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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 is a country with a high production of biofarmaca plants. Biofarmaca refers to plants that are beneficial for disease prevention and are widely used in the pharmaceutical industry. One of the biofarmaca plants is Zingiber officinale, commonly known as ginger, which grows extensively throughout the provinces of Indonesia. This study focuses on clustering biofarmaca producers using the k-means clustering technique with two variables: harvest quantity and land area, employing four clusters for 38 provinces in Indonesia. The results indicate that there are 9 provinces in Cluster 1, 3 provinces in Cluster 2, 2 provinces in Cluster 3, and 24 provinces in Cluster 4. Cluster 1, consisting of 3 provinces, is the largest producer of ginger in Indonesia and has the largest land area compared to other provinces.
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