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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 135 Documents
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Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques Setiawan, Rudi; Hayatou Oumarou
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.129

Abstract

This research explored the efficacy of machine learning techniques, specifically the Bagging meta-estimator, in the classification of rice grain images. Utilizing a dataset composed of 45,000 images of Arborio, Basmati, and Jasmine rice varieties, a 5-fold cross-validation was employed to evaluate the model's performance. The results were highly promising, with the model consistently achieving over 96% in accuracy, precision, recall, and F1-score across all folds, indicating its robustness and reliability. The study confirmed that ensemble learning techniques could significantly improve the classification accuracy over single classifier systems in agricultural applications. The findings offer a significant contribution to automated agricultural processes, suggesting that machine learning can greatly enhance the efficiency and precision of rice variety classification. These results pave the way for further research into the integration of such models into agricultural quality control and provide a foundation for the exploration of advanced image processing and deep learning techniques for improved performance. Future research directions include expanding the model to encompass a wider variety of crops and integrating additional data modalities to refine classification accuracy further. Practical applications should explore the incorporation of this technology into existing agricultural systems to maximize the benefits of automation in quality control.
A Survey on Machine Learning Techniques for The Prediction of Solar Power Production Lamidi, L.O.; Oyelakin, Akinyemi Moruff; Akinbi, M. B
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.130

Abstract

Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting
Bibliometric Analysis of Mixed Text Using Transformer-Based Architecture in Africa Sekwatlakwatla, Sello Prince; Malele, Vusumuzi; Ramalepe, Phetole Simon; Modipa, Thipe
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.131

Abstract

Deep learning techniques based on neural networks have been developed for text creation, a critical sub-task of natural language generation that aims to create human-readable content. Natural language processing (NLP) tasks are utilized to recognize speech in code-mixed comments on social media platforms like Facebook and Twitter, which enable users to interact and exchange ideas, views, status updates, pictures, and videos with people all over the world. Although NLP is widely investigated in the world and Africa is home to approximately 3,000 languages, many of which are derived from significant language families, in this regard, there are challenges that Africa faces in Natural Language Processing (NLP), especially mixed text using transformer-based architecture. The purpose of this study is to investigate the prevalence of mixed text using transformer-based architecture in Africa. Bibliometric analysis was used to assess natural language and mixed text in Africa, utilizing transformer-based architecture. show that sentiment analysis is the holistic tool that is used for mixed text using transformers, where social media, deep learning, codes, computational linguistics, and social networking are critical tools in generating human-like quality text. Therefore, this study proposes artificial intelligence, artificial neutral networks, and neural networks, as well as a prediction to estimate the technique or fluctuation as the method for mixed text using transformer-based architecture in Africa. This research sets the path for future studies that use mixed text using transformer-based architecture in Africa
Adaptive Minimum Support Threshold for Association Rule Mining Ogedengbe, Matthew; Junaidu, Sahalu; Kana, Donfack
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.134

Abstract

In association rule mining (ARM), valuable rules are extracted from frequent itemsets, selecting appropriate minimum support thresholds is essential yet challenging. Arbitrary threshold selection often results in either an overwhelming number of uninteresting rules or the omission of relevant rules. To address this issue, this study introduces an Adaptive Minimum Support (SAd) algorithm designed to dynamically adjust the support threshold based on dataset characteristics, thereby facilitating the discovery of optimal association rules. The SAd algorithm was experimented on three real-world datasets, yielding optimal minimum support thresholds of 0.065, 0.133, and 0.057 respectively. Results demonstrate the algorithm's effectiveness in adapting the support threshold to each dataset's characteristics. By optimizing the threshold, the SAd algorithm enhances the quality of discovered association rules, offering more actionable insights for decision-making.
A Literature Review to Investigate Data Analytics Tools for The Allocation of Resources in Cloud Computing Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.136

Abstract

To ensure efficient operations and cost-effectiveness, resource management in cloud computing entails managing cloud resources to satisfy application needs, financial restrictions, and security. In this regard, utilizing data analytics tools for the allocation of resources in cloud computing to efficiently predict, track, allocate, and monitor resources enables businesses to make informed decisions based on real-time data, which plays a crucial role in resource allocation. Organizations adopting cloud computing services face increased network traffic, limiting traffic routing flexibility and causing excess traffic to reach unprepared physical nodes due to an inability to adjust to real-time traffic changes. This paper uses a systematic literature review to investigate the data analytics techniques used for resource allocation in cloud computing. It uses data from 2019 to 2024, sourced from different research databases. The results show that the majority of data analytics tools, including ARIMA and SVM, are employed for resource allocation in cloud computing. This study offers guidance to organizations regarding data analytics tools for the allocation of resources in cloud computing, and the recommendations can be utilized for the enhancement of the results in cloud computing, as well as to scholars by suggesting techniques to further investigate resource allocation to address the current gaps in cloud computing
Internet browsing and Web 2.0 Competencies as Correlate of Effective Management of Institutional Repository by Librarians in Federal Universities in South-South, Nigeria Robinson , Janet Nnenta; Ukaegbu, Bernadette C. N.
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.161

