Support Vector Machine with K-fold Validation to Improve the Industry’s Sustainability Performance Classification

Asrol, Muhammad; Papilo, Petir; Gunawan, Fergyanto E.

doi:10.1016/j.procs.2021.01.074

Cited by 33 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on these considerations, this study defines 24 qualitative and quantitative indicators to describe the economic (E), social (S), environmental (N), and resource (R) sustainability dimensions. Economic, social, and environmental dimensions are triple bottom line that have been theoretically and practically assigned to assess sustainability performance, as found in previous research [6], [17], [40]. Further, this study also considers the resource dimension as it is discusses the sugarcane agroindustry that encounters problems in resource allocation [41]- [44] and high utilization of natural resources with low machine performance [43], [44], which is found to be the main problem in developing countries [38].…”

Section: B Research Stagesmentioning

confidence: 79%

“…Therefore, the linear calculation model for comparison with the FIS sustainability model is shown in Equation 6. (6) Further, the decision to use the appropriate function and model in FIS for assessing supply chain sustainability is evaluated using the RMSE.…”

Section: Defuzzification and Fis Model Comparisonmentioning

confidence: 99%

“…Currently, due to challenges faced by humans and the adoption of Industry 4.0, the United Nations has resolved to fully implement sustainable development goals by 2030 [2]. Consequently, sustainable development goals have been implemented in many areas, including country and regional sustainability [3], [4], industrial development [5], [6], small medium enterprises [7], food processing and manufacturing [8], city infrastructure [9], industry, and fresh-cut vegetables [10]. Owing to the adoption of Industry 4.0, which supports the implementation of sustainability development [11], [12], there is a need for sustainability assessment and evaluation for industry.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

et al. 2022

Self Cite

View full text Add to dashboard Cite

Supply chain sustainability assessment is key to maintaining and improving the performance of agroindustry supply chain, particularly in agroindustry sustainable development. Assessment of the agroindustry supply chain performance is a complex and dynamic process. Hence, there is a need for an adaptive fuzzy multi-criteria sustainability assessment model as an alternative method for analysis and improvement. This study aims to design an adaptive fuzzy multi-criteria sustainability assessment and improvement model of the sugarcane agroindustry supply chain. In this study, (1) fuzzy inference system (FIS) was developed to assess the performance of sustainability dimensions. This study proposed 24 indicators of 4 dimensions, namely, economic, social, environment, and resource. (2) Adaptive neuro fuzzy inference system (ANFIS) was designed for aggregating the overall supply chain sustainability performance. (3) The proposed fuzzy multi-criteria assessment model was compared with the common multidimensional scaling (MDS) and linear models. This study proved that the proposed synthesis of the FIS and ANFIS models was powerful and adaptive for evaluating supply chain sustainability and providing accurate results. (4) The strategy to improve sustainability performance was developed using the cosine amplitude method (CAM). The proposed model determined that the overall supply chain sustainability value was 68.58%, which is almost sustainable. Several strategies have been suggested to improve sustainability performance, including maintaining sugarcane supply by strengthening the partnership program and improving the mill's overall recovery, followed by factory revitalization or new factory investment.

show abstract

Section: B Research Stagesmentioning

confidence: 79%

Section: Defuzzification and Fis Model Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, the k-fold cross validation procedure [ 77 ] was used to develop the algorithms for LAI estimation. The samples were randomly split into k mutually exclusive sets (k = 10 in this study, which is a common number used in many studies [ 29 , 65 , 77 , 78 ]) and they were trained and validated for k times. For each time, k-1 sets are used iteratively as training data for calibrating the coefficients (Coef i ) of the relationship, and the remaining set is used as the validation data to obtain estimation accuracy: Root mean square error (RMSE i ), coefficient of variation (CV i ) and Bias i [ 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

et al. 2021

View full text Add to dashboard Cite

Background Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production. Methods This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. Results The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. Conclusions The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

show abstract

“…The use of this method can reduce bias in sampling because the data is divided randomly into several (k) parts for training using several parts; and tested in other sections. In relation to this process, the final accuracy is the average accuracy of the number of processes [22]. The illustration of data partition using k-fold cross validation is showed in Fig.…”

Section: G Evaluation Of Classification Performancementioning

confidence: 99%

Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

Ardhani

Chamidah

Saifudin

2021

JISEBI

View full text Add to dashboard Cite

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function

show abstract

Support Vector Machine with K-fold Validation to Improve the Industry’s Sustainability Performance Classification

Cited by 33 publications

References 13 publications

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

Contact Info

Product

Resources

About