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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya EIGEN MATHEMATICS JOURNAL Indonesian Physical Review Jurnal Abdi Insani Jurnal Pepadu

Syamsul Bahri

Program Studi Matematika Universitas Mataram

Author-ID : 3777866

Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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MODEL PREDIKSI CURAH HUJAN HARIAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION komang nonik afsari dewi; Syamsul Bahri; Irwansyah Irwansyah
Indonesian Physical Review Vol 2, No 1 (2019)
Publisher : Universitas Mataram

Weather is an atmospheric condition that occurs in a narrow area with a short space of time. Observations of weather elements are needed in everyday life. for it can affect the safety of air transportation. The weather element that is often predicted is rainfall. Rainfall in tropical regions such as Indonesia is one of the parameters that can describe weather conditions in general. The method used to predict rainfall was artificial neural network with backpropagation algorithm. The purpose of this paper is to apply the model of artificial neural networks with backpropagation algorithm to predict daily rainfall and to determine prediction accuracy based on Mean Square Error (MSE). The network used has 3 layers namely input layer. hidden layer. and output layer with 7 input neurons, 12 hidden neurons, and 1 output neuron. The activation function used were bipolar sigmoid function and linear function. Based on data analysis carried out using network architecture and parameters that had been determined with 578 data at the training stage. MSE values of 25,0639 was obtained and based on the results of the network training process. the prediction was quite well. In the testing stage. the model developed using data as much as 145. MSE value of 405,1994 was obtained. MSE obtained during the testing stage was greater than that of obtained during the training process due to several factors. one of them is because the weather is volatile so the weather conditions vary every year and global warming causes weather conditions to be unpredictable.

PENERAPAN METODE WAVELET THRESHOLDING UNTUK MENGAPROKSIMASI FUNGSI NONLINIER Muhammad Luthfie Janariah; Syamsul Bahri; Nurul Fitriyani
Indonesian Physical Review Vol. 4 No. 3 (2021)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v4i3.98

The wavelet thresholding method is an approximation method by reducing noise, which is known as the denoising process. This denoising process will remove noise while closed the important information in the data. In this research, the wavelet thresholding method is used to approximate the nonlinear function. The data used for the simulation is a representation of several functions that represent several events that often occur in the real world, which consists of the types of functions Blocks, Bumps, Doppler, and HeaviSine. Based on simulation results based on the indicator value of the Cross-Validation (CV), the best approximation of the nonlinear function using the wavelet thresholding method for the four simulation cases are: (i) the Blocks function is given by Haar wavelet with a soft of thresholding function and the 10-th resolution level ; (ii) the Doppler function is given on the 2-nd order of Symlets wavelet with a soft of thresholding function and the 10-th resolution level; (iii) the Bumps function is given on the 6-th order of Daubechies wavelet with a soft of thresholding function and the 10-th resolution level; and (iv) the HeaviSine function is given by the 3-rd order of Coiflet wavelet with a soft of thresholding function and the 7-th resolution level.

Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island Syamsul Bahri
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 3 (2020): Vol. 11, No. 03 December 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i03.p06

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.

Penerapan aritmatika modulo untuk menguji validitas dan mengembangkan nomor ISBN (International Standard Book Number) Lukman Ibrahim; Syamsul Bahri; Irwansyah -
Eigen Mathematics Journal In Press Desember 2018
Publisher : University of Mataram

International Standard Book Number or ISBN is a code that contains information about the title, the publisher, the different types of materials for making the book, and publisher group from a book. The ISBN code of a book along with its development need to be checked for validity, because the more books are published, the more chance the book will be copied so that it has a double ISBN number. This research show that the use of modulo arithmetic in arranging ISBN for a book, especially ISBN-10 and ISBN-13. In this research too discussed about validation ISBN-10 and ISBN-13 using modulo arithmetic and expanded by developing an ISBN-n, for a natural number n greater than 10. Validation will be carried out in two stages, namely manually using modulo arithmetic calculation and then computing, by compiling java-based application to validate an ISBN. The development of ISBN-n for n ∊ ℕ and n ≥ 11 use the advantages of ISBN-10 and ISBN-13 and (Memorandum of Understanding/MoU) ISBN agency. Case studies in the Department of Library and Archives of West Nusa Tenggara Province on the ISBN validity of additional collection books for the 2015-2016 period showed that the ISBN validity of these books is 96%.

Peramalan Indeks Harga Konsumen Kota Mataram Menggunakan Vector Autoregressive Integrated Moving Average Moudy Puspita Ayudhiah; Syamsul Bahri; Nurul Fitriyani
Eigen Mathematics Journal Vol. 3 No. 1 Juni 2020
Publisher : University of Mataram

Penelitian ini bertujuan untuk meramalkan data Indeks Harga Konsumen (IHK) sub-kelompok padi-padian, umbi-umbian dan hasilnya serta sub-kelompok bumbu-bumbuan di Kota Mataram. Data yang digunakan adalah data tahun 2014 sampai dengan tahun 2017, yang digunakan untuk meramalkan nilai IHK pada tahun 2018. Metode yang digunakan dalam penelitian ini adalah metode Vector Autoregressive Integrated Moving Averageatau disebut VARIMA. Hasil penelitian menunjukkan bahwa model terbaik yang diperoleh adalah model VARIMA (1,1,0) dengan akurasi model untuk IHK padi-padian, umbi-umbian dan hasilnya berdasarkan nilai MAPE sebesar 0,7359% yang menyatakan bahwa hasil peramalan dapat dikategorikan sangat baik, sedangkan akurasi model untuk IHK bumbu-bumbuan berdasarkan nilai MAPE sebesar 10,6736% yang menyatakan bahwa hasil peramalan dapat dikategorikan baik.

