Arsyelina Husni Johan (1), Edy Waluyo (2)
This study aims to determine the predicted demand for office stationery (ATK) for the upcoming year. The availability of sufficient and appropriate ATK reflects good governance within an organization. To achieve this, accurate and data-driven predictions of ATK needs are essential in the budget planning process. In this study, the method used to predict ATK demand is Second-Order Polynomial Regression. The accuracy of this method is assessed using the Mean Absolute Percentage Error (MAPE). The lower the MAPE value, the better the prediction accuracy. The results of the study indicate that the Polynomial Regression method provides reasonably accurate predictions, with an average MAPE of 22.41%.
Herlina Dini Damayanti, Dani Sasmoko, & Andik Prakasa Hadi. (2024). INFORMATION SYSTEM INVENTORY OF OFFICE WRITING EQUIPMENT AT PT LION SUPER INDO MAJAPAHIT SEMARANG BRANCH. Journal of Engineering, Electrical and Informatics, 3(2), 55–67. https://doi.org/10.55606/jeei.v3i2.2866
Maskikit, C. (2021). IMPLEMENTASI PENGADAAN BARANG DAN JASA ALAT TULIS KANTOR PADA BAGIAN UMUM SETDA KABUPATEN MERAUKE. Madani Jurnal Politik Dan Sosial Kemasyarakatan, 13(03), 205–224. https://doi.org/10.52166/madani.v13i03.2798
Merici, A., & Saprudin, U. (2024). PERAMALAN PERSEDIAAN BARANG MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE DI CV. MULTIPAPER STATIONERY. Jurnal Indonesia?: Manajemen Informatika Dan Komunikasi, 5(2), 1685–1694. https://doi.org/10.35870/jimik.v5i2.742
Ordila, R. (2020). EFFICIENCY OF STMIK HANG TUAH PEKANBARU STATIONERY INVENTORY OFFICE USING MONTE CARLO METHOD. Journal of Applied Engineering and Technological Science (JAETS), 1(2), 77–84. https://doi.org/10.37385/jaets.v1i2.63
Prawita, R., Sumijan, S., & Nurcahyo, G. W. (2020). SIMULASI METODE MONTE CARLO DALAM MENJAGA PERSEDIAAN ALAT TULIS KANTOR (STUDI KASUS DI IAIN BATUSANGKAR). Jurnal Informatika Ekonomi Bisnis. https://doi.org/10.37034/infeb.v3i2.69
Schwartz, Z., Ma, J., & Webb, T. (2024). THE MSAPEMER: A SYMMETRIC, SCALE-FREE AND INTUITIVE FORECASTING ERROR MEASURE FOR HOSPITALITY REVENUE MANAGEMENT. International Journal of Contemporary Hospitality Management, 36(6), 2035–2048. https://doi.org/10.1108/IJCHM-01-2023-0088
Sedera, D., & Atapattu, M. (2019). POLYNOMIAL REGRESSION AND RESPONSE SURFACE METHODOLOGY: THEORETICAL NON-LINEARITY, TUTORIAL AND APPLICATIONS FOR INFORMATION SYSTEMS RESEARCH. Australasian Journal of Information Systems, 23, 1–35. https://doi.org/10.3127/ajis.v23i0.1966
Simangunsong, A. (2023). PENERAPAN METODE MONTE CARLO DALAM SIMULASI PENGELOLAAN PERSEDIAAN ALAT TULIS KANTOR. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 22(2), 280. https://doi.org/10.53513/jis.v22i2.8718
Sopia, A., & Syahrizal, M. (2020). APPLICATION OF DATA MINING TO PREDICT PROCUREMENT OF OFFICE WRITING IN AL-IKHWAN MIDDLE SCHOOL USING NAÏVE BAYES METHOD. The IJICS (International Journal of Informatics and Computer Science), 4(2), 39. https://doi.org/10.30865/ijics.v4i2.2119
Steposhina, S. (2024). IMPROVING THE ACCURACY OF REGRESSION MODELS BY METHODS OF POLYNOMIAL APPROXIMATION. Transport Engineering, 2024(4), 4–12. https://doi.org/10.30987/2782-5957-2024-4-4-12
Taufik Hidayat, M., Suarna, N., & Rahaningsih, N. (2023). IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PERSEDIAAN BARANG PT. DILMONI CITRA MEBEL INDONESIA. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 693–699. https://doi.org/10.36040/jati.v7i1.6310