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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Statistika Jurnal Pendidikan Matematika RAFA Science and Technology Indonesia Jurnal Penelitian Sains Jurnal Pengabdian Pada Masyarakat Seminar Nasional Lahan Suboptimal M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Journal of Engineering and Scientific Research (JESR) Indonesian Journal of Engineering and Science (IJES) Inovasi Matematika (Inomatika) Bulletin of Applied Mathematics and Mathematics Education Jurnal Pelita Sriwijaya Prosiding Seminar Nasional Unimus Mathematics Education Journal Journal on Mathematics Education

Yulia Resti

Jurusan Matematika Fakultas MIPA Universitas Sriwijaya,Jl. Raya Palembang-Prabumulih Km.32 Inderalaya 30662, Ogan Ilir, SumateraSelatan

Author-ID : 359641

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests Yulia Resti; Chandra Irsan; Muflika Amini; Irsyadi Yani; Rossi Passarella; Des Alwine Zayantii
Science and Technology Indonesia Vol. 7 No. 3 (2022): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach Yulia Resti; Firmansyah Burlian; Irsyadi Yani; Des Alwine Zayanti; Indah Meiliana Sari
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes Yulia Resti; Firmansyah Burlian; Irsyadi Yani
Science and Technology Indonesia Vol. 5 No. 4 (2020): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression Yulia Resti; Endang Sri Kresnawati; Novi Rustiana Dewi; Des Alwine Zayanti; Ning Eliyati
Science and Technology Indonesia Vol. 6 No. 2 (2021): April 2021
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2021.6.2.96-104

Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.

Prediction of Plastic-Type for Sorting System using Fisher Discriminant Analysis Irsyadi Yani; Yulia Resti; Firmansyah Burlian; Ansyori Yani
Science and Technology Indonesia Vol. 6 No. 4 (2021): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2021.6.4.313-318

Recycling is a more environmentally friendly method of managing and reducing plastic waste that can significantly reduce land degradation, pollution, and greenhouse gas emissions. According to its composition, an essential first step in the recycling process is sorting out plastic waste. However, inadequate sorting of plastic types can result in cross-contamination and increasing industrial operating costs. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. The purpose of this paper is to demonstrate how to use Fisher Discriminant Analysis (FDA) to predict the plastic type from a digital image of the RGB model and then evaluate the performance using cross-validation. This work has four main steps: collecting plastic digital image data, forming statistical tests, predicting plastic types, and evaluating prediction performance. FDA is quite effective for predicting the type of plastic. Performance measures the accuracy of 87.11 %, the recall-micro of 91.67 %, the recall-micro of 80.97 %, the specificity-micro of 90.33 %, and the specificity-macro of 90.38 %, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor Yulia Resti; Chandra Irsan; Mega Tiara Putri; Irsyadi Yani; Ansyori Ansyori; Bambang Suprihatin
Science and Technology Indonesia Vol. 7 No. 1 (2022): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

Majority Voting as Ensemble Classifier for Cervical Cancer Classification Anita Desiani; Endang Sri Kresnawati; Muhammad Arhami; Yulia Resti; Ning Eliyati; Sugandi Yahdin; Titania Jeanni Charissa; Muhammad Nawawi
Science and Technology Indonesia Vol. 8 No. 1 (2023): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.1.84-92

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.

A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests Yulia Resti; Chandra Irsan; Jeremy Firdaus Latif; Irsyadi Yani; Novi Rustiana Dewi
Science and Technology Indonesia Vol. 8 No. 2 (2023): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.2.288-297

Control of diseases and pests of maize plants is a significant challenge to ensure global food security, self-sufficiency, and sustainable agriculture. Classification or early detection of diseases and pests of corn plants is intended to assist the control process. Random forest is a classification model in tree-based statistical learning in making decisions. This approach is an ensemble method that generates many decision trees and makes classification decisions based on the majority of trees selecting the same class. However, tree-based methods are often unstable when small changes or disturbances exist in the learning data. Such instability can produce significant variances and affect model performance. This study classifies diseases and pests of the corn plant using a random forest method based on bootstrap-aggregating. It fits multiple models of a single random forest, then combines the predictions from all models and determines the final result using majority voting. The results showed that the bootstrap aggregating could improve the classification of diseases and pests of maize using a random forest if the number of trees is optimal.

Ensemble Method of Multiple Decision Trees with Crisp and Fuzzy Discretization for Axial Surface Roughness Prediction Resti, Yulia; Yani, Irsyadi; Puspitasari, Dewi; Thamrin, Ismail; Saputra, M.A. Ade
Science and Technology Indonesia Vol. 10 No. 3 (2025): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.3.678-689

