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All Journal International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Bulletin of Social Informatics Theory and Application
Rayner Alfred
Faculty of Computing and Informatics, Jalan UMS, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Malaysia
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Journal : knowledge engineering and data science

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Handwriting Character Recognition usingVector Quantization Technique Haviluddin, Haviluddin; Alfred, Rayner; Moham, Ni’mah; Pakpahan, Herman Santoso; Islamiyah, Islamiyah; Setyadi, Hario Jati
Knowledge Engineering and Data Science
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This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Putra, Gubtha Mahendra; Hasnida, Rima Yustika; Alfred, Rayner
Knowledge Engineering and Data Science
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The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.
Comparative Analysis of BPNN and LVQ for Sundanese Character Recognition Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Nurpadillah, Dinda Izmya; Setyadi, Hario Jati; Taruk, Medi; Alfred, Rayner
Knowledge Engineering and Data Science
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The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster convergence compared to LVQ, which reached a maximum accuracy of 66.66%. Additionally, BPNN demonstrated better generalization and robustness. At the same time, LVQ was highly sensitive to learning rate variations, leading to unstable accuracy and slower training times. The findings highlight that BPNN is a more effective model for Sundanese script recognition, providing a reliable approach for preserving and digitizing traditional scripts. Future research should explore hybrid models, deep learning approaches, and larger datasets to enhance recognition accuracy and system robustness.
Network Traffic Time Series Performance Analysisusing Statistical Methods Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Alfred, Rayner; Gaffar, Achmad Fanany Onnlita
Knowledge Engineering and Data Science
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This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting
Co-Authors Achmad Fanany Onnilita Gaffar Burhandenny, Aji Ery Emmilya Umma Azizah Gaffar Gaffar, Achmad Fanany Onnlita Hasnida, Rima Yustika Haviluddin Haviluddin Herman Santoso Pakpahan Irwan Gani Islamiyah Islamiyah, Islamiyah Moham, Ni’mah Nurpadillah, Dinda Izmya Pratama, Arief Ardi Purnawansyah Purnawansyah Puspitasari, Novianti Putra, Gubtha Mahendra Setyadi, Hario Jati Taruk, Medi Wati, Masna Wong, Kelvin