Meta-Analisis Tingkat Akurasi Peramalan Menggunakan Metode Wavelet
Abstract
Abstrak: Peramalan adalah salah satu unsur yang sangat penting dalam pengambilan keputusan. Salah satu metode yang digunakan dalam peramalan adalah Metode Wavelet merupakan komponen yang frekuensi yang berbeda atau alat analisis yang biasa digunakan untuk menyajikan data. Tujuan dalam meta-analysis ini untuk menganalisis kembali hasil-hasil penelitian yang berkaitan dengan peramalan menggunakan metode Wavelet. Datanya dikumpulkan melalui database pengindeks seperti Scopus, DOAJ, Worldcat dan Google Scholar. Data yang difilter adalah hasil penelitian yang memuat nilai dari jumlah data (N), uji korelasi (r) dan klasifikasi, kemudian yang dianalisis menggunakan meta-analysis melalui effect size dan standar error untuk melihat summary effect size. Hasil analisis data menggunakan software JASP menunjukkan bahwa nilai estimate pada tingkat akurasi peramalan menggunakan metode Wavelet sebesar 0.827 yang termasuk kategori tinggi, Kemudian diperoleh nilai estimate dari metode modifikasi sebesar 0.828 dan nilai p-Rank-testnya 0.591, sedangkan non-modifikasi nilai estimatenya adalah 0.831 dan nilai p-Rank-testnya 0.76.
Abstract: Forecasting is one of the most important elements in decision making. One of the methods used in forecasting is the Wavelet Method is a component of which is a different frequency or analysis tool commonly used to present data. The purpose of this meta-analysis is to reanalyze the results of research related to forecasting using the Wavelet method. Its data is collected through indexer databases such as Scopus, DOAJ, Worldcat and Google Scholar. Filtered data is the result of research that contains values from the amount of data (N), correlation test (r) and classification, then analyzed using meta-analysis through effect size and error standards to see summary effect size. The results of data analysis using JASP software showed that the estimate value at the forecasting accuracy rate using the Wavelet method was 0.827 which belonged to the high category, then obtained the estimate value of the modification method of 0.828 and the p-Rank-test value was 0.591, while the non-modification of the estimate value was 0.831 and the p-Rank-test value was 0.76.
References
Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian Physical and Engineering Sciences in Medicine, 38(1), 139–149. https://doi.org/10.1007/s13246-015-0333-x
Amrinsani, F., Arief, Z., & Gunawan, A. I. (2019). Identifikasi Sinyal Elektromiografi Otot Vastus Medialis dan Erector Spinae dalam Transisi Gerakan untuk Kontrol Robot Kaki. INOVTEK POLBENG, 9(2), 219. https://doi.org/10.35314/ip.v9i2.1011
Andrian, Y., & Putra, P. H. (2017). Analisis Penambahan Momentum Pada Proses Prediksi Curah Hujan Kota Medan Menggunakan Metode. Seminar Nasional Informatika, 165–172.
Armansyah, A., Syahputra, G., & Perangin-angin, M. I. (2020). Analisis Kemampuan Akademik Mahasiswa Berdasarkan Latar Belakang Keluarga, Tempat Tinggal, Pertemanan, Sikap Belajar, Konsep Diri, Iklim Kampus, Dan Tenaga Pengajar Dengan Jaringan Syaraf Tiruan Backpropagation Armansyah*, Guntur. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 19(1), 75. https://doi.org/10.53513/jis.v19i1.227
Balubaid, M., Sattari, M. A., Taylan, O., Bakhsh, A. A., & Nazemi, E. (2021). Applications of discrete wavelet transform for feature extraction to increase the accuracy of monitoring systems of liquid petroleum products. Mathematics, 9(24). https://doi.org/10.3390/math9243215
Banerjee, S., & Mitra, M. (2014). Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Transactions on Instrumentation and Measurement, 63(2), 326–333. https://doi.org/10.1109/TIM.2013.2279001
Beura, S., Majhi, B., & Dash, R. (2015). Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing, 154, 1–14. https://doi.org/10.1016/j.neucom.2014.12.032
Chato, L., Chow, E., & Latifi, S. (2018). Wavelet transform to improve accuracy of a prediction model for overall survival time of brain tumor patients based on MRI Images. Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018, 441–442. https://doi.org/10.1109/ICHI.2018.00091
Christnatalis, C., Bachtiar, B., & Rony, R. (2020). Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 3(2), 202–209. https://doi.org/10.31289/jite.v3i2.3154
Dan, D., & Kategori, P. (2017). Arcing Pada Hubung Singkat Tegangan.
