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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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Feature Selection Analysis for Diagnosing Narcissistic Personality Disorder (NPD) Using Principal Component Analysis and the Naïve Bayes Model Sarwadi, Sarwadi; Rosnelly, Rika; Triandi, Budi
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.v9i1.24086

Abstract

The mental health illness known as narcissistic personality disorder (NPD) affects a person's capacity to preserve harmonious social interactions. Early diagnosis plays a crucial role in providing timely intervention and treatment. This study examines the effectiveness of Principal Component Analysis (PCA) for feature selection in diagnosing NPD using the Naïve Bayes algorithm. The dataset utilized in this research was sourced from Open Psychometrics via Kaggle, followed by preprocessing, including data cleaning and dimensionality reduction through PCA. This study compares the performance of three Naïve Bayes models, Gaussian, Bernoulli, and Multinomial, to identify the most suitable classification approach. The findings reveal that Gaussian Naïve Bayes, when integrated with PCA, achieves the highest accuracy (91%), surpassing Bernoulli Naïve Bayes (80%) and Multinomial Naïve Bayes (69%). Implementing PCA significantly enhances computational efficiency and improves classification performance by eliminating irrelevant features and reducing data dimensionality. These results suggest combining PCA with Gaussian Naïve Bayes is a promising strategy for automating NPD diagnosis. Additionally, this study highlights the potential of machine learning in mental health evaluation and establishes the framework for further studies on hybrid models or other methods to improve prediction accuracy.
Integrating Self-Organizing Maps and K-Means in a Multidimensional Approach to Enhance Private University Market Segmentation Alifah, Amalia Nur; Rochmah, Wachda Yuniar; Mesak, Evellyn Verity
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.v9i1.24430

Abstract

Educational institutions face challenges in attracting prospective students while maintaining academic quality and resource efficiency. This study applies a hybrid approach that integrates Self-Organizing Maps (SOM) and K-Means to cluster schools based on four attributes, namely the number of accounts, average UTBK scores, geographical distance, and parental income. The analysis's findings produce three distinct clusters. With a high degree of attribute variation, Cluster 2 (279 schools) is a dominant group that suggests the possibility of extensive marketing campaigns. Clusters 1 (45 schools) and 3 (81 schools), on the other hand, are more uniform and call for a more specialized and focused strategy. These results imply that a data-driven approach can help institutions create interventions that are specific to each segment's profile and increase the efficacy of educational marketing strategies. In order to improve segmentation accuracy in the future, this study creates opportunities for investigating new features and dynamic clustering techniques.
Zero Inflated Poisson Regression for Analyzing Excess Zeros in Job Transition Data: A Case Study of Tourism Workers in Malang Regency Berliana, Sarni Maniar; Karim, Rafidah Abd; Yuliana, Rita; Budyanra, Budyanra; Prasetyo, Achmad; Kusuma, Arya Candra; Priatmadani, Priatmadani
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.v9i1.24581

Abstract

This study examines job transition patterns among 868 tourism workers in Malang, Indonesia, a key tourism hub, using 2023 Fieldwork Training data from Politeknik Statistika STIS. It aims to model transitions while addressing the high number of workers with no job changes using a Zero-Inflated Poisson (ZIP) model, which better captures these patterns than standard models. The ZIP model, including sex, age, relationship to the head of household, education, and foreign language proficiency, shows that proficient workers are more likely to remain in stable roles, while men and younger workers exhibit greater mobility, particularly when leveraging language skills. These findings support Indonesia’s Sertifikasi Kompetensi SDM Pariwisata program by justifying targeted interventions: language training to enhance mobility for non-proficient workers, mentorship for female tour guides to address gender disparities, and digital skills programs for older workers to boost employability. These strategies align with government efforts to strengthen Malang’s tourism workforce resilience.
Expert System Analysis of Divorce Case Handling in Islamic Law Using Breadth First Search Algorithm Apdilah, Dicky; Harmayani, Harmayani; Salim Siregar, Emiel; Siregar, Aris; Andriani, Dian Ayu; Ikhwan, Ali; H Aly, Moustafa
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.v9i1.24342

Abstract

Divorce is a separation between spouses often triggered by domestic violence, adultery, mental illness, gambling, or polygamy. Many first-time divorce plaintiffs are unaware of the proper legal procedures and tend to rush through the process. To address this, an expert system was developed using the Breadth First Search (BFS) algorithm to provide a more structured and measurable approach to divorce filing. The system aims to simplify and clarify the divorce procedure. The development process followed Rapid Application Development (RAD), with data collected through observations, interviews, and literature review. Experimental results with a sample of 100 users showed that 85% reported increased confidence in making decisions, the time to obtain accurate information decreased by 40%, and understanding of the divorce procedure improved by 90%. The system achieved a 92% user satisfaction rate, demonstrating its effectiveness. The BFS algorithm proved to be efficient and reliable, with statistically significant improvements in user experience.
Drivers and Impacts of Agricultural Land Conversion: Regression Modelling with Spatial Dependence in West Bandung and Purwakarta Regencies, Indonesia Wijayanto, Arie Wahyu; Prasetyo, Rindang Bangun; Putri, Salwa Rizqina; Sugiarto, Sugiarto; Marsisno, Waris; Wahyuni, Krismanti Tri; Pasaribu, Ernawati; Maghfiroh, Meilinda F N
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.v9i1.23939

