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All Journal EKSAKTA: Journal of Sciences and Data Analysis JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Unisda Journal of Mathematics and Computer Science (UJMC) EIGEN MATHEMATICS JOURNAL Square : Journal of Mathematics and Mathematics Education Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS LOSARI: Jurnal Pengabdian Kepada Masyarakat Journal Of Human And Education (JAHE) Journal of Data Insights Emerging Statistics and Data Science Journal Amalgamasi: Journal of Mathematics and Applications
Ihsan Fathoni Amri
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Transportation Other

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Forecasting the Volatility of Tuna Fish Prices in North Sumatra using the ARCH Method in the Period January - April 2024 Multiyaningrum, Riska; Amri, Ihsan Fathoni; Haris, M. Al; Salsabilla, Havinka Angel; Ginasputri, Heppy Nur Asavia; Sintya, Salsabila Dhea
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.236

Abstract

Tuna (Euthynnus affinis) is one of the most important fisheries commodities in Indonesia with significant economic value, especially in its contribution to fisheries export revenue. However, the price of tuna experiences significant fluctuations that can affect local and national economic stability. This study analyzes the daily price fluctuations of tuna in the North Sumatra market from January 1, 2024 to April 29, 2024 using a time series analysis approach. Daily price data were collected and analyzed to identify existing price patterns and volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) model was selected to address the heteroscedasticity in the data, which suggests that the volatility of tuna prices can be well predicted based on past price behavior. The analysis steps include identifying the optimal ARCH model using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as well as testing parameter significance and normality assumptions to validate the model fit. The results show that the ARMA (1,0,0) model is the optimal one to model the price volatility of yellow tuna with the MAPE obtained of 2.382. compared to the ARMA-ARCH method with the MAPE value obtained of 2,747. Because it still contains heteroskedasticity effects, even though the results are good, the prediction results do not closely match the original data. The model is effective in improving price forecasting accuracy, which is important to support decision-making in risk management and economic planning in the fisheries sector. The findings contribute to understanding the dynamics of the yellowtail market and optimizing strategies for fisheries management.
Peramalan dan Permodelan Volatilitas Harga Penutupan Crypto Tether dengan Metode GARCH pada Periode Januari - Juni 2024: Peramalan dan Permodelan Volatilitas Harga Penutupan Crypto Tether dengan Metode GARCH pada Periode Januari - Juni 2024 Syaharani, Nabbila Dyah; Khikman, Muhammad Alvaro; Wahid, Siti Nurasriyanti; Watur, Annisa Cahyaningrum; Amri, Ihsan Fathoni; HARIS, M. AL
Emerging Statistics and Data Science Journal Vol. 2 No. 3 (2024): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol2.iss.3.art29

Abstract

Penelitian ini bertujuan untuk memodelkan dan meramalkan volatilitas harga penutupan cryptocurrency Tether (USDT) menggunakan metode Generalized Autoregressive Conditional Heteroscedasticity (GARCH) pada periode Januari - Juni 2024. Data diperoleh dari platform investing.com. Metode GARCH digunakan karena volatilitas tinggi dalam harga cryptocurrency. Hasil analisis menunjukkan bahwa harga penutupan Tether memiliki rata-rata sebesar 1.000016 dengan standar deviasi 0.000446812. Uji Augmented Dickey-Fuller (ADF) menunjukkan bahwa data harga penutupan sudah stasioner. Model Autoregressive Moving Average (ARMA) digunakan untuk mendukung model GARCH, dan model ARIMA terbaik yang ditemukan adalah ARMA (1,0). Uji signifikansi parameter, uji normalitas, dan uji autokorelasi menunjukkan bahwa model tersebut valid untuk prediksi. Model GARCH digunakan untuk mengestimasi volatilitas dan hasilnya menunjukkan bahwa model ini mampu menangani fluktuasi dan heteroskedastisitas dalam data. MAPE GARCH terbaik yang ditemukan sebesar 0.0264701, menunjukkan bahwa model ini sangat akurat dalam meramalkan volatilitas harga penutupan Tether. Penelitian ini memberikan panduan bagi investor dalam mengelola risiko dan mengoptimalkan return investasi di pasar cryptocurrency.
Itu Analisis faktor kejadian batu empedu menggunakan model regresi logistik biner. Amri, Ihsan Fathoni; Rohim, Febrian Hikmah Nur; Nurul Azka, M. Ilham; Rakhmawati, Muji Silvi
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art3

