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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 230 Documents
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Prediksi Harga Cabai Merah Berbasis Long Short-Term Memory dan Komparasi ARIMA pada Data Runtun Waktu Alisa Apriliani; Ade Ima Afifa Himayati; Findasari Findasari
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39298

Abstract

The price of red chili in Jepara Regency, Central Java, shows high and nonlinear fluctuations due to seasonal factors, weather, and supply dynamics. This instability affects food inflation as well as the income of farmers and traders. This study aims to build a prediction model for red chili prices in Jepara Regency using the Long Short-Term Memory (LSTM) method and compare its performance with the Autoregressive Integrated Moving Average (ARIMA) method. The data used are weekly red chili prices from January 2020 to December 2025, obtained from the official hargajateng.org website. The research steps include data preprocessing, normalization using Min-Max Scaling, forming time series data with a sliding window, training the LSTM model using the Adam optimizer, Mean Squared Error (MSE) as the loss function, a maximum of 200 epochs, batch size of 8, and applying EarlyStopping to get the best model. Performance evaluation was done using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The study results show that the LSTM model can follow chili price change patterns well, producing an RMSE of 16,707.02, an MAE of 12,348.54, and a MAPE of 19.31% on the testing data. Additionally, the 16-week forecasting results give an idea of the price trend in the upcoming period. Compared to the ARIMA model, which produced an RMSE of 20,992.15, an MAE of 13,867.26, and a MAPE of 22.70%, the LSTM model shows better prediction performance with lower error rates. The study indicates that the LSTM method is more effective in modeling nonlinear patterns in red chili price data compared to the ARIMA method.
Integrating Customer Segmentation, Predictive Modelling, and Uplift Modelling for Retail Voucher Targeting Michael Calvin Charmelino Wijaya; Maria Zefanya Sampe
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39943

Abstract

The rapid growth of e-commerce has encouraged retail companies to optimize data-driven promotional strategies to achieve higher targeting precision and cost efficiency. Mass promotional campaigns without proper segmentation often lead to budget inefficiencies and reduced profit margins. This study aims to optimize voucher targeting strategies for transactions involving growing-up milk products during the January-December 2024 period by integrating customer segmentation, predictive modelling, and uplift modelling. Customer segmentation was performed using K-Means Clustering, resulting in seven distinct clusters representing heterogeneous purchasing behaviors. The clustering output was incorporated as an additional feature in classification models based on XGBoost and CatBoost to predict the probability of targeted voucher redemption. Model evaluation using ROC-AUC, PR-AUC, confusion matrix, and classification report indicated that XGBoost with cluster features achieved the best performance, with a ROC-AUC of 0.9972 and a PR-AUC of 0.5479. Since conventional classification does not measure the causal impact of promotions, uplift modelling with a two-model approach was implemented to estimate the incremental effect of voucher distribution. The results reveal that approximately 1,027 customers (0.46% of 222,036 total customers) belong to the persuadable segment, generating an uplift of 1.91%. These findings demonstrate that uplift-based targeting is more efficient than mass campaigns or probability-based predictive targeting alone. The integration of segmentation, predictive modelling, and uplift modelling provides a more precise and measurable framework for data-driven promotional strategies in retail e-commerce.
Perbandingan Kinerja Algoritma Cheng and Church dan Qualitative Biclustering pada Data Produksi Perikanan Budidaya Indonesia Dewi Zulyani Pomalingo; Muhammad Nur Aidi; Kusman Sadik
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39884

Abstract

Biclustering is a two-dimensional clustering approach that simultaneously identifies relationships between objects and variables, making it more suitable than conventional clustering methods for analyzing data with local patterns. Although numerous biclustering algorithms have been developed, comparative studies evaluating their performance on official statistical data remain limited. This study aimed to evaluate the performance of the Cheng and Church (CC) and Qualitative Biclustering (QUBIC) algorithms using Indonesia’s aquaculture production data for 2023. The dataset comprised the production of 14 aquaculture commodities across 34 provinces. Optimal parameters for both algorithms were determined through a manual tuning procedure. Their performance was subsequently evaluated using the Mean Squared Residue (MSR), Average Squared Residue (ASR), and the Liu–Wang index. The results showed that the optimal parameter for the CC algorithm was δ = 0.009, while the optimal QUBIC parameters were r = 1, q = 0.06, and c = 0.75. CC produced 7 biclusters with an ASR of 0.0052, whereas QUBIC produced 8 biclusters with an ASR of 0.4360. The lower MSR and ASR values of CC compared with QUBIC indicate that CC was more effective in forming homogeneous biclusters in aquaculture production data. The number of province memberships showed that both algorithms were able to capture patterns across most provinces, although CC had slightly broader coverage. The Liu and Wang index value of 0.1951 indicated a relatively low similarity between the bicluster membership structures produced by CC and QUBIC. Thus, although both algorithms were applied to the same aquaculture production data, CC and QUBIC tended to produce different clustering structures.
Optimization of Subject Timetabling Based on Integer Linear Programming Using Microsoft Excel Solver Ayu Larasati; Alma Aulia Hanifah; Sugiyanto Sugiyanto
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39668

