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All Journal Jurnal Industri dan Inovasi (INVASI) Industrial & System Engineering Journals (ISEJOU)
Okpri Meila
Universitas Syiah Kuala
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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K-Means Klastering Tanaman Biofarma Zingiber officinale Indonesia Tahun 2023 Ade Aisyah Arifna Putri; Nur Irhamni Sabrina; Okpri Meila
Industrial & System Engineering Journals (ISEJOU) Vol. 4 No. 1 (2025): ISEJOU, Vol 4, No 1 Desember 2025
Publisher : Universitas Katolik Darma Cendika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37477/isejou.v4i1.728

Abstract

Indonesia merupakan negara yang banyak memproduksi tanaman biofarma, Biofarma adalah tanaman yang bermanfaat untuk mencegah penyakit dan banyak di gunakan pada industri farmasi. Salah satu tanaman biofarma adalah Zingiber officinale atau dikenal dengan jahe yang banyak tumbuh di seluruh provinsi di Indonesia. Penelitian ini berfokus dalam melakukan pengelompokan penghasil tanaman biofarmaka dengan menggunakan teknik k-means klastering dengan menggunakan 2 variabel yaitu jumlah panen dan luas lahan dengan menggunakan 4 klaster untuk 38 provinsi di Indonesia. Hasilnya menunjukkan bahwa bahwa terdapat 9 provinsi pada klaster 1, 3 provinsi pada klaster 2, 2 provinsi pada klaster 3 dan 24 provinsi pada klaster 4. Klaster 1 yang berjumlah 3 provinsi adalah penghasil jahe yang paling banyak di Indonesia dan memiliki luas lahan yang paling besar dibandingkan dengan provinsi lainnya.
Analisis Peramalan Permintaan Omeprazole Injeksi di Rumah Sakit XYZ Nur Irhamni Sabrina; Okpri Meila; Dhea Nur Fadhilah; Syaubari Syaubari
Jurnal Industri dan Inovasi (INVASI) Vol 3, No 1 (2025): Vol 3, No 1 (September 2025)
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/invasi.v3i1.14613

Abstract

Inaccurate drug inventory planning can lead to stock shortages or excess inventory, which negatively affects the efficiency of hospital services. One drug with highly fluctuating demand is Omeprazole injection 40 mg at XYZ Hospital. This study aims to forecast the demand for Omeprazole injection 40 mg using time series forecasting methods, namely Single Moving Average, 2-month Moving Average, and 3-month Moving Average. This research employed a descriptive quantitative approach using historical demand data from January to September as the basis for forecasting demand for the period of October to December. Forecast accuracy was evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Single Moving Average method produced the lowest MAD value of 91.33 and MSE value of 9,948.67, making it the most effective method in minimizing absolute and squared forecasting errors. The 2-month Moving Average method resulted in the lowest MAPE value of 57.91% but showed the highest MAD and MSE values, while the 3-month Moving Average method demonstrated more moderate and stable performance with error values between the other two methods. The high MAPE values across all methods indicate substantial demand variability; therefore, MAD and MSE are considered more relevant indicators for selecting the appropriate forecasting method. The findings of this study are expected to support more effective and efficient decision-making in planning the procurement of Omeprazole injection 40 mg at XYZ Hospital.
Co-Authors Ade Aisyah Arifna Putri Dhea Nur Fadhilah Nur Irhamni Sabrina Syaubari Syaubari