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All Journal Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Seroan, Miracle
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Earth & Planetary Sciences Mathematics

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Pemodelan Matematika untuk Perkiraan Jumlah Penduduk Kabupaten Minahasa pada Tahun 2026 dan 2027 Menggunakan Model Logistik Seroan, Miracle; Annisa Husin; Windy Tunas; Gracela Songkilawang; Arbi Maarial; Fitri Damogalad
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 2 No. 4 (2024): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v2i4.100

Abstract

This study aims to estimate the population in Minahasa Regency at t=7 or t=8 in 2026-2027 using a logistic population model. Based on data obtained from the Minahasa Regency Central Statistics Agency (BPS) from 2019 to 2022, it can be assumed that the capacity limit (K) = 400,000. The results of this research show that the accurate model to use in determining the estimated population of Minahasa Regency is the model with k = 0.0588644467, and the relative growth rate is 5.88% per year. Then, using the logistic method, the general form of the solution is obtained, namely P(t )=400,000/(e^(-0.0588644467t) (0.16921458)+1). Furthermore, the results obtained show that the predicted population of Minahasa Regency in 2026-2027 is 359,690 and 361,774 people.
Forecasting new student enrollment numbers using simple linear regression Seroan, Miracle; Sulistyaningsih, Murni; Pitoy, Cori
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 8 No. 3 (2025): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v8i3.3376

Abstract

This study aims to forecast the number of new students at Manado State University using historical enrollment data from the past five years (2020–2024). A simple linear regression model was developed based on this data and subsequently used to predict new student admissions for the next five academic years, from 2025/2026 to 2029/2030. The results indicate that the simple linear regression method is sufficiently effective for predicting new student enrollment, despite certain limitations inherent in linear modeling. Model performance was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). At the university level, the forecasting results yielded a MAPE value of 5.7% and an MSE of 31,018.64, indicating high predictive accuracy. At the faculty level, MAPE and MSE values varied, with the Faculty of Languages and Arts (3.92% and 574), Faculty of Engineering (5.55% and 4,512.4), Faculty of Economics and Business (7.23% and 11,104.3), Faculty of Mathematics, Natural Sciences, and Earth Sciences (7.32% and 1,421.1), Faculty of Social and Legal Sciences (8.52% and 9,619.2), Faculty of Sports Science and Public Health (18.84% and 29,529.2), and Faculty of Education and Psychology (23.08% and 74,977.2). These findings suggest that simple linear regression can serve as a practical and data-driven tool to support strategic planning and decision-making in new student admissions at Manado State University.
Co-Authors Annisa Husin Arbi Maarial Fitri Damogalad Gracela Songkilawang Pitoy, Cori Windy Tunas