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Ariska Fitriyana Ningrum
Department of Data Science, Universitas Muhammadiyah Semarang
Computer Science & IT

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Journal : Scientific Journal of Computer Science

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Analysis of Suspected Factors in Tuberculosis Cases in Semarang City Using a Logistic Regression Model Ihsan Fathoni Amri; Febrian Hikmah Nur Rohim; Muhammad Ivan Ardiansyah; Farid Sam Saputra; Supriyanto; Ariska Fitriyana Ningrum; Arman Mohammad Nakib
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.32

Abstract

Tuberculosis (TB) is one of the world's deadliest infectious diseases, with Indonesia being among the countries with the highest TB burden. Semarang City, as an urban area with a dense population, faces significant challenges in controlling TB, particularly among vulnerable populations. This study identifies significant risk factors influencing TB incidence in Semarang City using a binary logistic regression model. Descriptive analysis reveals an imbalance in the data, with the majority of patients categorized as "not indicated for TB." Chi-Square tests show that variables such as shortness of breath, persistent fever for more than one month, diabetes mellitus, and household contact are significantly associated with TB incidence. The logistic regression model demonstrates overall significance (G statistic = 275.13; p-value = 1.23×10−55), with shortness of breath and diabetes mellitus emerging as major risk factors based on odds ratio interpretation. However, the model's performance in detecting the "indicated for TB" category is very low (Precision 36.36%; Recall 2.05%; F1-Score 3.88%), despite an overall accuracy of 87.25%. The poor performance in the "1" category and the Pseudo R2 value of 7% are likely related to data imbalance, where the number of cases in the "1" category is much smaller than in the "0" category, leading to bias toward the majority class. Additionally, the distribution of predictor variables that do not provide sufficient information to distinguish the "1" category from the "0" category further contributes to the model's limited ability to explain data variability overall.
Waiting Time Analysis of Willingness to Pay for Rice Farming Insurance Premiums Using Cox Proportional Hazard Modeling and Weibull Method Siti Mutiah; Yan Nazala Bisoumi; Elsa Nudyawati; Khamidah Arsyad Daud; Rofiah Ainun Nisa; Dwi Sulistiani; Ihsan Fathoni Amri; Ariska Fitriyana Ningrum; Ahmed A. Mostfa
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.34

Abstract

Rice is a primary commodity in Indonesia's agricultural sector but is highly vulnerable to climate risks such as floods, droughts, and pest infestations. To mitigate these risks, the government, in collaboration with PT. Asuransi Jasa Indonesia (Jasindo), launched the Rice Farming Insurance Program (AUTP) in 2015. This study aims to analyze the willingness-to-pay time of farmers for AUTP premiums in Jayaraksa Village, Cimaragas Subdistrict, Ciamis Regency, using Weibull regression and Cox Proportional Hazard models. Factors such as education, secondary employment, rice production, and farming costs were examined to understand their influence on farmers' participation. Based on the analysis, the Weibull regression model, with a lower AIC value compared to Cox Proportional Hazard (270.4431 vs. 330.9111), demonstrated better performance in explaining the data. This research contributes to the development of more effective AUTP policies by identifying key factors influencing farmers' participation.
Co-Authors Ahmed A. Mostfa Arman Mohammad Nakib Dwi Sulistiani Elsa Nudyawati Farid Sam Saputra Febrian Hikmah Nur Rohim Ihsan Fathoni Amri Khamidah Arsyad Daud Muhammad Ivan Ardiansyah Rofiah Ainun Nisa Siti Mutiah Supriyanto Yan Nazala Bisoumi