Abstract

Gallstone disease (cholelithiasis) is a digestive system disorder with a globally increasing prevalence. This study aims to identify risk factors contributing to the occurrence of gallstones using a logistic regression model. The data were obtained from the UC Irvine Machine Learning Repository, comprising a total of 319 outpatients from Ankara VM Medical Park Hospital, Turkey. The analysis was conducted on 23 independent variables, including demographic characteristics, body composition, medical history, and laboratory results. The Chi-Square test identified four significant variables, while the Wald test revealed six statistically significant predictors of gallstone occurrence: age, comorbidities, diabetes mellitus, visceral fat rating, visceral fat area, and vitamin D levels. Diabetes mellitus emerged as the most dominant risk factor (OR = 11.5), whereas higher levels of vitamin D showed a protective effect. The logistic regression model demonstrated a classification accuracy of 77%, indicating good predictive performance. These findings are expected to support early detection, clinical decision-making, and preventive interventions for more effective gallstone prevention.
The ARIMA-GARCH Method in Case Study Forecasting the Daily Stock Price Index of PT. Jasa Marga (Persero) Amri, Ihsan Fathoni; Wulan Sari; Widyasari, Velia Arni; Nurohmah, Nufita; Haris, M. Al
Eigen Mathematics Journal Vol 7 No 1 (2024): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i1.174

Abstract

PT Jasa Marga is a large company in Indonesia that develop and operation the toll roads and is known as one of the blue chip companies with LQ45 shares. However, share prices have high volatility or rise and fall quickly so their value is always changing. Therefore, forecasting is needed to predict the share price of PT Jasa Marga in the future in order to know the movement of its share price. The Autoregressive Integrated Moving Average (ARIMA) method is a method that can predict data with high volatility, but has the disadvantage of residuals containing heteroscedasticity. So, the addition of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was carried out to overcome the heteroscedasticity problem that was initially caused by the ARIMA model so it could predict data with high volatility more optimally. Therefore, this research applies the ARIMA-GARCH method to find the best model for forecasting the daily share price index of PT Jasa Marga. The data used comes from the daily closing stock price index of PT Jasa Marga (Persero) for the period January 2015 to May 2023. The measurement of forecasting accuracy uses the Mean Absolute Percentage Error (MAPE). The forecasting results show that the best model uses ARIMA (2,1,1) - GARCH (1,3) with a MAPE value of 6.825728%, which indicates very good forecasting results because the MAPE value is <10%.
Survival Analysis Using Kaplan-Meier and Cox Regression in Hypertension Patients at Kefamenanu Regional Hospital Khikman, Muhammad Alvaro; Multiyaningrum, Riska; Kholifah , Revika Inta Nur; Sa'adah , Lydia Nur; Safira, Elfina Latifah; Sarah, Albertus Dion; Amri, Ihsan Fathoni; Haris, M. Al
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.270

Abstract

Hypertension is a chronic disease with a steadily increasing global prevalence and is one of the leading causes of serious complications. Indonesia is among the countries with a high prevalence of hypertension, necessitating an understanding of the factors influencing patient treatment duration to enhance the effectiveness of healthcare services. This study aims to analyze differences in the survival rates of hypertensive patients at Kefamenanu Hospital based on gender. The Kaplan-Meier method was used to estimate patient survival rates, while Cox Proportional Hazards regression was used to evaluate the influence of gender on survival time. The Kaplan-Meier analysis results showed that female patients had a higher probability of survival than male patients during hospitalization. However, the Cox Proportional Hazards regression analysis indicated that this difference was not statistically significant. These findings suggest that while there are differences in survival patterns, gender is not the primary determinant of the duration of care for hypertensive patients. The results of this study are expected to provide input for hospitals in designing more effective care strategies that focus on other factors that may influence patient survival time.
Pemodelan ARIMA dan ARIMAX untuk Memprediksi Jumlah Produksi Padi di Kota Magelang Amri, Ihsan Fathoni; Ramadhan, Wulan Nur; Ainurrofiah, Safira; Haris, M. Al
Square : Journal of Mathematics and Mathematics Education Vol. 5 No. 2 (2023)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2023.5.2.17059

