Akbar Priyo Santosa, Muhammad Reesa, Lubna Mawaddah, Muhamad Akrom
Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.
腐蚀是一个对石油和天然气行业有重大影响的问题，会造成重大损失。这一点值得研究，因为腐蚀在全球石油和天然气生产公司的年度总成本中占了很大一部分，而且会对环境造成严重问题，对社会产生影响。使用抑制剂是一种相当有效的防腐蚀方法。本研究是一项实验研究，旨在对缓蚀剂的效率实施机器学习（ML）。考虑到量子计算技术的日益发展，本研究在 ML 方法中使用了量子支持向量回归（QSVR）算法，旨在产生比经典 ML 算法更好的评估矩阵值。实验发现，与其他模型相比，QSVR 算法结合（TrainableFidelityQuantumKernel、ZZFeatureMap/PauliFeatureMap 和线性纠缠）获得了更好的均方根误差（RMSE）和模型训练时间，其值分别为 6、19 和 92，可用于预测缓蚀剂的效率。该研究模型的成功可以为量子计算机算法提高矩阵评估值的能力和 ML 预测缓蚀剂效率的能力提供新的启示，特别是在大型工业规模上。
{"title":"Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis","authors":"Akbar Priyo Santosa, Muhammad Reesa, Lubna Mawaddah, Muhamad Akrom","doi":"10.26877/asset.v6i3.601","DOIUrl":"https://doi.org/10.26877/asset.v6i3.601","url":null,"abstract":"Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"3 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alifia Salwa Salsabila, C. A. Sari, E. H. Rachmawanto
Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50 trees estimator with 96.84% accuracy before feature selection and 96.92% accuracy after feature selection has the best accuracy compared to the Decision Tree classification. Besides, Decision Tree has only 95.64% accuracy before feature selection and increases to 96.07% accuracy after feature selection. Trees estimator also affect the accuracy of Random Forest classification. After comparing the results, Random Forest with 50 trees estimators using feature selection provides best accuracy and it will be used to predict some similar movies and shows recommendation
{"title":"Classification of Movie Recommendation on Netflix Using Random Forest Algorithm","authors":"Alifia Salwa Salsabila, C. A. Sari, E. H. Rachmawanto","doi":"10.26877/asset.v6i3.676","DOIUrl":"https://doi.org/10.26877/asset.v6i3.676","url":null,"abstract":"Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50 trees estimator with 96.84% accuracy before feature selection and 96.92% accuracy after feature selection has the best accuracy compared to the Decision Tree classification. Besides, Decision Tree has only 95.64% accuracy before feature selection and increases to 96.07% accuracy after feature selection. Trees estimator also affect the accuracy of Random Forest classification. After comparing the results, Random Forest with 50 trees estimators using feature selection provides best accuracy and it will be used to predict some similar movies and shows recommendation","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"76 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PT BSB is a company engaged in steel fabrication manufacturing. There are various kinds of projects carried out at the company, namely bridges, steel structures, buildings, towers, etc. and the irregular placement of steel in the warehouse results in problems in the steel storage warehouse at PT. BSB where the company does not have an arrangement regarding the layout of raw materials for steel retrieval has difficulty because the steel is stored randomly without paying attention to its type and is only placed in an empty place. Therefore, it is necessary to design a layout using the dedicated storage and class-based storage methods to improve the warehouse layout by designing a warehouse layout so that it can make it easier to find and minimize steel search time. Results of research carried out obtained that distance travelled using the dedicated storage method is amounting to 7790,85 metres with material handling time 15581,7 minutes, whereas with use class based storage method can be obtained distance 8382,05 metres with material handling time 16764,1 minutes. Concluded that design results Select the selected layout is with use possible dedicated storage method reduce distance travel and time at PT. BSB warehouse becomes more practice and efficient
