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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Buletin Ilmiah Sarjana Teknik Elektro Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Statistika dan Aplikasinya Journal Of Human And Education (JAHE) Journal of Data Insights Scientific Journal of Computer Science
Ariska Fitriyana Ningrum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Scientific Journal of Computer Science

 1 
Identification of Dominant Topics in Public Discussions on IKN using Latent Dirichlet Allocation (LDA) and BERTopic Ningrum, Ariska Fitriyana; Talirongan, Florence Jean B.; Tangaro, Diana May Glaiza G.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.19

Abstract

This study aims to analyze public opinion related to the relocation of Indonesia's National Capital City (IKN) through topic modeling on Twitter data. The two main approaches used are Latent Dirichlet Allocation (LDA) based on Bag of Words and BERTopic based on Transformer language model. LDA was chosen for its ability to identify topic distribution in large text collections, while BERTopic was used to overcome the limitations of LDA in capturing semantic meaning in short and informal texts such as tweets. The analysis was conducted on a collection of tweets discussing the relocation of IKN, with the aim of uncovering the main themes and public perceptions. The result of LDA showed three main topics in the public discussion, namely (1) political debate and nationalism related to the relocation, (2) policy implementation and project execution, and (3) economic justification and challenges facing Jakarta. Mean-while, BERTopic identified topics with more contextual representations, including aspects of investment, economic impact construction progress, and public perception. Dominant topics include urban relocation, investment in IKN, and socio-economic impacts. The novelty of study lies in the comparison of two topic modeling approaches in the context of social media sentiment analysis related to major public policy issues. These findings not only enrich the understanding of the narratives that develop in society, but also provide important insights for policy makers in responding to public opinion more appropriately and contextually.
Analysis of Suspected Factors in Tuberculosis Cases in Semarang City Using a Logistic Regression Model Amri, Ihsan Fathoni; Rohim, Febrian Hikmah Nur; Ardiansyah, Muhammad Ivan; Saputra, Farid Sam; Supriyanto; Ningrum, Ariska Fitriyana; Nakib, Arman Mohammad
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
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 Mutiah, Siti; Bisoumi, Yan Nazala; Nudyawati, Elsa; Daud, Khamidah Arsyad; Nisa, Rofiah Ainun; Sulistiani, Dwi; Amri, Ihsan Fathoni; Ningrum, Ariska Fitriyana; Mostfa, Ahmed A.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
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 Alfian Ma’arif Alwan Fadlurohman Amri, Saeful Anggun Lestari, Febi Asno Azzawagama Firdaus Aura Hisani, Zahra Bisoumi, Yan Nazala Dannu Purwanto Daud, Khamidah Arsyad Dhani, Oktaviana Rahma Dwi Sulistiani Furizal, Furizal Ihsan Fathoni Amri Kariyamin, Kariyamin Khaira, Mulil Kusuma Wardani, Amelia M. Al Haris Mostfa, Ahmed A. Muhammad Ivan Ardiansyah Musa, Fitri Diana mutiah, siti Nakib, Arman Mohammad Nisa, Rofiah Ainun Nudyawati, Elsa Prizka Rismawati Arum Rahma Dhani, Oktaviana Rohim, Febrian Hikmah Nur Saputra, Farid Sam Sitti Sahara Supriyanto Talirongan, Florence Jean B. Tangaro, Diana May Glaiza G. Wardani, Amelia Kusuma Wijaya, Setiawan Ardi Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani