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p-Index From 2021 - 2026
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All Journal UNP Journal of Statistics and Data Science Journal of Organizational Analysis and Performance
Rahmat Kurniawan
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

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Analisis Faktor-Faktor Penentu Kualitas Laporan Keuangan Pemerintah: Studi Empiris pada Pemerintah Kota Magelang Rahmat Kurniawan
Journal of Organizational Performance and Analysis Vol. 1 No. 2 (2025): Journal of Organizational Analysis and Performance
Publisher : Athallah Publishing Globalindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64845/optimanus.v1i2.83

Abstract

Kombinasi antara sistem informasi akuntansi, kepemimpinan yang kuat, dan pengendalian internal yang kokoh menginspirasi penelitian yang disajikan dalam tesis ini. Mengetahui bagaimana sistem informasi akuntansi, tata kelola yang baik, dan sistem pengendalian internal memengaruhi keakuratan pelaporan keuangan pemerintah Kota Magelang merupakan tujuan utama penelitian ini. Dengan menggunakan respons survei sebagai sumber data utamanya, analisis kuantitatif ini menarik kesimpulan. Uji validitas dan reliabilitas, normalitas, multikolinearitas, heteroskedastisitas, pengujian hipotesis, dan regresi linier berganda semuanya digunakan dalam penelitian studi ini. Untuk mengumpulkan data, survei dikirimkan dan kemudian dianalisis menggunakan perangkat lunak SPSS. Menurut temuan penelitian, ada hubungan positif dan signifikan antara variabel sistem pengendalian internal dan kualitas laporan keuangan, sedangkan variabel sistem informasi akuntansi dan variabel tata kelola yang baik keduanya tidak berpengaruh pada kualitas laporan. Sistem pengendalian internal, sistem informasi akuntansi, dan tata kelola yang efektif merupakan tiga faktor yang sangat memengaruhi keakuratan pelaporan keuangan Kota Magelang.
Sentiment Analysis of Public Opinion on Rupiah Redenomination on Twitter Using Naive Bayes Classification FIGO RAHMATULLAH; Dila Sari; Rahmat Kurniawan; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss2/484

Abstract

This study examines public opinion on the Rupiah redenomination policy through sentiment analysis of Twitter data. Redenomination refers to the simplification of currency denominations without changing their real value, a policy that often triggers varied public responses due to concerns such as inflation perception and money illusion. In the digital era, Twitter (currently X) serves as a major platform for real-time public expression, generating large volumes of unstructured textual data suitable for analysis. The objective of this research is to classify public sentiment toward the Rupiah redenomination policy into positive, negative, and neutral categories using the Naive Bayes Classifier, as well as to evaluate the model’s performance. The dataset consists of Indonesian-language tweets collected via the Twitter API using keywords related to redenomination. Data processing involves several stages, including data cleaning, manual labeling, text preprocessing (case folding, tokenization, stopword removal, and stemming), and feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF). The classification results are evaluated using a confusion matrix. The Naive Bayes Classifier achieved an accuracy of approximately 74.84% and a precision of 80%, indicating that the model performs adequately in identifying sentiment patterns. The findings show that neutral sentiment dominates the discussion, suggesting that most users tend to provide informational or observational opinions rather than strong support or opposition. These results are expected to provide insights for policymakers, particularly Bank Indonesia and the government, regarding public acceptance of the redenomination policy, while also contributing to the development of sentiment analysis research on Indonesian social media data.
Evaluation of Prognosis and Duration of Survival in Breast Cancer Patients Using the Cox PH Model Dela Meliza; Tessy Octvia Mukhti; Riza Sasmita; Celsy Aprotama; Rahmat Kurniawan
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/422

Abstract

Breast cancer is the leading cause of cancer-related deaths among women in Indonesia. Late detection and delayed treatment contribute significantly to this high mortality rate, as many patients seek medical care only after reaching advanced stages. Early detection through Breast Self Examination (BSE) and timely intervention can improve survival rates and quality of life. This study aims to evaluate the survival duration and influencing factors for breast cancer patients using clinical and genomic data from the METABRIC dataset, encompassing 1.980 primary breast cancer cases. The study employs survival analysis using Kaplan-Meier curves, Log-rank tests, and Cox proportional hazards regression to analyze the data. Results indicate significant differences in survival rates based on type of surgery and chemotherapy, while age at diagnosis shows no significant effect. The Cox proportional hazards model reveals that patients undergoing mastectomy have a 0.725 lower risk of death compared to those not undergoing the procedure, and patients receiving chemotherapy have a 1.869 higher risk of death. The findings underscore the importance of early and appropriate treatment in improving survival outcomes. This study contributes to the understanding of factors influencing breast cancer survival, aiding in better clinical decision-making and patient management strategies. Keywords: Breast Cancer, Cox Regression, Kaplan-Meier, Survival Analysis, Treatment Factors.
Co-Authors Celsy Aprotama Dela Meliza Dila Sari Fadhilah Fitri FIGO RAHMATULLAH Riza Sasmita Tessy Octvia Mukhti