Abstract

Subject timetabling is an essential component of educational management because it involves allocating subjects, teachers, and instructional time while satisfying multiple scheduling constraints. Manual timetabling practices commonly used in schools often result in scheduling conflicts and require considerable effort when timetable revisions are necessary. Therefore, this study aims to develop and implement an Integer Linear Programming (ILP)-based subject timetabling model using Microsoft Excel Solver for MTs Nurul Ummah. The proposed model was formulated using binary decision variables, an objective function that minimizes scheduling penalties, and a set of constraints representing curriculum requirements, teacher availability, subject allocation, and instructional time. The model was implemented using Microsoft Excel Solver to generate a feasible timetable that satisfies the predefined scheduling constraints. The findings demonstrate that Microsoft Excel Solver provides a practical platform for implementing ILP-based subject timetabling in secondary schools without requiring specialized optimization software.
Association Between TikTok Use and Insecurity Among Indonesian Generation Z: A Spearman Rank Correlation Study Qori' Afiata Fiddina; Elok Kharismatul Ula; Vani Krismo Anggoro; Aninda Hawa Hanipah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39053

Abstract

The rapid growth of TikTok among Generation Z users has raised concerns about its psychological effects, particularly feelings of insecurity. This study examines the relationship between TikTok usage and insecurity among Generation Z using a quantitative correlational design. Data were collected via an online questionnaire from 121 Generation Z respondents aged 13--28 years who were active TikTok users and selected through purposive sampling. TikTok usage was measured through three indicators—attention, appreciation, and duration of use—while insecurity was measured through low self-esteem, anxiety, and fear of judgment by others. Both instruments demonstrated good validity and reliability, with Cronbach's Alpha values of 0.775 and 0.897, respectively. Data were analyzed using Spearman's rank correlation in SPSS. The results revealed a statistically significant, moderate positive correlation between TikTok usage and insecurity ($r_s=0.541$, $p0.001$, $n=121$), indicating that higher TikTok engagement is associated with greater insecurity, although this does not necessarily imply a direct causal effect. These findings support the view that intensive engagement with visually oriented social media platforms is associated with psychological factors such as low self-esteem, anxiety, and fear of negative evaluation among Generation Z. The study underscores the importance of balanced and self-regulated social media use and suggests further research into mediating factors such as social comparison and self-esteem.
Systematic Literature Review on the Integration of Technology in Enhancing Problem-Solving Skills in Mathematics Education Sianne Sarif; Ismail Djakaria; Hasan S. Panigoro
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39501

Abstract

This systematic literature review explores how technology is being used in math instruction students and the effect on this issue of problem-solving. Based on nine chosen studies, the review synthesises evidence about which kind of technological tools help improve problem-solving skills and the advantages and disadvantages of their use. The results show that the integration of technology positively helps students improve their problem-solving performance within pedagogically aligned learning activities, one that fosters strategic thinking, metacognitive skills and collaborative involvement. Some suitable tools are dynamic mathematics software, computer algebra system, Interactive learning tool and TPACK (technological pedagogical content knowledge) based technology. It also notes that benefits come with the caveat of schools not yet being prepared to teach, curriculum alignment and sufficient access to materials for learning, while challenges include equity of technology infrastructure, inadequate training and the speed at which digital tools change. The synthesis provides overall evidence of a conditional but significant effect of technology on developing problem-solving skills in mathematics education.
Integrating Islamic Contexts into Visual Thinking Approach to Enhance Mathematical Representation Skills Risa Cahya Hairani; Dindin Sobiruddin; Khamida Siti Nur Atiqoh
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39959

Abstract

Mathematical representation ability is an essential competency in mathematics learning because it enables students to express and communicate mathematical ideas through various forms of representation. However, students' mathematical representation ability remains relatively low, indicating the need for instructional approaches that better support its development. This study aimed to analyze differences in mathematical representation ability between students taught using the Visual Thinking approach integrated with Islamic contexts and those taught using Direct Instruction. The study employed a quantitative quasi-experimental method using a post-test-only nonequivalent group design. The sample consisted of 28 students in the experimental class and 23 students in the control class selected through cluster sampling. The research instrument was an essay test of mathematical representation ability that satisfied the required validity and reliability criteria. Data were analyzed using the Mann–Whitney U test at a 5% significance level, followed by effect size analysis. The results showed that the experimental class obtained a higher mean post-test score (84.54) than the control class (72.43). The Mann–Whitney U test indicated a statistically significant difference in the distribution of post-test scores between the two groups (U = 186.50, Z = −2.623, p = .009). The rank-biserial correlation coefficient of 0.421 indicated a small effect. Within the context of this study, students in the experimental class obtained higher post-test mathematical representation scores than students in the control class.
Konstruksi Lapangan Hasil Bagi dari Daerah Integral sebagai Landasan Aljabar Kriptografi Kurva Eliptik Miftah Sigit Rahmawati; Muhammad Fadli Hasa
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.40284

