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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ilmu Sosial dan Humaniora Jurnal Ketahanan Nasional Jurnal Ilmiah Pangabdhi Jurnal Sains Matematika dan Statistika JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Jurnal Varian Jurnal ABDINUS : Jurnal Pengabdian Nusantara Inferensi Elastisitas Jurnal Ekonomi Pembangunan Journal of Mathematics Education and Science UNP Journal of Statistics and Data Science Eksponensial Jurnal Pendidikan Matematika Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics-IJCSAM
Dita Amelia
Fakultas Sains Dan Teknologi, Universitas Airlangga
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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ANALISIS BIPLOT PADA BERBAGAI FAKTOR KEMISKINAN DI INDONESIA BERDASARKAN PROVINSI Wieldyanisa, Ezha Easyfa; Ismi, Ferissa Maulida; Putri, Refa Berliana; Dwitya, Shabrina Nareswari; Elly Pusporani; Amelia, Dita
Elastisitas : Jurnal Ekonomi Pembangunan Vol. 7 No. 2 (2025): Elastisitas, September 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/e-jep.v7i2.09

Abstract

Kemiskinan merupakan permasalahan kompleks yang dipengaruhi oleh berbagai faktor sosial dan ekonomi. Berdasarkan hal tersebut, penelitian ini bertujuan untuk melihat hubungan antara provinsi di Indonesia dan berbagai faktor yang berpengaruh terhadap kemiskinan seperti pendidikan, kesehatan, dan infrastruktur dasar menggunakan analisis biplot. Data sekunder tahun 2024 dari BPS digunakan dengan delapan variabel utama, meliputi usia harapan hidup, produk domestik regional bruto (PDRB) per kapita, angka melek huruf, rumah tangga dengan sanitasi layak, akses air layak, akses listrik, angka partisipasi sekolah, dan rata-rata lama sekolah. Hasil analisis menunjukkan bahwa 81,772% keragaman data dapat dijelaskan oleh dua komponen utama dalam grafik biplot. Provinsi-provinsi dikelompokkan ke dalam empat kuadran berdasarkan kesamaan karakteristik kemiskinan. Faktor dengan keragaman tertinggi adalah rumah tangga dengan sanitasi layak, sedangkan faktor dengan keragaman terendah adalah PDRB per kapitaKorelasi antar variabel menunjukkan bahwa angka melek huruf dan akses listrik memiliki hubungan paling kuat, yang berarti semakin tinggi tingkat melek huruf suatu daerah, semakin besar pula kemungkinan masyarakatnya memiliki akses terhadap listrik. Sebaliknya, hubungan terlemah terdapat antara PDRB dan akses listrik. Penelitian ini menunjukkan bahwa memahami kemiskinan memerlukan pendekatan terhadap berbagai faktor yang saling berkaitan serta perlunya kebijakan pembangunan yang disesuaikan dengan karakteristik daerah masing-masing.
Identifikasi Faktor yang Mempengaruhi Kemiskinan di Papua dengan Principal Component Analysis Ain, Dzuria Hilma Qurotu; Kusuma, Shalwa Oktavia; Zahrani, Vista Vanadya; Suryono, Alda Fuadiyah; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Journal of Mathematics Education and Science Vol. 7 No. 1 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i1.1336

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor kemiskinan terhadap pengentasan kemiskinan di Provinsi Papua. Metode yang digunakan yaitu Analisis Komponen Utama (AKU). Cakupan data yang digunakan dalam penelitian ini adalah data statistik kesejahteraan rakyat Provinsi Papua pada bulan Maret tahun 2021 yang diperoleh dari Badan Pusat Statistik (BPS). Hasil penelitian ini menunjukkan bahwa faktor-faktor yang mempengaruhi kemiskinan di Kabupaten dan Kota Provinsi Papua dapat dikategorikan menjadi tiga komponen yaitu Komponen 1 : “Pendidikan dan Kependudukan“, Komponen 2 : ”Fasilitas Imunisasi dan Penerangan”, serta Komponen 3 :  “Fasilitas Teknologi dan Kesehatan”. Dengan demikian,  penelitian  ini  bermanfaat  bagi  para  pembuat  kebijakan  baik pemerintah  pusat maupun  daerah  untuk  memperhatikan  faktor-faktor  yang  mempengaruhi terjadinya peningkatan kemiskinan di Provinsi Papua. Kemiskinan merupakan prioritas pada SDGs yang dinyatakan pada poin pertama yaitu no poverty (tanpa kemiskinan).
Pengelompokan Provinsi di Indonesia berdasarkan Ketimpangan Akses Layanan Kesehatan Tahun 2024 Menggunakan Pendekatan Cluster Hirarki Nabila Rahma Na’ifa, Ariza; Rohayah, Dewi; Yuliati, Intan; Tsabita Amalia Shofa, Nayla; Pusporani, Elly; Amelia, Dita
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/v16i2.1560

