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134 results on '"Arisanty, Deasy"'

1. Effectiveness of Problem-Based Learning Models to Improve Learning Outcomes of Geography in the New Normal Learning Era

Author

Aristin, Nevy Farista, Hastuti, Karunia Puji, Arisanty, Deasy, Adyatma, Sidharta, and Donna, Christiana

Abstract

Online learning causes teachers to be considered more active than students, so students' higher-order thinking skills decrease. Limited face-to-face learning is a new normal policy in the field of education. Learning models can be one way to recreate active learning to improve student's learning outcomes. This study determines the effectiveness of the problem-based learning (PBL) model in enhancing student learning outcomes of geography in new normal learning. This research is a nonequivalent pretest-posttest control group design. The population is all class XI social study (IPS) at Senior High School 13 Banjarmasin, Indonesia. The sample is used as the experimental group, namely XI IPS 3 totaling 17 students, and the sample is used as the control group, namely XI IPS 2 totaling 14 students. Data collection techniques use tests, non-test, interviews, and documentation. The results are the independent t-test in the experimental and control groups showed that the PBL model effectively improved student learning outcomes in geography in the new normal learning era. The implication is the PBL model can be applied to new normal learning by teachers by elaborating with learning technology which is still adapted to the characteristics of students so that learning objectives are achieved.

Published
2023

2. Application of Make a Match Model to Improve Geography Learning Outcomes

Author

Arisanty, Deasy and Riyah

Abstract

This research was conducted to analyze the application of make a match learning model to improve the learning outcome of geography in class X of SMA PGRI 4 Banjarmasin with the material of geography principles. This research employed a quasi-experimental design method, quantitative approach and non-equivalent of pre-test and post-test control group design. The research included two groups namely the control and experimental groups. The number of the population was 67 students. All students in class X were used as the samples, where class X 1 was as the experimental class and X2 was as the control class. Students in class X 1 and X-2 were 35 students and 32 students. The result of t-test showed that the value of post-test for experimental class using make a match learning model was 54.87. The post-test result from the control class using the conventional model was 48.05. The post-test using the make a match had the higher learning outcome than the conventional model. The make a match learning model had increased the student learning outcome of geography.

Published
2019

4. Sustaining Peatlands: Community-driven Fire Reduction and Land Management.

Author

Arisanty, Deasy, Hastuti, Karunia Puji, Putro, Herry Porda Nugroho, Aristin, Nevy Farista, Angriani, Parida, Alviawati, Eva, and Syahril

Subjects
PEATLANDS, LAND management, PEATLAND management, BURNING of land, ARSON, LOCAL knowledge, POVERTY reduction
Abstract

This study aimed to analyze sustainable peatland management to reduce the risk of peatland fires. Data were collected through observations and interviews with 20 informants, including farmers, civil servants, heads of government offices, and the Masyarakat Peduli Api (MPA) or Fire Care Community. Findings confirmed that knowledge of peatland characteristics, economic morals, local wisdom, and adaptation carried out by the community has an impact on sustainable peatland management. Local knowledge about the peatland, economic morals, and adaptations have been passed on from generation to generation by reducing land preparation by burning and using appropriate agricultural equipment and farming systems. Managing peatlands while maintaining local wisdom through the tapulikampar system is a better option to reduce the risk of land fires. If the community continues to maintain the local values in managing peatlands, the risk of fires on peatlands can be reduced. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Prediction of forest fire occurrences in peatlands - unbalanced - Data using hybrid ADASYN-machine learning method.

Author

Rosadi, Dedi, Arisanty, Deasy, and Andriyani, Widyastuti

Subjects
*FOREST fires, *BLENDED learning, *PEATLANDS, *LITERATURE reviews, *WILDFIRE prevention, *MACHINE learning, *FOREST fire prevention & control
Abstract

The literature review found that only a few studies in Indonesia have been applied to modeling peatlands fire occurrences. In our previous studies, various machine learning classification approaches are applied to predict forest fire occurrence in the peatlands area. Our previous empirical study found that the datasets of fire hotspot areas, especially for data from the South Kalimantan area, need to be more balanced. In this paper, we consider ADAptive SYNthetic (ADASYN) method for the sampling approach to balance the datasets. We found that by balancing the data, the performance of the classification method can improve about 2-4%. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Balancing peatlands fire data using ANS-SMOTE method for improvement prediction of peatlands fire occurrence.

