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All Journal JUITA : Jurnal Informatika Journal of Intelligent Decision Support System (IDSS)
I Made Dwi Putra Asana
Institut Bisnis dan Teknologi Indonesia, Indonesia
Computer Science & IT

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Implementation of Artificial Neural Network on Sales Forecasting Application Ketut Jaya Atmaja; Ida Bagus Nyoman Pascima; I Made Dwi Putra Asana; I Gede Iwan Sudipa
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 4 (2022): Desember: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Sales forecasting is an effort to fulfill customer demands. The existence of a sales forecast, can help trade business owners in carrying out stock management to deal with customer demands in the future. Data owned in the past is used in predicting and estimating a condition in the future. Quantitative data used as a reference in the forecasting process can be time series data based on a certain period containing the number of sales. Artificial Neural Networks (ANN) are one of the human efforts to model the way the human nervous system functions in carrying out certain tasks. This modeling is based on the ability of the human brain to organize brain cells called neurons. Neurons are information processing units that are the basis of artificial neural network operations. ANN can be used to solve forecasting problems based on continuous data such as time series data from a sale based on a certain period. The research stages that will be carried out consist of analyzing needs, training the model, testing the model, forecasting sales.
Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System I Made Dwi Putra Asana; I Dewa Gede Ari Oka; I Made Oka Widyantara; I Made Subrata Sandhiyasa
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21990

Abstract

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.
Co-Authors Atmaja, Ketut Jaya I Dewa Gede Ari Oka I Gede Iwan Sudipa I Made Oka Widyantara I Made Subrata Sandhiyasa Ida Bagus Nyoman Pascima