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All Journal Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Mosharafa: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting
Mukhti, Tessy Octavia
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Physics Social Sciences Other

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Journal : unp journal of statistics and data science

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Metode Density Based Spatial Clustering of Applications with Noise (DBSCAN) dalam Mengelompokkan Provinsi di Indonesia Berdasarkan Kasus Kriminalitas Tahun 2022 Miftahurrahmi, Syifa; Zilrahmi; Amalita, Nonong; Mukhti, Tessy Octavia
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/203

Abstract

Based on Central Statistics Agency 2023 data, in 2022 there was a significant increase in the number of crime cases in Indonesia compared to 2021, from 239,481 cases to 372,965 cases. The increase in the number of criminal acts occurred along with community activities that began to loosen up after the Covid-19 pandemic. The types of crimes that occur in Indonesia themselves vary, ranging from murder, theft, drug-related crimes, and others. This research will cluster provinces in Indonesia based on crime cases with certain types of crimes in 2022 using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) method. The results of the study are expected to help the government and police in an effort to deal with crime in Indonesia. Clustering using the DBSCAN method produces 2 clusters with a silhouette coefficient value of 0,68. The resulting cluster is cluster 0 with noise category consisting of 5 provinces with a high number of crime cases, while cluster 1 consists of 29 provinces with a low number of crime cases.
Classification of Determining Factors for Eligibility of Extreme Poverty Social Assistance Recipients in Dumai City for 2024 Using CHAID Pajrini, Nurul Hasni; Fitria, Dina; Mukhti, Tessy Octavia
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/354

Abstract

Poverty is one of the goals of the Sustainable Development Goals (SDGs). Poverty is a condition in which an individual falls below the standard minimum value of basic needs, both food and non-food. One of the efforts by the Indonesian government to alleviate poverty is through fulfilling needs in various sectors. Although the distribution of social assistance has been successfully implemented, there are still issues in determining beneficiaries who are not properly targeted. Therefore, it is necessary to identify the significant factors influencing the eligibility of social assistance recipients. The application of the CHAID method in classifying the determining factors for eligibility of extreme poverty social assistance recipients in Dumai City for 2024 shows that the significant factors influencing the eligibility status of extreme poverty social assistance recipients in Dumai City for 2024 are house size and neighbors' testimonies. The classification model's accuracy in determining the eligibility factors for extreme poverty social assistance recipients in Dumai City for 2024 is 87.70%.
Stratified Cox Regression Approach to Identifying Prognostic Factors for Survival in Breast Cancer Patients Ervandi, Dhio; Novriani, Aisyah; Luthfiyah, Andini Diva; Siregar, Fauzan Al Hamdani; Mukhti, Tessy Octavia
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-iss4/418

Abstract

The most common type of cancer that affects women is Breast cancer. In 2022, 2.3 million women were diagnosed with breast cancer, and 670,000 deaths were recorded globally. By 2040, it is estimated that breast cancer will increase by 40%, reaching 3 million annually with the number of deaths increasing by 50% to 1 million in 2020. This highlights breast cancer as a serious threat to world health. This study utilized secondary data from METABRIC or the Molecular Taxonomy of Breast Cancer International Consortium obtained from the website www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric/data. The independent variables analyzed were, Age at Diagnosis (X­­1), Surgery Type (X­­2), Chemotherapy (X­­3), Hormone Therapy (X­­4), Tumor Size (X­­5), Radio Therapy (X­­6), Pam50. The dependent variables were Survival Time (Overall Survival Month) and Patient Status. In this study, we used the Stratified Cox model to predict the predictor variables of survival time. The total number of patients used was 18886, with 1080 censored patients and 788 uncensored patients. The Stratified Cox model without interaction revealed that the patients who underwent breast-conserving surgery had a 1.35 times higher risk of death compared to those who underwent mastectomy. Patients who received chemotherapy had a 2.01 times higher risk of death than those who did not, while patients who did hormone therapy had a 1.83 times higher risk of death than those who did not undergo this therapy.
Co-Authors Adrianingsih, Narita Y. Dina Fitria, Dina Dodi Vionanda Ervandi, Dhio Fitri, Fadhilah Khairani, Putri Rahmatun Luthfiyah, Andini Diva Miftahurrahmi, Syifa Nonong Amalita Novriani, Aisyah Padafani, Lekison Pajrini, Nurul Hasni Permana, Dony Rizki Akbar Salma, Admi Sari, Nilam N. Sari, Widia Kemala Siregar, Fauzan Al Hamdani Suliswati, Yeni Syafriandi Syafriandi Taslim, Fauziah Yenni Kurniawati Yusra, Zulmi Yusra Zamahsary Martha Zilrahmi, Zilrahmi