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All Journal International Journal Software Engineering and Computer Science (IJSECS) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian
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Journal : International Journal Software Engineering and Computer Science (IJSECS)

 1 
Grouping Production Goods Requirements Using the K-Means Clustering Method Setiawan, Dani Yuda Dwi; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2863

Abstract

The inventory management of production goods presents several challenges, including difficulties in distinguishing between necessary and unnecessary items, leading to overstocking and manual data processing issues. Additionally, the risk of data loss can impede the data processing workflow. Data testing is conducted to evaluate the accuracy of calculations and the functionality of the applied methods. The objective is to optimize production results and inventory levels in warehouses. The K-means algorithm, known for its simplicity and effectiveness, is utilized to identify clusters within the data. The first cluster (C0) has centroids at (60.33, 70.33) and includes stock data categorized as having no potential. This cluster comprises 35 records. The second cluster (C1) has centroids at (10.94, 7.11) and includes stock data categorized as available, consisting of 15 records. Testing with the RapidMiner Studio application confirms similar insights, with each cluster containing members that are divided into two clusters, each having optimal centroid values of (60.33, 70.33) for Cluster 1 (C0) and (10.94, 7.14) for Cluster 2 (C1), and a Davies-Bouldin Index evaluation score of 0.666.
Estimating Distributor Demand for Fishing Gear Products Using Linear Regression Algorithm Keswanto; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2864

Abstract

Fishing equipment plays a critical role in both recreational and commercial fishing activities across various aquatic environments. The challenge of managing inventory effectively is heightened by the fluctuating demand and the need to avoid overstocking, which can result in increased operational costs. To address this, a linear regression algorithm is utilized to predict demand for fishing products, using relevant independent variables to model the relationship with dependent variables such as monthly sales figures. This predictive model aims to provide actionable insights that can assist businesses in making informed decisions regarding inventory management and distribution strategies. The study employs the RapidMiner Studio application to develop and evaluate the model's performance, with the analysis yielding a Root Mean Square Error (RMSE) of 140.200. This relatively low RMSE value demonstrates the model's accuracy and effectiveness in forecasting demand, suggesting that the algorithm can be a valuable tool for optimizing inventory levels and ensuring product availability while minimizing excess stock.
Predicting Consumer Demand Based on Retail Stock Using the K-Nearest Neighbors Algorithm Putri N.A, Anindya; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2865

Abstract

Inefficient stock management, such as improper stock management, will result in excess or shortage of goods. Excess stock can cause high storage costs and the risk of unsold goods. Predict consumer needs based on stock. Analyze inefficient stock to improve shortages. One effective method for making this prediction is using the K-Nearest Neighbors (K-NN) algorithm. The K-NN algorithm is a simple but powerful machine-learning technique that can be used for classification and regression. The model scenario results show 24 objects in the Low-needs group and 14 in the High-needs group. Evaluation and performance testing using the Rapid Miner tool can also produce a relevant picture of the modelled scenario. The model implemented using the K-NN algorithm has an Accuracy value of 97.50% with a Standard Deviation of +/- 750%, then a Precision value of 100%, and a Recall value of 950%. By measuring model performance with cross-validation, the resulting accuracy has a standard deviation value, which aims to see the distance between the average accuracy and the accuracy of each experiment (iteration)
Co-Authors Abdul Ghofir Andri Firmansyah Elkin Rilvani Gema Sukmawati Suryadi Iwan Mulyana Keswanto Muhtajuddin Danny Purwanto Putri N.A, Anindya Rahardjo, Sugeng Sanudin Setiawan, Dani Yuda Dwi Siti Rahayu Turmud Zy, Ahmad Wahyu Hadikristanto Wiyanto