Reviewerly/exHarmony huggingface dataset:
Analisis Sentimen pada Twitter menggunakan Word Embedding dengan Pendekatan Word2Vec

In this day and age, the use of social media is familiar to some circles. The existence of social media can be analyzed for certain interests. This analysis can also be carried out for the benefit of knowing the opinions or sentiments that contain it. Therefore, a sentiment analysis is needed to get a classification of existing opinions. The use of sentiment analysis cannot be separated from the document or text representation stage. This usually takes the form of the bag of word (BOW). However, BOW has a weakness, namely it produces a lot of features so that the classification accuracy results are less than optimal. Therefore we need the Word Embedding method to represent documents in vector form. The use of this method results in fewer features so that data training time can be shorter. Apart from that, the syntax and semantics of the words that compose the tweet are also considered. So, Word Embedding produces meaningful vectors.
Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method

Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification
LEARNING LANGUAGE WITH TECHNOLOGY OUT OF CLASSROOM SELF-ORGANIZED

Various studies have focused on language learning by using technology in/outside the classroom. For more investigation, this proposal study examined self-independent learning English outside the classroom; it aims to determine the most frequent technological tool that learners preferred, and what is the most effective online platform. Because of the overlap, several studies published from 2004 up to 2021 have been reviewed. Self-organized as a theoretical framework has been adopted. Participants from three different nationalities: 15 from Indonesian, three Sudanese, and two from Malaysia. Findings show participants used technological means for learning English independently; the smartphone is more used; among many apps, youtube is the best platform for learning English through videos and movies. In sense of wide opportunities created by technology, materials availability, and easy accessibility to improve language learning out of the classroom.
How the Connectedness Between Humans and Nature Can Be Increased? Comparing ChatGPT’s Language Use to Scientific Literature

The artificial intelligence chatbot ChatGPT can answer questions and solve tasks posed by the user within seconds. Using an exemplary task, this study investigates the reliability and con-sistency of the answers given by ChatGPT. For this purpose, it was analyzed whether the an-swers differ when a question is asked repeatedly and whether selected content categories and associated word combinations coincide with those from scientific literature. ChatGPT was asked 400 times, how the connectedness between humans and nature can be increased. The responses were classified into a categorization system and the most frequently occurring word combina-tions for each category were extracted. These word combinations were used for a search of re-lated scientific literature. In addition, the Normalized Mutual Information (NMI) between the 400 individual responses was calculated. The results show that the phrases used by ChatGPT within the categories are also found in the scientific literature, however, the number of mentions between scientific literature and ChatGPT differs. In addition, the NMI suggested that there are large differences between the categories mentioned in the individual answers. These results in-dicate that ChatGPT currently still has difficulties in answering complex questions and cannot yet replace scientific literature research or academic writing.
Ball and Beam Control: Evaluating Type-1 and Interval Type-2 Fuzzy Techniques with Root Locus Optimization

This study evaluates the performance of three control systems, namely the root locus method, type-1 Mamdani fuzzy logic system (FLS), and interval type-2 Mamdani FLS, in noise-free and noisy ball and beam systems. The main contribution of this study is enabling improved design and implementation of control systems in real-world applications by offering a comprehensive understanding of each control system's performance. The methodology involves conducting four tests focusing on various input types, including a 0.8-meter step input and sine wave function, and assessing the presence of noise in the system. The performance of each control system is analyzed using parameters such as rise time, setting time, and percentage overshoot, with the interval type-2 Mamdani FLS further examined by varying footprint of uncertainty values. Results from noise-free tests reveal that the root locus method has shorter rise and setting times, but a higher percentage overshoot compared to the type-1 Mamdani FLS and type-2 Mamdani FLS. In noisy environments, the type-2 Mamdani FLS with varying Footprint of Uncertainty values outperforms the type-1 Mamdani FLS with reduced rise time, setting time, and percentage overshoot. The root locus method shows a significantly higher percentage overshoot in noisy conditions compared to the other two control systems. In conclusion, the type-2 Mamdani FLS control system demonstrates superior capability under changing conditions compared to the type-1 Mamdani FLS, with its performance varying based on footprint of uncertainty values. This study highlights the importance of selecting the appropriate control system depending on specific needs and environmental factors.
Application of Talking Stick-Based Learning Media to Improve Students' Critical Thinking

