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All Journal Bulletin of Scientific Contribution : Geology Journal of Geoscience, Engineering, Environment, and Technology JOURNAL OF EARTH ENERGY ENGINEERING International Journal of Educational Dynamics (IJEDS) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Scientific Contributions Oil and Gas
Novia Rita
FBS UNP
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Physics Public Health Social Sciences

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Contribution of Teachers in Achieving the Goals of Basic Education: A Literature Review on Best Practices and Challenges Rita, Novia; S, Neviyarni; Irdamurni, Irdamurni
International Journal of Educational Dynamics Vol. 5 No. 2 (2023): International Journal of Educational Dynamics (IJEDs)
Publisher : Postgraduate School, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijeds.v5i2.420

Abstract

Teachers play a crucial role in achieving the goals of basic education. This article aims to conduct an in-depth literature review on the contribution of teachers in realizing the objectives of basic education by identifying the best practices employed by teachers and analyzing the challenges they face in reaching these goals. The article is a descriptive-analytical research study that collects data through searching and analyzing various relevant articles, journals, and scholarly literature. The data is gathered by searching literature through academic databases and digital libraries. Relevant articles are selected based on predetermined inclusion and exclusion criteria. Subsequently, the data is systematically analyzed, and significant findings are identified. The research findings indicate that teachers have significant contributions in achieving the goals of basic education. Teachers utilize innovative teaching methods, consider individual student differences, provide constructive feedback, collaborate with students and parents, and build positive relationships with students. However, there are challenges, including limited resources, student individual differences, and time constraints and high workload.
Implementation of Shared and Webbed Learning Model in Elementary Schools: A Qualitative Study Through Interviews with Teachers Rita, Novia; Fitria, Yanti; Erita, Yeni
International Journal of Educational Dynamics Vol. 5 No. 2 (2023): International Journal of Educational Dynamics (IJEDs)
Publisher : Postgraduate School, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijeds.v5i2.421

Abstract

The development of integrated thematic learning in elementary schools is an increasingly popular approach to building foundational knowledge, skills, and positive attitudes in children. This article aims to explain the implementation of the shared and webbed learning models in elementary schools through qualitative research using teacher interviews. The research sample consisted of elementary school teachers who have implemented the shared and webbed learning models in integrated thematic learning. Data obtained from the teacher interviews were analyzed using a descriptive analysis approach to identify emerging themes and patterns. The results of the study indicate that the shared and webbed learning models have been implemented in various ways in elementary schools but also face challenges in terms of time, teacher understanding, and comprehensive assessment. The benefits obtained include improved student understanding, the development of collaborative skills, and student motivation in learning. In further development, adequate support is needed in terms of time and resources, teacher training, and the development of relevant assessment instruments to enhance the implementation of shared and webbed learning models in elementary schools.
The Integration of Hybrid Capacitance Resistance Model and Machine Learning: A Data-Based Workflow for Optimizing Waterflood Performance and Reservoir Management Muhendra, Syifa Alviola; Rita, Novia; Ambia, Fajril; Dahlia, Agus
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1928

Abstract

This study aims to minimize uncertainty in waterflood performance by employing a data-driven workflow that combines the Capacitance Resistance Model (CRM) with Machine Learning. Two CRM variants, CRM-P (Producer-based) and CRM-IP (Injector-Producer-based), are utilized to evaluate interwell connectivity and time constants on three reservoir models: homogeneous, heterogeneous, and a real field scenario (Volve Field). The model is evaluated using R² and Mean Absolute Percentage Error (MAPE) and is compared against the Random Forest and eXtreme Gradient Boosting (XGBoost) techniques. The results indicate that CRM-IP provides more realistic estimates than CRM-P, particularly for response time. XGBoost consistently demonstrates superior prediction accuracy, achieving R² values of 0.76–0.98 and MAPE values of 0.5–10%. Three-dimensional (3D) visualizations of interwell connectivity and streamline analysis strengthen the understanding of fluid flow and sweep efficiency. This further demonstrates that integrating CRM and Machine Learning serves as a decision-support tool for Enhanced Oil Recovery optimization, as evidenced by R² and MAPE analyses that characterize sweep efficiency and the reservoir's capacity to accommodate additional injection.
Comparative Study of Capacitance Resistance Model and Machine Learning for Sensitivity Analysis of Polymer Injection Performance Rizal, Azri Agus; Ambia, Fajril; Rita, Novia; Herawati, Ira
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1929

