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All Journal Journal of ICT Research and Applications International Journal of Artificial Intelligence Research 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 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.
Scalable and Efficient Student Behavior Prediction using Parallelized Clustering and AHP-weighted KNN Guozhang, Li; Alfred, Rayner; Pailus, Rayner; Fengchang, Xu; Haviluddin, Haviluddin
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.3

Abstract

This study proposes a scalable and efficient approach for predicting student behaviour in large-scale educational environments. It introduces a parallelized hybrid model that combines Density-Based Optimized K-Means clustering, Analytic Hierarchy Process (AHP) feature weighting, and Hierarchical K-Nearest Neighbours (KNN), implemented using Apache Spark. The main research question is how to improve scalability, accuracy, and computational efficiency of student behaviour prediction when dealing with large, complex datasets. The model addresses key limitations of traditional methods, such as handling heterogeneous data, treating all features equally, and high computational cost. Two main innovations are presented. First, AHP is used to assign structured importance to features, allowing critical factors like attendance and study time to have greater influence on prediction accuracy. Second, clustering and prediction are parallelized using Spark, enabling efficient real-time processing of large datasets. The approach was evaluated using 18,586 student records and more than 20 million behavioural entries. Results show that Hierarchical KNN consistently outperforms standard KNN as dataset size increases. While traditional KNN shows unstable error rates, peaking at 9.4%, Hierarchical KNN maintains lower and more stable errors between 5.16% and 6.08%. Execution time was also significantly reduced through parallel processing, though gains were limited by communication overhead. Overall, the proposed model offers a robust framework for real-time behaviour analysis, academic risk detection, and targeted educational intervention.
Co-Authors Achmad Fanany Onnilita Gaffar Burhandenny, Aji Ery Fengchang, Xu Gaffar, Emmilya Umma Aziza Guozhang, Li Haviluddin Haviluddin Irwan Gani Pailus, Rayner Pratama, Arief Ardi Purnawansyah Purnawansyah Setyadi, Hario Jati Wati, Masna Wong, Kelvin