Articles
<![CDATA[Analisis Kemampuan Daya Serap Mahasiswa Pada Mata Kuliah Geometri Transformasi ]]>
<![CDATA[Putri, Darvi Mailisa]]>;
<![CDATA[Amalina, Amalina]]>
<![CDATA[ Math Educa Journal Vol 2, No 2 (2018) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/mej.v2i2.190
<![CDATA[The student's absorption of learning material is one of the factors that can influence the efforts of students to achieve learning outcomes. Absorption can be a benchmark for knowing how far a student understands learning material. Therefore, this study aims to analyze the level of absorptive ability of the UIN Imam Bonjol Padang Tarbiyah Faculty of Mathematics Tadris Study Program at the transformation geometry subject. The study uses statistical estimation, namely the ability of the absorption of students in the transformation geometry subject to the lowest or equal to 75% of the maximum value. The data analysis of the research was carried out manually by using the parametric statistical test formula to find out the truth of the statement or the hypothesis that had been hypothesized. In this study z-test one sample in one direction left side. In addition, hypothesis testing is also carried out using the help of SPSS applications. The results show that and. This means that Ho is accepted. Thus it can be seen that the ability of student absorption in the transformation geometry subject is the lowest or equal to 75% of the maximum value. In other words, the average absorption of students towards transformation geometry courses is more than or equal to 75. So it can be concluded that the absorption of students in the geometry subject of transformation is quite good.Keywords: student absorption, transformation geometry]]>
<![CDATA[VALIDITAS PERANGKAT PEMBELAJARAN E-LEARNING BERBASIS EDMODO PADA MATA KULIAH ALJABAR LINEAR ]]>
<![CDATA[Amalina, Amalina]]>;
<![CDATA[Putri, Darvi Mailisa]]>;
<![CDATA[Aswirna, Prima]]>
<![CDATA[ Math Educa Journal Vol 3, No 2 (2019) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/mej.v3i2.883
<![CDATA[The purpose of this study is to describe the validity of Edmodo-based E-learning learning tools in linear algebra courses. Learning tools consist of RPS, Teaching Materials and Final Tests that are developed using the ADDIE model, namely Analysis, Design, Develop, Implementation, and Evaluation. The results of this study are Edmodo-based E-Learning learning tools in linear algebra courses for students of Mathematics Tadris FTK UIN Imam Bonjol Padang. Validation involves four validators, namely 3 mathematics education lecturers and 1 language education expert lecturer using a Likert scale learning tool validation sheet on RPS, Teaching Materials, and Final Tests. The results of the study of validity were stated to be very valid by the validators with the RPS obtaining an average score of 3.63, teaching materials obtained a score of 3.38, and the final test obtained a score of 3.41.]]>
<![CDATA[Pemodelan Laju Perubahan Nilai Tukar Rupiah (IDR) terhadap Dolar Amerika (USD) dengan Metode Markov Switching Autoregressive (MSAR) ]]>
<![CDATA[Yurinanda, Sherli]]>;
<![CDATA[Putri, Darvi Mailisa]]>
<![CDATA[ JOSTECH: Journal of Science and Technology Vol 1, No 1: Maret 2021 ]]>
Publisher : <![CDATA[UIN Imam Bonjol Padang ]]>
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DOI: 10.15548/jostech.v1i1.2438
<![CDATA[This study aims to determine the modeling of the exchange rate change rate of the Rupiah (IDR) against the US dollar (USD) using the Markov Switching Autoregressive (MSAR) method. The research data is sourced from secondary data through website investing to see the Rupiah exchange rate against the US dollar with a time span from January to December 2020. The results show that the best model obtained is MS(2)AR(3) with parameters = 0.031119 and = -0.000504, where when state = 1, the average rate of change in the rupiah exchange rate against the US dollar is 0.031119 per day, while when state = 2 the average rate of change in the rupiah exchange rate against the US dollar is -0.000504 per day. ]]>
<![CDATA[PENERAPAN GERAK BROWN GEOMETRIK PADA DATA SAHAM PT. ANTM ]]>
<![CDATA[Putri, Darvi Mailisa]]>;
<![CDATA[Hasibuan, Lilis Harianti]]>
<![CDATA[ MAp (Mathematics and Applications) Journal Vol 2, No 2 (2020) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/map.v2i2.2258
