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All Journal Journal of Information Technology and Computer Engineering Indonesian Journal of Social and Environmental Issues (IJSEI) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Wangmo, Sonam
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Determinants of Participation in Different Livelihood Diversification Strategies Among Rural Households in Western Bhutan Wangmo, Sonam; Dorji, Ugyen; Dorji, Nedup
Indonesian Journal of Social and Environmental Issues (IJSEI) Vol. 5 No. 2 (2024): August
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijsei.v5i2.1496

Abstract

Strategies for diversifying one's source of income are crucial for the development of rural households' rain-fed agricultural economies in developing nations like Bhutan. Participating in off-farm and non-farm activities supports households in tackling a variety of difficulties, such as drought. Nonetheless, little study has been done on determining the factors that affect households’ decisions about livelihood choices in the Bhutanese context. Therefore, this study aims to examine the factors influencing rural households’ decisions to diversify their livelihood diversification strategies in western Bhutan. A multi-stage stratified random sampling method was employed to select 384 rural household heads as the study area's sample. Primary data were collected using structured questionnaires from sampled households. The factors affecting rural household heads' decision to select livelihood strategies were determined using a multivariate Probit Regression Model. The model's result showed that, while on-farm livelihood strategy was negatively and significantly correlated with distance to market, it had a strong correlation with male-headed households and land holdings. The non-farm livelihood strategy was demonstrated to be significantly and positively affected by the total income, education level, and dependency ratio; whereas, the gender of the household head had a negative and significant impact. Landholding had a negative and significant impact on off-farm livelihood strategy, while the gender of the household head had a positive and significant effect.  Therefore, the study recommends policies and initiatives aimed at enhancing rural livelihood should prioritize expanding rural infrastructures, enhance smallholder households’ sustainable livelihood ability, and help to participate in income-generating activities in different ways.
Smartphone-Based Heart Disease Classification Using Machine Learning Techniques Jamtsho, Yonten; Wangmo, Sonam
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12504

Abstract

Patients having heart diseases are diagnosed with a severe delay at times and further diagnosis in the absence of medical personnel can be fatal if the prediction is inaccurate. Therefore, this paper proposes the use of heart disease datasets to predict heart disease using various machine learning methods (Logistic Regression, Naive Bayes, Random Forest, k-nearest Neighbor, Support Vector Machine, Decision Tree Classifier, XGBoost Classifier, Artificial Neural Network). Cleveland, Hungarian, Switzerland, Long Beach VA and Statlog (Heart) datasets were used in this study which has 11 features of 1190 instances. The dataset was split into train and test sets with a ratio of 80:20. The performance was evaluated based on the accuracy, precision, recall, and F1 score for each of the models. From the eight models, the XGBoost Classifier outperformed other models with an accuracy of 93.7%. The trained model was integrated with the Android Studio framework to create the mobile application for the classification of heart disease.
Deep Learning-Based Dzongkha Handwritten Digit Classification Jamtsho, Yonten; Yangden, Pema; Wangmo, Sonam; Dema, Nima
JITCE (Journal of Information Technology and Computer Engineering) Vol. 8 No. 1 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.1.1-7.2024

Abstract

In computer vision applications, pattern recognition is one of the important fields in artificial intelligence. With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. The purpose of conducting the research is to create the first-ever Dzongkha handwritten digit dataset and develop a model to classify the digit. In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. The model was trained on three different image dimensions: 28 by 28, 32 by 32, and 64 by 64. Compared to image dimensions 28 by 28 and 32 by 32, 64 by 64 gave the highest train, validation, and test accuracy of 98.66%, 98.9%, and 99.13% respectively. In the future, the sample of digits needs to be increased and use the transfer learning concept to train the model.
Co-Authors Dema, Nima Dorji, Nedup Dorji, Ugyen Jamtsho, Yonten Yangden, Pema