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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 222 Documents
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Text Mining and News Sentiment Analysis of the PPRT (Domestic Worker Protection) Bill in Three Online News Media From 2004 to 2024 Kristiyani, Dian Novita; Mayopu, Richard Gordon
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26837

Abstract

The Domestic Worker Protection Bill (RUU PPRT) has been a critical issue in Indonesia, yet its legislative process has stagnated for two decades, leading to intense public discourse. This study aims to analyze the sentiment and narrative dynamics of RUU PPRT news coverage in online media, as well as the media's role in shaping public opinion. Employing a Text Mining and Lexicon-Based Sentiment Analysis approach, enhanced with adaptations for the Indonesian lexicon, this research analyzes 387 news articles from three prominent online media outlets (Tempo, Kompas, and VOA News) published between 2004 and 2024. The findings reveal that positive sentiment dominates with 58.1%, followed by negative sentiment at 31.3%, and neutral sentiment at 10.6%. Tempo was identified as the most active media outlet covering this issue. These results indicate that the mass media plays a significant role in shaping the pattern of public discourse regarding the PPRT Bill, particularly through the dominance of positive sentiment in its reporting and confirm that lexicon-based sentiment analysis can systematically capture the dynamics of complex socio-political narratives.
Weather-Driven Loss Modeling for Rice Farmers' Losses Using Cobb-Douglas and VaR-ES Nurviana, Nurviana; Amelia, Amelia; Sari, Riezky Purnama; Nabilla, Ulya; Mawarni, Mawarni; Masthura, Masthura
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26842

Abstract

Weather variability poses significant risks to rice production, leading to potential income losses for farmers and increased uncertainty in agricultural planning. This study integrates a Cobb-Douglas production function with Value at Risk (VaR) and Expected Shortfall (ES) measures to assess weather-driven production losses in Aceh Besar using secondary data on rainfall, temperature, and wind speed from 2010 to 2023. Rice production is first modeled to estimate output sensitivity to climatic factors, after which production losses are derived from forecast-based outcomes. Several candidate parametric probability distributions are fitted to the loss data, and the most suitable distribution is selected based on goodness-of-fit ranking. The results indicate that weather variables significantly reduce rice output and that the production process exhibits decreasing returns to scale. The selected distribution yields a potential loss of IDR 774,352 and an expected loss of IDR 940,160 per hectare at the 95% confidence level. These findings provide a quantitative basis for weather-based agricultural risk assessment and support evidence-based risk mitigation strategies for farmers and policymakers.
Evaluating Semantic Geometry of Indonesian News Texts: Agglomerative Clustering Study using IndoBERT Embeddings Sitopu, Joni Wilson
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26549

Abstract

This study aims to evaluate the effectiveness of various Agglomerative Clustering configurations in unveiling the Semantic Geometry of a large corpus of Indonesian news texts, represented using IndoBERT Embeddings. The IndoBERT transformer model addresses the limitations of traditional methods (such as TF-IDF) in capturing semantic equivalence despite lexical variations. However, this research finds that the dense (homogeneous) nature of the embeddings necessitates a meticulous clustering methodology. The use of Cosine Similarity resulted in a highly uneven cluster distribution, with one cluster dominating over 99% of the documents, demonstrating a limitation in distinguishing thematic nuances due to the high vector directional similarity. Conversely, the combination of Euclidean Distance with UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction proved optimal. UMAP, as a non-linear technique, successfully decomposed the finer data structure, yielding clusters with the most balanced size (ranging from 4254 to 8204 documents) and being thematically representative. The thematic profiling of the UMAP-Euclidean clusters successfully identified five distinct and granular main themes: Politics, Health & Technology, Macroeconomics & Finance, Economy & Industry, and Education & Social Issues. This research concludes that non-linear dimensionality reduction (UMAP) is a crucial step for clarifying the Semantic Geometry and achieving granular and meaningful clustering on IndoBERT embeddings.
Structural Equation Modeling Multigroup on Waste Economy Management Rizqia, Anggun Fadhila; Solimun, Solimun; Nurjannah, Nurjannah; Junianto, Fachira Haneinanda; Hidayat, Kamelia
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (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.v9i2.26774

Abstract

In recent years, urban waste has increased, leading to the demand for sustainable waste management based on circular economy. Facilities infrastructure and waste banks, play a role in improving 3R practices and the economic value of the community. This study used Structural Equation Modeling (SEM) with a Multigroup approach to analyze the effect of facility and infrastructure quality and waste banks on 3R-based waste management and waste economic management, moderated by environmental quality. In both groups, the impact of waste bank usage and 3R-based management was significant but stronger in moderate environments, with a significant difference (p-value = 0.032). Moderation also appeared in the influence of waste bank usage on waste economic management, where the difference was significant (p-value = 0.041). The results reveal that environmental quality moderates waste banks usage, 3R-based waste management and economic benefits, especially in environments with better quality.
Automated Social Media Advertising Poster Design Using a Multi-Agent AI Framework Putri, Christiana Naida; Wiharja, Kemas Rahmat Saleh
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.27563

Abstract

This study proposes an automated system for social media advertising poster generation using a role-based multi-agent architecture implemented with CrewAI. The system decomposes the design task into three sequential agents responsible for text generation, visual asset recommendation, and grid-based layout optimization. A formal 12×12 discrete layout model is employed to represent spatial constraints, enabling consistent and structured poster composition. System performance was evaluated through user testing involving five respondents using a five-point Likert scale. The results show mean scores of 3.2 for content completeness, 3.6 for layout consistency, 3.8 for text relevance, and an overall performance mean of 3.4 (SD = 0.15), indicating satisfactory usability. From an applied mathematics perspective, this work contributes a computational layout formulation using grid discretization and rule-based optimization, as well as a quantitative evaluation of multi-agent coordination efficiency. The proposed framework demonstrates that agentic AI can effectively support structured visual content generation while maintaining user-controlled refinement.
Comparative Performance of Statistical and LSTM Based Arbitrage in the Indonesian Stock Market Yunita, Yunita; Indwiarti, Indwiarti; Saepudin, Deni
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.27373