{"title":"Designing Steel Warehouse Layouts: A Comparative Study of Dedicated Storage and Class-Based Storage Methods at PT. BSB","authors":"Maghrobi Muzzaky Pratama, Said Salim Dahda","doi":"10.26877/asset.v6i3.737","DOIUrl":"https://doi.org/10.26877/asset.v6i3.737","url":null,"abstract":"PT BSB is a company engaged in steel fabrication manufacturing. There are various kinds of projects carried out at the company, namely bridges, steel structures, buildings, towers, etc. and the irregular placement of steel in the warehouse results in problems in the steel storage warehouse at PT. BSB where the company does not have an arrangement regarding the layout of raw materials for steel retrieval has difficulty because the steel is stored randomly without paying attention to its type and is only placed in an empty place. Therefore, it is necessary to design a layout using the dedicated storage and class-based storage methods to improve the warehouse layout by designing a warehouse layout so that it can make it easier to find and minimize steel search time. Results of research carried out obtained that distance travelled using the dedicated storage method is amounting to 7790,85 metres with material handling time 15581,7 minutes, whereas with use class based storage method can be obtained distance 8382,05 metres with material handling time 16764,1 minutes. Concluded that design results Select the selected layout is with use possible dedicated storage method reduce distance travel and time at PT. BSB warehouse becomes more practice and efficient","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"81 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The synthesis of the portable adsorbent material CMC-Fe3O4-Chitosan-TiO2 begins by inserting the CMC-Chitosan mixture into the leaching solution. Next, concentrated NaOH and 3% CaCl2 were added, then decanted and dried at room temperature. After that, the composite was coated with TiO2 and then dried in an oven at a temperature below 100 oC. The success of the synthesis was indicated by the presence of specific absorption in FT-IR. 3429 cm-1 hydroxyl group, 2926 cm-1 for the CH/CH3 group, 1631 cm-1 for the carbonyl group (C=O), 1642 cm-1 which is the CH/CH3 group, as well as ) and ( at 400-600 cm-1. In addition, the different surface morphology of the material formed from its basic components is based on SEM characterization sails. Adsorption test results for Hg (II) metal ions were 53% while dyes were 38% with a time of 40 minutes. This research is good for handling watermaster
{"title":"Synthesis and Characterization Materials Modern (CMC-Fe3O4-Chitosan-TiO2) As Portable Adsorbent Toxic Metal (Hg) and Dye Substance (Rh B)","authors":"Kaharuddin Kahar","doi":"10.26877/asset.v6i3.709","DOIUrl":"https://doi.org/10.26877/asset.v6i3.709","url":null,"abstract":"The synthesis of the portable adsorbent material CMC-Fe3O4-Chitosan-TiO2 begins by inserting the CMC-Chitosan mixture into the leaching solution. Next, concentrated NaOH and 3% CaCl2 were added, then decanted and dried at room temperature. After that, the composite was coated with TiO2 and then dried in an oven at a temperature below 100 oC. The success of the synthesis was indicated by the presence of specific absorption in FT-IR. 3429 cm-1 hydroxyl group, 2926 cm-1 for the CH/CH3 group, 1631 cm-1 for the carbonyl group (C=O), 1642 cm-1 which is the CH/CH3 group, as well as ) and ( at 400-600 cm-1. In addition, the different surface morphology of the material formed from its basic components is based on SEM characterization sails. Adsorption test results for Hg (II) metal ions were 53% while dyes were 38% with a time of 40 minutes. This research is good for handling watermaster","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"14 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency
{"title":"Implementation of DenseNet121 Architecture for Waste Type Classification","authors":"Munis Zulhusni, C. A. Sari, E. H. Rachmawanto","doi":"10.26877/asset.v6i3.673","DOIUrl":"https://doi.org/10.26877/asset.v6i3.673","url":null,"abstract":"The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.
