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13 pages, 3712 KiB

Open AccessArticle

Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data

by Hady Suryono, Heri Kuswanto and Nur Iriawan

Sustainability 2022, 14(22), 15252; https://doi.org/10.3390/su142215252 - 17 Nov 2022

Cited by 1 | Viewed by 1500

The United Nations Sustainable Development Goals (SDGs) have had a considerable impact on Indonesia’s national development policies for the period 2015 to 2030. The agricultural industry is one of the world’s most important industries, and it is critical to the achievement of the [...] Read more.

The United Nations Sustainable Development Goals (SDGs) have had a considerable impact on Indonesia’s national development policies for the period 2015 to 2030. The agricultural industry is one of the world’s most important industries, and it is critical to the achievement of the SDGs. The second major aspect of the SDGs, i.e., zero hunger, addresses food security (SDG 2). To measure the status of food security, accurate statistics on paddy production must be accessible. Paddy phenological classification is a way to determine a food plant’s growth phase. Imbalanced data are a common occurrence in agricultural data, and machine learning is frequently utilized as a technique for classification issues. The current trend in agriculture is to use remote sensing data to classify crops. This paper proposes a new approach—one that uses two phases in the bootstrap stage of the random forest method—called a two-phase stratified random forest (TPSRF). The simulation scenario shows that the proposed TPSRF outperforms CART, SVM, and RF. Furthermore, in its application to paddy growth phase data for 2019 in Lamongan Regency, East Java, Indonesia, the proposed TPSRF showed higher overall accuracy (OA) than the compared methods. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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12 pages, 523 KiB

Open AccessArticle

Modeling the Level of Drinking Water Clarity in Surabaya City Drinking Water Regional Company Using Combined Estimation of Multivariable Fourier Series and Kernel

by Andi Tenri Ampa, I Nyoman Budiantara and Ismaini Zain

Sustainability 2022, 14(20), 13663; https://doi.org/10.3390/su142013663 - 21 Oct 2022

Cited by 1 | Viewed by 1225

Abstract

The purpose of this study is to propose an appropriate model to predict chemical composition during water purification at the Regional Water Company (PDAM) Surabaya, in order to achieve proper drinking water standards. Drinking water treatment is very expensive, so the model serves [...] Read more.

The purpose of this study is to propose an appropriate model to predict chemical composition during water purification at the Regional Water Company (PDAM) Surabaya, in order to achieve proper drinking water standards. Drinking water treatment is very expensive, so the model serves as a basis for determining the composition of chemicals used in the water purification process at PDAM Surabaya. This study examines a model of the relationship between the level of clarity of drinking water and the composition of the chemicals used. The government can obtain important benefits from the forecasting model to formulate policies for the company. One of the objectives of developing the estimation method involved in this research is to efficiently determine the exact chemical composition resulting from the water purification process, which will inform the financing and control of water quality. We used a multivariable linear approach for some parametric components, a multivariable Fourier Series approach for some nonparametric components, and a multivariable Kernel approach for semiparametric regression. Using the penalized least square (PLS) approach, a mixed estimator of the Fourier and Kernel Series was obtained with semiparametric regression. The smoothing parameters were selected using a common cross-validation technique (GCV). The performance of this technique was evaluated using the Gaussian Kernel and Fourier Series with data trends in the drinking water clarity level obtained from PDAM Surabaya. The findings showed that this technique performed well, so we recommend that the government conduct an in-depth analysis to determine correct chemical composition so that the cost of water treatment can be minimized. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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12 pages, 2667 KiB

Open AccessArticle

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

by Chien-Chih Wang and Yu-Hsun Li

Sustainability 2022, 14(14), 8575; https://doi.org/10.3390/su14148575 - 13 Jul 2022

Cited by 2 | Viewed by 2206

Abstract

Many dyeing and finishing factories generally use old-fashioned dyeing machines. A key issue when using these machines is that the dyeing tank cannot detect entanglement problems, which may result in a lower dyeing quality. In this paper, imbalanced data with ensemble machine learning, [...] Read more.