Abstract

Health disparities remain a major challenge in Indonesia, particularly in terms of access to healthcare services across provinces. This study aims to classify 38 Indonesian provinces based on inequality in healthcare access in 2024 using a hierarchical clustering approach. Three key indicators were used: the number of hospitals, the number of medical personnel, and the percentage of people experiencing health complaints who opted for self-medication. The analysis identified the average linkage method as the most suitable model, supported by the highest cophenetic correlation coefficient (0,911). The results revealed two distinct clusters. The first cluster includes most provinces outside Java Island, characterized by limited healthcare infrastructure and personnel. The second cluster comprises four provinces on Java Island with advanced healthcare facilities but a high rate of self-medication. These findings suggest that healthcare access inequality is influenced not only by infrastructure but also by social and behavioral factors. Therefore, policy recommendations should be tailored accordingly: infrastructure improvement and equitable distribution of medical personnel for the first cluster, and health education interventions for the second. This study contributes to evidence-based policy design in line with the Sustainable Development Goals (SDGs), particularly the goal of ensuring equitable healthcare access for all.
Analisis Survival Distribusi Lomax dengan Estimasi Maximum Likelihood Victoria Anggia Alexandra; Aprilia Prastyaningrum; Ardi Kurniawan; Dita Amelia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3373

Abstract

Survival analysis is a statistical technique used to test the durability and reliability of a component. Life time data obtained from a life test experiment is often in the form of type III censored data, which occurs when observations enter at different times and last for varying durations. In survival analysis, data is expected to follow a certain probability distribution. To determine the characteristics of a population, a point estimate of the probability distribution parameters is conducted. This study aims to obtain parameter estimators of the Lomax distribution on type III censored data with the Maximum Likelihood Estimation (MLE) and Newton Raphson methods. Application of parameter estimation results on post-heart surgery survival data in one of the Jakarta hospitals. The result of estimating the parameter value in the post-heart surgery patient data is 1.552 and the result of estimating the parameter in the post-heart surgery patient data is 20.38. Based on these results, it can be concluded that the estimated probability of survival of a post-heart surgery patient for more than 49 days is 14.94%.
Pengaruh Suplementasi Vitamin D dan BMI terhadap LVEF dengan Pendekatan Generalized Additive Models Longitudinal Dita Amelia; Suliyanto Suliyanto; Victoria Anggia Alexandra; Adelia Frielady Yosifa; Syavrilia Alfiatur Rakhma; Agnes Happy Julianto
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3378

