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All Journal Jurnal Sains Matematika dan Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Variance : Journal of Statistics and Its Applications Jurnal Varian Inferensi Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Journal of Mathematics Education and Science Journal of Scientific Research, Education, and Technology Jurnal Statistika dan Komputasi (STATKOM) Eksponensial Economic Development Analysis Journal PARADIGM : JOURNAL OF MULTIDISCIPLINARY RESEARCH AND INNOVATION Jurnal Keselamatan Transportasi Jalan
Elly Ana
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Analyzing Gender Equality Indicators Using Principal Component Analysis in Central Papua and Papua Highland Shafira Renianti, Fayza; Suliyanto; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.972

Abstract

The Gender equality is one of the key targets in Sustainable Development Goal (SDG) 5 and remains a major challenge in Central Papua and Papua Highland, where gender development indicators are among the lowest in Indonesia. This study aims to identify the dominant factors influencing gender equality in these two provinces using Principal Component Analysis (PCA) on seven indicators representing education, health, economic conditions, and political representation of women. The analysis results show that two main factors are formed with a total variance explained of 77.248%. The first factor reflects women’s economic participation and basic living conditions, while the second factor represents resource capacity and socio-political representation. These findings suggest that limited access to education, health services, and participation in the labor market and political institutions are the primary contributors to gender inequality in this region. Therefore, empowerment-oriented policies and improved service accessibility are required to achieve more equitable gender development in Papua.
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.
PREDICTION OF STUDENT LEARNING MASTERY IN INFORMATICS USING A MACHINE LEARNING APPROACH Tianda, Izhar Muhammad; Ana, Elly
PARADIGM : Journal Of Multidisciplinary Research and Innovation Vol 3 No 02 (2025): PARADIGM : Journal Of Multidisciplinary Research and Innovation
Publisher : Pusat Studi Ekonomi Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/paradigm.v3i02.1994

Abstract

This study aims to develop a robust machine learning framework for predicting student learning mastery in Informatics subjects. The research employs a supervised learning approach using assessment-based features derived from student academic records. Due to limitations commonly found in real educational data, including imbalance and data leakage risks, synthetic data generation and feature engineering were applied to support controlled experimentation. Several classification models were implemented to evaluate the stability and consistency of the proposed framework. The results indicate that the models were able to consistently distinguish between students who achieved learning mastery and those who did not. The comparable performance across different modeling approaches suggests that the predictive capability is driven by the methodological design rather than dependence on a specific algorithm. This study demonstrates that machine learning can provide a reliable and interpretable tool to support data-driven evaluation and early intervention in Informatics education.
Co-Authors Abdillah, Adrian Wahyu Adma Novita Sari Aflaha, Nabila Shafa Agnes Happy Julianto Ain, Dzuria Hilma Qurotu Aldawiyah, Najwa Khoir Amalia, Nadinta Kasih Ameliatul 'Iffah Anggriawan, Muhammad Rizal Ardi Kurniawan Ariyawan, Jovansha Astuti, Aprillia Aulia, Niswa Faizah Budijono, Gabriella Agnes Darmawan, Kezia Eunike Davina Shafa Vanisa Dewanty, Sanda Insania Dewi, Berlianti Alisa Dita Amelia Dita Amelia Dita Amelia, Dita Dwiyanto, Adelia Sukma Fauzi, Doni Muhammad Fitri, Marfa Audilla Fitriyani, Mubadi’ul Fortunata, Regina Ghasani, Anisah Nabilah Gunawan, Syifa' Azizah Putri Hardiansyah, Fernanda Rizky Humaira, Edla Putri Inneztiana, Alya Rahma Khairian, Farhan Aldan Kurnia, Rizky Dwi Kusuma, Shalwa Oktavia M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Marpaung, Josua Ronaldo Davico Marthabakti, CitraWani Mochammad Baihaqi Muhammad Rosyid Ridho Az Zuhro Nitasari, Alfi Nur Nugroho, Hariawan Widi Nurdin, Nabila Pambudi, Daffa Satrio Pratama, Fachriza Yosa Putra, Mochamad Rasyid Aditya Putri, Farah Fauziah Rahmada, Indrastanto Oktodian Rahmanita, Tentri Ryan Ramadhan, Achmad Wahyu Ramadhani, Maulana Syah Putra Ramadhanty, Devira Thania Salsabila, Fatiha Nadia Sari, Ni Wayan Widya Septia Sediono, Sediono Shafira Renianti, Fayza Siagian, Kimberly Maserati Suliyanto Suliyanto Suliyanto Suliyanto Suryono, Alda Fuadiyah Syaugi Sungkar, Salman Tagawa, Dustin Nathanael Tianda, Izhar Muhammad Toha Saifudin Toha Saifudin Vanisa, Davina Shafa Wibawa, Yoga Setya Wulandari, Indana Zulfa Zahrani, Vista Vanadya Zhafirab, Azizah Atsariyyah