<![CDATA[Penelitian ini akan mengkaji aplikasi gerak Brown geometrik pada data harga saham PT. Antm. Data harga saham yang digunakan adalah data harga saham penutupan dari tanggal 02 Januari 2019 sampai dengan 30 Desember 2019 dengan periode harian. Dalam mengaplikasikan data harga saham PT. Antm pada gerak Brown geometrik diperlukan nilai return harga saham yang memenuhi asumsi dari gerak Brown geometrik. Selanjutnya melalui parameter-parameter yang diperoleh dari return harga saham dan membangkitkan data berdistribusi normal atau  sebanyak data yang diamati dan harga awal yang telah diketahui maka didapat plot hasil dari data harga saham PT. Antm yang telah memenuhi asumsi gerak Brown geometik.AbstractThis research will examine the application of geometric Brownian motion on the stock price of PT. Antm. The stock price data used is the closing stock price data from January 02nd 2019 to December 30th 2019 with a daily period. In applying the stock price of PT. Antm on geometric Brownian motion requires a stock price return value that satisfies the assumptions of geometric Brownian motion. Futhermore, through the parameters obtained from the stock price return and generate normally distributed data or   as much as the observed data and the known intial price, then we get the plot of PT. Antm has fulfilled the assumption of geometric Brownian motion.]]>
<![CDATA[LEFT INVERTIBLE SEMIGRUP PADA RUANG HILBERT ]]>
<![CDATA[Asfa’ani, Ezhari]]>;
<![CDATA[Hasibuan, Lilis Harianti]]>;
<![CDATA[Jannah, Miftahul]]>;
<![CDATA[Putri, Darvi Mailisa]]>
<![CDATA[ MAp (Mathematics and Applications) Journal Vol 2, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/map.v2i1.1640
<![CDATA[Analisis Fungsional merupakan salah satu cabang dari ilmu Matematika yang membahas tentang ruang vektor serta pemetaan di antara ruang - ruang tersebut. Pada artikel ini membahas tentang semigrup pada ruang Hilbert yang dapat dibalik dan mempunyai balikan. Untuk Semigrup yang sangat kontinu pada Ruang Hilbert, disini disajikan bukti singkat dari fakta-fakta bahwa inverse kiri dari semigrup yang dapat dibalik dan dapat dipilih menjadi semigrup juga. Lebih jauh pada tulisan ini akan ditunjukkan pula bahwa semigrup ini tidak perlu unik.AbstractFunctional Analysis is one branch of Mathematics that deals with vector spaces and mapping between these spaces This article to discuss about semigroups on Hilbert Space. For strongly continous semigroups on Hilbert space, we present a short proof of the fact that the left inverse of a left invertible semigroup can be chosen to be a semigroups as well. Furthermore, we show that this semigroups need not to be unique.Keywords: three-five word(s) or phrase(s), that it’s representative for the article.]]>
<![CDATA[NULLITAS MAKSIMUM MATRIKS HERMITIAN DIGAMBARKAN OLEH GRAF G ]]>
<![CDATA[Syafii, Mohamad]]>;
<![CDATA[Putri, Darvi Mailisa]]>;
<![CDATA[Rahman, Alfit]]>
<![CDATA[ MAp (Mathematics and Applications) Journal Vol 3, No 1 (2021) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/map.v3i1.2784
<![CDATA[Penelitian ini bertujuan untuk mendapatkan sebuah pola matriks Hermitian yang digambarkan graf G. Tentunya banyak kemungkinan matriks Hermitian yang didapatkan. Dengan berbantuan program Matlab, peneliti merumuskan pola matriks Hermitian yang didapatkan dengan tujuan memperoleh nullitas maksimum. Pada penilitian ini nullitas terbesar (maksimum) dari matriks Hermitian yang digambarkan graf G dapat dituliskan dengan M(G)=maks{null(A):A∈H(G), G(A)=G). Adapun graf yang digunakan pada penelitian adalah graf komplit, graf lintasan, graf sikel, graf bipartisi komplit, dan graf star. Teorema pendukung yang digunakan dalam penelitian ini untuk menentukan M(G) adalah M(G)+mr(G)=|G|, dengan mr(G) adalah minimum rank dari matriks Hermite yang digambarkan oleh graf G dan |G| adalah order dari G atau banyaknya sisi pada Graf G. ]]>
<![CDATA[APLIKASI TEOREMA HUKUM LEMAH BILANGAN BESAR PADA PEMBUKTIAN TEOREMA APROKSIMASI WEIERSTRASS ]]>
<![CDATA[Ul Hasanah, Fitri Rahmah]]>;
<![CDATA[Putri, Darvi Mailisa]]>
<![CDATA[ MAp (Mathematics and Applications) Journal Vol 3, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/map.v3i2.3354