Darwan, D. (2021). Penggunaan Jaringan Syaraf Tiruan dan Wavelet Pada Citra EKG 12 Lead. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 20(2), 355–366. https://doi.org/10.30812/matrik.v20i2.1075
Gutiérrez-Gnecchi, J. A., Morfin-Magaña, R., Lorias-Espinoza, D., Tellez-Anguiano, A. del C., Reyes-Archundia, E., Méndez-Patiño, A., & Castañeda-Miranda, R. (2017). DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomedical Signal Processing and Control, 32, 44–56. https://doi.org/10.1016/j.bspc.2016.10.005
Hapsari, K. D., Cholissodin, I., & Santoso, E. (2016). Optimasi Radial Basis Function Neural Network Menggunakan Hybrid Particle Swarm Optimization Dan Genetic Algorithm Untuk Peramalan Curah Hujan. DORO: Repository Jurnal …, January.
Kutlu, H., & Avcı, E. (2019). A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks. Sensors (Basel, Switzerland), 19(9). https://doi.org/10.3390/s19091992
Lareno, B. (2015). ANALISA DAN PERBANDINGAN AKURASI MODEL PREDIKSI RENTET WAKTU ARUS LALU LINTAS JANGKA PENDEK. CSRID (Computer Science Research and Its Development Journal), 6(3), 148. https://doi.org/10.22303/csrid.6.3.2014.148-158
Li, F., Wang, L., Liu, J., Wang, Y., & Chang, Q. (2019). Evaluation of leaf N concentration in winter wheat based on discrete wavelet transform analysis. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111331
Liu, B., Zhang, L., Wang, Q., & Chen, J. (2021). A Novel Method for Regional NO2Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/6631614
Maheshwari, S., Pachori, R. B., & Acharya, U. R. (2017). Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images. IEEE Journal of Biomedical and Health Informatics, 21(3), 803–813. https://doi.org/10.1109/JBHI.2016.2544961
Murugappan, M., Alshuaib, W., Bourisly, A. K., Khare, S. K., Sruthi, S., & Bajaj, V. (2020). Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography. PLoS ONE, 15(11 November), 1–17. https://doi.org/10.1371/journal.pone.0242014
Pan, L., Pipitsunthonsan, P., Daengngam, C., Channumsin, S., Sreesawet, S., & Chongcheawchamnan, M. (2020). Method for Classifying a Noisy Raman Spectrum Based on a Wavelet Transform and a Deep Neural Network. IEEE Access, 8, 202716–202727. https://doi.org/10.1109/ACCESS.2020.3035884
Peng, Y., Li, G., Zhou, M., Wang, H., & Lin, L. (2017). Dynamic spectrum extraction method based on independent component analysis combined dual-tree complex wavelet transform. RSC Advances, 7(18), 11198–11205. https://doi.org/10.1039/c6ra28647j
Qin, S., Zhu, Z., Zou, Y., & Wang, X. (2019). Facial expression recognition based on Gabor wavelet transform and 2-channel CNN. International Journal of Wavelets, Multiresolution and Information Processing. https://doi.org/10.1142/S0219691320500034
Rissacher, D., & Galy, D. (2015). Cardiac radar for biometric identification using nearest neighbour of continuous wavelet transform peaks. 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015. https://doi.org/10.1109/ISBA.2015.7126356
Subasi, A., Tuncer, T., Dogan, S., Tanko, D., & Sakoglu, U. (2021). EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. Biomedical Signal Processing and Control, 68. https://doi.org/10.1016/j.bspc.2021.102648
Sui, K., & Kim, H. G. (2019). Research on application of multimedia image processing technology based on wavelet transform. Eurasip Journal on Image and Video Processing, 2019(1). https://doi.org/10.1186/s13640-018-0396-1
Sutera, M. B. M., Magdalena, R., & Hidayat, B. (2020). Steganalisis Menggunakan Metode Wavelet Dan Svm Dengan Penyisipan Teks Melalui Aplikasi Steganography Android. eProceedings of Engineering, 7(2), 4244–4250.
Tascikaraoglu, A., Sanandaji, B. M., Poolla, K., & Varaiya, P. (2016). Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform. Applied Energy, 165, 735–747. https://doi.org/10.1016/j.apenergy.2015.12.082
Van, M., & Kang, H. J. (2016). Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(2), 291–302. https://doi.org/10.1177/0954406215573976
Wafira rahmania. (2018). MENGGUNAKAN JARINGAN SYARAF TIRUAN ( BACKPROPAGATION ) DAN PRAPROSES MEL- Sebagai Salah Satu Syarat untuk Memperoleh Sebagai Salah Satu Syarat untuk.
Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., & Ou, C. (2021). Automatic ECG classification using continuous wavelet transform and convolutional neural network. Entropy, 23(1), 1–13. https://doi.org/10.3390/e23010119
Zhang, J., Guo, Y., Shen, Y., Zhao, D., & Li, M. (2018). Improved CEEMDAN-wavelet transform de-noising method and its application in well logging noise reduction. Journal of Geophysics and Engineering, 15(3), 775–787. https://doi.org/10.1088/1742-2140/aaa076
Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B. S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060–34067. https://doi.org/10.1109/ACCESS.2019.2900719
1.png)
.png){kind=small}