Many dyeing and finishing factories generally use old-fashioned dyeing machines. A key issue when using these machines is that the dyeing tank cannot detect entanglement problems, which may result in a lower dyeing quality. In this paper, imbalanced data with ensemble machine learning, such as Extreme Gradient Boosting (XGBoost) and random forest (RF), are integrated to predict the possible states of a dyeing machine, including normal operation, entanglement warning, and entanglement occurrence. To verify the results obtained using the proposed method, we worked with industry−academia collaborators. We collected 1,750,977 pieces of data from 1848 batches. The results obtained from the analysis show that after employing the Borderline synthetic minority oversampling technique and the Tomek link to deal with the data imbalance, combined with the model established by XGBoost, the prediction accuracy of the normal operation states, entanglement warning, and entanglement occurrence were 100%, 94%, and 96%, respectively. Finally, the proposed entanglement detection system was connected with the factory’s central control system using a web application programming interface and machine real-time operational parameter data. Thus, a real-time tangle anomaly warning and monitoring system was developed for the actual operating conditions. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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13 pages, 1310 KiB

Open AccessArticle

Coral Reef Bleaching under Climate Change: Prediction Modeling and Machine Learning

by Nathaphon Boonnam, Tanatpong Udomchaipitak, Supattra Puttinaovarat, Thanapong Chaichana, Veera Boonjing and Jirapond Muangprathub

Sustainability 2022, 14(10), 6161; https://doi.org/10.3390/su14106161 - 19 May 2022

Cited by 6 | Viewed by 5901

Abstract

The coral reefs are important ecosystems to protect underwater life and coastal areas. It is also a natural attraction that attracts many tourists to eco-tourism under the sea. However, the impact of climate change has led to coral reef bleaching and elevated mortality [...] Read more.

The coral reefs are important ecosystems to protect underwater life and coastal areas. It is also a natural attraction that attracts many tourists to eco-tourism under the sea. However, the impact of climate change has led to coral reef bleaching and elevated mortality rates. Thus, this paper modeled and predicted coral reef bleaching under climate change by using machine learning techniques to provide the data to support coral reefs protection. Supervised machine learning was used to predict the level of coral damage based on previous information, while unsupervised machine learning was applied to model the coral reef bleaching area and discovery knowledge of the relationship among bleaching factors. In supervised machine learning, three widely used algorithms were included: Naïve Bayes, support vector machine (SVM), and decision tree. The accuracy of classifying coral reef bleaching under climate change was compared between these three models. Unsupervised machine learning based on a clustering technique was used to group similar characteristics of coral reef bleaching. Then, the correlation between bleaching conditions and characteristics was examined. We used a 5-year dataset obtained from the Department of Marine and Coastal Resources, Thailand, during 2013–2018. The results showed that SVM was the most effective classification model with 88.85% accuracy, followed by decision tree and Naïve Bayes that achieved 80.25% and 71.34% accuracy, respectively. In unsupervised machine learning, coral reef characteristics were clustered into six groups, and we found that seawater pH and sea surface temperature correlated with coral reef bleaching. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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19 pages, 6185 KiB

Open AccessArticle

Revisiting Cluster Vulnerabilities towards Information and Communication Technologies in the Eastern Island of Indonesia Using Fuzzy C Means

by Faisal Anggoro, Rezzy Eko Caraka, Fajar Agung Prasetyo, Muthia Ramadhani, Prana Ugiana Gio, Rung-Ching Chen and Bens Pardamean

Sustainability 2022, 14(6), 3428; https://doi.org/10.3390/su14063428 - 15 Mar 2022

Cited by 9 | Viewed by 2380

Abstract

Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in [...] Read more.

Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in the last five years has shown a massive development of ICT indicators. The population using the internet also experienced an increase during the period 2016–2020, as indicated by the increasing percentage of the population accessing the internet in 2016 from around 25.37 percent to 53.73 percent in 2020. This study led to a review of the level of ICT vulnerability in eastern Indonesia through a machine learning-based cluster analysis approach. Implications: Data were collected in this study from Badan Pusat Statistik (BPS) through SUSENAS to obtain an overview of the socioeconomic level and SAKERNAS to capture the employment side. This study uses 15 variables based on aspects of business vulnerability covering 174 districts/cities. Practical implications: Cluster analysis using Fuzzy C Means (FCM) was used to obtain a profile of ICT level vulnerability in eastern Indonesia by selecting the best model. The best model is obtained by selecting the validation value such as Silhouette Index, Partition Entropy, Partition Coefficient, and Modified Partition Coefficient. Social implication: For some areas with a very high level of vulnerability, special attention is needed for the central or local government to support the improvement of information technology through careful planning. Socio-economic and occupational aspects have been reflected in this very vulnerable cluster, and the impact of the increase in ICT will provide a positive value for community development. Originality/value: From the modelling results, the best cluster model is two clusters, which are categorized as high vulnerability and low vulnerability. For each cluster member who has a similarity or proximity to each other, there will be one cluster member. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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18 pages, 2878 KiB

Open AccessArticle

Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

by Yuo-Hsien Shiau, Su-Fen Yang, Rishan Adha and Syamsiyatul Muzayyanah

Sustainability 2022, 14(5), 2896; https://doi.org/10.3390/su14052896 - 2 Mar 2022

Cited by 12 | Viewed by 2547

Abstract

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate [...] Read more.

