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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika
Ramadhani, Dini
IPB University, Bogor, Indonesia
Computer Science & IT Engineering Mathematics

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Metode Machine Learning-Based Univariate Time Series Imputation Method untuk Estimasi Nilai Hilang pada Data Non-Stasioner Dini Ramadhani; Agus Mohamad Soleh; Erfiani Erfiani
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36468

Abstract

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.
Perbandingan Kinerja Metode Arima, Multi-Layer Perceptron, dan Random Forest dalam Peramalan Harga Logam Mulia Berjangka yang Mengandung Pencilan Prasetyo, Teguh; Putri, Rizki Alifah; Ramadhani, Dini; Angraini, Yenni; Notodiputro, Khairil Anwar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127392

Abstract

Akurasi peramalan sebagai tolok ukur kinerja metode deret waktu bergantung beberapa hal, antara lain karakteristik data, pemilihan metode, dan jangka waktu, di samping fluktuasi data dan keberadaaan pencilan dalam data. Keberadaan pencilan dalam data sering kali tidak dapat dihindari sehingga dapat mengganggu akurasi dan presisi dari peramalan. Berdasarkan hal tersebut dalam artikel ini dibahas tentang hasil kajian perbandingan kinerja metode ARIMA, Multi-Layer Perceptron (MLP), dan Random Forest (RF) dalam peramalan data deret waktu yang mengandung pencilan, khususnya untuk data harga logam mulia berjangka (emas, perak, dan platina) berdasarkan nilai Mean Absolute Percentage Error (MAPE). Ditunjukkan bahwa kinerja metode ARIMA dengan Interpolasi Linier mampu menekan pengaruh pencilan lebih baik dibanding ARIMA dengan Winsorized Mean dan ARIMA tanpa penanganan data pencilan Dalam hal ini diperoleh nilai MAPE rata-rata berturut-turut sebesar 10,67% dibanding 12,33% dan 11,79% ketika dievaluasi menggunakan data uji. Selain itu, metode MLP memiliki kinerja yang tidak lebih baik dibanding ARIMA dengan Interpolasi Linier dengan nilai MAPE rata-rata sebesar 11,13% ketika dievaluasi menggunakan data uji. Secara keseluruhan kinerja terbaik dihasilkan oleh metode RF, yang memiliki nilai MAPE rata-rata jauh lebih kecil dibanding metode lainnya, yakni 2,85% ketika dievaluasi menggunakan data uji. Dalam kajian ini nampak bahwa Metode RF memiliki kinerja terbaik dibandingkan semua metode dalam peramalan data deret waktu yang dicobakan menggunakan data empiris yaitu harga loga mulia berjangka.
Characteristics of Machine Learning-based Univariate Time Series Imputation Method Dini Ramadhani; Agus Mohamad Soleh; Erfiani Erfiani
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23453

Abstract

Handling missing values in univariate time series analysis poses a challenge, potentially leading to inaccurate conclusions, especially with frequently occurring consecutive missing values. Machine Learning-based Univariate Time Series Imputation (MLBUI) methods, utilizing Random Forest Regression (RFR) and Support Vector Regression (SVR), aim to address this challenge. Considering factors such as time series patterns, missing data patterns, and volume, this study explores the performance of MLBUI in simulated Autoregressive Integrated Moving Average (ARIMA) datasets. Various missing data scenarios (6%, 10%, and 14%) and model scenarios (Autoregressive (AR) models: AR(1) and AR(2); Moving Average (MA) models: MA(1) and MA(2); Autoregressive Moving Average (ARMA) models: ARMA(1,1) and ARMA(2,2); and Autoregressive Integrated Moving Average (ARIMA) models: ARIMA(1,1,1) and ARIMA(1,2,1)) with different standard deviations (0.5, 1, and 2) were examined. Five comparative methods were also used in this research, including Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The research findings indicate that MLBUI performs exceptionally well in imputing successive missing values. The results of this study indicate that the performance of MLBUI in imputing consecutive missing values, based on MAPE, yielded values of less than 10% across all scenarios used.
Co-Authors Achmad Noerkhaerin Putra Agus Mohamad Soleh Erfiani Erfiani Putri, Rizki Alifah Yenni Angraini