<![CDATA[Teorema aproksimasi weierstrass dinyatakan sebagai fungsi kontinu pada selang tertutup dan terbatas yang daoat didekati dengan barisan suku banyak. Salah satu pembuktian teorema ini dengan menggunakan polinomial Bernstein ). Oleh karena,  dimana  untuk ukuran  cukup besar maka ) dirumuskan menjadi , dimana berlaku hukum lemah bilangan besar dengan . Oleh karena itu, dalam tulisan ini dibahas pembuktian teorema aproksimasi weierstrass dengan hukum lemah bilangan besar.Kata Kunci: bilangan besar, Weierstrass, polinomial Bernstein.]]>
<![CDATA[ESTIMASI MODEL TERBAIK UNTUK PERAMALAN HARGA SAHAM PT. POLYCHEM INDONESIA Tbk. DENGAN ARIMA ]]>
<![CDATA[Putri, Darvi Mailisa]]>;
<![CDATA[Aghsilni, Aghsilni]]>
<![CDATA[ MAp (Mathematics and Applications) Journal Vol 1, No 2 (2019) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/map.v1i2.1176
<![CDATA[Saham merupakan hal yang masih sangat menarik dibahas dalam dunia investasi. Investasi dalam bentuk saham sangat dihadapkan dengan resiko yang tinggi. Hal ini disebabkan harga saham bersifat fluktuatif dan stokastik. Sehingga bagi suatu perusahaan harus memiki dasar pengambilan keputusan yang tepat dan akurat agar bisa meminimalisir kerugian dalam berinvestasi. Analisis deret waktu merupakan analisis yang biasa digunakan untuk memodelkan data deret waktu. Analisis ini dapat digunakan untuk meramalkan harga saham kedepannya dengan menggunakan data sebelumnya. Model Autoregressive Integrated Moving Average (ARIMA) adalah salah satu model deret waktu yang dapat digunakan untuk memodelkan harga saham. Persamaan pada model ARIMA yang diperoleh akan membantu meramalkan harga saham periode selanjutnya. Pada penelitian ini digunakan data harga saham penutupan pada PT. Polychem Tbk. dengan periode harian. Data harga saham yang ada diolah dengan menggunakan program eviews. Melalui program eviews dikaji nilai AIC, SIC, dan HQC minimum untuk memilih model terbaik. Model ARIMA(1,1,0) menjadi model terbaik dalam meramalkan harga saham PT. Polychem Indonesia TbkAbstractThe stocks are very interesting matters discussed in the investment world. Investment in the form of shares is very faced by high risk. This is due to fluctuating and stochastic stock prices. So to minimize losses in investment, a company must have appropriate and accurate decision-making standards. Time series analysis is an analysis commonly used to model time series data. This analysis can be used to forecast future stock prices by using previous data. The Integrated Moving Average Autoregressive Model (ARIMA) is a time series model that can be used to model stock prices. Equations obtained in the ARIMA model will help predict future stock prices. The data used in this study is the closing stock price data at PT. Polychem Tbk. in daily periods. Existing stock price data is processed using the eviews program. Through the eviews program, the minimum AIC, SIC, and HQC values are examined to choose the best model. ARIMA model (1,1,0) is the best model in predicting the stock price of PT. Polychem Indonesia Tbk.]]>
<![CDATA[PEMODELAN DATA SAHAM MENGGUNAKAN ANALISIS TIME SERIES DENGAN PENDEKATAN COPULA GAUSSIAN ]]>
<![CDATA[Jannah, Miftahul]]>;
<![CDATA[Mardika, Fitria]]>;
<![CDATA[Hasibuan, Lilis Harianti]]>;
<![CDATA[Putri, Darvi Mailisa]]>
<![CDATA[ Math Educa Journal Vol 5, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Islam Negeri Imam Bonjol Padang ]]>
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DOI: 10.15548/mej.v5i2.3124
<![CDATA[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.]]>
<![CDATA[Pemodelan Harga Saham Menggunakan Geometric Brownian Motion ]]>
<![CDATA[Ul Hasanah, Fitri Rahmah]]>;
<![CDATA[Putri, Darvi Mailisa]]>
<![CDATA[ JOSTECH: Journal of Science and Technology Vol 2, No 1: Maret 2022 ]]>
Publisher : <![CDATA[UIN Imam Bonjol Padang ]]>
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DOI: 10.15548/jostech.v2i1.3801
<![CDATA[Stocks are something that is still interesting to this day to be discussed. Because the price tends to fluctuate, it is necessary to make predictions for the future in order to reduce losses for investors. Geometric Brownian Motion is a model for predicting stock prices by conducting a study through stock return data obtained. Stock return data is required to meet the assumptions of Geometric Brownian Motion. After that, the average value and volatility of the stock return data of PT. Aneka Tambang Tbk. from January 04th to June 30th 2021 amounted to  -0,002376925 and 0,0212161. Through stock return parameters and data generation with a standard normal distribution, a model that is very close to the actual stock price data is obtained.]]>
