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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ilmu Sosial dan Humaniora CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ketahanan Nasional Jurnal Ilmiah Pangabdhi Jurnal Sains Matematika dan Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan 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 Eksponensial Limits: Journal of Mathematics and Its Applications
Dita Amelia
Fakultas Sains Dan Teknologi, Universitas Airlangga
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation 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.
BAYESIAN ESTIMATION OF THE SCALE PARAMETER OF THE WEIBULL DISTRIBUTION USING THE LINEX AND ITS APPLICATION TO STROKE PATIENT DATA Rahmanita, Tentri Ryan; Kurniawan, Ardi; Ana, Elly; Sediono, Sediono; Amelia, Dita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0413-0426

Abstract

Survival analysis is used to study the timing of an event, such as recovery or death, in the context of medical data. One of the diseases that many people suffer from is stroke. Based on the survey results, the number of stroke sufferers in Indonesia reached 8.3% of 1000 people in Indonesia continues to increase every year, especially among the elderly. The research conducted aims to model the estimation of the type III censored Weibull distribution parameters with the Bayesian Linear Exponential Loss Function (LINEX) method. This study uses secondary data on stroke patients in the period January-November 2024 with a sample of 62 patients at the Haji Surabaya Regional General Hospital. Weibull distribution model with Bayesian approach using Linear Exponential Loss Function (LINEX) was applied to estimate the distribution parameters and survival function. The estimation results show that the parameter α is 6.32342 with an average hospitalization time of 5.9151646 days. MSE value is 0.000270555, which indicates that the estimation model is more accurate in predicting data for the length of hospitalization for stroke patients at the Haji Surabaya Regional General Hospital. The probability value of the survival function of stroke patients who have been hospitalized on the 5th day shows a probability of 82.4% so that no further hospitalization is needed, which indicates that the patient's health condition is improving. In addition, the hazard function analysis shows that the longer a patient is hospitalized, the greater the risk of the patient not recovering.
Modelling Factors Affecting the Middle Income Trap in Indonesia Using Generalized Additive Models (GAM) Amelia, Dita; Suliyanto, Suliyanto; Zhafira, Azizah Atsariyyah; Ramadhanti, Aulia; Suyono, Billy Christandy; Hizbullah, Firqa Aqila
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35119

Abstract

Indonesia is currently facing a significant challenge known as the Middle Income Trap (MIT), a condition where economic growth stagnates after reaching middle-income status, hindering progress toward becoming a high-income country. This study aims to identify and model the socio-economic factors influencing MIT at the provincial level in Indonesia during the 2020–2023 period. The Generalized Additive Model (GAM) is employed to estimate nonlinear relationships between predictors and the response variable while capturing complex patterns in panel data. GRDP per capita is used as an indicator of MIT, with six predictor variables: life expectancy, poverty rate, informal employment share, secondary education completion rate, food insecurity prevalence, and population density. The results showed that the best model was obtained based on the minimum GCV and AIC values of the Gaussian family with an identity link function and 5 knot points with the highest correlation of 99,9%. Five variables show nonlinear effects, while food insecurity exhibits a significant negative linear impact. The findings provide a valuable reference for designing inclusive and adaptive eco nomic policies based on each region’s socio-economic characteristics to mitigate MIT risks and also supports the achievement of Sustainable Development Goal (SDG) 8, which promotes decent work and sustained economic growth.
Spatial Analysis of Child Violence in West Java Using a Geographically Weighted Negative Binomial Regression Approach Suliyanto, Suliyanto; Amelia, Dita; Putri, Lisa Amanda; Anggakusuma, Aurellia Calista
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40390

