Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performanc...
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Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance.
Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung...
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ISBN:
(数字)9798331505530
ISBN:
(纸本)9798331505547
Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.
The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perfo...
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Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to rest...
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Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method.
In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating t...
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Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo cust...
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Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.
Tourism continues to be developed, as one of important sectors to support foreign exchange revenue and also support the economic sectors. The purpose of this research finds the mapping factor from digital economy and ...
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This paper presents a motion strategy for a fruitset-reagent spraying operation using a mobile manipulator-type robot. It is very time-consuming to search for and detect flowers and plan the manipulator’s trajectory ...
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ISBN:
(数字)9798350394276
ISBN:
(纸本)9798350394283
This paper presents a motion strategy for a fruitset-reagent spraying operation using a mobile manipulator-type robot. It is very time-consuming to search for and detect flowers and plan the manipulator’s trajectory on time. Therefore, we propose a strategy to effectively execute the manipulator’s trajectory planning in advance based on the flower positions obtained by the robot running in advance, and to execute the spraying and detecting of flowers by orbiting around greenhouse. As a result, the hourly spraying efficiency was improved compared to related methods. This achievement is important in that it enables on-time spreading process in an environment where the operation time is limited.
The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. ...
The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.
E-Government and Tourism are fields of research that are constantly evolving. Where tourism services are one of the major foreign exchange earners for most countries. Governments in various countries are trying their ...
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