Fuzzy Metric Space and Its Topological Properties Masriani Masriani; Qurratul Aini; Syamsul Bahri
Eigen Mathematics Journal Vol. 4 No. 2 Desember 2021
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v4i2.95

The fuzzy set theory is mathematics that applies fuzziness characteristics, so that gives the truth value at interval [0,1]. It is different from the crisp set that gives a truth value of 0 if it is not a member and 1 if it is a member. The theory of fuzzy sets has been developed continuously by scientists. One of the developments of the fuzzy set is the fuzzy metric space which the definition was introduced by George and Veeramani. Based on the analysis results, it is found that every metric space X if and only if X is fuzzy metric space. As a result, the topological properties of the metric space still apply to the fuzzy metric space

PENGUATAN DAN PENERAPAN PARADIGMA PROBLEM SOLVING METODE POLYA DALAM PEMBELAJARAN MATEMATIKA DI SMA NEGERI 1 PRAYA LOMBOK TENGAH Syamsul Bahri; Qurratul Aini; lailia Awalushaumi; Marliadi Susanto
Jurnal Abdi Insani Vol 7 No 2 (2020): Jurnal Abdi Insani Universitas Mataram
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v7i2.274

Metode pembelajaran merupakan salah satu faktor yang menentukan keberhasilan dalam suatu proses pembelajaran, termasuk didalamnya pembelajaran matematika. Metode problem solving merupakan salah satu metode pembelajaran yang berbasis masalah yang dapat membuat proses pembelajaran menjadi lebih menarik dan menyenangkan. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan dan pemahaman kepada siswa tentang penerapan prosedur dan strategi problem solving model Polya dalam menyelesaikan permasalahan matematika yang berifat rutin maupun tidak rutin. Hasil kegiatan memperlihatkan bahwa siswa SMA Negeri 1 Praya Lombok Tengah belum familiar dengan metode ini, yang tercermin dalam jawaban siswa terhadap angket yang diberikan. Terhadap penerapan metode problem solving model Polya dalam penyelesaian soal matematika, jawaban siswa berbeda untuk tipe soal rutin dan yang tidak rutin. Untuk soal tipe rutin 87,50 % siswa menjawab dengan benar, sedangkan untuk tipe soal yang tidak rutin 56,26 % siswa menjawab tetapi belum maksimal memanfaatkan metode ini untuk memperoleh solusi dari permasalahan yang diberikan.

Dynamic Neural Network Model Design for Solar Radiation Forecast Syamsul Bahri; Muhammad Rijal Alfian; Nurul Fitriyani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 2 (2022): Vol. 13, No. 2 August 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i02.p03

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.

Peramalan Harga Beras dengan Metode Double Exponential Smoothing dan Fuzzy Time Series (Study Kasus : Harga Beras di Kota Mataram) Sulpaiyah Sulpaiyah; Syamsul Bahri; Lisa Harsyiah
Eigen Mathematics Journal Vol. 5 No. 2 Desember 2022
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v5i2.123

Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 627400.307 and based on the MAPE value of 7.39%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method.

Jaringan Syaraf Tiruan untuk Memprediksi Kadar Polutan Ozon di Kota Mataram Nurul Hikmah; Syamsul Bahri; Irwansyah Irwansyah
Eigen Mathematics Journal Vol. 5 No. 2 Desember 2022
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v5i2.129

Ozone tropospher (O3) is one of the pollutants in the environment of Mataram City, Lombok, NTB, Indonesia. Based on the data obtained from the Agency of Environment and Forestry of West Nusa Tenggara Province, ozone pollutant concentrations in Mataram City have changed unpredictably. One time pollutant concentrations increase and then decrease, but then quickly increase again significantly. Therefore, the concentrations of ozone pollutant must be monitored because its presence at certain levels can cause various negative effects human health and the environment. Changes in ozone pollutant concentrations can be identified by carrying out a method of predicting ozone pollutant levels so that a decision can be taken to prevent the negative impact of the pollutant. In this research, a backpropagation artificial neural network is used to find the model prediction of the concentration of ozone in Mataram City. The input variables that are used in this network are air temperature (x_1 ), wind direction (x_2 ), wind speed (x_3 ), humidity (x_4 ), solar radiation (x_5 ), concentration of NO2 (x_6 ), the concentration of SO2 (x_7 ) and the concentration of O3 a day before (x_8 ) for the period of 6 July 2018 to 31 May 2019. The method in this study was to conduct trial and error on 60 different combinations of network architectures and parameters. Then all the network architectures performance will be compared based on the RMSE, MAPE and R2 indicators. Based on this research, the best neural network model to predict the concentration of ozone pollutant in Mataram City is the network with architecture 8-20-1, with logsig-purelin activation function and trainlm learning function. The performance of the training model is RMSE=0.011, MAPE = 1,043 % and R^2=0,9566. Meanwhile, the performance of the testing model is RMSE=0.001, MAPE = 0.749 % and R^2=0.497

Co-Authors Awalusshaumi, Lailia Azmi, Kamalatul Baiq Urfa Justitiaski Harsiyah, Lisa Harsyiah, Lisa Helmina Andriani Irwansyah Irwansyah komang nonik afsari dewi Lailia Awalushaumi lailia Awalushaumi Lailia Awalushaumi Lukman Ibrahim Marliadi Susanto Marwan - Marwan Marwan Masriani Masriani Moudy Puspita Ayudhiah Muhammad Luthfie Janariah Muhammad Rijal Alfian Nurul Fitriyani Nurul Fitriyani Nurul Hikmah Nuzla Af'idatur Robbaniyyah Qurratul Aini Sulpaiyah Sulpaiyah

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