与传统模型相比，本研究通过整合 Catboost 回归和随机搜索超参数调整，提出了一种新颖的房价预测方法，取得了显著的改进。通过将这些先进的机器学习技术应用于金县数据集，我们进行了全面的回归分析和预测建模，从而显著提高了准确性。基线模型是一个传统的线性回归模型，它为比较、评估 R 平方和平均平方误差 (MSE) 等性能指标提供了基础。通过对 Catboost 模型进行细致的超参数调整，预测准确率有了显著提高，证明了先进的数据科学技术在房地产和物业评估中的功效。摘要中明确指出了与基线模型相比准确率的提高百分比。
{"title":"Improving the Accuracy of House Price Prediction using Catboost Regression with Random Search Hyperparameter Tuning: A Comparative Analysis","authors":"Faezal Hartono, Muljono Muljono, Ahmad Fanani","doi":"10.26877/asset.v6i3.602","DOIUrl":"https://doi.org/10.26877/asset.v6i3.602","url":null,"abstract":"Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Palm oil production in Indonesia currently meets 40% of the world’s consumption needs. Control and planning of the production process in maximizing performance results that are useful for maximizing profits and minimizing losses can be archived through optimization and scheduling. One method that can be used in scheduling optimization is the Campbell Dudek Smith (CDS) method. By using the CDS algorithm, each treatment to be completed must go throught the work process on each production machine (flowshop) to get the minimum makespan value. This method can be used to sort jobs in the palm oil production process which is carried out at several stations. Where each machine works according to the production process sequence schedule. The results of the optimal job sequence in the palm oil production process using CDS are : J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 J13 with an optimal makespan value of 404 minutes.
{"title":"Optimization Of Crude Palm Oil Production Machine Scheduling Using The Campbell Dudek Smith (CDS) Method","authors":"Ratri Nurfitriah, Fibri Rakhmawati","doi":"10.26877/asset.v6i3.731","DOIUrl":"https://doi.org/10.26877/asset.v6i3.731","url":null,"abstract":"Palm oil production in Indonesia currently meets 40% of the world’s consumption needs. Control and planning of the production process in maximizing performance results that are useful for maximizing profits and minimizing losses can be archived through optimization and scheduling. One method that can be used in scheduling optimization is the Campbell Dudek Smith (CDS) method. By using the CDS algorithm, each treatment to be completed must go throught the work process on each production machine (flowshop) to get the minimum makespan value. This method can be used to sort jobs in the palm oil production process which is carried out at several stations. Where each machine works according to the production process sequence schedule. The results of the optimal job sequence in the palm oil production process using CDS are : J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 J13 with an optimal makespan value of 404 minutes.","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
员工在场是支持工作场所需求的一个关键因素。目前，PT.AirNav Indonesia Semarang 分公司的员工到岗系统仍使用指纹和基于 RFID 的员工 ID 卡进行身份验证。这种基于 RFID 的系统可能会允许员工滥用彼此的身份证，从而增加员工欺诈行为。为了避免这种欺诈行为，需要建立一个系统，该系统将使用人脸识别技术作为主要的身份验证方法，并采用 Haar Cascade 算法。这种算法的优点是计算速度快，因为它只依赖于矩形内的像素数量，而不是图像的每个像素。除了计算速度快之外，这种算法还具有识别相对较远物体的优势。在采用 Haar Cascade 算法后，结果表明，人脸识别技术能够根据面部角度检测系统内注册员工的脸部，准确率为 60%；根据表情检测的准确率为 100%；根据眼镜和面具等障碍参数检测的准确率为 33.33%。从不同的摄像机角度检测物体、识别不同表情的人脸以及识别受参数阻碍的物体的能力，可以作为需要实施该算法的理由
{"title":"The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade","authors":"Fidela Azzahra, C. A. Sari, E. H. Rachmawanto","doi":"10.26877/asset.v6i3.672","DOIUrl":"https://doi.org/10.26877/asset.v6i3.672","url":null,"abstract":"The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research is motivated by the need for a new learning model with different and engaging learning media, particularly in javelin throwing education. The aim of this research is to create an Android-based learning media for javelin throw. The method in this research is Research and Development (R&D). The subjects and location of this research are 11th-grade students of SMA Negeri 1 Bantarbolang, Pemalang Regency. In the small-scale research, the sample consists of 21 11th-grade students, while in the large-scale research, the sample consists of 272 11th-grade students. The data collection technique used is a questionnaire as the instrument. The quantitative data analysis technique in this research utilizes descriptive statistical analysis. The final validation results from media experts indicate that all aspects are rated as "Good" with a score of 78%. Meanwhile, the final validation from subject matter experts indicates that all aspects are rated as "Excellent" with a score of 82%. According to the data analysis results in the small-scale trial, the percentage obtained is 86.17%.