Abstract

Cardiovascular diseases (CVD) are the leading cause of global mortality, with Left Ventricular Ejection Fraction (LVEF) being a key indicator of heart function. This study explores the impact of vitamin D supplementation and Body Mass Index (BMI) on LVEF using Generalized Additive Models (GAM) in longitudinal data from 47 elderly patients with hypovitaminosis D undergoing orthopedic surgery. LVEF was measured before surgery and at 1, 3, and 6 months post-intervention. GAM was employed to capture nonlinear relationships between variables with working correlation structures such as Independence, Exchangeable, Unstructured, and Autoregressive-1 (AR-1). The findings revealed a significant increase in vitamin D levels and LVEF following supplementation, while BMI remained relatively stable throughout the observation period. The best GAM model with AR-1 correlation structure achieved the lowest Quasi Information Criterion (QIC) score of 443.47, indicating a complex relationship between vitamin D and LVEF and a linear relationship between BMI and LVEF. Vitamin D demonstrated a significant nonlinear effect on LVEF improvement, whereas a 1-point increase in BMI raised LVEF by 0.291%. This study underscores the importance of vitamin D supplementation in enhancing heart function among elderly patients with hypovitaminosis D, supporting the development of evidence-based health policies
Indeks Pembangunan Gender Indonesia dalam Perspektif Pendekatan Spasial dengan Pembobot Queen Contiguity Dita Amelia; Made Riyo Ary Permana; Adelia Frielady Yosifa; Ardi Kurniawan; Suliyanto
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 2 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Isu gender menjadi fokus global karena ketimpangan dalam hak-hak dan kontribusi laki-laki dan perempuan dalam pembangunan. Pencapaian Indeks Pembangunan Gender (IPG) menjadi tolok ukur penting dalam upaya mencapai kesetaraan gender dan pembangunan manusia yang inklusif di Indonesia. Penelitian ini bertujuan untuk memodelkan Indeks Pembangunan Gender di Indonesia dengan pendekatan regresi spasial dengan variabel-variabel yang diduga mempengaruhi IPG. Metode yang digunakan dalam penelitian ini adalah regresi spasial dengan pembobot Queen Contiguity . Berdasarkan penelitian yang telah dilakukan dengan tiga jenis pemodelan didapatkan model terbaik dalam pemodelan Indeks Pembangunan Gender di Indonesia adalah model regresi spasial error dengan nilai AIC sebesar 154,950 dan nilai R 2 sebesar 0,6643. Analisis spasial mengungkapkan adanya korelasi dan heterogenitas spasial antar wilayah, menyoroti pentingnya mempertimbangkan aspek spasial dalam merancang kebijakan untuk meningkatkan pembangunan gender di Indonesia. Dengan demikian, upaya perbaikan dan kesetaraan gender sebaiknya diterapkan dengan mempertimbangkan variabilitas spasial serta fokus pada aspek-aspek yang telah diidentifikasi melalui pemodelan ini.
Analisis Nilai Inflasi Bulanan Indonesia Menggunakan Regresi Nonparametrik Estimator Kernel Azzah Nazhifa Wina Ramadhani; Adelia Sukma Dwiyanto; Nuzulia Anid; Muhammad Hafidzuddin Nahar; Dita Amelia
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 2 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

High levels of inflation are plaguing Indonesian society. Inflation occurs due to price increases as indicated by the increase in most expenditure group indices. This can lead to a higher poverty rate in Indonesia. This study aims to identify the best method that can be used to estimate Indonesia's monthly inflation value based on a nonparametric regression approach with a kernel estimator and analyze the results of predicting Indonesia's monthly inflation value for the next four months. The data used in this study is secondary data sourced from Bank Indonesia, with the variable used is the value of Indonesian inflation during the period January 2019 to July 2024. The collected data were analyzed using descriptive statistics and analytical statistics in the form of nonparametric regression with kernel estimators and predictions using kernel estimator and non-seasonal ARIMA methods. The results showed that triweight kernel regression was the best kernel function model with a minimum bandwidth value of 1.214, value of 99.990, MSE of 0.00016, and MAPE of 0.348%. The results of data prediction for the next thirteen months provide that triweight kernel estimator was better than non seasonal ARIMA method, with a MAPE value of 10.92%, so that the nonparametric regression method with the triweight kernel function is good or accurate in predicting data, which also can be used to analyze and predict Indonesia's monthly inflation data.
Analyzing the Influence of Gross Domestic Product on the Human Development Index Worldwide in 2021 Using a Nonparametric Regression Approach Based on Penalized Spline Estimator Dita Amelia; Azizah Atsariyyah Zhafira; Bryan Given Christiano Ginzel; Fery Yulian Putra; Yoga Setya Wibawa
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8851

