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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal
Nurdin, Nabila
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Penentuan Kategori Kelulusan Mahasiswa Menggunakan Metode Analisis Diskriminan Nurdin, Nabila; Aulia, Niswa Faizah; Ramadhani, Maulana Syah Putra; Marbun, Barnabas Anthony Philbert; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
Zeta - Math Journal Vol 9 No 1 (2024): Mei
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2024.9.1.31-40

Abstract

Kelulusan tepat waktu merupakan salah satu cara menilai kualitas sebuah perguruan tinggi. Artikel ini membahas penerapan analisis diskriminan dalam menentukan kelulusan mahasiswa dengan mempertimbangkan lima indikator utama, yaitu Indeks Prestasi Kumulatif (IPK), Pelatihan Pengembangan Diri (PPD), Prestasi, Kegiatan Organisasi (KO), dan Forum Komunikasi Kampus (FKK). Dengan menganalisis data historis, penelitian ini bertujuan untuk mengidentifikasi kontribusi dari masing-masing indikator terhadap kecepatan lulus mahasiswa. Hasil analisis menunjukkan bahwa ada perbedaan signifikan antara kelompok responden yang lulus cepat dan tidak lulus cepat dengan indikator IPK sebagai variabel paling efisien dalam membedakan kedua kelompok tersebut. Fungsi diskriminan yang dihasilkan mempunyai nilai ketepatan klasifikasi sebesar 73,3%, sehingga dapat digunakan untuk pengklasifikasian kategori kelulusan. Hasil analisis dapat memberikan wawasan mendalam tentang peran dari setiap indikator, membantu mahasiswa agar lebih fokus dalam meningkatkan IPK, dan memungkinkan institusi pendidikan untuk mengembangkan strategi yang lebih efektif dalam mendukung mahasiswa menuju kelulusan. Pendekatan analisis diskriminan pada lima indikator ini membuka pintu bagi perbaikan kontinu dalam sistem evaluasi kelulusan, menciptakan landasan bagi kebijakan pendidikan yang berorientasi pada hasil dan memberikan dampak positif pada kualitas Pendidikan.
ANALYSIS OF FACTORS AFFECTING PNEUMONIA IN INDONESIAN TODDLERS USING NONPARAMETRIC REGRESSION WITH LEAST SQUARE SPLINE AND FOURIER SERIES METHODS Saifudin, Toha; Suliyanto, Suliyanto; Nurdin, Nabila; Christiano Ginzel, Bryan Given; Oktavia, Sabrina Salsa; Ariyawan, Jovansha; Ubadah, Mohammad Noufal
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/barekengvol20iss1pp0087-0104

Abstract

Pneumonia is the leading cause of death among children under five, with the highest prevalence in Indonesia found in West Papua Province (75%) and the lowest in North Sulawesi (0.3%). This study aims to analyze the factors influencing the prevalence of pneumonia in Indonesian toddlers using nonparametric regression approach by comparing Least Square Spline (LS-Spline) and Fourier Series. Data sourced from the Indonesian Ministry of Health website, consisting of 34 provinces in Indonesia in 2023, with one response variable (Y) and five predictor variables (X). The analyzed factors include the coverage of vitamin A supplementation, malnutrition rates, low birth weight prevalence, measles immunization coverage, and exclusive breastfeeding rates. The analysis was conducted by modeling with nonparametric Least Square Spline regression using up to three optimal knot points, then performing analysis using nonparametric regression with the Fourier series approach. The two methods were compared based on GCV and R², with the best model having lower GCV and higher R². The results showed that LS-Spline was better than Fourier Series, with a GCV value of 233.16 and a coefficient of determination of 92.5%. The findings reveal that the relationships between predictor factors and pneumonia prevalence are nonlinear, with varying influence patterns across different variable ranges. These results indicate that LS-Spline has a strong ability to explain data variability. The Fourier series is limited in this study because it is best suited for periodic data, unlike pneumonia data and its causal factors which do not show such patterns. The weakness of the Fourier Series in this study lies in its suitability for periodic data, while pneumonia cases and their causal factors do not follow such patterns. This study offers insights into health policy making to reduce pneumonia cases, improve their lives, in line with the SDGs target on Good Health and Well-being.
Co-Authors Ariyawan, Jovansha Aulia, Niswa Faizah Christiano Ginzel, Bryan Given Dita Amelia Elly Ana M. Fariz Fadillah Mardianto Marbun, Barnabas Anthony Philbert Oktavia, Sabrina Salsa Ramadhani, Maulana Syah Putra Suliyanto Suliyanto Toha Saifudin Ubadah, Mohammad Noufal