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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian
Fauzi, Doni Muhammad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Classification Classification of Criminal Events Based on Biplot Analysis Fauzi, Doni Muhammad; Dewanty, Sanda Insania; Putri, Farah Fauziah; Inneztiana, Alya Rahma; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3795

Abstract

Kriminalitas merupakan suatu perilaku yang melanggar hukum dan aturan dalam masyarakat. Penelitian ini dilakukan untuk menganalisis data biplot jumlah kejahatan di berbagai provinsi di Indonesia. Biplot merupakan analisis yang berguna untuk menafsirkan hubungan antara variabel dan objek dalam bentuk grafik tunggal. Sumber data dalam penelitian ini adalah data sekunder yang berasal dari website Badan Pusat Statistik yang berjudul “Statistik Kriminal 2022”. 34 kepolisian daerah yang mewakili setiap provinsi di Indonesia menjadi objek pengamatan dan 9 klasifikasi kejahatan menjadi variabel. Metode penelitian ini menggunakan analisis biplot dengan bantuan fiton. Dari nilai Dekomposisi Nilai Singular, keragaman data yang dapat dijelaskan sebesar 73,714%. Pada grafik analisis biplot hubungan antar observasi diperoleh bahwa observasi atau objek polda dari setiap provinsi tersebar terpusat pada satu kuadran. Hubungan antar variabel yang paling tinggi adalah korelasi antara variabel kejahatan narkotika dengan kejahatan yang berkaitan dengan penggelapan, penipuan, dan korupsi, sedangkan hubungan yang paling rendah adalah korelasi antara kejahatan narkotika dengan kejahatan terhadap ketertiban umum. Dalam hubungan observasi dengan variabel diperoleh 4 kelompok. Keberagaman variabel yang paling tinggi terletak pada kejahatan terhadap kebebasan masyarakat, sedangkan keberagaman variabel yang paling rendah terletak pada kejahatan terhadap kesusilaan.
PREDICTION OF THE INDONESIA COMPOSITE INDEX (ICI) USING THE ARCH GARCH APPROACH AND THE FOURIER SERIES Fadillah Mardianto, M. Fariz; Valida, Hanny; Putri, Farah Fauziah; Fauzi, Doni Muhammad; Pusporani, Elly
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0271-0286

Abstract

The Indonesia Composite Index (ICI) is a key indicator of stock market performance in Indonesia, often experiencing high volatility due to various domestic and global economic factors. In recent years, ICI has shown a significant upward trend, influenced by both local and international factors. In 2024, from June to October, the ICI saw a notable increase, reaching its highest value since 2020 at Rp 7,670. Despite fluctuations in stock prices, the rise in ICI reflects a positive outlook for the Indonesian stock market, attracting both domestic and foreign investors. This study aims to predict ICI movements using ARIMA-GARCH and Fourier Series approaches. The ARIMA model is employed to analyze time series data, while the ARCH-GARCH model addresses heteroskedasticity in residual variance. For comparison, the Fourier Series Estimator is applied to capture seasonal patterns in the data. Although ICI volatility is driven by a range of external macroeconomic and geopolitical factors, this study focuses on univariate modeling to evaluate the predictive capability of the index’s own historical movements, without involving exogenous variables. The data used comes from Investing.com. Weekly ICI data from March 2020 to June 2024 is used, split into training and testing sets. The analysis results indicate that the ARIMA-GARCH method provides higher accuracy, with a Mean Absolute Percentage Error (MAPE) of 5% (out-sample), compared to the Fourier Series method, which has a MAPE of 8.57%. This suggests that ARIMA-GARCH is more effective in predicting ICI trends, reflecting its ability to account for volatility and market changes more accurately.
FORECASTING THE INFLATION RATE IN INDONESIA USING ARIMA-GARCH MODEL Saifudin, Toha; Suliyanto, Suliyanto; Afifa, Fitriana Nur; Arrofah, Aini Divayanti; Fauzi, Doni Muhammad; Pratama, Fachriza Yosa; Adyatma, Isryad Yoga
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp0955-0970

Abstract

Inflation is a key economic indicator that affects purchasing power, economic growth, and financial stability. Accurate forecasting is essential for policymakers to implement effective monetary and fiscal policies. However, traditional models like ARIMA (Autoregressive Integrated Moving Average) mainly capture general trends and often fail to address inflation volatility. This study enhances inflation forecasting accuracy by applying the ARIMA-GARCH hybrid model, which combines trend estimation with volatility modelling. Focusing on Indonesia’s inflation patterns using recent data, it addresses a gap in existing research. This type of research uses quantitative methods, and the data were obtained from the official website of Bank Indonesia. The dataset consists of 240 monthly Indonesian inflation data points spanning from September 2004 to August 2024. The ARIMA (0,1,1)-GARCH (2,0) model is used to analyze inflation trends and volatility dynamics. The model evaluation shows strong predictive performance, with a Mean Absolute Percentage Error (MAPE) of 2.73% and Root Mean Squared Error (RMSE) of 0.74 for training data. Testing data results in a MAPE of 18.95% and RMSE of 0.702, which remains within an acceptable range. These findings highlight the importance of incorporating volatility modelling in inflation forecasting to enhance economic decision-making. A reliable forecast mitigates economic uncertainty, thereby providing a stronger foundation for achieving long-term economic growth. This study contributes by demonstrating the practical application of ARIMA-GARCH in Indonesia’s inflation modelling, providing valuable insights for policymakers in managing inflation-related risks.
Co-Authors Adyatma, Isryad Yoga Afifa, Fitriana Nur Arrofah, Aini Divayanti Dewanty, Sanda Insania Dita Amelia, Dita Elly Ana Elly Pusporani Fadillah Mardianto, M. Fariz Inneztiana, Alya Rahma M. Fariz Fadillah Mardianto Pratama, Fachriza Yosa Putri, Farah Fauziah Suliyanto Suliyanto Toha Saifudin Valida, Hanny