Abstract

The objective of this study was to evaluate the performance of polymer injection in the Volve Field by validating full-physics tNavigator simulation results. This process was performed using two independent data-driven approaches: the Capacitance Resistance Model (CRM) and machine-learning algorithms Random Forest and XGBoost. This validation framework addresses uncertainty in flow-parameter and ensures that simulated production responses align with data-driven injection–production behavior. The simulation model was constructed using 20 years of historical field data, consisted of five years of polymer injection at 1000–3000 ppm, followed by 15 years of chase water flooding. The simulation results showed that polymer injection increased the oil recovery factor from 21.12% to 21.30% in the best-case scenario, indicating a modest improvement in sweep efficiency. CRM, applied through CRM-P and CRM-IP configurations, successfully reconstructed production profiles and quantified interwell connectivity (R² = 0.94; MAPE < 10%). Machine-learning validation further confirmed these results, with Random Forest achieving R² = 0.92 (MAPE < 1%) and XGBoost achieving R² = 0.99 (MAPE < 1%). Overall, CRM and machine learning provide effective and independent validation pathways, enhancing confidence in simulation outcomes and allowing for reliable assessment of polymer-injection performance in field applications.
Comparative Analysis of Capacitance-Resistance Models and Machine Learning for Co₂-Eor Production Forecasting: A Case Study of Dynamic Connectivity in Heterogeneous Reservoir Rafsanjani, Reyhan; Dahlia, Agus; Ambia, Fajril; Rita, Novia; Husbani, Ayyi
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1930

Abstract

This study evaluates an integrated forecasting framework that combines Capacitance-Resistance Models (CRMP and CRMIP) with ensemble machine learning algorithms (Random Forest and XGBoost) to predict CO₂-Enhanced Oil Recovery performance in the heterogeneous Volve Field. Reservoir simulation was performed using tNavigator with CO₂ injection at 941 tons/day (35 MMSCF/day) over 20 years. The results demonstrate the critical influence of CO₂-specific characteristics, with a determined Minimum Miscibility Pressure of 3299.68 psi and a corresponding oil Swelling Factor of 1.19. Machine learning models, particularly XGBoost, significantly outperformed conventional CRM methods, achieving exceptional accuracy (R² = 0.99-1.00, MAPE = 0.44-2.24%) compared to CRMP/CRMIP (R² = 0.55-0.72, MAPE = 16-23%). The CO₂ injection scenario substantially enhanced oil recovery, achieving a cumulative production of 15.73 MMSTB (RF 20.45%) compared to 9.38 MMSTB (RF 12.19%) for waterflooding, representing a 67.7% improvement and incremental recovery of 6.35 MMSTB. Interwell connectivity analysis revealed dynamic reservoir responses with time constants ranging from 916 to 927 days. The integration of physics-based models with non-linear machine learning algorithms significantly improves prediction accuracy while providing comprehensive insights into reservoir dynamics, allowing for optimal CCUS implementation in heterogeneous reservoir systems.
Co-Authors Adi Novriansyah, Adi Agus Dahlia Ambia, Fajril Fitria, Yanti Husbani, Ayyi Ira Herawati, Ira Irdamurni Marliaty, Toety Muhammad Ariyon Muhendra, Syifa Alviola Mursyidah Umar, Mursyidah Neviyarni, Neviyarni Novrianti Pradana, Aulia Putri, Rizka Haswinda Rafsanjani, Reyhan Ramadhan, Romal Rizal, Azri Agus Rosalia M Taufand, Rosalia M Sapitri Sapitri, Sapitri Tiggi Choanji, Tiggi Tomi Erfando, Tomi Yeni Erita Yuniarti Yuskar, Yuniarti