Reliabilitas, Uji Normalitas, Uji Homogenitas, Uji Multikolinearitas, Analisis Deskriptif, Regresi Linier Berganda, Uji T, Uji F, dan Uji Koefisien Determinasi.Hasil penelitian pada uji korelasi menujukkan bahwa nilai f hitung sebesar 11, 567 dengan nilai f tabel adalah 3,06. Sehingga nilai f hitung besar dari f tabel atau 11,567 > 3,06. Dengan tingkat signifikan 0,00 kecil dari 0,05 maka H0 ditolak dan H3 diterima, dapat disimpulkam bahwa variabel lingkungan keluarga (X1) dan motivasi belajar (X2) secara bersama-sama berpengaruh signifikan terhadap hasil belajar siswa pada SMA Negeri 2 Siak Hulu. Koefisien determinasi terdapat pada nilai adjusted r square sebesar 0,134. Hal ini berarti kemampuan variabel bebas dalam menjelaskan variabel terikat sebesar 13,4 % sisanya 86,6% dijelaskan oleh variabel lain yang tidak dibahas di dalam penelitian ini.
Kata Kunci: Lingkungan Keluarga, Motivasi Belajar, dan Hasil Belaja
A geological overview of the limestone members of the Woyla Group of Sumatra, Indonesia
Get PDFMesozoic limestone units of the Woyla group were identified in many places across the northern part of Sumatra, Indonesia. Even though these sedimentary rocks may play an important role as an element of the potential Pre-Tertiary hydrocarbon play of Sumatra, their characteristics are still not well understood. This study tries to fill this research gap and aims to better understand the characteristics of the limestone members of the Woyla group. There are three objectives of this study: (1) to characterise structural features, and deformation of the Woyla Group; (2) to provide sedimentary characteristics of the limestone members of the Woyla Group; and (3) to understand the main influences on the development of the limestone members of the Woyla Group. An integrated geological analyses, including structural scanline analysis, petrographic analysis, and acid digestion analysis, was conducted to achieve the objectives of this study.
Findings from this research show that the limestone members of the Woyla group were strongly deformed, and structural features such as bedded strata, faults, folds, and joints were identified within these rocks. The limestone units of the Woyla group consist of at least six microfacies. These are wackestone, packstone, wackestone-packstone, packstone-rudstone, fossiliferous sandstone, and fossiliferous shale. Depositional processes, sea level fluctuations, tectonisms, and climatic variations are interpreted as the main factors influencing the development and evolution of these limestone units. It is expected that the results of this study could advance our understanding of the Pre-Tertiary carbonate rocks in general, and the Woyla group of Sumatra in particular
Corn Leaf Diseases Recognition Based on Convolutional Neural Network
Get PDFMaize or known as corn is one of the most important agricultural commodities in Indonesia beside rice. Indonesia is located in a tropical area which has high rate of rainfall and humidity which makes it easy for fungi and bacteria that caused plant disease to thrive. It could be a threat which is a decrease of corn harvest due to plant diseases. To prevent this, a deep learning approach can be implemented to recognize plant diseases automatically based on visual pattern on leaves. In this study, we proposed a CNN-based model for corn leaf diseases recognition. Based on the results, the proposed method has great performance which accuracy score of 93%. Besides that, the proposed method achieved up to 100% precision and recall, and up to 99% F1 score