Abstract

People’s welfare is a universal goal that is the main focus of all countries in the world. One of the indicators used to measure welfare is the Human Development Index (HDI), which includes education, health and per capita income. On the other hand, Gross Domestic Product (GDP) is the main measure of a region’s economic growth. This research aims to highlight how regional economic dynamics affect human welfare in the world in 2021 and the data source was obtained from OurWorldInData. This research uses nonparametric regression with a penalized spline estimator approach. Penalized Spline analysis shows that the best model for predicting HDI based on GDP per capita is to use 2 knot points, namely k1=8000 and k2=50000. This model produces a Mean Squared Error (MSE) value of 0.0018 and Generalized Cross Validation (GCV) of 0.0019. In addition, this model has the ability to explain response variability of R2=91.58%. The grouping of countries by GDP per capita reveals that economic improvement impacts human development differently across income levels. By tailoring strategies to specific income groups, policymakers can more effectively enhance human development outcomes, fostering a more equitable and prosperous society
Pemodelan Faktor yang Mempengaruhi Indeks Demokrasi Indonesia Menggunakan Spline Truncated Hanny Valida; M. Fariz Fadillah Mardianto; Dita Amelia; Suliyanto Suliyanto
Jurnal Pendidikan Matematika Vol. 3 No. 2 (2026): February
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v3i2.2478

Abstract

Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi Indeks Demokrasi Indonesia (IDI) menggunakan pendekatan regresi spline linier terpotong nonparametrik. Data yang digunakan merupakan data sekunder cross-section dari 34 provinsi di Indonesia pada tahun 2024, dengan variabel prediktor berupa Indeks Pemberdayaan Gender, Indeks Kebebasan Pers, dan Indeks Pembangunan Manusia. Pemilihan model dilakukan menggunakan kriteria Generalized Cross Validation (GCV) untuk menentukan jumlah dan posisi knot yang optimal. Hasil analisis menunjukkan bahwa model terbaik diperoleh dengan tiga titik knot, menghasilkan nilai GCV sebesar 6,79 dan koefisien determinasi (R²) sebesar 89,37 persen. Hasil penelitian menunjukkan adanya hubungan nonlinier antara variabel prediktor dan IDI. Indeks Pemberdayaan Gender memberikan pengaruh positif pada tingkat rendah hingga menengah, namun berubah menjadi negatif pada tingkat yang lebih tinggi. Indeks Kebebasan Pers menunjukkan pengaruh positif pada tingkat rendah tetapi cenderung negatif setelah melewati titik tertentu. Sementara itu, Indeks Pembangunan Manusia memberikan pengaruh positif yang konsisten terhadap IDI. Temuan ini menunjukkan bahwa kualitas demokrasi di Indonesia dipengaruhi oleh dinamika sosial dan institusional yang kompleks.
Penalized Multivariate Adaptive Regression Splines with Generalized Cross Validation for Modeling Health Insurance Ownership in East Java Deby Victoria; Addina Nurkamila; Yanuar Ibnu Ridho; Rafly Tawekal; Dita Amelia
Inferensi Vol 9 No 1 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i1.8747