{"title":"\"My Javelin Throw\" Android-based Javelin Throw Learning Application","authors":"Ibnu Fatkhu Royana, Deni Wahyu Jumawan, Bertika Kusuma Prastiwi, Pandu Kresnapati, Tubagus Herlambang, Donny Anhar Fahmi, Muh Isna Nurdin Wibisana, Danang Aji Setyawan, Utvi Hinda Zhannisa","doi":"10.26877/asset.v5i1.16849","DOIUrl":"https://doi.org/10.26877/asset.v5i1.16849","url":null,"abstract":"This research is motivated by the need for a new learning model with different and engaging learning media, particularly in javelin throwing education. The aim of this research is to create an Android-based learning media for javelin throw. The method in this research is Research and Development (R&D). The subjects and location of this research are 11th-grade students of SMA Negeri 1 Bantarbolang, Pemalang Regency. In the small-scale research, the sample consists of 21 11th-grade students, while in the large-scale research, the sample consists of 272 11th-grade students. The data collection technique used is a questionnaire as the instrument. The quantitative data analysis technique in this research utilizes descriptive statistical analysis. The final validation results from media experts indicate that all aspects are rated as \"Good\" with a score of 78%. Meanwhile, the final validation from subject matter experts indicates that all aspects are rated as \"Excellent\" with a score of 82%. According to the data analysis results in the small-scale trial, the percentage obtained is 86.17%.","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.26877/asset.v5i2.16508
A. Handayani, A. Fadhilah, Ibnu Ziad, N. Husni, Sri Chodidjah, Mega Hasanul Huda, Nur Agustini, Mieska Despitasari, Riswal Hanafi Siregar
This research proposes a multifunctional wireless health monitoring tool with a display for Android and iOS devices. This research aims to develop a realistic solution for real-time and conveniently accessible health monitoring via mobile devices. The device allows users to test and track health factors such as heart rate, blood pressure, blood oxygen levels, body temperature, and blood glucose. It collects data properly by using wireless technology and sensors. The data is subsequently supplied to the appropriate apps on Android and iOS devices. The data is presented visually in the program, making it instructive and user-friendly. The device's development technique involved extensive testing and validation against established comparators to assure accuracy. The results of this study show that this digital, multi-purpose health monitoring device works well and reliably to give real-time health information. This innovation promotes health monitoring and digital health information access.
{"title":"Design of Android and IoS Applications for Mobile Health Monitoring Devices","authors":"A. Handayani, A. Fadhilah, Ibnu Ziad, N. Husni, Sri Chodidjah, Mega Hasanul Huda, Nur Agustini, Mieska Despitasari, Riswal Hanafi Siregar","doi":"10.26877/asset.v5i2.16508","DOIUrl":"https://doi.org/10.26877/asset.v5i2.16508","url":null,"abstract":"This research proposes a multifunctional wireless health monitoring tool with a display for Android and iOS devices. This research aims to develop a realistic solution for real-time and conveniently accessible health monitoring via mobile devices. The device allows users to test and track health factors such as heart rate, blood pressure, blood oxygen levels, body temperature, and blood glucose. It collects data properly by using wireless technology and sensors. The data is subsequently supplied to the appropriate apps on Android and iOS devices. The data is presented visually in the program, making it instructive and user-friendly. The device's development technique involved extensive testing and validation against established comparators to assure accuracy. The results of this study show that this digital, multi-purpose health monitoring device works well and reliably to give real-time health information. This innovation promotes health monitoring and digital health information access.","PeriodicalId":414022,"journal":{"name":"Advance Sustainable Science Engineering and Technology","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117242116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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