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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Science and Technology Indonesia NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Matematika: MANTIK Jurnal Elementer (Elektro dan Mesin Terapan) Desimal: Jurnal Matematika Syntax Literate: Jurnal Ilmiah Indonesia JWP (Jurnal Wacana Politik) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Buletin Ilmiah Nagari Membangun Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Journal of Applied Engineering and Technological Science (JAETS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Journal of Science and Technology Makara Journal of Health Research Lattice Journal : Journal of Mathematics Education and Applied STATISTIKA Limits: Journal of Mathematics and Its Applications
Yanuar, Ferra
Universitas Andalas
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Pemodelan dan Peramalan Volatilitas Memori Panjang pada Return Saham ANTM Studi Komparatif Model GARCH dan FIGARCH Rafulta, Elfa; Yanuar, Ferra; Devianto, Dodi; Maiyastri
Lattice Journal : Journal of Mathematics Education and Applied Vol. 5 No. 1 (2025): Juni 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/lattice.v5i1.9525

Abstract

This study aims to model and forecast the volatility of ANTM stock returns using FIGARCH and GARCH models to capture both short- and long-memory dynamics. Daily return data spanning from January 1, 2014, to December 31, 2024, were analyzed after stationarity confirmation via ADF test. A mean model was estimated using MA (4), followed by conditional variance modeling with GARCH (1,1) and FIGARCH (1, d,1). Diagnostic tests confirmed the presence of heteroskedasticity and long memory, justifying FIGARCH usage. The FIGARCH (1, d,1) model indicated significant long-memory effects (d = 0.461007), while GARCH (1,1) effectively captured short-term volatility clustering. Forecast performance comparison showed that although both models yielded equal RMSE (0.029000), GARCH (1,1) performed better in terms of MAE (0.019531 vs. 0.019529) and MAPE (192.0809 vs. 192.3617). However, FIGARCH demonstrated superior ability in modeling persistent volatility patterns with smoother conditional variance distribution and better long-term uncertainty estimation. These findings suggest that while GARCH is preferable for short-term predictive accuracy, FIGARCH offers more robust insights into long-term volatility persistence, making it suitable for strategic financial risk management.   Penelitian ini bertujuan untuk memodelkan dan meramalkan volatilitas return saham ANTM menggunakan model GARCH dan FIGARCH guna menangkap dinamika volatilitas jangka pendek dan panjang. Data return harian dari 1 Januari 2014 hingga 31 Desember 2024 dianalisis setelah melalui uji stasioneritas ADF. Model rata-rata ditentukan menggunakan MA (4), dilanjutkan dengan pemodelan varian bersyarat menggunakan GARCH (1,1) dan FIGARCH (1, d,1). Uji diagnostik menunjukkan adanya heteroskedastisitas dan efek memori panjang, mendukung penggunaan model FIGARCH. Hasil estimasi menunjukkan bahwa model FIGARCH (1, d,1) memiliki nilai d = 0,461007, mengindikasikan adanya efek long memory yang signifikan, sedangkan GARCH (1,1) efektif dalam menangkap klaster volatilitas jangka pendek. Evaluasi kinerja peramalan menunjukkan kedua model memiliki nilai RMSE yang sama (0,029000), namun GARCH (1,1) lebih unggul dalam MAE (0,019531 vs. 0,019529) dan MAPE (192,0809 vs. 192,3617). Meskipun demikian, FIGARCH menunjukkan keunggulan dalam menangkap pola volatilitas jangka panjang yang stabil. Dengan demikian, GARCH cocok untuk akurasi prediksi jangka pendek, sementara FIGARCH lebih direkomendasikan untuk estimasi risiko jangka panjang dalam pengelolaan keuangan strategis.
Implementasi Metode Fuzzy C-Means Dan Fuzzy Subtractive Clustering Dalam Pengklasteran Kabupaten/Kota Di Provinsi Sumatera Barat Berdasarkan Faktor Penyebab Stunting Ferra Yanuar; Putri Aulia; Aidinil Zetra; Hazmira Yozza
Limits: Journal of Mathematics and Its Applications Vol. 23 No. 1 (2026): Limits: Journal of Mathematics and Its Applications Volume 23 Nomor 1 Edisi Ap
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v23i1.3459

