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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Square : Journal of Mathematics and Mathematics Education Majalah Ilmiah Matematika dan Statistika (MIMS) Prosiding Seminar Nasional Unimus Journal of Mathematics, Computation and Statistics (JMATHCOS)
Rochdi Wasono
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Health Professions Mathematics Mechanical Engineering Nursing Physics Public Health Transportation

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Analisis risiko pada Saham PT. Unilever Indonesia dengan metode expected shortfall berdasarkan model GBM with jump diffusion Utami, Rossy Prima Nada; Haris, M. Al; Wasono, Rochdi
Majalah Ilmiah Matematika dan Statistika Vol. 23 No. 2 (2023): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v23i2.38459

Abstract

Stock investment activities had a high level of profit and a high level of risk as well. The risk could be known from fluctuations in stock price data on stock returns. Fluctuations in stock price data on stock returns in each period could not be controlled, so predicting stock prices through returns was relatively difficult to do. The Geometric Brownian Motion (GBM) with Jump Diffusion model was proposed because it was able to capture fluctuations in stock return value data. The GBM with Jump Diffusion model was used when the data has extreme data or jumps that did not meet the assumption of normality, for example stock price data. This research was conducted to calculate the estimated risk of predicting the value of stock returns at PT Unilever Indonesia (UNVR) data for the period January 4, 2021, to January 27, 2023. Based on the results of the analysis, the estimated investment risk in UNVR stocks using the Expected Shortfall method showed that at a confidence level of 95% was generated a risk value of 0.05229, at 90% confidence level resulted in a risk value of 0.04436, at 85% confidence level resulted in a risk value of 0.03747 and 80% confidence level resulted in a risk value of 0.03645. So it could be said that the higher the level of trust, the higher the level of risk.Keywords: GBM, jump diffusion, PT Unilever Indonesia, expected shortfallMSC2020: 62P05, 91G70
Peramalan Harga Emas Menggunakan Metode Fuzzy Time Series Chen dalam Investasi untuk Meminimalisir Risko Sofiyanti , Elvia Nanda; Ulinuha, Samikoh; Okiyanto, Rizal; Haris, M. Al; Wasono, Rochdi
Journal of Mathematics, Computations and Statistics Vol. 7 No. 1 (2024): Volume 07 Nomor 01 (April 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i1.1955

