FORECASTING ANALYSIS OF NEW STUDENTS ACCEPTANCE USING TIME SERIES FORECASTING METHOD

FORECASTING ANALYSIS OF NEW STUDENTS ACCEPTANCE USING TIME SERIES FORECASTING METHOD

• By S, Suhardi Suhardi Suhardi Suhardi;
• Jurnal Nasional Tidak Terakreditasi
• 323
• 10-23

• New students admission (PMB) is a routine activities for education institution. Therefore, it was necessary for the institution to be aware of the mechanism of PMB to produce competent and qualified prospective students. Forecasting of PMB needed to predict the number of students and to prepare the institution infrastucture facilities to welcome the new freshmen. The research discussed the determination of forecasting number of Universitas Bina Sarana Informatika PSDKU Karawang students by using time series forecasting with three different method: moving everage, exponential smoothing and trend analysis. It also compare Mean Absolute Presentage Error (MAPE) calculated by QM for Windows. The result of the research showed that trend analysis was the most effective method which had 41.84 MAD, 2324.28 MAD, 4.99 % of MAPE and 557 students for next forecasting. The conclussion from data analysis processing showed that Universitas Bina Sarana Informatika should provide all of infrastructure facilities for the teaching and learning process with the number of 557 students in 2020.

• Katakunci Demand forecasting, moving average, exponential smoothing, trend analysis
• URL http://akrabjuara.com/index.php/akrabjuara/article/view/846/744
• Penerbit Jurnal Akrab Juara, Pekanbaru Riau

REFERENSI

Aqsa, M. A., & Fitrasyah, H. 2017. Pengaruh Kompetensi Terhadap Kinerja Karyawan Pada Pt. Silver Silk Tour And Travel Pekanbaru. Eko Dan Bisnis: Riau Economic and Business Review, 8(1), 97–106. 

 

Basri, H., Eko Indrajit, R., Studi Magister Ilmu Komputer, P., & Nusa Mandiri Jakarta, S. 2017. Implementasi Information Retrivals Untuk Meningkatkan Pemasaran Produk. Jurnal Pilar Nusa Mandiri, 13(2), 249–254. 

 

Bee, D., Weku, W. C. D., & Rindengan, A. J. (n.d.). Aplikasi Penentuan Tingkat Kesegaran Ikan Selar Berbasis Citra Digital Dengan Metode Kuadrat Terkecil Application Of Mackerel Freshness Level Determination Based On Digital Image With Least Square Method. 

Hani’ah, U. 2015. Implementasi Adaptive Neuro-Fuzzy Inference System (Anfis) untuk Peramalan Pemakaian Air di Perusahaan Daerah Air Minum Tirta Moedal Semarang.  

Heizer, J., & Render, B. 2009. Manajemen Operasi Buku 1. Edisi. 

 

Herawati, T. 2014. Pengaruh Sistem Pengendalian Intern Terhadap Kualitas Laporan Keuangan (Survei Pada Organisasi Perangkat Daerah Pemda Cianjur). STAR–Study & Accounting Research, 11(1), 1–14. 

 

Iwan, Iviq, Eneng Rahayu, H., & Yulianto, A. 2018. Analisa Peramalan Permintaan Mobil Mitsubishi Xpander dengan Tiga Metode Forecasting. Cakrawala, 18(2). Retrieved from doi: https://doi.org/10.31294/jc.v18i2 

 

Sarjono, H. 2012. Prediksi Jumlah Penerimaan Siswa SMK Swasta Tahun Ajaran 2011/2012. Binus Business Review, 3(2), 923–933. 

 

Sugandi, A. I., & Bernard, M. 2018. Penerapan Pendekatan Kontekstual Terhadap Kemampuan Pemahaman Dan Komunikasi Matematis Siswa Smp. Jurnal Analisa, 4(1), 172–178. https://doi.org/10.15575/ja.v4i1.2364 

 

Suhardi. 2019. Pengaruh Gaya Hidup , Demografi Dan Kualitas Produk Terhadap Keputusan Pembelian Konsumen Alfamart Sukaseuri Cikampek. Perspektif, 17(1), 96–103. 

 

 

 

 

 

 

 

 

 

Suparti. 2018. ANALISIS KEBUTUHAN PENGEMBANGAN MEDIA AUDIO BAHASA INGGRIS UNTUK PEMBELAJARAN MENYIMAK A Needs Analysis on Developing English Audio Media for Listening Skills. 6(1), 1–22. Retrieved from https://jurnal kwangsan. kemdikbud.go.id/index.php/jurnalkwang san/article/view/47 

Supriyatno, A. 2018. The Effect Of Corporate Social Responsibility On Word Of Mouth With Trust And Corporate Reputation As Intervening Variable. Jurnal Manajemen Bisnis, 9(2), 161– 174. https://doi.org/10.18196/mb.9263 

Tarnoto, N. 2017. PermasalahanPermasalahan Yang Dihadapi Sekolah Penyelenggara Pendidikan Inklusi Pada Tingkat Sd. Humanitas, 13(1), 50. https://doi.org/10.26555/humanitas.v13i 1.3843 

Tetlock, B. P., & Gardner, D. 2016. Superforecasting : The Art and Science of Prediction . 2–6. https://doi.org/10.3390/risks4030024 

Yuwono, B., & Istiani, N. P. 2007. Panduan Menggunakan POM for Windows. Yogyakarta: UPN Veteran.