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All Journal INFORMAL: Informatics Journal SPIRIT Journal of Applied Agricultural Science and Technology
Muhamad Arief Hidayat
Universitas Jember
Agriculture, Biological Sciences & Forestry Computer Science & IT

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KLASIFIKASI BERBASIS GRAVITASI DATA DAN PROBABILITAS POSTERIOR Muhamad Arief Hidayat; Arif Djunaidy
SPIRIT Vol 7, No 1 (2015): SPIRIT
Publisher : STMIK YADIKA BANGIL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (755.016 KB) | DOI: 10.53567/spirit.v7i1.23

Abstract

The classification method based on data gravitation (DGC) is one of the new classification techniques that uses data  gravitation as the criteria of the classification. In the case of DGC, an object is classified on the basis of the class that creates the largest gravitation in that object. However, the DGC method may cause inaccurate result when the training data being used suffer from the class imbalanced problem. This may be caused by the existence of the training data containing a class having excessively big mass that will in turn tend to classify an uknown object as a member of that class due to the high degree of the data gravitation produced, and vice versa. In this research, a modification to the DGC method is performed by constructing a classificaion method that is based on both the data gravitation and posterior probability (DGCPP). In DGCPP, the mass concept defined in the DGC method as the prior probability is replaced by the posterior probability. By using this modification, data gravitation calculation process is expected to produce more accurate results in compared to those produced by the DGC method. In addtion, by improving the data gravitation calculation, it is expected that the DGCPP method willproduce more accurate classification results in compared to those produced by the DGC method for both normal dataset as well as dataset having class imbalanced problems. A thorough tests for evaluating the classification accuracy are performed using a ten-fold cross-validation method on several datasets containing both normal andimbalanced-class datasets. The results showed that DGCPP method produced positive average of accuracy differences in compared to those produced by the DGC method. For the tests using the entire normal datasets showed that the average of accuracy differences are statistically significant with a 95% confidence level. In addition, results of the tests using the four imbalanced-class datasets also showed that the average accuracy differences are statistically significant with a 95% confidence level. Finally, results of the tests for evaluating the computing times required by the classification program showed that the additional computing time needed by DGCPP method to perform the classification process is insignificant and less than the human response time, in compared to that needed by DGC method for running all datasets being used.  Keywords—data gravitation-based classification, class imbalanced problem,posterior probability 
Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree Muhamad Arief Hidayat
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.28396

Abstract

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%
Rice Deep Knowledge Graph-Based Expert System: An Intelligent Solution for Identifying Rice Pests and Diseases Muhammad Ariful Furqon; Muhamad Arief Hidayat; Windi Eka Yulia Retnani; Gayatri Dwi Santika
Journal of Applied Agricultural Science and Technology Vol. 10 No. 1 (2026): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i1.332

Abstract

Accurate diagnosis of rice pests and diseases is essential but often challenging using traditional methods, which are time-consuming and prone to human error. In this study, we propose the Rice Deep Knowledge Graph (RiceDKG) Expert System, which integrates deep learning techniques, particularly Long Short Term Memory (LSTM), with a Knowledge Graph to enhance symptom pattern-based diagnosis accuracy. This hybrid approach captures relationships among rice plant symptoms while leveraging systematically constructed domain knowledge. The system was evaluated on a dataset of 25 test cases, encompassing various symptoms such as brown spots, leaf curling, and fungal damage. Evaluation results demonstrate an overall accuracy of 84%, with 21 out of 25 cases correctly diagnosed, compared to expert evaluations. These findings indicate that integrating LSTM with knowledge graphs improves the system's ability to handle diverse diagnostic scenarios.
Co-Authors Arif Djunaidy Furqon, Muhammad Ariful Gayatri Dwi Santika Windi Eka Yulia Retnani