Reviewerly/exHarmony huggingface dataset:
REFORMS ARE ONGOING IN THE HIGHER EDUCATION SYSTEM

&#x0D; This article discusses the reforms carried out in the higher education system, practical work in improving the quality of education, and the knowledge necessary for training future specialists. Information is provided about the social necessity of teaching the science of educational management, the integration of the preparation of students for professional activity with educational management. The opinions of the representatives of management are presented.&#x0D;
Examining the Developing Influence of Emerging Technologies in the Media Sector of Afghanistan

The study sets the stage by outlining the growing significance of digital platforms in Afghan media and the transformative potential of emerging technologies in this landscape. The purpose of this research is to comprehensively analyze the current state of affairs, challenges, and opportunities associated with integrating emerging technologies into Afghanistan's media sector. Employing a mixed-methods approach, the study utilizes questionnaires and interviews to gather data from a sample size of 80 participants representing diverse media outlets in Afghanistan. Quantitative analysis involves descriptive statistics and chi-square tests, while qualitative data from interviews are thematically analyzed to provide deeper insights. The results shed light on notable disparities in technology adoption rates, with cloud computing and artificial intelligence emerging as frontrunners. Anticipated integration levels vary across technologies, with cloud computing and AI garnering significant interest. Perceived challenges in integration range from moderately to extremely challenging, while opportunities for media development are viewed as promising. In conclusion, the study underscores the transformative potential of emerging technologies in Afghanistan's media landscape, despite existing challenges. It offers recommendations such as enhancing media literacy and addressing infrastructure gaps to fully harness the benefits of technological advancements.
Sistem Pakar Diagnosa Penyakit Angsa Menggunakan Metode Forward Chaining Berbasis Web

Geese have many benefits for human needs. Starting from meat, eggs, feathers, fat, and other uniqueness such as guarding other livestock if there are animals or strangers approaching their territory. However, in Indonesia, goose utilization is less desirable and only a few do it. This poses a probӏem for goose breeders due to the absence of knowӏedge about the diseases experienced and the handӏing soӏutions. An expert system is an artificiaӏ inteӏӏigence that supports expert decision-making. The forward chaining method provides conclusions through rules derived from existing facts. This Expert System for Diagnosing Swan Diseases Using the Web-Based Forward Chaining Method is designed to make it easier for farmers or users to consult about the diseases experienced and treatment solutions with 90% diagnostic results. This expert system is made based on a website that can be accessed easily and at any time. Based on black box testing, the results obtained show 100% system functionality, so this expert system application can be used.
Sistem Pendukung Keputusan Penentuan Lokasi Pabrik Baru Menggunakan Metode ROC dan MAUT

Factories are places where goods and services are mass produced. Factories collect and integrate various resources, such as labor, capital, and machines. Factories have an important role in a country's economy, because they produce goods and services that society needs. Apart from that, factories can also create jobs and improve community welfare. However, factories can also have a negative impact on companies and the environment. Companies that produce goods with certain raw materials must consider the availability of raw materials at the factory location. If raw materials are not available at that location, the company must bring them in from other locations, which can increase production costs. Apart from that, companies must also consider the availability of adequate infrastructure, such as roads, electricity, air and telecommunications. If infrastructure is inadequate, companies have to invest to build it, which can also increase production costs. In this study, the ROC method and the MAUT method were used, the ROC method was used to determine the level of importance of the criteria used in selecting the factory location. The MAUT method is used to rank factory locations. The five criteria used in selecting factory locations are: availability of raw materials, availability of infrastructure, labor costs, transportation costs, and environmental impact. A total of 10 factory locations were selected based on predetermined criteria. The final results show that the best factory location is location E with a final score of 0.682, followed by location H and location F with a final score of 0.619 and 0.453 respectively.
Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.
Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web

Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
Penerapan Metode Support Vector Machine Dalam Memprediksi Prediksi Cuaca

