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Sunggingwati, Dyah; Haviluddin, Universitas Mulawarman, Samarinda, Indonesia; Purnawansyah, Universitas Muslim Indonesia, Makassar, Indonesia; Trahutomo, Dinnuhoni, Universitas Mulawarman, Samarinda, Indonesia

Abstract

Critical literacy within Life-Based Learning (LBL) serves as a transformative educational approach that empowers students to engage critically with texts, media, and societal narratives, examining the ideologies, power dynamics, and cultural contexts embedded within them. This chapter examines the role of critical literacy in LBL, highlighting its significance in enabling students to understand and critically engage with the broader social structures that shape their lives. It discusses the expansion of critical literacy beyond traditional reading and writing to include digital and media literacy, driven by the technological advancements of the twenty-first century. The integration of technology in education facilitates new forms of content creation and analysis, enabling students to engage with and deconstruct media actively. The chapter also highlights how LBL fosters real-world application of critical literacy through projects that tackle local and global issues, such as environmental sustainability, social justice, and media representation. Through activities like media analysis, community research, and role-playing, students are encouraged to explore multiple perspectives and develop an understanding of power and equity. The chapter concludes with the assertion that critical literacy within LBL not only prepares students to analyze and critique information but also empowers them to become active, informed citizens capable of addressing complex societal challenges.

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Purnawansyah; Adnan, Adam; Darwis, Herdianti, Faculty of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM 5, Makassar, 90231, Indonesia; Wibawa, Aji Prasetya, Universitas Negeri Malang, Jl. Semarang No. 5, Malang, 65145, Indonesia; Widyaningtyas, Triyanna; Haviluddin, Universitas Mulawarman, Jl. Kuaro, Samarinda, 75119, Indonesia

Abstract

Facial Expression Recognition (FER) plays a crucial role in humancomputer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various labelto- unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study's reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.

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Purnawansyah, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jl. Semarang, Malang, 65145, Indonesia; Department of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90231, Indonesia; Darwis, Herdianti; Azis, Huzain, Department of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90231, Indonesia; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jl. Semarang, Malang, 65145, Indonesia; Haviluddin, Department of Computer Science, Universitas Mulawarman Jl. Kuaro Gunung Kelua, Samarinda, 75119, Indonesia

Abstract

This study aims to assess the effectiveness of various algorithms in the realms of supervised and semi-supervised learning applied to three multiclass facial image datasets: JAFFE, Georgia tech, and Yale. The datasets were partitioned into proportions of 80:20, 75:25, and 50:50 for supervised learning, while semi-supervised learning was conducted with labelled and unlabeled data ratios of 20:80, 25:75, and 50:50. The evaluated algorithms include convolutional neural networks (CNNs), decision tree, long short-term memory, K-nearest neighbors (K-NNs), multilayer perceptron, and support vector classifier (SVC), each with varying parameters. Experimental outcomes reveal that the performance of models depends on the dataset partitioning strategies and the type of algorithms used. Specifically, linear and polynomial SVC consistently yield favorable results in supervised learning, particularly demonstrating efficacy on the Georgia tech dataset. Conversely, on the JAFFE and Yale dataset, linear SVC and K-NN emerge as optimal choices. The inclusion of semi-supervised learning enhances insights, particularly evident in the Georgia tech dataset, where the combination of labeled and unlabeled data significantly improves accuracy, especially when leveraging linear SVC algorithm. Although there are some instances of sub-optimal performance in certain algorithms like CNN on specific datasets, this research provides comprehensive insights into the effectiveness of various models in contexts of limited-label learning. The implications of these findings are crucial in advancing the development of adaptive and robust facial recognition systems, especially in navigating datasets characterized by diverse variations and complexities.

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Purnawansyah, Department of Information Systems, Universitas Muslim Indonesia, Indonesia; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia; Haviluddin, Department of Informatics, Universitas Mulawarman, Indonesia; Raja, Roesman Ridwan; Darwis, Herdianti, Department of Informatics, Universitas Muslim Indonesia, Indonesia; Nafalski, Andrew, UniSA Education Futures, School of Engineering, University of South Australia, Australia

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.