Abstract

Kekerasan terhadap anak tetap menjadi isu kritis di Indonesia, dengan Jawa Barat secara konsisten melaporkan jumlah kasus yang tinggi. Studi ini meneliti faktor-faktor sosioekonomi yang memengaruhi jumlah kasus kekerasan terhadap anak di 27 kabupaten dan kota, dengan fokus pada tingkat kemiskinan, rata-rata tahun sekolah, tingkat perceraian, Tingkat Partisipasi Angkatan Kerja (PFPR), dan Tingkat Pengangguran Terbuka (OUR). Tes diagnostik mengidentifikasi heterogenitas spasial dan overdispersi, yang mendukung penggunaan model Regresi Binomial Negatif Berbobot Geografis (GWNBR). Model GWNBR mengungguli model Poisson dan Binomial Negatif global, yang ditunjukkan oleh nilai Akaike Information Criterion (AIC) terendah sebesar 193,23, yang menunjukkan kemampuannya untuk menangani data hitungan spasial yang overdispersi. Hasil penelitian mengungkapkan variasi spasial yang substansial dalam pengaruh faktor-faktor sosioekonomi. Rata-rata tahun sekolah dan tingkat perceraian signifikan di sebagian besar wilayah, sementara Kota Bandung adalah satu-satunya wilayah di mana kelima prediktor tersebut signifikan. Temuan ini menunjukkan struktur risiko yang bervariasi secara geografis yang tidak dapat ditangkap oleh model global. Studi ini menyoroti pentingnya pemodelan adaptif spasial dalam analisis sosial dan demografis serta menyarankan agar karakteristik spesifik wilayah dipertimbangkan dalam perumusan kebijakan. Temuan ini mendukung strategi perlindungan anak yang terarah dan selaras dengan SDG 3, SDG 4, dan SDG 16.
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%.
Co-Authors Abdillah, Adrian Wahyu Aditya Syarifudin Akbar Adma Novita Sari Aflaha, Nabila Shafa Agnes Happy Julianto Ain, Dzuria Hilma Qurotu Aini Divayanti Arrofah Alya Rahma Inneztiana Ameliatul 'Iffah Ana, Elly Andi Vania Ghalliyah Putrie Anggakusuma, Aurellia Calista Anida, Nuzulia Anisa Laila Azhar Annisa Putri Nayumi Aprilia Prastyaningrum Ardi Kurniawan Ardi Kurniawan Ariyawan, Jovansha Aulia Ramadhanti Aulia, Niswa Faizah Azzah Nazhifa Wina Ramadhani Bintang Alyaa Sabila Budijono, Gabriella Agnes Cynthia Anggelyn Siburian Davina Shafa Vanisa Deshinta Arrova Dewi Doni Muhammad Fauzi Dwitya, Shabrina Nareswari Dwiyanto, Adelia Sukma Dwiyanto, Adelia Sukma Elly Ana Elly Ana Elly Ana Elly Ana Elly Pusporani Faradilla Harianto Farah Fauziah Putri Firda Aulia Pratiwi Fortunata, Regina Ghasani, Anisah Nabilah Grace Lucyana Koesnadi Hizbullah, Firqa Aqila Humaira, Edla Putri Ismi, Ferissa Maulida Isna Nurul Izza Amalia Karina Rubita Makhbubah Karina Tri Handayani Kurniawan, Ardi Kusuma, Shalwa Oktavia M. Fariz Fadillah Mardianto M. Nabil Saputra Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Maria Setya Dewanti Marthabakti, CitraWani Mochammad Baihaqi Muhammad Fikry Al Farizi 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 Nahar, Muhammad Hafidzuddin Nur Chamidah Nurdin, Nabila Nurrohmah, Zidni ‘Ilmatun 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, Lisa Amanda Putri, Refa Berliana Putu Eka Andriani Rahmada, Indrastanto Oktodian Rahmanita, Tentri Ryan Ramadhan, Achmad Wahyu Ramadhani, Azzah Nazhifa Wina Ramadhani, Maulana Syah Putra Ramadhanti, Aulia Rani, Lina Nugraha Rohayah, Dewi Sanda Insania Dewanty Sediono, Sediono Siagian, Kimberly Maserati Simamora, Antonio Nikolas Manuel Bonar Siregar, Naufal Ramadhan Al Akhwal Sofia Andika Nur Fajrina Suliyanto Suliyanto Suliyanto Suliyanto, Suliyanto Suryono, Alda Fuadiyah Suyono, Billy Christandy Tagawa, Dustin Nathanael Toha Saifudin Toha Saifudin Tsabita Amalia Shofa, Nayla Victoria Anggia Alexandra Wibawa, Yoga Setya Wieldyanisa, Ezha Easyfa Wulandari, Indana Zulfa Yosifa, Adelia Frielady Yuliati, Intan Zah, Alfian Iqbal Zahrani, Vista Vanadya Zhafira, Azizah Atsariyyah Zhafirab, Azizah Atsariyyah