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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13 pages, 1540 KiB

Open AccessArticle

COVID-19 Pandemic’s Impact on Return on Asset and Financing of Islamic Commercial Banks: Evidence from Indonesia

by Gama Putra Danu Sohibien, Lilis Laome, Achmad Choiruddin and Heri Kuswanto

Sustainability 2022, 14(3), 1128; https://doi.org/10.3390/su14031128 - 19 Jan 2022

Cited by 20 | Viewed by 3130

Abstract

The aim of this study is to propose appropriate models to forecast Return on Asset (ROA) and financing of Indonesia Islamic Commercial Banks during COVID-19 pandemic. In particular, we study the models which involve reciprocal relation between ROA and financing and incorporate COVID-19 [...] Read more.

The aim of this study is to propose appropriate models to forecast Return on Asset (ROA) and financing of Indonesia Islamic Commercial Banks during COVID-19 pandemic. In particular, we study the models which involve reciprocal relation between ROA and financing and incorporate COVID-19 pandemic’s impact. It is crucial because the government would benefit from forecasting results to formulate the policy for the banks related to ROA and financing. We consider two models: Vector Autoregressive with exogenous variable (VARX) and spline regression, since both models are able to exploit the multivariate structure of ROA and financing and to include COVID-19 impact as predictor. The results show that the VARX outperforms spline regression in terms of RMSE. Using VARX, we deduce that ROA and financing have a positive reciprocal relationship, meaning that when ROA increases, financing would increase, and vice versa. In addition, the pandemic has significant impact on the decline of the ROA. We recommend that banks conduct an in-depth analysis to determine the appropriate form of restructuring for debtors so that it does not have a significant impact on the decrease in ROA. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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19 pages, 5714 KiB

Open AccessArticle

AI-Based Quantification of Fitness Activities Using Smartphones

by Junhui Huang, Sakdirat Kaewunruen and Jingzhiyuan Ning

Sustainability 2022, 14(2), 690; https://doi.org/10.3390/su14020690 - 9 Jan 2022

Cited by 3 | Viewed by 3006

Abstract

To encourage more active activities that have the potential to significantly reduce the risk of people’s health, we aim to develop an AI-based mobile app to identify four gym activities accurately: ascending, cycling, elliptical, and running. To save computational cost, the present study [...] Read more.

To encourage more active activities that have the potential to significantly reduce the risk of people’s health, we aim to develop an AI-based mobile app to identify four gym activities accurately: ascending, cycling, elliptical, and running. To save computational cost, the present study deals with the dilemma of the performance provided by only a phone-based accelerometer since a wide range of activity recognition projects used more than one sensor. To attain this goal, we derived 1200 min of on-body data from 10 subjects using their phone-based accelerometers. Subsequently, three subtasks have been performed to optimize the performances of the K-nearest neighbors (KNN), Support Vector Machine (SVM), Shallow Neural Network (SNN), and Deep Neural Network (DNN): (1) During the process of the raw data converted to a 38-handcrafted feature dataset, different window sizes are used, and a comparative analysis is conducted to identify the optimal one; (2) principal component analysis (PCA) is adopted to extract the most dominant information from the 38-feature dataset described to a simpler and smaller size representation providing the benefit of ease of interpreting leading to high accuracy for the models; (3) with the optimal window size and the transformed dataset, the hyper-parameters of each model are tuned to optimal inferring that DNN outperforms the rest three with a testing accuracy of 0.974. This development can be further implemented in Apps Store to enhance public usage so that active physical human activities can be promoted to enhance good health and wellbeing in accordance with United Nation’s sustainable development goals. Full article

(This article belongs to the Special Issue Recent Research on Statistics, Machine Learning, and Data Science in Sustainability and Penta Helix Contribution)

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