Abstract

The rapid conversion of farmland to non-agricultural uses in West Java threatens food security, farmer livelihoods, and environmental sustainability. This study investigates the causes and consequences of land conversion in West Bandung and Purwakarta Regencies using a mixed-source data, including geotagging, CAPI, and secondary data from satellite images, focusing on landowners who converted farmland between 2013 and 2021. Multiple linear regression and spatial models, including Spatial Lag Model (SLM), were applied to assess key determinants. The results revealed economic pressures as the main driver, with rice fields most affected and various geographic and infrastructure factors influencing outcomes. The findings underscore the need for targeted policies to balance development with sustainable land and food system management.
Integer Linear Programming for Patchwork Production Planning Optimization with Demand Uncertainty Afnaria, Afnaria; Sari, Rina Filia; Octariani, Dhia; Rambe, Isnaini Halimah
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.v9i1.24349

Abstract

This study develops an Integer Linear Programming (ILP) model to optimize monthly production planning for handcrafted patchwork MSMEs in Medan, Indonesia. The model incorporates key operational constraints, including production time, material and handling costs, labor, electricity, capital limits, and uncertain demand. Demand uncertainty is modeled deterministically using upper and lower bounds derived from historical field data. The objective function maximizes total profit while ensuring resource feasibility. A real-world case study involving five products across five artisans is presented, resulting in a maximum profit of IDR 12,469,900. The model is implemented using LINGO 18.0 and validated through sensitivity analyses. Results show that a 50% reduction in demand may reduce profit by up to 33.8%, while an increase in lead time can lower profit by 17.1%. These findings demonstrate the model’s robustness and its potential to serve as a decision-support tool for MSMEs facing volatile market conditions and operational constraints.
Optimization Of a Smart GPS Tracker System to Measure Truck Speed Performance Yuniar, Dewi; Yulfadli, Zony; Alnakhli, Mohammad
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.v9i1.24660

Abstract

Smart GPS v 3.3" offers features that traditional systems lack, such as real-time monitoring and advanced travel behavior analysis. This study evaluates the speed of trucks and travel performance using Smart GPS v 3.3 and a tracking system from the stockpile to the port (93.89 km). The methods used were field observation and data collection through smart GPS software. The results show that the trucks average speed/month is 31 km/h for loaded trucks and 57 km/h for empty trucks. The average travel time for loaded trucks is 3:05:36, while for empty trucks it is 2:13:48. In the morning, the travel time for loaded trucks is 2:50:49, and at night it is 3:31:28. The travel time for empty trucks in the morning is 2:50:49 and at night is 2:29:11. The use of GPS serves as an evaluation tool for the coal transport for companies to streamline distribution.
Evaluating Robust Estimators in Geographically Weighted Regression for Stunting Analysis at the District-Level Across Java: A Focus on Outlier Handling Setyowati, Silfiana Lis; Aidi, Muhammad Nur; Syafitri, Utami Dyah; Ernawati, Fitrah
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.v9i1.24064

Abstract

Rate remains high and lags behind neighboring countries such as Vietnam and Thailand. This slow progress underscores the need for region-specific interventions and a deeper understanding of local factors driving stunting to meet the 14% national target. This study applies RGWR, an improvement over GWR for handling outliers.  This method uses M, S, and MM estimators applied to the analysis of the prevalence of stunting among children under five the 2018 Riskesdas data across 85 districts in Java. Immunization reduces disease risk, growth monitoring detects stunting early, ARI management mitigates disease impact, parental height influences stunting risk, and working mothers improve family income and healthcare access, all contributing to reduced stunting. Given the regional variation in impact, stunting reduction policies should be spatially tailored, the MBG program should be prioritized in eastern Java regions.
Choosing the Right Tool: Practical Considerations for GLMM and GEE in Longitudinal Studies, with a Focus on Data Challenges Sihombing, Pardomuan Robinson; Erfiani, Erfiani; Notodiputro, Khairil Anwar; Kurnia, Anang
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.v9i1.24602

Abstract

The proposed research systematically reviews the comparative issues between GLMM and GEE for longitudinal data. The review discusses the competing arguments regarding the practical strengths and weaknesses of the two arrests. Empirical evidence demonstrates that GLMM generally provides subject-specific estimates and performs better than GEE in hierarchical and individual variance. In contrast, GEE provides resilient population-level findings, which are crucial for policy. The choice of method depends on the data structure and scope of inference. GLMM is consistently better when characterizing individuals, for example, in studies where we assume random effects are drawn from a complex distribution. GEEs shine most brightly in large datasets, obtaining robust population-level estimates even when the working correlation is misspecified. Finally, the results provide hands-on recommendations for researchers from various domains who apply statistical models to longitudinal studies to select solid, context-fitting statistical models for long-term studies.
A Data-Driven Framework for Integrating Decision-Making and Operational Efficiency in Multi-Product Retail: A Case Study with Experimental Evaluation Aryza, Solly; Novelan, Muhammad Syahputra; Islam, Muhammad Remanul
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.v9i1.24301

Abstract

In today’s highly competitive retail and industrial landscape, multiproduct retail systems face growing challenges due to complex operations, fluctuating demand, and market uncertainty. This paper presents a data-driven framework for optimizing integrated decision-making and enhancing operational efficiency. By utilizing historical transaction data and advanced analytical techniques, the model combines key operational functions—including demand forecasting, inventory management, and resource allocation—to support real- time, data-informed decisions. The approach employs predictive modeling and optimization algorithms to minimize operational costs while maintaining product availability and service level targets. The initial model features five interconnected components: inspection, distribution, disposal, recovery, and retail centers. However, it currently excludes forward logistics, fleet operations, and is limited to a single product and planning period. To address supplier uncertainty, a deterministic equivalent formulation is introduced, relying on the estimation of statistical moments from limited data. Since supplier selection is critical to effective sourcing strategies, improving this process directly enhances supply chain performance. The study highlights that accurately identifying and modeling operational uncertainties is essential for achieving robust and optimal outcomes in retail environments.

Page 13 of 19 | Total Record : 184

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