Aulia, Niswa Faizah
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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.
Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method Suliyanto, Suliyanto; Saifudin, Toha; Naura, Sheila Sevira Asteriska; Dewanty, Sanda Insania; Wulandari, Indana Zulfa; Aflaha, Nabila Shafa; Aulia, Niswa Faizah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28392

Abstract

Hypertension is one of the important public health problems in Indonesia, which contributes to the high prevalence of non-communicable diseases. This study aims to model the prevalence of hypertension in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method to identify significant predictors and their interactions. The data used was secondary data from the 2023 Indonesian Health Survey, including variables such as smoking prevalence, physical inactivity, dietary habits (consumption of fatty and sweet foods), lack of fruit and vegetable consumption, and obesity prevalence. The MARS method was used to analyse the nonlinear relationships and interactions between these predictors. After a trial-and-error process to determine the optimal number of basis functions (BF), maximum interactions (MI), and minimum observations (MO), the best model was achieved with BF = 18, MI = 3, and MO = 1. This model produced a Generalised Cross Validation (GCV) value of 13.428 and R-Square of 0.278. This fairly low R-Square value indicates that the factors analysed have contributed to the variation in hypertension prevalence, but there are still other aspects that can be taken into account to improve the predictive power of the model. The significant predictor variables were consumption of fatty foods (X3), lack of physical activity (X2), and consumption of sweets (X4), with the highest importance on X3 (100%). The findings reveal that interactions between variables, such as dietary habits and physical inactivity, significantly influence the prevalence of hypertension. For example, higher consumption of fatty and sweet foods combined with low physical activity increases the risk of hypertension. These results demonstrate the effectiveness of the MARS method in capturing complex and nonlinear relationships and serve as findings that highlight the need for health policies that focus on healthy diets and increased physical activity, in line with Goal 3 of the SDGs, “Good Health and Well-Being,” which aims to reduce premature mortality from noncommunicable diseases. Recommended interventions include nutrition education campaigns and community-based exercise programs to reduce the prevalence of hypertension in Indonesia.