Abstract

One of the problems of malnutrition that is still quite high in Indonesia is stunting. Stunting can occur as a result of malnutrition, especially during the 1000 HPK (First Day of Life). The government continues to make various efforts to emphasize the prevalence of stunting in toddlers, but these efforts are not effective enough. One way that is quite effective is to carry out cluster analysis. This research aims to group districts/cities in West Sumatra based on factors that cause stunting. The methods used in this research are fuzzy c-means and fuzzy subtractive clustering. The cluster validity tests used in this research are Modified Partition Coefficient (MPC), Partition Entropy (PE), and Xie-Beni index (XB). Based on the calculation of the three validity indices, it was found that the optimum number of clusters in clustering based on the FCM method was two clusters (c = 2). Meanwhile, in the fuzzy subtractive clustering method, the optimum number of clusters is found in clustering with radius (r) = 0.90 with the number of clusters formed being three clusters. In this research, the results showed that the fuzzy subtractive clustering method was better than the fuzzy c-means method because the resulting CE and XB validity index values were lower. Using the fuzzy subtractive clustering method, it was found that clusters one and two consisted of nine regions, while cluster three only consisted of one region
Integrating Mathematical Modeling and Deep Learning for Uncertainty-Aware Fault Diagnosis in Industrial Rotating Machinery Primawati Primawati; Ferra Yanuar; Dodi Devianto; Remon Lapisa; Fazrol Rozi; Arda Yunianta
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/a6wnmz27

Abstract

In Industry 4.0, reliable fault diagnosis is critical for minimizing downtime and preventing catastrophic failures in rotating machinery. However, conventional deep learning models often operate deterministically, lacking the ability to quantify prediction uncertainty—a limitation that hinders risk-based maintenance decisions. This study aims to develop a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bayesian inference for uncertainty-aware fault diagnosis. The model extracts spatial features from Short-Time Fourier Transform (STFT) spectrograms via CNN, models temporal dynamics from raw vibration signals via LSTM, and quantifies prediction uncertainty using Monte Carlo Dropout (T=50). Evaluated on the benchmark Case Western Reserve University (CWRU) bearing dataset with an 80/20 data partitioning under six operating conditions, the hybrid architecture achieves an accuracy of 99.14% and an F1-score of 0.9914, significantly outperforming standalone CNN (97.42%) and LSTM (84.12%) models. The integration of probabilistic inference enhances decision reliability by providing confidence estimates for each prediction. This work contributes a robust, uncertainty-aware model that effectively captures both spatial and temporal patterns, offering significant implications for safety-critical industrial predictive maintenance systems.
Modeling Classification Of Stunting Toddler Height Using Bayesian Binary Quantile Regression With Penalized Lasso Lilis Harianti Hasibuan; Ferra Yanuar; Dodi Devianto; Maiyastri Maiyastri
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 2 (2025): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v10i2.928