Author

Rosadi, Dedi, Arisanty, Deasy, and Andriyani, Widyastuti

Subjects
*PEATLANDS, *MACHINE learning, *FORECASTING
Abstract

It is known that the studies of peatlands fire occurrences in Indonesia are less studied before. In our previous study, the prediction of the peatlands fire occurrence was modeled using various machine learning classification approaches. It is found that using South Kalimantan Province data, in the empirical study we previously found that the datasets are unbalanced, i.e., the occurrence and the nonoccurrence of fire hotspots areas. In the study presented in this paper, to improve the classification performance, we consider Adaptive Neighbor Synthetic Majority Oversampling Technique (ANS-SMOTE) approach to balance the data. Using the considered empirical data, we found that this method did not always gives improvement in the classification results. [ABSTRACT FROM AUTHOR]

Published
2024
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12. From Glimmer to Grind: Unveiling the Conflict Potential of South Kalimantan's Diamonds.

Author

Rochgiyanti, Rochgiyanti, Arisanty, Deasy, Rajiani, Ismi, Hastuti, Karunia Puji, Jumriani, Jumriani, and Muhaimin, Muhammad

Subjects
*DIAMOND mining, *STRUCTURAL equation modeling, *DIAMONDS, *CONFLICT theory, *SOCIAL conflict, *ENVIRONMENTAL degradation
Abstract

This study delves into the intricate social dynamics of South Kalimantan's traditional diamond mines, revealing a potential for conflict amidst the promise of economic uplift. South Kalimantan is one of the provinces in Indonesia, known nationwide for its diamond production. Going beyond conventional economic and environmental perspectives, the research employs Galtung's Conflict Theory, examining the interplay of attitudes, behaviours, norms, values, and political influences. By adopting this nuanced approach, the study unveils hidden fault lines within these communities, providing a comprehensive understanding of how diverse factors converge to create discord. The consequences of unaddressed conflict include shattered livelihoods, environmental degradation, and fractured communities. The study employs rigorous quantitative methods to empower stakeholders in proactive conflict management, including questionnaires and Structural Equation Modeling (SEM) with data from 400 respondents. The findings underscore the significant roles of behaviours, attitudes, norms, and politics in fueling tension, with values demonstrating surprisingly less direct influence. This insight is a roadmap for conflict prevention and sustainable development, allowing stakeholders to tailor interventions based on specific conflict-driving factors. The study urges a shift from reactive to proactive measures, envisioning a future where prosperity continues and communities thrive in harmony, free from conflict. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Prediction of forest fire using hybrid fuzzy-clustering - Bagging method.

Author

Rosadi, Dedi, Andriyani, Widyastuti, and Arisanty, Deasy

Subjects
FOREST fires, SUPPORT vector machines, WILDFIRE prevention, FOREST fire prevention & control
Abstract

Various classification methods have been considered to predict the occurrence of the forest fire, including the recent ensemble methods, such as bootstrap aggregating (bagging) method and its extension. Here we consider a hybrid approach between fuzzy c-means clustering and bagging. As the weak learner in the bagging approach, we consider multinomial logistic (multilogit) regression and Support Vector Machines (SVM) classification approaches. To see their empirical performance, the proposed approaches are applied to the public data set, where the bagging approach has not been considered as the classification method in the other studies yet. From the empirical results, we obtain the hybrid fuzzy c-means clustering - the bagging multilogit regression approach has the best accuracy for classifying the size of forest fire data. [ABSTRACT FROM AUTHOR]

Published
2023
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