Abstract

This article develops a conceptual framework that connects quotient field construction, kurva eliptik curve group structures, and symbolic operations in Elliptic Curve Cryptography (ECC), enabling the mathematical mechanisms of ECC to be analyzed explicitly before being implemented over finite fields. Although practical implementations of ECC employ finite fields as the basis for arithmetic operations, understanding quotient field construction from an integral domain remains important as the mathematical foundation of kurva eliptik curve operations. This article investigates the algebraic structure of the quotient field and its application to kurva eliptik curve theory, focusing on point addition and the verification of the Abelian group properties of the point set . This approach allows every algebraic operation to be represented symbolically (symbolic computation), enabling kurva eliptik curve point operations to be analyzed more explicitly. Such an understanding bridges the concepts of abstract algebra and kurva eliptik curve group structures. Therefore, the study of quotient fields provides a conceptual framework that supports the theoretical understanding of ECC. This article aims to provide a deeper understanding of the mathematical foundations underlying the use of kurva eliptik curves in cryptography as a step toward developing a conceptual framework for explaining ECC.
Optimasi Biaya Distribusi Biji Kopi: Perbandingan Metode Northwest Corner Method, Least Cost Method, Vogel's Approximation Method, dan Modified Distribution Method Zahrotul Janah; Siti Nur Afifatun; Muhammad Asadi Almaruf; Winarno Winarno
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.40357

Abstract

This study aims to analyze transportation costs in distributing Mr. Rusman's coffee beans from Candiroto and Bejen plantations to five destination stores: Seleksi, Muneng, Plosogaden, Lancar, and Berkah, using the Northwest Corner Method (NWC), Least Cost Method (LC), Vogel's Approximation Method (VAM), and Modified Distribution Method (MODI). Data were obtained through direct interviews with plantation owners regarding capacity, demand, and distribution costs. The results showed that the NWC resulted in a cost of Rp610,000, the LC Rp538,500, and the VAM Rp524,500. Testing using the MODI showed that the NWC and LC methods were not optimal and required iterations for improvement, while the VAM method was immediately optimal. After optimization, the minimum cost was obtained at Rp524,500. The calculation results also used POM QM for Windows V5 software and showed similar results to manual calculations. 
Embedding Struktur Sekunder lncRNA Berbasis Variational Autoencoder untuk Klasifikasi Kanker Hati dengan GraphSAGE Theodora Tantri Trisnawati; Mohammad Isa Irawan
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.40387

Abstract

Identifying liver cancer-associated lncRNAs is a classification problem that can leverage various biological characteristics, such as secondary structure, expression levels, and molecular associations with RNA-binding proteins (RBPs). This study develops a VAE-GraphSAGE framework to integrate this information within a heterogeneous graph. A Variational Autoencoder (VAE) is employed for representation learning to extract lncRNA secondary structure embeddings through a reconstruction process that does not rely on class labels. These embeddings are combined with expression features to form node features, while data on lncRNA similarity, lncRNA-RBP interactions, and protein-protein interactions (PPI) are used to construct the heterogeneous graph. Subsequently, GraphSAGE serves as the classifier, utilizing inter-node relationship information to classify the lncRNAs. The dataset comprises 1,000 lncRNAs (400 positive and 600 negative) and 85 RBPs (as protein nodes), with a training-validation-testing split of 70:15:15. Comparative results indicate that VAE-GraphSAGE achieves the highest recall among the tested models (outperforming AE-GraphSAGE, MLP-GraphSAGE, CNN-GraphSAGE, and ResNet-GraphSAGE); meanwhile, MLP-GraphSAGE yields the highest accuracy, precision, specificity, and F1-score, and CNN-GraphSAGE produces the highest ROC-AUC. Furthermore, using the same VAE embeddings, VAE-GraphSAGE demonstrates superior performance compared to VAE-HGT and VAE-RGCN across the evaluated classification metrics. These results demonstrate that the VAE effectively generates secondary structure representations that facilitate the detection of positive lncRNAs, while GraphSAGE successfully leverages biological relationships within the heterogeneous graph for the classification task.

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