Weather is a natural that has a big impact on humans. Information about weather predictions is really needed by humans, especially in the city of Medan. This information is very useful for knowing weather events around us. Data mining is a process of collecting important information from large data, so that it can help make decisions and make accurate predictions. So researchers are interested in conducting research in predicting the weather in the city of Medan using the Support Vector Machine (SVM) method as a solution for predicting the weather in the city of Medan. The application of data mining using the SVM method helps produce precise accuracy for weather based on predetermined criteria. This method is suitable for weather predictions because it is able to provide clear and accurate assessments with weather predictions of 54.55%.
Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat

Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.
An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.
Design a Desktop-Based Load and Customer Calculation Application Information System (SIAPEL)

In today's technological developments, many people are using technology to make work easier, as is PT. PLN Persero Customer Service Implementation Unit (UP3) Tasikmalaya. Several parts of this company, especially the section for recording expenses and customers by the PDKB Team, still use manual methods, namely by calculating using a calculator. This method is very risky, especially as it has the potential for errors in calculations or writing of the recorded numbers. Given these problems, a desktop-based load and customer calculation application information system (SIAPEL) was built. Information system solutions for related load and customer data calculation applications (SIAPEL) so that the results obtained are faster and more accurate. By making direct observations or observations, actively communicating with related fields through the interview process, and looking for research materials that support building an application as a solution to the problems faced. The load and customer calculation application information system (SIAPEL) is an application that can calculate load and customer data and can store the data as a form of company archive. The system development used is waterfall with stages or processes carried out sequentially from the system. The software used to build the load and customer calculation application information system (SIAPEL) is NetBeans 8.2, Java Development Kit 1.8, and MySql. Users can process load calculation data and process customer calculation data. And users can print reports from data that has been entered into the system database. With SIAPEL, it is hoped that it can reduce the risk of information errors and make it easier for users to calculate, store and process data. And it can be used as a more effective way to process data compared to using manual methods
Prediksi Tingkat Angkatan Kerja Terhadap Pengangguran Terbuka Di Semarang Menggunakan Regresi Linier

Unemployment is a situation where a person who does not have a job is caused by many factors, not only because they are lazy to look for work but mostly in this region of Indonesia unemployment is caused by limited employment opportunities, a lot of competition in the world of work, the large number of the labor force, lack of experience in the world of work, and also too choosy in working. The unemployment that occurs in Semarang is caused by the high number of labor force that makes the unemployment rate more and more. In this research, the author predicts the level of unemployment in Semarang. This research is a quantitative research whose data is taken from BPS Semarang. In this research, the author uses linear regression algorithm. The algorithm is widely used in cases to predict a problem, this research produces an RMSE (Root Mean Square Error) value of 0.07 with an R Square value of 91%. The results obtained can be used as a reference for the government to see the high unemployment rate in Semarang.
UTAPIS Indonesian Word Error Detection Application: Design and Development

People misspell and incorrectly write Indonesian words without even recognizing it. Poor and incorrect language proficiency is a common cause of word spelling mistakes. Since PT Tribun Digital Online publishes between 600 and 700 articles a day, the U-Tapis website might be of great assistance to journalists in identifying grammatical problems. Three separate cities—Jakarta, Makassar, and Solo—have participated in the testing of the U-tapis website. @ Tribun Digital Online, PT. This department is in charge of using the Flask framework to create the U-tapis website design. This study develops software or applications that are comparatively brief, succinct, and quick using the RAD (Rapid Application Development) technique. In addition to system development methods. The implementation of this method uses the U-tapis Sentence Syntax Error Detection algorithm which functions to detect an algorithm comparing two pieces of data, calculated using a formula called the Jaccard Index to check spelling errors in words. UAT test results produce all the features according to the wishes of the company owner based on the problems faced.
Modeling of Wind Power Generation in Tegal Region using Matlab Simulink