The reason for this study is learners' low critical thinking ability on the material "economic activity". This study aims to describe the results of the application of Talking Stick-based learning media to students' critical thinking skills on the material of economic activities carried out in class VII I of Al Maksum Stabat Private Junior High School. This research method uses a qualitative method with a descriptive approach. The data collection techniques used are interviews and documentation. The research acts as a data collection instrument. According to Miles and Huberman, the data analysis techniques used are data reduction, data presentation (data display), and conclusion drawing/verification. The research subjects used are students of class VII I of the 2022/2023 school year. The number of students is 25 people. The results of this study showed learners' critical thinking ability on the material of economic activity. Seven participants in Students were able to answer when they got their turn in the talking stick game, while 2 students were still reluctant to answer questions. Researchers use questionnaires to discover the development and influence of the application of Talking Stick learning media.
Rancang Bangun Sistem Informasi Akademik Menggunakan Framework Codeigniter Pada universitas Muhammadiyah Purworejo

Academic Information System is a system that provides services that are academic data information designed in accordance with business processes that run to improve the performance and quality of academic services. This academic information system has many benefits for institutions in the field of education, be it in the processing of teaching data, value data, and other data related to academic learning in this case, especially universities. The problem that exists today is the absence of an academic information system at the University of Muhammadiyah that is able to store all academic data. Therefore, academic information systems that can meet the needs are very necessary. This research discusses the design of web-based systems using codeigniter 3 framework which is expected to produce an academic information system that meets the needs.
Designing, Planning, and Prototyping a Google Cloud Network

Like in a data center, computers are physically interconnected with cables, and data are transmitted across the network(s) using bridges, repeaters, switches, and routers; in the cloud, the compute elements of your workload—for example, Compute Engine instances (commonly known as virtual machines—VMs), Google Kubernetes Engine (GKE) nodes, Cloud Run container instances, App Engine instances—are also interconnected, but using a different technology, that is, software-defined networking.
Explore the potential of deep learning and hyperchaotic map in the meaningful visual image encryption scheme

Abstract In recent years, meaningful visual image encryption schemes that the plain image is compressed and encrypted and then hidden into the carrier image have received increasing attention. This paper proposes a new meaningful visual image encryption scheme, which consists of three stages: compression (compression network)—encryption (2D‐SLC hyperchaotic map)—hiding (matrix encoding). First, the advantages of deep learning are explored. It can compress the width, height, channel, and pixel values of the plain image simultaneously. Second, a new 2D‐SLC hyperchaotic map is designed to ensure security. It has a larger chaotic space and better randomness. Finally, to obtain a high‐quality cipher image, the secure secret image is hidden in the grey carrier image by matrix encoding. The scheme can compress and encrypt the grey or colour plain image and then hide it in a grey carrier image. In addition, the theoretical peak signal‐to‐noise ratio (PSNR) between the cipher image and the carrier image is improved from 40.9292 to 42.1785 dB. The total running time is only about 0.35, 0.87 and 3.1 s for a 256 × 256, 512 × 512 and 1024 × 1024 grey or colour plain image, respectively.
Improving Students‘ Motivation in Learning Phonics through Games

This research was designed to improve students' motivation in learning Phonics through games.It is a classroom action research with two cycles.The procedures of each cycle were planning, acting, observing, and reflecting.The research subjects in this research were 21 K2 students of Tunas Bangsa Kindergarten.The data collected qualitatively by using classroom observation, interview, and documents.The research finding showed that the students' motivation in learning Phonics through games has increased due to the application of games during teaching and learning process.The students were more engaged in learning when lesson presented involved games, and they also put more concerned about their assignments, attendance, and activities during the learning process.They stated that it became easier to understand the lesson because they can directly practice it the game application in the learning process.From this, the games guaranteed to increase students' motivation in learning Phonics..
ANALISIS MOTIVASI BELAJAR SISWA PADA MATA PELAJARAN IPS DI KELAS IV SDN 43 TAKKALA KOTA PALOPO

