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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNEJ e-Proceeding BAREKENG: Jurnal Ilmu Matematika dan Terapan Buana Matematika : Jurnal Ilmiah Matematika dan Pendidikan Matematika Martabe : Jurnal Pengabdian Kepada Masyarakat PeTeKa MAJAMATH: Jurnal Matematika dan Pendidikan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Math Educa Journal Bakti Cendana: Jurnal Pengabdian Masyarakat Logaritma : Jurnal Ilmu-ilmu Pendidikan dan Sains Journal of Science and Technology Mathematics and Applications (MAp) Journal Jurnal Pengabdian dan Pemberdayaan Masyarakat Kepulauan Riau (JPPM KEPRI) Journal of Social Outreach Jurnal Pengabdian Pada Masyarakat
Hasibuan, Lilis Harianti
Universitas Islam Negeri Imam Bonjol Padang
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Journal : Math Educa Journal

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PEMODELAN DATA SAHAM MENGGUNAKAN ANALISIS TIME SERIES DENGAN PENDEKATAN COPULA GAUSSIAN Jannah, Miftahul; Mardika, Fitria; Hasibuan, Lilis Harianti; Putri, Darvi Mailisa
Math Educa Journal Vol 5, No 2 (2021)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/mej.v5i2.3124

Abstract

One method of predicting stock prices is to use the time series analysis method. In this method, a linear prediction model is made to see patterns from historical stock price data to assess future prices. The stock data used in this study is the daily stock data of PT. Telkom and PT. Indosat in 2020-2021. Autoregressive (AR) model is a time series model that is often used with the assumption that its volatility does not change with time (Homoscedastic). After analyzing the AR Model(1) data for the stock data of PT. Telkom and PT. Indosat has a non-independent error, therefore the AR(1)-N.GARCH(1,1) time series model construction was carried out to model the error (ϵ_(i,t)). Furthermore, the error of the AR(1)-N.GARCH(1,1) model is independent of t, so it can be modeled using Copula. After the Copula model was applied to the data and obtained the value of the fit of the Gaussian Copula distribution error model. From the values generated from the Gaussian Copula C({ϵ_(i,t) }_(t=1)^T ),T=1,2,…, and approximates a uniform distribution. So the stock data of PT. Telkom and PT. It can be said that Indosat is not suitable to be modeled with the Gaussian Copula.
PREDIKSI JUMLAH PENUMPANG PESAWAT PADA MASA COVID-19 DENGAN METODE EXPONENTIAL SMOOTHING Darvi Mailisa Putri; Fitri Rahmah Ul Hasanah; Lilis Harianti Hasibuan; Miftahul Jannah
Math Educa Journal Vol 6, No 1 (2022)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/mej.v6i1.3896

Abstract

Forecasting is a study that is still interesting today. With the forecasting method, a person can make predictions about something based on previously available data. In this study, will be carried out on the prediction of the number of airplane passengers on domestic during the COVID-19 period. The data taken is data on domestic airplane passengers at Minangkabau International Airport Padang city. Data by month for the period 2016 to 2020. The method that will be applied to the data is the exponential smoothing type forecasting method, especially the Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES) methods. The results of the study concluded that if analyzed from the MAPE value, the DES method was better with a MAPE value of 77. However, if analyzed from the MAD and MSD values, the SES method was better with a value of 32609 and 2044501652, respectively. Furthermore, analyzing the prediction results of the two methods, it was obtained that for the first four months the DES method showed better results than the SES method. But two months later the SES method was much better.
ANALISIS METODE SINGLE EXPONENTIAL SMOOTHING DAN METODE REGRESI LINEAR UNTUK PREDIKSI HARGA DAGING AYAM RAS Lilis Harianti Hasibuan; Darvi Mailisa Putri; Miftahul Jannah; Syarto Musthofa
Math Educa Journal Vol 6, No 2 (2022)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/mej.v6i2.3872

Abstract

Prediction of the price of broilers in the future is intended to control the excess and shortage of broiler stock can be minimized. When the price of purebred chicken can be predicted accurately, the fulfillment of consumer demand can be managed on time. This study aims to analyze the prediction accuracy of broiler prices using the Single Exponential Smoothing (SES) method compared to using the linear regression method, so that a more accurate method will be obtained to predict the price of broilers. The percentage of prediction error values is the most important criterion in analyzing the prediction accuracy of these two methods. The results showed that the average percentage of error in predicting the quantity of sales of broilers using the SES method with the smoothing parameter value =0.5 is the method that has the highest predictive accuracy (MAPE=0.00258%) compared to using the linear regression method (MAPE= 0.05%).
Co-Authors Afrimayani, Afrimayani Amelia Putri Annur, Lathifah Arham, Aulia Asfa'ani, Ezhari Asfa’ani, Ezhari Audinta Sakti Firmansyah Bagus Pribadi, Bagus Darvi Mailisa Putri Devianto, Dodi Fadhila Putri Indriani Fitri Rahmah Ul Hasanah Gusti Muhammad Rosyadi Hijriati, Naimah Indah Permata Sari Indah Permata Sari Juwita Lasterina La Ode Nashar Lilis Suriani M Syahrul Ridho Nasution Maiyastri, Maiyastri Mardika, Fitria MIFTAHUL JANNAH Miftahul Jannah Miftahul Jannah Miftahul Jannah Miftahul Jannah Mochammad Idris Mohamad Syafi’i Muhammad Naufan Rizqullah Musthofa, Syarto Nor Hidayati Novia Lestari Novita Hasanah Novizal Wendry, Novizal Nur, Rizki Amalia Putri, Rani Kurnia Rahma Yulia Rani Kurnia Putri Rani Kurnia Putri Raudhatul Jannah Rianjaya, Ilham Dangu Ririn Hayatun Nufus Riza, Ozzy Secio Rudiyanto Rudiyanto, Rudiyanto Sabran, La Ode Salsabila, Unik Hanifah Saman Abdurrahman Sari, Anisa Rizki Sepri, Domi Sheryn Amelia Puteri Sholih, Ahmad Shubhi Subhan Ajrin Sudirman Susi Sulastri Lubis Syahadah, Nadila Syarto Musthofa Syarto Musthofa Ul Hasanah, Fitri Rahmah Winda Fransiska Yanuar, Ferra Yuharnida, Yuharnida