Abstract

Peran emas yang nilainya diakui secara luas diseluruh dunia menyebabkan minat para investor untuk berinvestasi lebih tinggi dibandingkan dengan investasi lainnya. Informasi yang signifikan mengenai harga emas dapat digunakan sebagai prediktor yang penting untuk memperkirakan pergerakan harga emas dimasa mendatang. Risiko yang umum dalam investasi emas adalah berfluktuasinya harga disetiap periode/satuan waktu yang menyulitkan bagi investor untuk menduga arah pergerakan harga emas. Agar dapat mencapai keuntungan sesuai dengan rencana yang dibuat investor perlu menggunakan teknik peramalan yang akurat, salah satunya dengan metode Fuzzy time series dengan algoritma Chen. Hasil analisis menunjukkan bahwa penerapan metode Fuzzy time series yang dikembangkan oleh Chen menghasilkan tingkat kesalahan berdasarkan nilai MAPE sebesar 3.92% atau dengan kata lain tingkat akurasinya sangat baik. Hasil prediksi harga emas pada 1 Juli 2023 menghasilkan nilai ramalan sebesar 1907.36 USD.
Peramalan Uang Kartal Provinsi Jawa Barat Menggunakan Hybrid ARIMAX-QR DAN QRNN Biru, Pelangi Langit; Utami, Tiani Wahyu; Wasono, Rochdi
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Penelitian ini membahas peramalan aliran uang kartal (inflow dan outflow) di Provinsi Jawa Baratmenggunakan dua model hybrid, yaitu ARIMAX-QR dan Quantile Regression Neural Network (QRNN).Aliran uang kartal yang diatur oleh Bank Indonesia memiliki peran penting dalam perekonomian, sehinggadiperlukan model yang akurat untuk memprediksi pergerakannya. Penelitian ini menggunakan data inflowdan outflow uang kartal dari Kantor Perwakilan Bank Indonesia Provinsi Jawa Barat untuk periode Januari2011 hingga Desember 2023. Model hybrid ARIMAX-QR digunakan untuk memprediksi komponen linear, sedangkan QRNN digunakanuntuk menangkap pola non-linear dalam data. Selain itu, penelitian ini juga membandingkan akurasi darikedua model hybrid tersebut dalam memprediksi aliran uang kartal. Hasil penelitian menunjukkan bahwamodel hybrid memberikan akurasi yang lebih tinggi dibandingkan model individu, dengan QRNNmenunjukkan performa terbaik dalam memodelkan fluktuasi non-linear. Penelitian ini memberikankontribusi dalam pengembangan model peramalan yang lebih presisi dan akurat, yang dapat membantuBank Indonesia dalam merencanakan dan membuat kebijakan terkait pengelolaan uang kartal. Manfaat dari penelitian ini tidak hanya untuk memberikan metode peramalan yang lebih akurat tetapi jugasebagai rujukan kebijakan bagi Bank Indonesia dalam pengelolaan aliran uang kartal di Jawa Barat. Modelyang dikembangkan juga dapat diterapkan pada berbagai konteks peramalan keuangan lainnya yangmembutuhkan akurasi tinggi dalam kondisi yang dinamis dan tidak pasti. Kata Kunci : Uang Kartal, Hibrid, QRNN, ARIMAX-QR, Jawa Barat
Pemodelan HIV dan AIDS di Provinsi Jawa Timur Menggunakan Metode Regresi Bivariat Poisson Invers Gaussian (BPIG) Fitriyah, Novina Indah; Arum, Prizka Rismawati; Wasono, Rochdi
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Regresi Poissonnadalah metode regresi yang digunakannuntuk memodelkannhubungannantara variabeldependen diskrittdalam bentuk data hitungan (count). Namun, data hitungan pada variabel dependenseringgkali mengalami masalah overdispersi atau underdispersi, yang berarti bahwa variansinya lebih besaratau lebih kecil daripada rata-rata. Masalah ini tidak sesuai dengan asumsi regresi Poisson, di manadiasumsikan bahwa rata-rata sama dengan varians (equidispersi). Untuk mengatasi masalah ini, salah satumodel yang dapat digunakan adalah Bivariate Poisson Inverse Gaussian. Model ini dapat menjelaskanhubungan antara dua variabel dependen, seperti HIV dan AIDS, dengan beberapa variabel independen.Kesehatan dianggap sebagai unsur kunci dalam perkembangan ekonomi Negara dan permasalahankesehatan, terutama HIV dan AIDS menjadi isu utama dalam rangka mencapaiSSustainable DevelopmentGoals (SDGs) di Indonesia. Sehingga diperlukan penelitiannuntukkmengetahuiifaktor-faktor yangberpengaruh terhadap jumlah kasus HIV dan AIDS di Provinsii Jawa Timur tahun 2022. Penaksir parameterdilakukan dengannmetodeeMaximum Likelihood Estimation (MLE). Hasil penelitian menunjukkan modelregresi Bivariat Poisson Invers Gaussian adalah λ̂1 = exp(4,30692 + 0,00004X1 + 0,00048X2 + 0,00006X3+ 0,01657X4 + 0,00403X5 - 0,02719X6) dan λ̂2 = exp(2,52020 + 0,00034X1 + 0,00560X2 + 0,00006X3 –0,00257X4 + 0,00303X5 + 0,00497X6), di mana variabel kepadatan peduduk per kilometer, presentasedaerah yang berstatus desa, presentase pasangan usia subur pengguna kondom, presentase pendudukk yangmaksimal tamat SMA, presentase penduduk miskin, dan presentase penderita infeksi menular seksual,berpengaruh secara signifikan terhadap kasus HIV dan AIDS dengan nilai AIC sebesarr 5994.888.Kata Kunci : AIDS, HIV, Overdispersi, Regresi Poisson Bivariat, Poisson Invers Gaussian.
FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD Wasono, Rochdi; Fitri, Yulia; Haris, M. Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0427-0436

Abstract

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.
ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION WITH LEAST ABSOLUTE DEVIATION (LAD) ESTIMATION AND M-ESTIMATION ON GRDP OF WEST JAVA PROVINCE Arum, Prizka Rismawati; Ridwan, Mohammad; Alfidayanti, Ina; Wasono, Rochdi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1573-1584