Abstract

This study compares the performance of traditional statistical arbitrage and Long Short-Term Memory (LSTM)-based deep arbitrage strategies in generating returns and risk-adjusted performance in the Indonesian stock market. A quantitative approach is employed using long-only trading simulations on daily closing prices of blue-chip financial sector stocks listed on the Indonesia Stock Exchange from April 2015 to April 2025. Stock pairs are selected based on correlation and cointegration criteria, while spread volatility is modeled using a GARCH (1,1) framework. To ensure a genuine out-of-sample evaluation, the sample is divided into an in-sample period from April 2015 to August 2021 for model training and parameter optimization, and an out-of-sample period from September 2021 to April 2025 for performance assessment. Strategy performance is evaluated using portfolio return and Sharpe ratio. The empirical results show that both strategies are feasible in the Indonesian market; however, the LSTM-based deep arbitrage strategy significantly outperforms the traditional statistical arbitrage approach, achieving a higher out-of-sample portfolio return (735% versus 482%) and a superior Sharpe ratio (1.67 versus 0.69). These findings indicate that deep learning-based arbitrage can provide substantial improvements in both return and risk-adjusted performance under long-only trading constraints in an emerging market context.
Modeling and Forecasting Volatility through EGARCH-X and EGARCH-CJ Models Nugroho, Didit Budi; Putri, Benita Dwitya; Susanto, Bambang
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26532

Abstract

This study compares the performance of EGARCH-X and EGARCH-CJ models in forecasting financial market volatility using daily TOPIX data (2004-2011). Model parameters were estimated using an efficient Bayesian MCMC framework. The results indicate that the EGARCH-CJ model, which decomposes volatility into continuous and jump components, provides a superior in-sample fit. More importantly, in out-of-sample forecasting, the EGARCH-CJ model demonstrates significantly better accuracy for medium- and long-term horizons (e.g., MSE reductions up to 30% at the 5-day horizon, with significant Diebold-Mariano statistics). In contrast, the standard EGARCH model remains more effective for short-term forecasts. These findings underscore the importance of explicitly modeling jump dynamics for medium-term risk management in the Japanese stock market, offering valuable insights for financial modelers and risk managers.
Integrating Triple-Bottom-Line Goals and Uncertainty in Aggregate Production Planning Using Fuzzy Goal Programming Indra, Nabila Zakia; Santosa, Budi; Siswanto, Nurhadi
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26953

Abstract

This study develops a Sustainable Aggregate Production Planning (SAPP) model based on Fuzzy Goal Programming (FGP) that integrates economic, environmental, and social objectives under uncertainty. Conventional aggregate production planning primarily focuses on cost minimization, often resulting in excessive overtime, high emissions, and workforce instability. To address these limitations, the proposed model simultaneously considers total cost, carbon emissions, energy consumption, waste generation, workforce stability, and worker satisfaction within a unified fuzzy optimization framework. From a mathematical perspective, the main contribution of this study lies in the explicit formulation of a max-min FGP structure using aspiration-based linear membership functions for all sustainability objectives, enabling a balanced compromise solution without relying on deviation-variable-based goal programming commonly adopted in existing SAPP models. The resulting formulation is a linear mixed-integer optimization model that preserves tractability while accommodating conflicting sustainability goals. Numerical experiments are conducted using illustrative demand and operational data adapted from a reference study, solely for mathematical calibration and validation of the proposed model rather than empirical inference. The results indicate a global satisfaction level of λ = 0.67, representing a balanced max-min compromise among economic, environmental, and social objectives. Compared to the baseline scenario, the optimized plan achieves notable improvements in cost efficiency and waste reduction while keeping emissions, energy consumption, and workforce-related indicators within predefined fuzzy tolerance limits. Overall, the proposed SAPP-FGP model provides a transparent and flexible decision-support framework for sustainability-oriented production planning, offering clear insights into trade-offs among competing objectives and contributing to the applied mathematical literature on multi-objective production planning under uncertainty.
Ensemble Bagging Support Vector Machine-Kernel Discriminant Analysis Model for Stunting Potential Classification Nasywa, Alfiyah Hanun; Solimun, Solimun; Efendi, Achmad; Fernandes, Adji Achmad Rinaldo; Sianipar, Celia; Junianto, Fachira Haneinanda
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26818

Abstract

Considering the maternal knowledge, economic status, and maternal nutritional status, the current study created an optimal risk assessment model to detect childhood stunting risk. At the same time, these variables are unbalanced and interrelated in non-linear fashion. Then, to these ends, an Ensemble Bagging model consisting of Support Vector Machine and Kernel Discriminant was trained by voting on the aggregation of the majority of 100 bootstrapped samples, which countered variance and overfitting reducing, hence improving generalization. The primary data were sourced from the mothers of toddlers in the Wajak District. The model predicators were 3 out of the primary ones accounting for the stunting risk. The model also recorded an accuracy of 95%, sensitivity level of 80%, as well as a 100% specificity score. Non-linear relationships were detected and the variance was also reduced, supporting the study to place itself in the realms of novelty by being the first research to fuse the Ensemble Bagging with Kernel methods for Detected stunting risk, The model, hence, fits best as a decision Support System for detecting stunting risk at an early stage.
Closed-Form Formulas for Fibonacci Numbers of Broom and Double Star Graphs Yudhi, Yudhi; Neki, Eliana; Fran, Fransiskus; Raventino, Raventino
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26839

Abstract

Let

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