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Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Published by Universitas Negeri Gorontalo
ISSN : 20879393     EISSN : 27763706     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi is a national journal intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in the research. Euler disseminates new research results in all areas of mathematics and their applications. Besides research articles, the journal also receives survey papers that stimulate research in mathematics and its applications. The scope of the articles published in this journal deal with a broad range of mathematics topics, including: Mathematics Applied Mathematics Statistics and Probability Applied Statistics Mathematics Education Mathematics Learning Computational Mathematics Science and Technology
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Volume 12 Issue 2 December 2024" : 15 Documents clear
Model Pembelajaran Problem Based Learning dengan Pendekatan Culturally Responsive Teaching untuk Meningkatkan Hasil Belajar Peserta Didik Rochaminah, Sutji; Baid, Nurfaida; Lantang, Nortje D.J.
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27409

Abstract

This study is classroom action research which purpose to improve student learning outcomes through a Problem Based Learning (PBL) model and a Culturally Responsive Teaching (CRT) approach through daily life problems by considering cultural background in order to motivate students to be actively involved in learning. The subjects in this research were 31 class VIII B-Mawar students. To collect data in this  study, it was done through test results and observations. The stages of research carried out are planning, implementing, observing and reflecting. This study was carried out in stages with two cycles, namely cycle one and cycle two. The research results show that using a PBL model with a CRT approach can improve student learning outcomes. This is proven by the increase in student learning outcomes from cycle one to cycle two, namely 29%. Student learning activities were declared quite active in cycle one and increased to active in cycle two, namely with a percentage of 20,50%.
Implementasi Metode Goal Programming Untuk Optimasi Produksi Cokelat Pada UMKM Pradjaningsih, Agustina; Andora, Ela; Santoso, Kiswara Agung
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.26904

Abstract

Chocolate is a food made from cocoa beans, namely Theobroma Cacao. Cocoa beans harvested are then processed to prevent rotting, which can reduce their quality. Currently, many chocolate manufacturers produce various variants of chocolate products. Each production company tries to achieve maximum profits with minimal costs. Production optimization problems can be addressed using objective programming, which is a method used to develop mathematical models of optimization problems involving multiple objectives or constraints. In goal programming, each goal is expressed as a goal constraint. Objective programming methods involve determining decision variables, objective constraints, and objective functions. Optimization problems are solved using the objective programming method with the help of Lingo software. Optimization calculations using Lingo software show that the production of each chocolate product has reached optimality. Production after optimization reached Rp. 10,380,000 per month, whereas production costs were only Rp. 10,500,000 per month before optimization. The availability of raw materials needed after optimization reached 85 recipes per month, whereas it was 90 recipes per month before optimization. The profit obtained is also optimal, namely Rp. 4,267,000 in one month.
Implementasi Artificial Neural Network (ANN) dalam Memprediksi Nilai Tukar Rupiah terhadap Dolar Amerika Sakti, Adam Indra; Saputra, Lianda; Suhendra, Helen; Halim, Nikken; Alviari, Irfaliani; Ilham, Muhammad Rozan Nur; Putri, Marwah Hotimah Nada; Dalimunthe, Desy Yuliana
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.26654

Abstract

The exchange rate of one country's currency against other countries takes an important role in the development and economic activities for a nation. This condition of the Indonesian currency exchange rate, namely the rupiah, is now continuously increasing, meaning that the exchange rate is weakening and experiencing depreciation. Apart from that, the rupiah exchange rate also experiences fluctuations, so forecasting is needed to find solutions to problems that will arise if the currency exchange rate increases. This research purpose is to find the best of network archictecture and to predict the selling rate of the rupiah (Rp) per 1USD for one year. The forecasting method used in this research is using an Artificial Neural Network (ANN) with Backpropagation algorithm. This method is suitable for use in time series analysis because the algorithm is able to adjust the data and has a relatively small error. The data used is the rupiah exchange rate against the USD in the form of time series data, which from March 1, 2019 to February 28, 2024. The data scenario of 90% training and 10% testing at the training stage obtained the best architecture 4-20-1 with MSE is 0.0010385. The data scenario is 80% training and 20% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00089412. The data scenario is 70% training 30% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00099221. Thus, the prediction prices used are predictions for the 80% training data scenario and 20% testing data, because the accuracy results (MSE) are better than the other two scenarios.
Support Vector Machine-Radial Basis Function Kernel and K-Nearest Neighbor Differences for Classification Superior Varieties of Rice in Indonesia Chintyana, Alissa; Kertanah, Kertanah; Hastuti, Siti Hariati; Khotimah, Husnul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27605

Abstract

Rice is the primary food source for the Indonesian population, making it a priority commodity in Indonesia. Rice production plays a significant role in Indonesia's economic development, with high-yield rice varieties being crucial for enhancing national rice output. Ensuring food security requires the selection of superior rice varieties with optimal quality. This study evaluates various high-yield rice varieties, including INPARA, INPARI, INPAGO, and HIPA, based on characteristic data collected in 2023. Machine learning algorithms, increasingly central to data analysis, were applied, leveraging labeled data suitable for supervised learning methods. During the pre-processing stage, it was determined that the data did not meet the linearity assumption. Thus the Support Vector Machine (SVM) algorithm was modified with the Radial Basis Function (RBF) kernel to better handle non-linear data. Additionally, the K-Nearest Neighbor (KNN) algorithm, a traditional method, was used for comparison. The results indicate that SVM with the RBF kernel achieved faster processing times and the accuracy value reaches 96%, nearly 10% higher than the KNN algorithm.
Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting Vianti, Febi; Khaulasari, Hani; Farida, Yuniar; Swantika, Cicik; Efendi, Havid
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.28072

Abstract

Stunting is a child growth disorder that is evident from a lack of height for age. Jember Regency has a stunting prevalence rate of 34.90% in 2022, making it the region with the highest stunting cases in East Java. The purpose of this research is to map the factors that influence stunting in Jember Regency with a spatial analysis approach. The method applied in this study is Geographically Weighted Regression (GWR) to analyze the spatial relationship between predictors and responses. GWR uses an optimal kernel to determine the spatial weights based on distance accurately, as well as the AIC and  goodness criteria to calculate the goodness of the model. The research variables include the number of stunting cases in Jember Regency as the response variable (Y), and the predictor variables (X) are chronic energy deficiency pregnant women (), anemic pregnant women (), exclusive breastfeeding (), proper sanitation (), pregnant women consuming TTD at least 90 days (), complete basic immunization (), and wasting (). The results of the study using the adaptive gaussian kernel with the minimum CV compared to other kernels can improve accuracy, so it can be applied to data analysis.  The GWR model obtained an accuracy of 80.59% and AIC 360.  indicates the ability to explain 80.59% of the variability of the response data, and the AIC value is 360, which reflects the efficiency and suitability of the model to spatial data. From the GWR parameters, 14 groups were formed where there are several different factors in each area in the sub-districts in Jember Regency.

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