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All Journal Indonesian Journal of Data and Science Internet of Things and Artificial Intelligence Journal
Nurmisba, Nurmisba
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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Optimization of K-Means Clustering Method by Using Elbow Method in Predicting Blood Requirement of Pelamonia Hospital Makassar Anggreani, Desi; Nurmisba, Nurmisba; Setiawan, Dedi; Lukman, Lukman
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.755

Abstract

Hospitals require an adequate supply of blood to meet patient needs. Accurate prediction of blood demand is essential to optimize inventory management and avoid shortages or overstocks. This study aims to predict blood demand at Pelamonia Hospital using K-Means Clustering and Elbow methods. Historical data on blood demand at Pelamonia Hospital was collected and processed. The Elbow method is used to determine the optimal number of clusters in the K-Means Clustering algorithm. Sum of Squared Errors (SSE) or Within-Cluster Sum of Squares (WCSS) values were calculated for various clusters, and the elbow point on the graph of SSE/WCSS vs. number of clusters was identified as the optimal number of clusters. Once the optimal number of clusters is determined, the K-Means Clustering algorithm is applied to the blood demand data, resulting in grouping the data into specific clusters. Each cluster is analyzed to find interesting patterns or characteristics, such as clusters with high or low blood demand. From the results of the SSE calculation process on 1057 blood demand data, the result that has the biggest decrease is at k = 4 with a difference value of 2754.90. The clustering results and patterns found are used to predict future blood demand by identifying which cluster best fits the current or expected conditions. The characteristics of the clusters are used to estimate the likely blood demand. This approach provides valuable insights into blood demand patterns and enables hospitals to better anticipate blood demand, thereby optimizing inventory management and improving the quality of healthcare services.
A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia Anggreani, Desi; Nurmisba, Nurmisba; Abd Rahman, Aedah
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.330

Abstract

Accurately predicting export values is key for a country in formulating its economic plans. Unfortunately, export data often exhibits complex time series patterns that are difficult to predict, characterized by non-linearity, high volatility, and complex temporal dependencies. This study offers a solution by testing a combined deep learning model, specifically a fusion of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), to address the challenges of export time series forecasting. This study uses this approach to forecast Indonesia's monthly export time series data from 2016 to 2023, covering various sectors ranging from oil and gas, non-oil and gas, agriculture, industry, mining, and others. The core idea is to leverage the CNN's ability to identify hidden features within time series patterns, while the BiLSTM is tasked with understanding the temporal flow of data from both directions to capture the inherent long-term temporal dependencies within economic time series data. As a result, this combined model proved to be far superior to the standard BiLSTM model in handling the complexity of export time series. In the Non-Oil and Gas sector, the proposed model achieved a high level of accuracy with an MSE value of 3,330,239.74, an RMSE of 1,824.89, and an average prediction error (MAPE) of only 8.17%, representing a significant improvement of 69% over the baseline BiLSTM model. Similar success was also found in all other sectors, proving that this hybrid approach is highly promising for complex economic time series analysis
Co-Authors Abd Rahman, Aedah Anggreani, Desi Dedi Setiawan Wiwik Handayani