A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data

Haviluddin, .; Alfred, Rayner; Obit, Joe Henry; Hijazi, Mohd Hanafi Ahmad; Ibrahim, Ag Asri Ag

doi:10.1166/asl.2015.6490

Cited by 21 publications

(11 citation statements)

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“…Beberapa metode dalam statistik untuk melakukan pengukuran akurasi suatu algoritma seperti mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), dan mean absolute percentage error (MAPE). Pengukuran algoritma bertujuan untuk mendapatkan nilai terbaik (Haviluddin, Alfred, Obit, Hijazi, & Ibrahim, 2015;Rojas & Rojas, 2011;Susanti et al, 2018). Dalam penelitian ini, metode MSE dipilih untuk mengukur akurasi dengan menggunakan persamaan (1).…”

Section: F Performa Akurasiunclassified

Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

Moham

Dwiyanto

Pakpahan

et al. 2019

JSAKTI

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Artikel ini bertujuan untuk menjelaskan langkah-langkah kerja metode Backpropagation Neural Network (BPNN) dalam mengenali pola Aksara Lontara Bugis Makassar dan menjelaskan seberapa akurat dalam mengenali pola aksara Lontara Bugis Makassar. Dari hasil pengujian, diperoleh tingkat akurasi sebesar 76.08%, dengan parameter learning rate sebesar 0,02, epoch maksimum sebesar 50 epoch dan hidden layer sebanyak 90 neuron berdasarkan ciri 8. Adapun, performa mean square error (MSE) sebesar 0.00424 telah diperoleh. Namun demikian, waktu yang dibutuhkan saat proses pembelajaran terbilang cukup lama yaitu 16 menit 56 detik. Berdasarkan hasil pengujian metode BPNN dapat direkomendasikan untuk mengenali pola aksara Lontara Bugis Makassar dalam rangka menunjang pembelajaran kepada masyarakat.

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Section: F Performa Akurasiunclassified

Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

Moham

Dwiyanto

Pakpahan

et al. 2019

JSAKTI

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“…Some methods in statistics to measure the accuracy of an algorithm are mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The measurement algorithm aims at attaining the best value [23] [24][25] [26]. In this study, the MSE method was chosen to measure accuracy.…”

Section: F Performance Accuracymentioning

confidence: 99%

Handwriting Character Recognition using Vector Quantization Technique

Haviluddin

Alfred²,

Moham³

et al. 2019

Kno. Eng. Da. Sc.

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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.

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“…Along with the development of the informatics field, GA is applied to a variety of issues, such as forecasting, optimization, scheduling, and so on. In the soft computing field, GA is widely used to obtain optimal parameter values [5]- [7]. Some researchers have conducted research regarding scheduling using GA, including Reballo and Casella and Pandey [8].…”

Section: Introductionmentioning

confidence: 99%

Lecture Scheduling Automation Using Genetic Algorithm

Herman¹,

Syafie²,

Lantara³

2019

IJEAT

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This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

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A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data

Abstract: The task of analyzing and forecasting time-series data is very crucial task as this Time Series Analysis (TSA) task is used for many applications such as Economic Forecasting,

Cited by 21 publications

References 0 publications

Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

Handwriting Character Recognition using Vector Quantization Technique

Lecture Scheduling Automation Using Genetic Algorithm

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