Using the Arima Method with Minitab Applications for Forecasting Work Order Projects of Casting Construction (Case Study: PT. Bumi Sarana Beton)

Authors

  • Fatmawaty Rachim Universitas Fajar
  • Sudirman Sudirman Universitas Fajar
  • Ritnawati Ritnawati Universitas Fajar
  • Erdawaty Erdawaty Universitas Fajar
  • Fitriah Fitriah Universitas Fajar

DOI:

https://doi.org/10.30736/cvl.v8i2.1092

Keywords:

Arimamethode, Application, Homogeneous, Minitab, Production of K-225, Production of K-400

Abstract

The ARIMA method is a non-stationary homogeneous time series model that uses the procedure for applying the Autoregressive model or scheme and the Moving Average in preparing its forecasts. The purpose of this study was to determine the application of the Autoregressive Integrated Moving Average (ARIMA) method and minitab application at PT. Bumi Sarana Beton in planning the estimated number of work orders. It can be concluded that for K-225 the best model is the ARIMA model (2,0,0) because it has the lowest MSE value and for K-400 the best model to use is the ARIMA model (2,0,2 ) because it has the smallest value. From the results of the research that has been done, it can be concluded that the production of K-225 in 2022 is 3573.50 m3, while using the ARIMA method in 2023 the total production is 3920.61 m3 and in 2024 the total production is 3573.50 m3. to 3824.36 m3. Production of K-400 in 2022 was 2015.00 m3, while using the ARIMA method in 2023 a total production of 1857.07 m3 was obtained and in 2024 a total production of 2045.89 m3 was obtained.

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References

H. Hartati, “Penggunaan Metode Arima Dalam Meramal Pergerakan Inflasi,” J. Mat. Sains Dan Teknol., Vol. 18, No. 1, Pp. 1–10, 2017, Doi: 10.33830/Jmst.V18i1.163.2017.

Sutarti, “Penggunaan Metode Analisis Runtun Waktu Dengan Bantuan Minitab 11 For Window Untuk Forecasting Produksi Textil Pada Pt. Primatexco Indonesia Kabupaten Batang Tahun 2009,” P. 56, 2009, [Online]. Available: Https://Lib.Unnes.Ac.Id/1280/1/4850.Pdf.

E. M. Tumanggor, “Analisa Dan Implementasi Data Mining Untuk Memprediksi Jumlah Material Bangunan Menggunakan Algoritma Autoreggresive Intergrated Moving Average (Arima),” Tin Terap. Inform. Nusant., Vol. 2, No. 6, Pp. 373–377, 2021.

H. A. Hidayah, R. F. Mu’affifah, And U. Chotijah, “Estimasi Jumlah Work Order Project Konstruksi Menggunakan Metode Arima (Autoregressive Integrated Moving Average),” J. Inform. Univ. Pamulang, Vol. 4, No. 3, P. 79, 2019, Doi: 10.32493/Informatika.V4i3.3169.

I. Teknologi Bandung Oleh Wafa Fatimah Rastiadi And P. Studi Fisika, “Karya Tulis Sebagai Salah Satu Syarat Untuk Memperoleh Gelar Sarjana Dari,” Vol. 33214011, No. November 2018, 2019.

R. J. Santoso, “Penggunaan Metode Arima Dengan Aplikasi Minitab Untuk Peramalan Data Pendapatan Perusahaan Pecah Batu Putra Mandiri,” 2020.

P. Minggu, “Minitab Menggunakan Pendekatan Arima,” No. 40, Pp. 13–19.

Mawardi, “Pembuatan Jadwal Pelaksanaan Untuk Simultan Dengan Memperhatikan Ketersediaan Sumber Daya Konstruksi,” Pp. 19–24.

N. E. Maitimu And A. Tonapa, “Analisis Perancangan Bahan Baku Berbasis Listrik Berdasarkan Metode Material Requirement Planning (Mrp) Pada Pt. Pln (Persero) Pusat Listrik Masohi,” Arika, Vol. 13, No. 1, Pp. 1–16, 2019, Doi: 10.30598/Arika.2019.13.1.1.

A. T. Abidin, “Penerapan Akuntansi Pertanggungjawaban Pada Pt Bumi Sarana Beton Makassar (Proyek Hadji Kalla Pare-Pare),” 2017.

Fatmawaty Rachim, St., Mt. (2022). Manajemen Proyek. Isbn 978-623-97118-2-5. Cetakan Pertama Maret 2022.

Box, G. E. P., & G. M. Jenkins. (1976). + Time series analysis forecasting and control. Holden-Day. Sa Fransisco.

Makridakis, S., Wheelwright, S.C., & McGee, V.E. (2002). Metode aplikasi dan peramalan. Jakarta. Binarupa Aksara Publisher.

Pimpi, La. (2013). Penerapan metode ARIMA dalam meramalkan indeks harga konsumen (IHK) Indonesia Tahun 2013. Jurnal paradigma, Vol. 17; Hal. 35.

Ruslan, R., Agus Salim Harahap., & Pasukat Sembiring. (2013). Peramalan nilai ekspor di propinsi Sumatera Utara dengan metode ARIMA Box Jenkins. Jurnal saintia matematika, Vol. 1; Hal 579.

Saluza, I. (2015). Aplikasi metode jaringan syaraf tiruan backpropagation dalam meramal tingkat inflasi di Indonesia. Jurnal gradien, Vol. 11; hal.1075. Universitas Bengkulu.

Slutsky, E.E. (1937). The summation of random causes as the source of clclical processes. Econometrica, 5; 46-105.

Stehpani, C. A, Agus Suharsono, & Suhartono. (2015). Peramalan inflasi nasional berdasarkan faktor ekonomi makro menggunakan pendekatan time series klasik dan ANFIS”. Jurnal sains dan seni, Vol. 4; hal. D-67.

Wold. H. (1938). A study in the, analysis of stationary time series, 1st ed. Uppsala: Almqvist and Wiksells.

Wulandari, N., Setiawan., & Imam Safawi Ahmad. (2016). Peramalan inflasi kota surabaya dengan pendekatan ARIMA, variasi kalender, dan intervensi. Jurnal sains dan seni, Vol. 5; Hal. D-90.

Yule, G. U. (1927). On a method of investigating periodicities in disturbed series, with special reference to wolfer’s sunspot numbers. Philosopical transactions of the royal society A: Mathematical, phisycal, and engineering science, 226(636-646); 226-267.

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Published

2023-09-12

How to Cite

Rachim, F., Sudirman, S., Ritnawati, R., Erdawaty, E., & Fitriah, F. (2023). Using the Arima Method with Minitab Applications for Forecasting Work Order Projects of Casting Construction (Case Study: PT. Bumi Sarana Beton). Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan, 8(2), 121–136. https://doi.org/10.30736/cvl.v8i2.1092

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Jurnal CIVILA