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All Journal Saintika Medika: Jurnal Ilmu Kesehatan dan Kedokteran Keluarga. Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Official Statistics
Efri Diah Utami
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Medicine & Pharmacology Public Health Social Sciences

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Influence Of Health Behaviour Factors On Basic Immunisation In Indonesia 2022 Syifa Faikhatul Ilmi; Efri Diah Utami
Saintika Medika : Jurnal Ilmu Kesehatan dan Kedokteran Keluarga Vol. 20 No. 2 (2024): December 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/sm.Vol20.SMUMM2.35004

Abstract

Background: The coverage of complete basic immunisation in Indonesia from 2020 to 2022 never reached the target of the 2020-2024 National Medium-Term Development Plan despite various efforts made by the government. As a result, there are many cases of infectious diseases in children. Therefore, this study aims to determine the overview of the coverage of complete basic immunisation in Indonesia in 2022 and the effect of antenatal care visits, doctor ratio, and ownership of immunisation documents on the coverage of complete basic immunisation in Indonesia in 2022. Methods: This study uses data from the Central Bureau of Statistics publication and the Ministry of Health publication. The analysis method used in this study is multiple linear regression analysis. Results: Antenatal care visits and the ratio of doctors have a positive and insignificant effect on the coverage of complete basic immunisation in Indonesia in 2022 while ownership of immunisation documents has a significant positive effect. Conclusion: The variable of ownership of immunisation documents is the most influential variable on the coverage of complete basic immunisation. This variable will increase the coverage of complete basic immunisation by 1.377 percent every 1 percent increase in the percentage of ownership of immunisation documents.
Model Klasifikasi Multilabel pada Publikasi Penelitian SDG dengan Pendekatan Multilevel dan Hierarki Berliana Sugiarti Putri; Lya Hulliyyatus Suadaa; Efri Diah Utami
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.16265

Abstract

The growing number of research publications complicates the identification of the implementation of research publications, especially related to sustainable development goals (SDGs). The research publication categorization into SDG levels has not been conducted. The Center for Research and Community Service (Pusat Penelitian dan Pengabdian Masyarakat, PPPM) Politeknik Statistika (Polstat) STIS needs this to monitor lecturers in implementing SDGs. This study aimed to implement and evaluate problem transformation methods and machine learning classification algorithms with a multilevel and hierarchical approach to categorize research publications into SDG levels. Problem transformation methods used were binary relevance, label powerset (LP), and classifier chains. Machine learning classification algorithms used were logistic regression (LR) and support vector machine (SVM). The inputs included titles, abstracts, and titles and abstracts. The best filter model that classified data into SDGs-non-SDGs was the model with titles and SVM, with an accuracy of 0.8634. The best level model for classifying data to SDG level was the model using titles, LP, and SVM with multilevel approaches. The level model classified data into four pillars, goals, targets, and indicators of SDGs, with an accuracy of 0.8067, 0.7501, 0.6792, and 0.6194, respectively. In comparison to other inputs with more comprehensive information, the results showed that title inputs yielded the best accuracy due to the simultaneous use of English and Indonesian. Future research can modify the model to utilize a single language input to optimize the term frequency-inverse document frequency (TF-IDF) process, hence, the word meanings from each language are not considered different important words.
Faktor-Faktor yang Memengaruhi Overeducation Mismatch Tenaga Kerja Berpendidikan Tinggi di DKI Jakarta Tahun 2025 Nilam Cahya Meilani; Efri Diah Utami
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2756

Abstract

The demographic bonus has increased the proportion of the working-age population in Indonesia, including college graduates. However, this increase has not been matched by the labor market's ability to provide suitable jobs, giving rise to the phenomenon of overeducation mismatch. Java, as the center of the national economy, has a relatively high percentage of overeducation mismatch among highly educated workers in each province, with the highest percentage in the DKI Jakarta region. This study aims to determine the picture of overeducation mismatch and the factors that influence it in the highly educated workforce in DKI Jakarta in 2025, as well as its tendency value using Sakernas 2025 data. The study used a binary logistic regression analysis method and determined overeducation mismatch with a normative approach based on the Indonesian Standard Occupational Classification (KBJI). This study found that age, working hours, employment contract ownership, education level, education department, training experience, and length of service significantly influence overeducation mismatch.
Co-Authors Afifah Siti Muslikhah Annisa Salsabila Aulia Berliana Sugiarti Putri Bertha Sani Lake Daniel M V Mone Dwi Ardian Firdaus Firdaus Khairunissa Balqis Zhahira Miko Oktavio Wijaya Nilam Cahya Meilani Nurul Dwi Afifah Retnaningsih Retnaningsih Suadaa, Lya Hulliyyatus Sukim Sukim Syifa Faikhatul Ilmi Tantri Retno Anasthasia Viki Tria Zianrini