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All Journal Jurnal Gaussian CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal Of Human And Education (JAHE)
Famuji, Ahmad
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Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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PENERAPAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (APARCH) TERHADAP HARGA MINYAK MENTAH DUNIA Famuji, Ahmad; Sriliana, Idhia; Agwil, Winalia
Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.13.1.99-109

Abstract

Heteroscedasticity poses a challenge in ARIMA modeling by causing residual variance to be non-constant, leading to less efficient estimates. This issue often arises in time series data due to volatility, which measures data fluctuation over time. To address heteroscedasticity, models like ARCH and GARCH incorporate variance changes into forecasting. However, they lack the ability to capture asymmetry, the difference in impact between good and bad news on volatility. The APARCH model, on the other hand, addresses this by modeling volatility with asymmetry elements. Daily world crude oil prices, known for high volatility, serve as a case study for this research. By employing the APARCH model, the study aims to forecast these prices accurately. Results indicate that the APARCH(1,1) model outperforms the best GARCH model, ARCH(2), as it yields a smaller Mean Absolute Percentage Error (MAPE) of 6.033487. This highlights the superior accuracy of APARCH in forecasting data with heteroscedasticity issues, particularly in the context of daily crude oil prices.
Desa Cantik, Desa Cakap Statistik Agwil, Winalia; Sriliana, Idhia; Rini, Dyah Setyo; Supianti, Filo; Oktarina, Cinta Rizki; Famuji, Ahmad
Journal Of Human And Education (JAHE) Vol. 4 No. 1 (2024): Journal Of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i1.708

Abstract

Dalam pembangunan desa tentunya diperlukan pengetahuan terkait dengan potensi desa yang dimiliki. Pengembangan potensi desa dapat dilakukan dengan penggalian data awal serta pengumpulan data untuk pemetaan potensi desa. Sayangnya, sering kali terdapat desa yang memiliki sumber daya dengan kompetensi yang masih belum memadai sehingga di perlukannya peningkatan kompetensi perangkat desa tentang pengumpulan dan pemanfaatan data. Program studi S1 Statistika dan Pojok Statistik Universitas Bengkulu bermitra bersama BPS melakukan pengembangan peningkatan kompetensi terhadap perangkat desa dengan melakukan pengabdian Desa Cantik yang diharapkan dapat meningkatkan kemampuan perangkat desa dalam memanfaatkan data. Pengabdian ini dilakukan pada Kelurahan Tanah Panah Kota Bengkulu. Melalui pengabdian ini, diharapkan pihak terkait dapat memanfaatkan data yang ada. Hasil dari pengabdian ini mampu memberikan pemahaman terkait dengan pemanfaatan data desa serta pengolahan data Microsoft excel dan Canva. Dimana perangkat desa mengetahui tipe-tipe data dan pengaplikasiannya dalam memvisualisasi data pada Microsoft Excel, mengetahui fitur-fitur pada canva yang dapat digunakan pada elemen-elemen infografis. Sebagai rekomendasi bentuk kegiatan pengabdian Desa Cantik patut dilakukan untuk desa-desa yang lain untuk meningkatkan kemampuan dalam memanfaatkan data-data desa.
Comparison Of Methods For Handling Imbalanced Datasets In Improving Classification Algorithm Performance Rini, Dyah Setyo; Agwil, Winalia; Agustina, Dian; Famuji, Ahmad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35780

Abstract

Data availability in large observations and dimensions is known as big data. There are several problems in processing big data, such as imbalanced datasets. In classification modeling, an imbalanced dataset is a common challenge. Data class predictions are more likely to be accurate in the majority class data and inaccurate in the minority class, resulting from the problem of imbalanced data. The data-level, the algorithm-level, and the ensemble method approach are the solutions that have been extensively researched. Some methods with a data-level approach are SMOTE, Undersampling, and Oversampling.  The algorithm-level method is NWKNN. And then, the ensemble approach is UnderBagging, RUSBoosting, SMOTEBoost, and SMOTEBagging. The goal of this study is to determine the best method for handling each case of the imbalanced dataset. There are three cases of imbalance, namely mild, moderate, and extreme. A simulation study was conducted for each imbalanced case to evaluate the accuracy of each method. Based on the AUC value, the SMOTEBagging method is the best for mild imbalance cases with an AUC value of 0.9581. For moderate imbalance cases, the SMOTEBagging method is the best method, with an AUC value of 0.9033. Meanwhile, for extreme imbalance cases, the UnderBagging method provides the best performance.
Co-Authors Agwil, Winalia Cinta Rizki Oktarina Dian Agustina Idhia Sriliana Rini, Dyah Setyo Supianti, Filo