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Astuti, Wistiani, Univeristas Muslim Indonesia; Wibawa, Aji Prasetya, Universitas Negeri Malang; Haviluddin, Universitas Mulawarman; Darwis, Herdianti, Univeristas Muslim Indonesia

Abstract

One prevalent conversational system within the realm of natural language processing (NLP) is chatbots, designed to facilitate interactions between humans and machines. This study focuses on predicting frequently asked questions by students using the Duel Intent and Entity Transformer (DIET) Classifier method and assessing the performance of this method. The research involves employing 300 epochs with an 80% training data and 20% testing data split. In this study, the DIET Classifier adopts a multi-task transformer architecture to simultaneously handle classification and entity recognition tasks. Notably, it possesses the capability to integrate diverse word embeddings, such as BERT and GloVe, or pre-trained words from language models, and blend them with sparse words and n-gram character-level features in a plug-and-play manner. Throughout the training process of the DIET Classifier model, data loss and accuracy from both training and testing datasets are monitored at each epoch. The evaluation of the text classification model utilizes a confusion matrix. The accuracy results for testing the DIET Classifier method are presented through four case studies, each comprising 25 text messages and 15 corresponding chatbot responses. The obtained accuracy values range from 0.488 to 0.551, F1-Score values range from 0.427 to 0.463, and precision range from 0.417 to 0.457.

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Setiawan, Ari; Dardjito, Hanandyo, Universitas Sarjanawiyata Tamansiswa; Andrian, Dedek, Universitas Islam Riau; Yuldashev, Azim Abdurakhmanovich, Chicrchik State Pedagogical University; Murlianti, Sri, Universitas Mulawarman; Khairas, Eri Ester, Politeknik Negeri Jakarta; Handoko, Lukman, Politeknik Perkapalan Negeri Surabaya; As'ad, Ihwana, Universitas Muslim Indonesia

Abstract

This research aims to evaluate the impact of education decentralization through the local curriculum in improving senior secondary school students' vocational and economic skills. This research uses a mixed methods approach with a sequential exploratory design. This design applied a qualitative approach followed by a quantitative approach. Data collection with the qualitative approach used in-depth interviews, while the quantitative approach used questionnaires and documentation of practical exam results. The participants in the qualitative approach were the vice principal and the traditional cookery subject teacher. The sample in the quantitative approach was students who took traditional cookery subjects. Qualitative data analysis used the Miles and Huberman formula, namely data display, data reduction, and conclusions, while quantitative data analysis used descriptive quantitative and MANOVA. The results showed that the impact of decentralization through the local content curriculum on improving students' vocational and economic skills was in the "good" category. The impact of decentralizing education through the local curriculum can reduce the dropout rate of poor students. MANOVA results showed no difference in the impact of the local curriculum on vocational skills across the six schools. However, there is a significant difference in the impact of the local curriculum on students' economic improvement in the six schools

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Budiman, Edy; Puspitasari, Novianti, Dept. of Informatics Engineering, Universitas mulawarman Samarinda, Indonesia; Alam, Sitti Nur, Dept. of informatisc engineering, Stimik sepuluh nopember jayapura, Jayapura, Indonesia; Akbar, Mohammad Aldrin, Dept. of Economics Faculty, University of yapis papua, Jayapura, Indonesia; Haeruddin, Dept. of mathematics education, Universitas mulawarman, Samarinda, Indonesia; Indra, Dolly, Universitas muslim indonesia, Makassar, Indonesia

Abstract

The problem of speed of page load time in the Borneo Biodiversity information system which is decreasing has an impact on the level of user satisfaction. The amount of resources used and the amount of data stored causes the system to be slow. The paper analyzes the load time of web pages based on key indicators performance for page loading timings. The variable analysis of webpage for calculates value; loading time (Time to First Byte, First paint time, First contentful paint time, DOM interactive time, DOM loaded time content, Onload time), total page size, and the number of requests. The results of the analysis have given a grade to the web performance and evaluated the results by providing a solution to optimize the low parameter values.

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