Twitter is a social networking platform that can be used as resources to find information in real time. Twitter is one of the social media that does not have writing criteria, users are free to express their thoughts ...
Twitter is a social networking platform that can be used as resources to find information in real time. Twitter is one of the social media that does not have writing criteria, users are free to express their thoughts and send messages. There are many messages and tweets that use abbreviations, foreign words, local and mixed languages, which are characteristic of informal language patterns. Informal language patterns do not rely on Indonesian, making the process of annotating word classes less precise and influential on the level of precision. The use of POS Tagging in previous research can annotate word classes well in formal sentences. The problem in informal sentences is solved by the process of word normalization after the preprocessing stage, where informal words are converted into formal words then Part-of-Speech Tagging is carried out on each word in the sentence such as nouns, verbs, adjectives, etc. The dataset used was 846 words, with a informal word count of 99 words. With the Hidden Markov Model commonly used in machine learning as well as the word normalization process, the system is able to increase the resulting precision value, there is a significant increase of up to 10% in the precision produced.
Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is u...
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In digital development 4.0, store brands are very important. The problem in this research is the lack of consumer trust to buy quality goods in e-commerce store accounts so that it affects consumer satisfaction. This ...
In digital development 4.0, store brands are very important. The problem in this research is the lack of consumer trust to buy quality goods in e-commerce store accounts so that it affects consumer satisfaction. This study aims to address this question feedback from the problems of the customer, then on the other hand a questionnaire with the PLS-SEM (Partial Least Squares Structural Equation Modeling) model to determine the dimensions of the variables selected according to customer experience. To achieve this aim, both negative and positive customer comments were compiled to assess customer satisfaction, employing a comparative analysis method through Naive Bayes algorithm. The overarching goal was to achieve optimal results and extract valuable insights regarding the determinants that influenced customer satisfaction within the domain of online transactions. This research also has an impact on buyers so they can have an understanding of the factors that support trust in customer satisfaction, so that individuals do not hesitate in making purchasing decisions to shop online. The results showed that the algorithm initially recorded a modest accuracy score of 0.37. Meanwhile, after implementing hyperparameter tuning, the accuracy increased significantly to 0.62. In the aspect of Smart PLS questionnaire analysis, a standardized Normed Fit Index (NFI) of 0.707 was recorded, which was slightly below the established threshold of 0.90. The standardized root mean square residual (SRMR) was measured at 0.071, falling below the specified value of 0.08, indicating a commendable model fit. However, the RMS theta value at 0.240 exceeded the threshold of 0.102.
Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green ...
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Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.
Context: Mentoring in Open Source Software (OSS) is important to its project’s growth and sustainability. Mentoring allows contributors to improve their technical skills and learn about the protocols and cultural nor...
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Context: Mentoring in Open Source Software (OSS) is important to its project’s growth and sustainability. Mentoring allows contributors to improve their technical skills and learn about the protocols and cultural norms of the project. However, mentoring has its challenges: mentors sometimes feel unappreciated, and mentees may have mismatched interests or lack interpersonal skills. Existing research has investigated the different challenges of mentoring in different OSS contexts, but we lack a holistic understanding. Objective: A comprehensive understanding of the current practices and challenges of mentoring in OSS is needed to implement appropriate strategies to facilitate mentoring. Method: This study presents a systematic literature review investigating how literature has characterized mentoring practices in OSS, including their challenges and the strategies to mitigate them. We retrieved 232 studies from four digital libraries. Out of these, 21 were primary studies. Using this, we performed backward and author snowballing, adding another 27 studies. We conducted a completeness check by reviewing the references of the 4 most relevant primary studies, which resulted in us adding 1 additional study. We then conducted a full-text review and evaluated the studies using a set of criteria;as a result, 10 papers were excluded. We then employed an open-coding approach to analyze, aggregate, and synthesize the selected studies. Results: We reviewed 39 studies to investigate the different facets of mentoring in OSS, encompassing motivations, goals, channels, and contributor dynamics. We then identified 13 challenges associated with mentoring in OSS, which fall into three categories: social, process, and technical. We also present a quick-reference strategy catalog to map these strategies to challenges for mitigation. Conclusions: Our study serves as a guideline for researchers and practitioners about mentoring challenges and potential strategies to mitigate these challenge
Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, a...
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Machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, mor...
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Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body’s immune system. Leukemias are divided into four main groups based on cell type and growth rate...
Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body’s immune system. Leukemias are divided into four main groups based on cell type and growth rate. Acute Myeloid Leukemia (AML) is one of them, namely leukemia that occurs in the myeloid series. AML itself is divided into several subtypes, according to the French-American-British (FAB), namely M0-M7. AML subtypes M1, M2, and M3 are influenced by the same cell type, namely myeloblast cells, so a more detailed analysis is needed to classify them. Therefore we need a system that can identify the presence of AML in the blood, this identification is done by classifying white blood cells on microscopic images. To detect the presence of AML in microscopic images, multiple processes will be used, including preprocessing, segmentation, feature extraction, and classification stages by applying the K-NN method. This classification procedure will result in a diagnosis of M1, M2, or M3 blood cells. At the end of phase 1 of this final project, pre-processing and segmentation processes and graphical user interface (GUI) designs have been carried out. This final project resulted in an accuracy of 80% for the identification of subtype M1, 70% for the identification of subtype M2, and 80% for the identification of subtype M3.
The increasing reliance on e-Government services has amplified the importance of digital forensic readiness (DFR) in ensuring effective incident response to mitigate the impact of cyber incidents. A specialized DFR fr...
The increasing reliance on e-Government services has amplified the importance of digital forensic readiness (DFR) in ensuring effective incident response to mitigate the impact of cyber incidents. A specialized DFR framework would streamline incident response procedures within e-Government services, facilitating prompt identification, containment, and remediation of cyber threats. One of the most notable characteristics of DFR for e-Government is the implementation of a formal policy on forensic readiness. This paper proposes novel DFR steps to guide the formulation of such a policy. The proposed framework is tailored by combining two DFR frameworks that Claims and Elyas et al. contributed. The new framework involves identifying crucial DFR parameters and mapping domain parameters between both frameworks. The framework was then implemented in a local government organization in West Java, Indonesia. The result is a security policy recommendation for Digital Forensic Readiness, which consists of 8 domains and 33 subdomains. The security policy provides a practical approach to enhance the DFR capabilities of e-Government services to enhance incident response and cyber resilience.
Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work...
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