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Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 184 Documents
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Decoding the Trends and Progress of Artificial Intelligence in E-commerce Over the Last Decade As'ad, Ihwana
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24423

Abstract

Through process improvement, growth acceleration, and business landscape transformation, artificial intelligence (AI) has transformed online businesses and accelerated digital transformation. The purpose of this study is to presents a conceptual overview of artificial intelligence (AI) in E-commerce. Potential research themes, explored through content analysis and visualization techniques, offer deeper understanding of the knowledge landscape in this field. This study utilized VOSviewer and R-bibliometrix to conduct data analysis and network visualization the scientific output of 811 research articles form Scopus, WoS and PubMed database from 2000 to 2024, including the number of publications, countries, journal, citations, authors, and keywords. The results of this research show that China and USA emerges as the country with the significant contributions to the development of research related to artificial intelligence in e-commerce, which is dominated by affiliations from Zhejiang University. In the analysis of the relationship between topics, two clusters were obtained, the most dominant topics with keyword “human” and “neural network”. Neural networks are included in AI algorithms that has characteristics similar to the human brain and have the ability to operate more efficiently and profitably in e-commerce. Researchers will gain important insights into the current landscape, as well as collaborative frameworks to suggest directions for future research.
The Trinomial Tree Method in Pricing European Gold Option with Volatility Forecasting Using the GARCH (1,1) Model Tazkya, Difa; Oktavia, Rini; Syahrini, Intan
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.25455

Abstract

This study enhances the pricing accuracy of European gold options by integrating GARCH (1,1)-based volatility forecast into the trinomial tree method. GARCH (1,1) captures key characteristics of financial return series, such as heteroscedasticity and volatility clustering, while the trinomial tree offers greater flexibility than traditional models by allowing three price movements at each node. This integration provides a more realistic and robust framework for option pricing under dynamic market conditions. Using gold price data from October 2017 to October 2024, the model forecast annualized volatilities of 16.59%, 17.33%, and 17.66% for one, two, and three months. For call options, prices increase with longer maturities, ranging from Rp194,048 to Rp207,385. Conversely, put options become more valuable when the strike price exceeds the market prices, reaching up to Rp107,778. The proposed model offers practical value for more accurate pricing and investment strategies.
eth Root Attack on Dual Modulus RSA Susanti, Bety Hayat; Silim, Tsamara Khadijah; Adiati, Nadia Paramita Retno; Ardyani, Mareta Wahyu
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24486

Abstract

The Rivest–Shamir–Adleman (RSA) algorithm relies on the presumed difficulty of integer factorization, making it vulnerable to certain attacks, particularly in the quantum era. One proposed variant, dual modulus RSA, is claimed to enhance resilience against specific cryptanalytic techniques. This study evaluates its security by applying an e^th-root attack using an advanced fraction method. The results demonstrate that the plaintext can be recovered without the private key, confirming that dual modulus RSA, like standard RSA, remains susceptible under particular conditions. Although dual modulus RSA incurs higher computational cost, the attack remains effective. These findings suggest that structural changes alone do not guarantee improved security and emphasize the need for rigorous cryptanalysis of RSA variants against established mathematical attacks.
A Mathematics Learning Outcome Test Characterized by Higher-Order Thinking Skills for Grade V Elementary School Students Ariyana, I Komang Sesara; Sariyasa, Sariyasa
ZERO: Jurnal Sains, Matematika dan Terapan Vol 8, No 2 (2024): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v8i2.21807

Abstract

This research was aimed at developing the results of learning mathematics characteristics of higher-order thinking skills (HOTS) in the form of multiple choice for grade V elementary school students. This research applied D & D model by Richey & Kleine (2007). HOTS in this study were able to transfer, critical thinking, and solve problems. These aspects are included in Analyzing, Evaluating, and Developing category. Math questions which HOTS were Multi step problems and non-routine questions. The sample of the test experiment was 112 students drawn from four elementary schools in Singaraja Town. The results showed that of 21 items that had been made, there were 20 valid items. The difficulty analysis showed five unfit grains, 14 items were categorized as difficult and one item was categorized as moderate. By the discrimination index, there were two items categorized as high, nine items were categorized as moderate, and four items were categorized as low. The effectiveness of the distractors was fulfilled in all questions. These fifteen items have a degree of reliability of 0.609.
Comparison of OLS Regression and Robust Regression in Overcoming Outlier Problems (Case Study: Cost of Living Data for Urban Areas in Indonesia) Susiana, Susiana; Chairunisah, Chairunisah; Refisis, Nice Rejoice
ZERO: Jurnal Sains, Matematika dan Terapan Vol 8, No 2 (2024): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v8i2.21345

Abstract

Multiple regression analysis in quantitative statistical studies describes the relationship between independent and dependent variables. On the other hand, outliers in a set of data can have an unfavourable influence on data analysis, such as high residuals, significant variances, and bias, and can even cause errors in decision-making. It can be done in several ways to overcome the outlier problem in multiple linear regression analysis, including using robust regression or Ordinary Least Square (OLS) Regression by removing data indicated as an outlier first. The OLS Regression method forms a regression model by minimizing the sum of squared residuals from the estimator of the regression equation. Meanwhile, robust regression is closer to the average parameters and variance-covariance of a particular estimator, namely by standardizing the estimator for the average parameters and variance-covariance in such a way as to produce a consistent estimator for these parameters. This research aims to compare the OLS Regression and robust regression methods as alternatives for dealing with outlier problems in data. The data used in this research is secondary data (cost of living) from the Cost of Living Survey conducted by The Central Statistics Agency of the Republic of  Indonesia in 2018. The stages of this research method are literature study, data collection, descriptive analysis to see the characteristics of the data, forming a regression model using the OLS Regression method, testing classical assumptions, creating a new regression model OLS Regression, forming a regression model with Robust Regression, calculating the MSE (Mean Square Error) of each regression model formed, determining the best regression model, The results of the research show that for the cost of living data, the best regression model is obtained through the OLS Regression method with data without outliers, namely .  
An Adaptive Control Framework for Optimizing Hybrid Electric Vehicle Performance Using Road Gradient Detection Lubis, Zulkarnain; Annisa, Selly; Beg, A. H.
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24302