Abstract

Geographically Weighted Regression (GWR) is an analytical method for data that contains spatial heterogeneity effects. However, parameter estimation in the GWR model has a weakness, namely it is prone to outliers and can cause the parameter estimation to be biased. This can be overcome by the Robust Geographically Weighted Regression (RGWR) method which is more robust against the presence of outliers. This method is suitable for Gross Regional Domestic Product (GRDP) data in West Java Province, which contains outliers and also has spatial effects. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of West Java Province. The purpose of this study is to compare the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation (LAD) Estimation and M-estimation and also to find out the factors that affect the Gross Regional Domestic Product (GRDP) in West Java Province in 2021 based on the model resulting from. Selection of the best model is seen based on the value of the coefficient of determination (R2) and Mean Squared of Error (MSE). The research results show that the Robust Geographically Weighted Regression (RGWR) method with M-estimation is much more effective in estimating the distribution of GRDP in West Java Province in 2021, seen from the larger coefficient of determination and the smaller Mean Square Error (MSE). The variables that have a significant influence on GRDP in West Java Province in 2021 are the variables of foreign investment and local income.
Principal Component Analysis on Convolutional Neural Network Using Transfer Learning Method for Image Classification of Cifar-10 Dataset Al Haris, M.; Dzeaulfath, Muhammad; Wasono, Rochdi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.3517

Abstract

The current era was defined by an overwhelming abundance of information, including multimedia data such as audio, images, and videos. However, with such an enormous amount of image data available, accurately and efficiently selecting the necessary images poses a significant challenge. To address this, image classification has emerged as a viable solution for organizing and managing large volumes of image data, thereby mitigating the issue of cluttered image datasets. One of the most popular algorithms for image classification is the Convolutional Neural Network (CNN), which reduces the complexity of network structure and parameters by leveraging local receptive fields, weight sharing, and pooling operations. CNN is a type of artificial neural network specifically designed to process grid-like data, such as images, using convolutional layers to automatically detect local features. Nonetheless, CNN faces several challenges, such as gradient diffusion, large dataset requirements, and slow training processes. To overcome these issues, Transfer Learning has been widely adopted in CNN-based image classification, and Principal Component Analysis (PCA) has been employed to accelerate the training process. PCA is a technique used to reduce data dimensionality by identifying the principal components that account for most of the variance in the data. This study tested the efficacy of PCA-based CNN architecture using the Transfer Learning method on the Cifar-10 dataset. The results demonstrated that the PCA-based CNN architecture achieved the highest accuracy, with a testing accuracy rate of 0.8982 (89%).
Pemodelan Tingkat Kemiskinan di Papua Barat dengan Pendekatan Binary Logistic Regression Aini, Anissa Nur; Octario Ashar, Andika Udistiyan; Lestari, Talia Indah; Nur, Indah Manfaati; Wasono, Rochdi
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.2023.5.2.17169

Abstract

Binary logistic regression model is a regression model used to predict the probability of a specific event occurring or not occurring based on predictor variable. In this model, the response variable is binary, meaning it has only two possible categories: a “failure”category and a “success” category. West Papua is included in the list of  seven provinces that are the main focus of efforts to combat extreme poverty. Therefore, it is necessary to monitor the factors that need to be considered in order to prevent an increase in the poverty rate. To identify the factors influencing the poverty rate in Papua Barat, the research method used is binary logistic regression modeling, which assesses the influence of independent variables on the poverty rate in West Papua. So the results obtained from this study are from three variables, namely the open unemployment rate, average per capita expenditure, and gross regional domestic product have a significant effect on the poverty rate in West Papua with a classification accuracy of 100%.
Co-Authors Aini, Anissa Nur Al Haris, M. Alfidayanti, Ina Biru, Pelangi Langit Dzeaulfath, Muhammad Fitriyah, Novina Indah Haris, M. Al Indah Manfaati Nur Lestari, Talia Indah M. Al Haris Mohammad Ridwan Octario Ashar, Andika Udistiyan Okiyanto, Rizal Prizka Rismawati Arum Tiani Wahyu Utami Ulinuha, Samikoh Utami, Rossy Prima Nada Yulia Fitri