This study aims to analyze atudent leraning motivation in social studies subject. This type of research is a descriptive study using a quantitative appoarch. This resesach was conducted in takkalala public elementary schools palopo city. The source of data in this study were 20 students of grade IV takkalala public elementary schools. Data collection techniques used were quiestonairre techniques dan interview techniques. Student learning motivation data were obtained form filling out questionnaire sheets given to 20 students containing 21 statement items and interviews with class teachers. Then the data obtained were analyzed using descriptive statisyic. The result showed that average score of the processed data was72 with good category.
Towards retention in airline industry using neutrosophic DEMATEL method: Does social media marketing activities affect passengers’ retention

This study examines the effect of social media marketing activities on passenger retention through brand image in the airline industry in the case of Fly Emirates. The survey included a sample of his 758 passengers traveling to Dubai from destinations around the world. The questionnaire was randomly distributed, and data was collected and returned from his 455 passengers. The purpose of this study is to examine the effect of social media marketing activities (trendiness, entertainment, interaction, word of mouth, and customization) on passenger retention through mediated brand image. This study applies structural equation modeling (PLS-SEM) to investigate the relationship among the constructs in the proposed model. These aspects are modeled using the neutrosophic decision-making trial and evaluation laboratory (DEMATEL) approach. The DEMATEL models the fuzziness related to domain specialists generating judgments inside the DEMATEL as single-valued neutrosophic numbers, while also handling the causal linkages among elements of social media marketing activities. The study concludes that all dimensions of social media marketing activities (trendiness, entertainment, interaction, word of mouth, and customization) have a positive effect on brand image. The findings also confirmed that brand image plays a mediation role between social media and passenger retention, with a positive brand image being associated with increased passenger retention. The study found that without effective social media marketing, Fly Emirates loses passengers, and brand image is a key driver of passenger retention. The study recommends that Fly Emirates, looking to improve passenger retention, invest heavily in attractive social media marketing activities in the marketing innovation segment.
Ethnomathematics: The exploration of fractal geometry in Tian Ti Pagoda using the Lindenmayer system

This study explores the concept of fractal geometry found in the Tian Ti Pagoda. Fractal geometry is a branch of mathematics describing the properties and shapes of various fractals. A qualitative method with an ethnographic approach is used in this study. Observation, field notes, interviews, documentation, and literature study obtained research data. The observation results were processed computationally using the Lindenmayer system method via the L-Studio application to view fractal shapes. The results show that the concept of fractal geometry is contained in the ornaments on the Tian Ti Pagoda. The length and angles of each part of the ornament influence the fractal shape of the Tian Ti Pagoda ornament. In addition, the length and angle modifications resulted in several variations of the Tian Ti Pagoda fractal. The findings from this study can be used as an alternative medium for learning mathematics lectures, especially in applied mathematics, dynamical systems, and computational geometry.
Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.
A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

Abstract Today cloud computing is at the heart of all information technologies. This prodigious technological paradigm relies on a very simple concept defined as the ability to deliver hardware and software resources as service directly over internet. A set of mechanisms cooperate to maintain the cloud reliability and to allow continuous delivery of these services while guaranteeing the same quality of service (QoS) and respecting the service-level agreement (SLA) for each client. Load balancing is one of those mechanisms and it ensures a crucial service, it can be defined as the ability of the system to ensure fairness in the distribution of workload over all servers. The most recent load balancing techniques are hybrid methods involving in major cases the combination between static and dynamic approaches, in other cases it can go further by integrating other mechanisms in order to improve the overall efficiency of the systems. The performance can be evaluated by parameters which generally refers to the degree of compliance with SLA and QoS. In order to enhance load balancing and tasks scheduling in cloud environment we propose in this paper a different hybrid approach which allows the decomposition of the problem and to operate on two levels by going through two stages : (i) first clusters are built for each datacenter grouping together sub-sets of servers that have close utilization rates, (ii) then tasks scheduling and load balancing operate at the datacenter level to deal with distribution over clusters and at the cluster level to ensure fairness between servers of the same cluster. Our method allows hot-deployment in already operating cloud environments and an excellent scalability. It also offers decoupling of missions and strong interoperability between the different mechanisms. To prove its validity, we implemented it on the standard cloudSim plus simulator, before carrying out a comparative study which shows better results than existing approaches in terms of makespan, reduces reaction time, number of migrations required and SLA violations.
IMAGE DETECTION EDGE IMAGE USING CANNY EDGE ALGORITHM