Abstract

This study was carried out to investigate Internet browsing and Web 2.0 competencies as correlate of effective management of institutional repository by Librarians in federal universities in South-South, Nigeria. Utilizing a correlational design, the study answered three questions and tested three hypotheses. The cohort of 242 Librarians from seven federal universities—115 academic and 127 non-academic—constituted the study's population. A stratified total census sampling approach was deployed to include all 242 librarians. Data gathering utilized two questionnaires: the "Librarian Internet Browsing and Web 2.0 Competencies Questionnaire (LIBWCQ)" and the "Effective Management of Institutional Repositories Questionnaire (EMIRQ)". Both tools were validated for face and content by experts and yielded Cronbach Alpha reliability coefficients of 0.75 and 0.77, respectively. Mean scores addressed the research questions, while regression analysis tested the hypotheses at a significance level of 0.05. Results indicated a 52% joint correlation between the Librarians' web competencies and effective management of repositories, notably in activities like social media engagement and collaboration with faculty and students using the digital space. The study concluded and recommended that Librarians should bolster their digital skills through professional development initiatives, such as workshops, webinars, and conferences, focusing on digital management and Web 2.0 tool application to keep up-to-date with technological advancements and best practices in management of institutional repository.
Dynamic Background Subtraction in Moving Object Detection on Modified FCM-CS Algorithm Dima Genemo, Musa
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.162

Abstract

This study uses deep learning for background subtraction in video surveillance. Scanned images often have unwanted background elements, making it difficult to separate objects from their backgrounds accurately. This affects how items are distinguished from their backgrounds. To solve this problem, this article introduces a model called the Improved Fuzzy C Means Cosine Similarity (FCM-CS). This model is designed to identify moving foreground objects in surveillance camera footage and address the associated challenges. The effectiveness of this model is evaluated against the current state-of-the-art, validating its performance. The results demonstrate the remarkable performance of the model on the CDnet2014 dataset
Predicting Online Gaming Behaviour Using Machine Learning Techniques Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.166

Abstract

Understanding player behaviour in online gaming is essential for enhancing user engagement and retention. This study utilizes a dataset from Kaggle, capturing a wide range of player demographics and in-game metrics to predict player engagement levels categorized as 'High,' 'Medium,' or 'Low.' The dataset includes features such as age, gender, location, game genre, playtime, in-game purchases, game difficulty, session frequency, session duration, player level, and achievements. The research employs a Gaussian Naive Bayes model, with data pre-processing steps including feature selection, categorical data encoding, and scaling of numerical features. The dataset is split into training (80%) and testing (20%) sets, and a 5-fold cross-validation is used to ensure model robustness. The model's performance is evaluated using accuracy, precision, recall, and F1-score. The results show consistent performance across different folds, with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. These findings indicate that the Gaussian Naive Bayes model can reliably predict player engagement levels, identifying significant predictors such as session frequency and in-game purchases. The study contributes to game analytics by providing a predictive model that can help game developers and marketers design more engaging gaming experiences. Future research should incorporate a broader range of features, including psychological and social factors, and explore other machine learning algorithms to enhance predictive accuracy. This study's insights are valuable for developing strategies to improve player retention and satisfaction in the gaming industry.
Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach Ilham, Ilham
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.167

Abstract

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity
Predictive Analysis of Online Course Completion: Key Insights and Practical Implications Riska, Riska; Syam, Rahmat Fuadi
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.168

Abstract

The rapid expansion of online education has brought significant attention to understanding factors that influence student engagement and course completion. This study aims to predict online course engagement using a dataset from Kaggle, encompassing user demographics, course-specific data, and engagement metrics. Employing a Decision Tree model with 5-fold cross-validation, the research identifies key predictors of course completion, including time spent on the course, the number of videos watched, and quiz scores. The model demonstrates robust performance with accuracy, precision, recall, and F1-scores consistently above 92%, indicating its effectiveness in predicting student outcomes. This predictive capability allows educators and online course providers to identify at-risk students early and implement timely interventions to enhance engagement and completion rates. The study's contributions lie in pinpointing critical engagement metrics and validating the use of Decision Trees in educational data mining. The findings align with existing educational theories that emphasize the importance of active engagement for academic success. Practical implications suggest that online platforms should focus on strategies to increase interaction with course content and provide timely feedback. Future research should explore additional datasets and machine learning models to further refine predictive accuracy and broaden the understanding of factors influencing online learning success. This research provides a foundation for developing more effective online education strategies, ultimately aiming to improve student retention and outcomes

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