Abstract

Memprediksi atau meramalkan perilaku observasi biasanya menggunakan pemodelan time series yang dilakukan secara berurutan. Prediksi jumlah produksi padi diharapkan dapat memberikan masukan bagi pemerintah dan dimanfaatkan oleh siapa saja sebagai pengembangan pada sektor pertanian serta sebagai bahan ajar penggunaan metode ARIMA (Autoregressive Integrated Moving Average) dan ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variable). Tujuan dari penelitian ini sebagai perbandingan dalam menemukan model terbaik dari metode ARIMA dan ARIMAX untuk memprediksi jumlah produksi padi pada tahun 2023 di kota Magelang. Data yang digunakan merupakan data produksi padi dan luas lahan tanam sebagai variabel eksogen di Kota Magelang pada bulan Januari 2019 sampai Desember 2022. Berdasarkan hasil analisis, diperoleh model ARIMA terbaik untuk meramalkan jumlah produksi padi di kota Malang adalah ARIMA (0,1,1), sedangnkan model ARIMAX terbaik adalah ARIMAX (0,0,1). Perbandingan kedua model tersebut berdasarkan nilai MAPE, model ARIMAX (0,0,1) menjadi model terbaik untuk meramalkan jumlah produksi padi di Kota Malang karena menghasilkan MAPE terkecil 6,31%. Hasil peramalan menggunakan model ARIMAX (0,0,1) menunjukkan data cenderung mengalami pola trend turun. Hal ini dikarenakan lahan pertanian yang semakin sempit setiap tahunnya sehingga menyebabkan jumlah produksinya semakin menurun.Kata Kunci: Pemodelan, metode ARIMA, metode ARIMAX, Produksi Padi.
Pemodelan ARIMAX untuk Meramalkan Harga Minyak Mentah Dunia Amri, Ihsan Fathoni; Wulandari, Ayu; Abidah, Khansa Ni'mal; Irawan, Alfian Chandra; Haris, M. Al
Square : Journal of Mathematics and Mathematics Education Vol. 5 No. 1 (2023)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2023.5.1.17074

Abstract

Perdagangan secara umum dikelompokkan menjadi dua yaitu, ekspor dan impor. Salah satu contoh perdagangan tersebut adalah minyak mentah. Diketahui saat ini harga pasar minyak mentah dunia mempengaruhi tingkat perekonomian global. Harga minyak yang terus berubah, tentu saja menjadi sumber kekhawatiran dan perhatian tersendiri, terutama dalam industri minyak. Dalam penelitian ini, akan mengkaji harga minyak mentah menggunakan model ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables). Model ARIMAX dipilih karena mampu mengintegrasikan variabel eksternal, seperti volume nilai tukar rupiah dan produksi minyak, yang mempengaruhi harga minyak mentah. Tujuan utama penelitian ini adalah untuk mengembangkan model prediktif yang akurat dengan mempertimbangkan pengaruh produksi minyak (Richard et al., 2021)dan nilai tukar rupiah terhadap harga minyak mentah dunia. Berdasarkan hasil analisis model ARIMAX (0,1,2) merupakan model terbaik dalam meramalkan harga minyak mentah dunia karena memiliki nilai AIC dan MAPE terkecil, yaitu AIC sebesar 408,49 dan MAPE 8,88. Berdasarkan hasil tersebut peramalan dengan model model ARIMAX (0,1,2) dapat dikategorikan sangat baik.Kata Kunci: ARIMAX, Perekonomian, Harga minyak mentah dunia, Nilai tukar rupiah, Produksi minyak.
Study on Import and Export Indicators in Indonesia Using Volatility and Markov Switching Model Combination: Kajian Indikator Impor dan Ekspor di Indonesia Menggunakan Kombinasi Model Volatilitas dan Markov Switching Fathoni Amri, Ihsan; Purwanto, Dannu
Journal of Data Insights Vol 1 No 1 (2023): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v1i1.137

Abstract

Krisis ekonomi tahun 1997 merupakan masalah yang terjadi di hampir semua negara berkembang termasuk Indonesia. Berdasarkan krisis ekonomi, diperlukan indikator performance knowledge. Impor dan ekspor merupakan indikator penting yang harus dilihat kinerjanya. Data bulanan impor dan ekspor merupakan data deret waktu karena dikumpulkan, dicatat, dan diamati dalam urutan waktu. Data impor dan ekspor mengandung masalah heteroskedastisitas pada model residual dan conditional change pada volatilitas. Kombinasi model volatilitas dan Markov switching dapat mengatasi permasalahan dalam penelitian ini. Penelitian ini dikembangkan dengan menggunakan data volatilitas dan smoothed probability, selanjutnya penelitian ini memperoleh tingkat akurasi dengan membandingkan probabilitas prediksi dengan probabilitas smoothed dari data aktual. Hasil dari penelitian ini diperoleh model SWARCH(4,1) dengan ARIMA(1,0,0) untuk rata-rata dan ARCH(1) untuk varians yaitu untuk total data impor dan model SWARCH(2,1) dengan ARIMA(1, 0,0) untuk rata-rata dan ARCH(1) untuk varian yang merupakan total data ekspor. Probabilitas prediksi perbandingan dan probabilitas pemulusan dari data aktual diperoleh akurasi 40,91% untuk indikator impor dan 100% untuk indikator ekspor, artinya untuk indikator impor harus mengubah nilai awal model SWARCH agar lebih akurat.
Forecasting Honda Car Retail Sales Using the Seasonal Autoregressive Integrated Moving Average Method: Peramalan Penjualan Retail Mobil Honda Menggunakan Metode Seasonal Autoregressive Integrated Moving Average Angelina, Lea; Permata, Alia; Arsusma, Jesicha; Masichah, Firochul; Al Haris, M.; Fathoni Amri, Ihsan
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.416