In general, edge detection is widely used in image processing to find the boundaries of objects in an image. In this study, a technique for detecting edges that is commonly used will be studied, namely the Canny method under different conditions using Visual Studio software. In the testing process that the author carried out, it was concluded that sooner or later a process of converting an object would be, that the following factors affect the conversion process, including; type of image, image resolution, image format, specifications of the camera used to take pictures as well as the specifications of a computer that has a processing speed that is expected to be able to process images (images). The use of the Canny method to detect the edges of an image is considered more accurate and effective because this method is able to provide detailed output by including a calculated execution time
PREDICTION OF PRODUCT SALES RESULTS USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

This study aims to optimize profits and minimize losses from product sales at X Market has achieved in the future. The data used in this study were obtained directly from X Market Trade by observing and interviewing. X Market Tradingis one of the industries that produces and sells food products to be marketed in the region and outside the region. The data to be processed is the result of the sale of food products at X Market uses the adaptive neuro fuzzy inference system (ANFIS) method which is a combination of Fuzzy Logic and Artificial Neural Networks. The data used is monthly sales data from 2018 to 2020 as many as 36 data. From a total of 36 data, it will be divided into 2 types of training and test data distribution, namely 90:10 with a total of 60 epochs and a learning rate range of 0.1 – 0.9. From the research results obtained the highest accuracy of 88.55% on 90% training data and 10% test data with a learning rate of 0.6. It was concluded that the ANFIS method could be implemented in predicting the sales of tofu. By doing this research is expected to provide input to X Market in optimizing profits and minimizing losses from product sales in the future.
Hybrid Cryptosystem Algorithm Vigenere Cipher and Base64 for Text Message Security Utilizing Least Significant Bit (LSB) Steganography as Insert into Image

Security message text is aspect important in modern communications for guard privacy and confidentiality information. Without exists guarantee security, of course just can raises risk when sensitive and valuable information are accessed by unauthorized persons responsible answer. Cryptography and steganography is two field used in a manner wide For reach objective this. Algorithm Vigenere Cipher and Base64 are method used for encryption message text and Least Significant Bit (LSB) steganography was used as method for insert message encrypted to in image. LSB makes use of the last bits from pixels image for keep information addition without bother image visual display in a manner significant. With utilise method here, order encrypted can hidden in a manner confidential in image that looks normal. This hybrid cryptosystem combine excess from third algorithm such, ie speed and effectiveness encryption use algorithm Vigenere Cipher as well ability Base64 characteristics, and levels security message more increase.
DEVELOPMENT OF HYBRID ENCRYPTION METHOD USING AFFINE CIPHER, VIGENERE CIPHER, AND ELGAMAL ALGORITHM TO SECURE TEXT MESSAGES IN DATA COMMUNICATION SYSTEM

With the development of technological advances in this day and age, it definitely requires a security system on messages and data. The way to maintain the security of data, messages or information requires a branch of science in its application, one of which is the algorithm or cryptography method. In its application, it requires more than one stage of the security process, because data security can be done by combining methods in its security techniques. This research aims to develop encryption methods using Affine Cipher, Vigenere Cipher, and ElGamal Algorithm to secure text messages. Affine cipher, Vigenere cipher and ElGamal are cryptography that can encrypt and decrypt text messages. Encryption is changing the message or plaintext into an unreadable message or ciphertext, on the other hand, decryption changes the ciphertext or message that initially cannot be read into a message that can be read or plaintext back in its original form. The result of this research is the development stage by doing three encryption and decryption processes. For the first encryption process using Affine Chiper which produces the initial ciphertext, then re-encrypted using Vigenere Cipher, then the previous encryption results are carried out ElGamal encryption which produces the final ciphertext. Conversely, the decryption process is first on ElGamal, then Vigenere Cipher, and finally Affine Cipher whose decryption results in plaintext back in the form of the initial text message. So that by developing and combining three algorithm methods can increase the security of information and text messages.