AISI 1045 is a steel used to make engine components for motor vehicles and aircraft. The quality of AISI 1045 is influenced by its surface roughness, both axial and tangential. The prediction of AISI 1045 surface roughness aims to produce quality products in a shorter time and at a lower cost. The axial surface roughness is in the form of certain grades according to ISO. This method is a prediction technique that combines several single prediction models to obtain better prediction results. In this work, the ensemble method is built using a voting system from an odd number of single prediction models of the decision trees. The proposed Single Model consists of one decision tree model with crisp discretization (DT1) and three models with fuzzy discretization (DT2, DT3, and DT4). The research data were obtained through experiments measuring the axial surface roughness of AISI 1045 steel using a wet machining system by considering cutting speed, feed motion, axial depth of cut, and tangential surface roughness. The study’s results indicate that not all proposed ensemble models are built to have better performance than single prediction models. Of the four proposed single prediction models, only one model has an accuracy above 80%, namely the decision tree model with fuzzy discretization using a combination of linear-trapezoidal fuzzy membership functions (DT4 model). The model performance based on accuracy, recall, precision, F1-score, and AUC is 80.73%, 48.53%, 73.47%, 58.44%, and 67.72%, respectively. For the four ensemble models formed from the combination of three Single decision tree models, only the combination of DT1, DT2, and DT3 does not perform better than the Single model. The other three ensemble methods have better accuracy, recall, and AUC than the performance of all proposed Single models with values of 81.33 - 82.67%, 51.62 - 55.19%, and 69.04 - 71.02%, respectively.

The Symmetric Pattern Fuzzy Discretizationin Predicting Plastic Type for a Sorting System Using Decision Tree Methods Yani, Irsyadi; Marwani; Puspitasari, Dewi; Resti, Yulia
Science and Technology Indonesia Vol. 10 No. 3 (2025): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.3.789-801

Plastics made from petroleum as the main ingredient cannot decompose quickly like organic materials, but rather take 500 years to 1000 years. Recycling plastic waste can significantly reduce the negative impacts that damage the environment. In addition, it can also reduce the use of natural resources as raw materials and energy use in the extraction process from mining to ready-for-use. In the process of recycling plastic bottle waste in industry, a sorting system is needed to sort the types of plastic bottle waste automatically, effectively, and efficiently. The system needs a prediction model with satisfactory performance. This research aims to build the prediction models of a plastic bottle waste sorting system with a fuzzy approach using the Decision Tree method. The main focus is fuzzy discretization with asymmetric pattern where the membership functions for the first and the last categories are balanced. Seven Decision Tree models are proposed in this study, six models with symmetric fuzzy discretization and one model with crisp discretization as a comparison. Three types of plastics are the objects of the study, namely Polyethylene Terephthalate (PET/PETE), High-Density Polyethylene (HDPE), and Polypropylene (PP). All three are types of plastics that are widely found in household waste. The unique contribution of this paper is that the symmetric pattern fuzzy discretization in the decision tree method can improve the performance of the decision tree model, but the combination of fuzzy membership functions used also contributes. Not all combinations used can improve the performance of the prediction model. Four of six models of symmetric fuzzy discretization have better performance than the decision tree model with crisp discretization. The combination of fuzzy membership functions consisting of linear and triangular functions provides the highest performance. Two models that do not perform better than crisp discretization are the linear-trapezoidal combination and the linear-Gaussian combination.

Co-Authors A. S. Mohruni Akbar Teguh Prakoso Ali Amran Ali Syahbana Alwine Zayanti, Des Amrifan Saladin Mohruni Andi Eka Putra Aneka Firdaus Anita Desiani Ansyori Ansyori Ansyori Ansyori Ansyori Yani Anthony Costa Arhami, Muhammad Astuti Astuti Bambang Suprihatin Bambang Suprihatin Bambang Suprihatin Burlian, F. Chandra Irsan Chandra Irsan Chandra Irsan Dendy Adanta Des A. Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti, Des Alwine Des Alwine Zayantii Desi Herlina Saraswati Dewi Puspita Sari Dewi Puspitasari Dewi Puspitasari Dewi, Novi R. Endang S Kresnawati Endang S. Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati F Nasution F. A. Alhamdini Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Fitri Puspasari Fusito Fusito Hasan Basri Hoiri, Sajiril I Yani Indah Meiliana Sari Irsyadi Yani Ismail Thamrin Jeremy Firdaus Latif Kresnawati, Endang S. M. Hasbi Ramadhan, M. Hasbi M. Rendy Kurniawan MARWANI Marwani Marwani, Marwani Mauizzatil Rahmayani Mega Tiara Putri Muflika Amini Muhammad Nawawi Muhammad Yanis Ning Eliyati Ning Eliyati Ning Eliyati Ning Eliyati Ning Eliyati Ning Elyati Noriszura Ismail Nova Yuliasari Novi R. Dewi Novi Rustiana Dewi Novi Rustiana Dewi Novi Rustiana Dewi Rahmayani, Mau’izatil Ratu Ilma Indra Putri Resnawati Rossi Passarella Saiful Hafizah Jaaman Saputra, M. A. Ade Saputra, M.A. Ade Setyo Cahyono, Endro Shohif Wijaya Sri Kresnawati, Endang Sugandi Yahdin Teguh Prakoso, Akbar Titania Jeanni Charissa Widya Ayu Amanda Winoto Chandra Yani, I. Yuli Andriani Zayanti, Des A. Zulkardi Zulkardi Zulkardi Zulkarnain Zulkarnain

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