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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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Penerapan Jaringan Heuristik untuk Prediksi Persentase Distribusi Produk Domestik Bruto (PDB) Atas Dasar Harga Berlaku Menurut Lapangan Usaha Gaffar, Emmilya Umma Aziza; Gaffar, Achmad Fanany Onnilita; Alfred, Rayner; Gani, Irwan; Haviluddin, Haviluddin
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 3, No 1 (2018): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (500.139 KB)

Abstract

Pertumbuhan ekonomi suatu negara diukur dengan tingkat pertumbuhan PDB yang sangat membantu untuk memprediksi situasi ekonomi dan pengembangan strategi pembangunan ekonomi. Pengukuran ini dapat dilakukan dengan menggabungkan konsep matematika komputasi dan teknologi komputer untuk menghasilkan prediksi pertumbuhan ekonomi secara ilmiah dan tepat. Metode statistik dan machine learning serta gabungan dari keduanya telah banyak digunakan untuk aktivitas prediksi maupun peramalan. Heuristik adalah salah satu filsafat ilmu pengetahuan dan matematika yang tergolong sebagai penalaran ampliatif, merupakan pendekatan pemecahan masalah, pembelajaran, atau penemuan yang menggunakan metode praktis yang tidak dijamin optimal atau sempurna, namun cukup signifikan untuk pencapaian tujuan, Di dalam studi ini, Jaringan Heuristik digunakan untuk memprediksi persentase distribusi PDB atas Harga Berlaku menurut Lapangan Usaha. Tujuan studi ini adalah melakukan prediksi secara simultan atas seluruh variabel lapangan usaha yang berkontribusi pada PDB. Hasil studi menunjukkan bahwa Jaringan Heuristik telah mampu melakukan prediksi dan peramalan secara optimal melalui proses komputasi yang cepat dengan hasil yang signifikan, serta menghasilkan error prediksi yang dapat diterima.
Big data: issues trends problems controversies in ASEAN perspective Haviluddin, Haviluddin; Alfred, Rayner
Bulletin of Social Informatics Theory and Application Vol. 3 No. 2 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v3i2.239

Abstract

Big Data has a characteristics is size, new opportunities and have the potential to transform corporations and government and its interactions with the public. This paper attempts to offer a broader definition of Big Data that captures it is other unique and defining characteristics. This paper presents a consolidated description of Big Data by integrating definitions from practitioners and academics. In addition, we summarize the issues, trends, problems and controversies related to Big Data (technology, applications, and people) from infrastructure (i.e., hardware and software), technology for Big Data Analytics (BDA), management, educational and scientists, and government-related to policies perspectives in order to support the Economic Community ASEAN (AEC) era.
An extraction of shapes and support vector machine methods for identification of decorative wall “Lamin” motifs of the Dayak Kenyah Pampang tribe Haviluddin, Haviluddin; Wati, Masna; Alfred, Rayner; Burhandenny, Aji Ery; Pratama, Arief Ardi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.475

Abstract

One of the Dayak cultures of Kalimantan Island, Indonesia is a traditional house called Lamin where each wall is decorated according to tribal characteristics. This study aims to identify the image on the Lamin wall using the Support Vector Machine (SVM) method based on the eccentricity and metric parameter values. The data of this study consisted of 50 types of images of the Lamin wall motifs of the Dayak Kenyah tribe consisting of tebengaang, dragon, crocodile, tiger, and arch which were taken from the tourist village, Pampang, Samarinda, East Kalimantan. Based on the experiment, the shape feature extraction method has produced the highest value of the eccentricity parameter which is 0.6979 and the metric parameter is 0.9953 on the image of the arch. Motif identification using the SVM method using linear, Gaussian/RBF, and polynomial kernel parameters has resulted in the highest accuracy with 80% image composition of kernel polynomial at 85%, Gaussian/RBF at 80%, and linear at 78%.
An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Setyadi, Hario Jati; Wong, Kelvin; Alfred, Rayner
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1806.11 KB) | DOI: 10.29099/ijair.v3i2.112

Abstract

This article aims to predict the inflation rate in Samarinda, East Kalimantan by implementing an intelligent algorithm, Backpropagation Neural Network (BPNN). The inflation rate data was obtained from the Provincial Statistics Bureau of Samarinda https://samarindakota.bps.go.id/ for the period January 2012 to January 2017. The method used to measure accuracy algorithm prediction was the mean square error (MSE). Based on the experiment results, the BPNN method with architectural parameters of 5-5-5-1; the learning function was trainlm; the activation functions were logsig and purelin; the learning rate was 0.1 and able to produce a good level of prediction error with an MSE value of 0.00000424. The results showed that the BPNN algorithm can be used as an alternative method in predicting inflation rates in order to support sustainable economic growth, so that it can improve the welfare of the people in Samarinda, East Kalimantan.
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
Publisher : citeus

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Abstract

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
Publisher : citeus

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Abstract

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
Publisher : citeus

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Abstract

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
Publisher : citeus

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Abstract

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