Abstract

This article discusses the forecasting of Honda car retail sales using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The study aims to forecast Honda car retail sales for the upcoming year. Various SARIMA models have been tested to determine the best model, and the results show that the SARIMA (1,1,0)(1,1,1)¹² model provides the lowest Mean Absolute Percentage Error (MAPE) among all tested models, which is 17,74%. Therefore, this model was chosen for forecasting sales over the next 12 months. The forecast results are expected to assist management in making optimal decisions regarding stock and marketing, as well as significantly enhancing operational efficiency and customer satisfaction in the future.
PERBANDINGAN MODEL ARIMA DAN ARIMAX UNTUK PERAMALAN TEMPERATUR DI KOTA SEMARANG Adhwaningrum, Arullah Salsabila; Amri, Ihsan Fathoni; Saputri, Atika Dwi; Diani, Nandini Lova; Pratama, Rifin Fadilla; Al Haris, M.
Amalgamasi: Journal of Mathematics and Applications Vol. 3 No. 2 (2024): Amalgamasi: Journal of Mathematics and Applications
Publisher : Universitas Pasifik Morotai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55098/amalgamasi.v3.i2.pp54-64

Abstract

Semarang mengalami banjir yang disebabkan oleh curah hujan yang tinggi dan penurunan temperatur yang drastis, terutama pada bulan Maret. Penelitian ini menganalisis perbandingan antara model AutoRegressive Integrated Moving Average (ARIMA) dan AutoRegressive Integrated Moving Average dengan Exogenous variables (ARIMAX) untuk prediksi suhu di kota Semarang pada tahun 2024. ARIMAX menggabungkan variabel eksogen ke dalam kerangka ARIMA dan memungkinkan pertimbangan faktor eksternal yang dapat mempengaruhi suhu. ARIMAX memberikan fleksibilitas tambahan dengan menyertakan variable eksternal seperti pola curah hujan, yang secara signifikan mempengaruhi variasi suhu. Data yang digunakan terdiri dari 150 observasi, dengan 135 data digunakan sebagai data training dan 15 data sebagai data testing. Setelah melakukan uji kestasioneran dan diferensiasi data, dilakukan pemodelan ARIMA dan ARIMAX. Model terbaik yang dipilih berdasarkan nilai AIC terkecil adalah ARIMAX (3,1,0) dengan nilai AIC sebesar 346.79 dan MAPE sebesar 2.2084%. Hasil menunjukkan bahwa model ARIMAX lebih akurat dibandingkan dengan model ARIMA, dengan kesalahan peramalan yang lebih rendah
Co-Authors Abdul Ghufron Abidah, Khansa Ni'mal Adhwaningrum, Arullah Salsabila Ainurrofiah, Safira Al Haris, M Alwan Fadlurohman Amri, Saeful Angelina, Lea Ariska Fitriyana Ningrum Arsusma, Jesicha Arya, Abimanyu Asrirawan Astuti, Sofi Anggi Aura Hisani, Zahra Ayu Wulandari Azzahrani, Rahma Dewi Dannu Purwanto Dhani, Oktaviana Rahma Diani, Nandini Lova Dwi Saputri, Atika Ginasputri, Heppy Nur Asavia Haris, M. Al Hikmah Nur Rohim, Febrian Irawan, Alfian Chandra Kaia Raissa Akmalia Khikman, Muhammad Alvaro Kholifah , Revika Inta Nur Laila Qadrini M. Al Haris Masichah, Firochul Muhammad Fahmuddin Multiyaningrum, Riska Nur Mahmudah Nurohmah, Nufita Nurul Azka, M. Ilham Permata, Alia Pranandira Rilvandri, Quinsy Pratama, Rifin Fadilla Priambodo, Danu Puspitasari, Linda Rahma Dhani, Oktaviana Rakhmawati, Muji Silvi Ramadhan, Wulan Nur Rohim, Febrian Hikmah Nur Sa'adah , Lydia Nur Safira, Elfina Latifah Salsabilla, Havinka Angel Saputri, Atika Dwi Sarah, Albertus Dion Sintya, Salsabila Dhea Suherdi, Andri Sulistiya, Indah Syaharani, Nabbila Dyah Tiani Wahyu Utami Wahid, Siti Nurasriyanti Wardani, Amelia Kusuma Watur, Annisa Cahyaningrum Widyasari, Velia Arni Wulan Sari Yolan Triky Zahra Aura Hisani