Abstract

This study introduces an adaptive control framework for hybrid electric vehicles (HEVs) that optimizes performance based on real-time road gradient conditions, such as uphill and downhill terrain. Utilizing an inclinometer and an accelerometer, the system continuously monitors road angle and vehicle dynamics. The adaptive control algorithm processes this data to adjust the output of both the electric motor and internal combustion engine, optimizing energy efficiency and vehicle performance. Experimental results on hilly routes show an 8% improvement in energy efficiency compared to conventional control systems. Additionally, the system ensures stable vehicle speed with an average deviation of ±2.5 km/h. These findings highlight the potential of gradient-based adaptive control to enhance HEV performance, especially on challenging terrains, by improving energy efficiency and driving stability. This approach offers a promising solution for future HEV applications in regions with varied topography.
The Application of a Domino Train Game-Based Learning System in Improving Middle School Students' Understanding of Algebraic Operations Warsali, Warsali; Kurniawan, Asep; Tabroni, Imam; Dewi, Maftuhah; Karim, Abdul
ZERO: Jurnal Sains, Matematika dan Terapan Vol 8, No 2 (2024): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v8i2.21338

Abstract

This study examines the effectiveness of using domino train games in teaching algebraic operations and how it improves students' understanding. The research employed Classroom Action Research (CAR) with both experimental and control groups. The experimental group was taught using the domino train game method, while the control group followed traditional teaching methods. The study was conducted at SMP Uswatun Hasanah during the 2019-2020 academic year, involving 64 seventh-grade students divided into two classes: class VII A (32 students) as the experimental group and class VII B (32 students) as the control group. Data were collected using pre-tests and post-tests to assess students' understanding of algebra, along with questionnaires to measure student engagement. The tests consisted of 10 essay questions on algebra. Validated closed-ended questionnaires were used to gather data on students' involvement in the learning process. Data analysis included normality tests, Mann-Whitney tests, and N-Gain calculations. Results showed that the experimental group experienced significant improvement, with an N-Gain of 0.493, while the control group had an N-Gain of -0.322. The findings indicate that the domino train game method is effective in enhancing students' understanding and participation in mathematics learning, providing an innovative alternative in teaching algebra. 
Predicting Student Stress Levels Based on Lifestyle Factors Using the Catbost Algorithm Rani, Putri Meuthia; Zufria, Ilka
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24537

Abstract

This study developed a machine learning model to classify student stress levels based on lifestyle factors using the CatBoost algorithm. Data were collected from 630 students of the SciTech Faculty at State Islamic University of North Sumatra through a questionnaire comprising 14 Likert-scale items. Instrument validation was confirmed using Pearson’s r (>0.821, p < 0.05) and Cronbach’s Alpha (0.866). Preprocessing included outlier removal with IQR, feature encoding, stratified train-test split (80:20), and 5-fold cross-validation. The training set was imbalanced and addressed using the SMOTE technique. Model evaluation used accuracy (85%), precision, recall, and F1-score per class, with high recall (0.97) for moderate and improved F1-score (0.79) for low stress. Final classification used a 20% test subset (126 samples). Feature importance analysis identified task procrastination, poor sleep quality, and weak time management as key predictors. These findings affirm CatBoost's reliability through consistent results, scalability, and balanced evaluation metrics beyond mere accuracy.
Integration of ARIMA Models and Machine Learning for Academic Data Forecasting: A Case Study in Applied Mathematics Simanungkalit, Erwinsyah; Husna, Mardhiatul; Tarigan, Jenny Sari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24148

Abstract

This study explores the use of ARIMA models and machine learning algorithms, specifically Random Forest and Multiple Linear Regression, to predict student academic performance. A mixed-method approach analyzed academic grades data from the past three years, with ARIMA identifying time series trends and machine learning models predicting academic outcomes based on various variables. Results show ARIMA effectively maps academic trends, while Random Forest excels in handling complex relationships, with an RMSE of 1.12 and an MAE of 0.94. These findings highlight the potential of combining statistical models and machine learning in developing adaptive learning strategies and data-driven decision-making. This approach offers a robust framework for improving educational outcomes and can guide future research in predictive analytics for educational systems.
Exploration of Mathematical Concepts in Oceanography: A Literature Review Dodi, Dodi; Zibar, Zan
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.23938

Abstract

Mathematics is crucial in oceanography, enabling the modeling of complex ocean phenomena such as currents, waves, tides, and pollutant dispersion. This paper highlights the use of partial differential equations, numerical methods, and spatial statistics in simulating physical ocean processes. Key models include the Navier–Stokes equations for fluid flow, advection–diffusion models for pollutant transport, and wave models for ocean surface dynamics. These models are vital for practical applications like climate prediction, disaster mitigation, and marine ecosystem management. For example, modeling sea surface temperature aids in forecasting El Niño and La Niña events that impact rainfall and fisheries. In Indonesia, a maritime country highly exposed to ocean hazards, mathematical tools support marine research, policy planning, and sustainable development. This study presents an overview of mathematical models in oceanography, emphasizing their analytical strength and value in addressing environmental and resource challenges.

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