Abstract

The average programmatic health insurance coverage is a key indicator of public welfare and the effectiveness of healthcare policies. This study proposes the use of Penalized Multivariate Adaptive Regression Splines (PMARS) with Generalized Cross Validation (GCV) to model the determinants of this average coverage rate across 38 regencies and cities in East Java Province, using secondary data from the 2025 BPS East Java publication. Several PMARS specifications are evaluated by varying polynomial degrees, numbers of basis functions, and penalty values to identify the optimal model structure. The PMARS approach effectively captures nonlinear relationships, interaction effects, and variable importance within a flexible regression framework. The optimal model is a quadratic specification consisting of 16 basis functions, a maximum interaction of two, and an optimal penalty parameter, explaining 94.46% of the variability in the average health insurance coverage with a minimum GCV value of 5.49654. Access to improved sanitation, clean water, and health complaints are identified as the most influential determinants, all exhibiting positive associations that drive the need for formal financial protection. These results demonstrate the effectiveness of the GCV-PMARS methodology for modeling complex socio-health data and provide empirical insights to support policies aimed at strengthening universal health coverage, ensuring equitable programmatic utilization, and advancing Sustainable Development Goal 3 on Good Health and Well-Being.
Co-Authors Abdillah, Adrian Wahyu Addina Nurkamila Adelia Frielady Yosifa Adelia Frielady Yosifa Adelia Putri Andini Adelia Sukma Dwiyanto Aditya Syarifudin Akbar Adma Novita Sari Aflaha, Nabila Shafa Agnes Happy Julianto Agnes Happy Julianto Ain, Dzuria Hilma Qurotu Aini Divayanti Arrofah Aini Divayanti Arrofah Alya Rahma Inneztiana Ameliatul 'Iffah Ana, Elly Andi Vania Ghalliyah Putrie Anida, Nuzulia Anisa Laila Azhar Anisah Nabilah Ghasani Annisa Putri Nayumi Antonio Nikolas Manuel Bonar Simamora Aprilia Prastyaningrum Ardi Kurniawan Ariyawan, Jovansha Aulia Ramadhanti Aulia, Niswa Faizah Azizah Atsariyyah Zhafira Azzah Nazhifa Wina Ramadhani Azzah Nazhifa Wina Ramadhani Bintang Alyaa Sabila Bryan Given Christiano Ginzel Budijono, Gabriella Agnes Cynthia Anggelyn Siburian Davina Shafa Vanisa Deby Victoria Deshinta Arrova Dewi Dinda Rahma Alya Dinnara Chairana Aisha Doni Muhammad Fauzi Dwi Syarifatun Nisya’ Dwika Maya Harsanti Dwitya, Shabrina Nareswari Dwiyanto, Adelia Sukma Dwiyanto, Adelia Sukma Elly Pusporani Faradilla Harianto Farah Fauziah Putri Faya Najwatus Silma Fery Yulian Putra Firda Aulia Pratiwi Fortunata, Regina Ghasani, Anisah Nabilah Grace Lucyana Koesnadi Hanny Valida Humaira, Edla Putri Ismi, Ferissa Maulida Isna Nurul Izza Amalia Julia Widiyanti Karina Rubita Makhbubah Karina Tri Handayani Kurniawan, Ardi Kusuma, Shalwa Oktavia Layyin Gisvira M. Fariz Fadillah Mardianto M. Nabil Saputra Made Riyo Ary Permana Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Maria Setya Dewanti Marisa Rifada Marthabakti, CitraWani Mochammad Baihaqi Muhammad Fikry Al Farizi Muhammad Hafidzuddin Nahar Muhammad Rizaldy Baihaqi Muhammad Rosyid Ridho Az Zuhro Muhammad Rosyid Ridho Az Zuhro Muhammad Walid Jumlat Mutyaravica, Astrid Na'imatul Lu'lu'a Nabila Rahma Na’ifa, Ariza Nadia Dwi Marwanda Nafla Nara Yonay Nahar, Muhammad Hafidzuddin Nike Meliana Rahmawati Nila Khoirun Naili Salam Nur Chamidah Nurdin, Nabila Nurrohmah, Zidni ‘Ilmatun Nuzulia Anid Nuzulia Anida Pambudi, Daffa Satrio Permana, Made Riyo Ary Pratama, Fachriza Yosa Pressylia Aluisina Putri Widyangga Previan, Anggara Teguh Putri Masyita Qomaryah Putri Nur Farida Putri, Ferdiana Friska Rahmana Putri, Refa Berliana Putu Eka Andriani Rafly Tawekal Rahmada, Indrastanto Oktodian Ramadhan, Achmad Wahyu Ramadhani, Azzah Nazhifa Wina Ramadhani, Maulana Syah Putra Rani, Lina Nugraha Rindiani Ahmada Alisiah Rohayah, Dewi Safira Salsabila Sanda Insania Dewanty Sediono, Sediono Siagian, Kimberly Maserati Siregar, Naufal Ramadhan Al Akhwal Slavina Sofia Andika Nur Fajrina Suliyanto Suliyanto Suliyanto Suryono, Alda Fuadiyah Syavrilia Alfiatur Rakhma Tagawa, Dustin Nathanael Thareq Alexander Manggala Napitupulu Toha Saifudin Tsabita Amalia Shofa, Nayla Tyo Anugrah Putra Utsna Rosalin Maulidya Victoria Anggia Alexandra Victoria Anggia Alexandra Wibawa, Yoga Setya Wieldyanisa, Ezha Easyfa Wulandari, Indana Zulfa Yanuar Ibnu Ridho Yoga Setya Wibawa Yosifa, Adelia Frielady Yuliati, Intan Zah, Alfian Iqbal Zahrani, Vista Vanadya Zhafirab, Azizah Atsariyyah