Abstract

Stunting is a child who has a height that is shorter than the age standard. One of the main indicators of stunting is a height that is lower than the standard for toddlers. Stunting in Indonesia is of great concern due to the high prevalence of stunting. Stunting children are at risk of impaired cognitive development, which will result in the development of human resources. This study aims to develop a classification model to detect stunted toddlers based on height using the Bayesian binary quantile regression method with LASSO (Least Absolute Shrinkage and Selection Operator). This method was chosen because of its ability to handle multicollinearity and variable selection problems automatically, as well as provide better estimates on non-normally distributed data. The data used in this study includes five independent variables such as age, weight at birth, gender, how to measure height and nutritional status. The results showed that independent variables that significantly affect the height of stunting toddlers can be a concern to reduce the problem of stunting in Indonesia. The results of model show that variable age, weight at birth, and nutritional status have a significant influence to classification of stunting toddler height. Indicator of model goodness is seen from the quantile that has the smallest MSE value. The model that has the smallest MSE is in quantile 0.25 with an MSE value of 0.1622.
Co-Authors Abdi Mulya Admi Nazra AMALIA DWI PUTRI Amalia Dwi Putri ANGGUN CITRA DELIMA ANNISA RAHMADIAH Arda Yunianta Arfarani Rosalindari Arrival Rince Putri Asdi, Yudiantri Astari Rahmadita ATIKAH RAHMAH PUTRI Azmi Arsa Bahri, Susila Baqi, Ahmad Iqbal Boby Canigia Budi Rudianto Catrin Muharisa Cichi Chelchillya Candra Cichi Chelchillya Candra Cici Saputri Cintya Mukti Des Welyyanti Deva, Athifa Salsabila Devianto, Dodi Dila Mulya Dina Monica DINIE ANEFI HAJARA Efendi Efendi Elfa Rafulta Ermanely Ermanely Fadilla Nisa Uttaqi Fajriyah, Rahmatika Farhah Anggana Fazrol Rozi Febriyuni, Rahmi Firdawati, Firdawati FITARI RESMALANI Fitri Aulia FITRI SABRINA Gusmanely Z Harahap, Vika Pradinda Haripamyu Haripamyu Hasibuan, Lilis Harianti Hazmira Yozza Helmi, Monika Rianti Ihsan Kamal Ikhlas Pratama Sandi Indah Pratiwi Izzati Rahmi HG Izzati Rahmi HG Jenizon Jenizon Kamarni, Neng Kartini Aboo Talib @Khalid Khatimah, Havifah Husnatul Lilis Harianti Hasibuan Livia Amanda M. Pio Hidayatullah M. Rizki Oktavian Maiyastri Maiyastri, Maiyastri Majbur, Ridha Fauza Mardha Tillah Mawanda Almuhayar MEILINA DINIARI Melisa Febriyana Mesi Oktafia Meutia Fikhri MIFTAHUL JANNAH HB Mira Serma Teti Mita Oktaviani Muhammad Iqbal Muhammad Qolbi Shobri Muharisa, Catrin Mutiara Fara Nabilla Nadia Cindi Eka Putri Nadiah Ramadhani NADYA PUTRI ALISYA Nadya Putri Alisya Narwen Narwen Nayla Desviona Nova Noliza Bakar Noverina Alfiany Nurmaylina Zaja Nurwijayanti Primawati Primawati Putri Aulia Qalbi, Latifatul Radhiatul Husna RAHMI HG, IZZATI Rahmi, Fatihatur Ramadhani, Eza Syafri Religea Reza Putri Remon Lapisa Rescha, Ratna Vrima Resti Mustika Sari Resti Nanda Yani Riau, Ninda Permata Ridhatul Ilahi Riri Lestari Riri Lestari Rudiyanto Rudiyanto, Rudiyanto SAIDAH . Sani, Ridha Fadhila Saputri, Ovi Delviyanti Sari, Putri Trisna Sarmada, Sarmada Selfinia, Selfinia SHINTA MUTIA KARNEVA Shinta Wulandari SHINTA YULIANA Silvia . SILVIA YUNANDA Sisi Andriani Siti Juriah SITI LATHIFAH IRMA SUMINDANG YUZAN Surya Puspita Sari, Surya Puspita Susi Marisa Syafwan, Mahdhivan Syauqi, Irfan Tari Adriana Musana Tasya Abrari Tasya Abrari Uswatul Hasanah VIKI ANDRIANI Widya Wijayanti WINDA LIDYA Winda Oktari WULANDARI, FRILIANDA Wulandari, Sintya wulandari, sisca Yanita Yanita Yosika Putri Yulmiati Yulmiati Yurinanda, Sherli Zahratul Aini Zetra, Aidinil Zetra, Aidinil Zulakmal, Zulakmal Zulhazizah .