The increasing energy demands require larger energy production. However, fossil energy sources are depleting, necessitating the use of renewable energy to replace fossil fuels and meet energy needs in Indonesia. Indonesia has a significant wind energy potential. Wind power generation modeling is carried out in one of the cities in Indonesia, namely Tegal. By using Matlab Simulink, it is expected to assist in predicting renewable energy production. Tegal is one of the regions with high wind speeds in Indonesia, specifically at 3 m/s. From the modeling, the optimal blade radius and rotor angular velocity that can generate maximum power at a wind speed of 3 m/s can be determined. Wind power generation in Tegal with a wind speed of 3 m/s has the potential to produce electrical power of 768.55 W.
SISTEM PENYIRAMAN DAN PEMUPUKAN OTOMATIS BERBASIS INTERNET OF THINGS (IOT) DENGAN PEMANTAUAN ANTARMUKA APLIKASI ANDROID

Technological developments in the field of agriculture are now expanding widely, various modern tools must be owned to assist farmers in cultivating plants. The owners of the chili plants do watering and fertilizing by collecting water or fertilizer and then giving it to the plants one by one. This is not a problem if the plant owner has free time. However, if the plant owner is busy with other things or is having business that makes the plant owner unable to care for the plant, then this becomes a problem because when the plant does not receive sufficient care, the plant will easily wither and die. Seeing the existing problems, this research will design a system that can automatically water and fertilize plants based on IoT with monitoring from an Android application. The design of this system uses an Arduino microcontroller as a controller, the nodeMCU esp2688 wifi module to send data from the sensor to the web server, a soil moisture sensor to determine soil moisture, a real time clock (RTC) for fertilization scheduling and a relay as an automatic switch on the water pump. This system aims to help the activities of farmers who still use physical force to water and fertilize plants. The IoT-based automatic watering and fertilizing system uses the prototype development method, namely by collecting system requirements, building prototyping, evaluating prototyping, coding the system, testing the system, evaluating the system and using the system
WEBSITE SISTEM PAKAR DIAGNOSA PENYAKIT KULIT DENGAN FORWARD CHAINING (STUDI KASUS : PUSKESMAS TANGGULANGIN)

A website-based expert system for diagnosing skin diseases using the Forward Chaining method and the Laravel Framework at the Tanggulangin Health Center. The main focus of research is to improve accessibility, accuracy of diagnosis, and public awareness of skin health. Quantitative approaches and exploratory design are applied through literature studies, interviews, surveys, system design, trials, evaluations, and improvements. The system development process involves collecting data from observations, interviews, and medical data to devise relational rules, tables of disease types, symptoms, and decision trees. The use of the Laravel Framework is the main foundation of system design, including user interface (UI) design, database structure, Forward Chaining inference logic, and diagnostic needs. System trials are conducted with test scenarios to evaluate the system's ability to diagnose various skin disease conditions. Data analysis is carried out quantitatively, especially on test results, to assess system reliability. The results of this study provide an empirical basis for recommendations for further development. Hopefully, this system can make a positive contribution in the process of diagnosing skin diseases at the Tanggulangin Health Center and increase overall public skin health awareness. This system is able to provide a diagnosis of skin diseases based on the symptoms inputted by the user. The evaluation carried out provides a positive picture regarding the effectiveness and satisfaction of users.
Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).
Automatic Text Labeling Method Based on Large Language Models

With the increasing demand for large amounts of training data for model development, this paper proposes LLM4Label, an automatic text labeling method based on large language models, to assist human labelers in annotating text data. LLM4Label first selects the most representative seed data using a clustering algorithm based on text similarity. It then constructs prompt dialogues with few-shot prompts to stimulate the language model’s performance on entity labeling tasks, enabling it to automatically and efficiently label more data. Finally, LLM4Label introduces human feedback to correct un- certain labeling results and retrains the model with the corrected annotations. Experiments show that LLM4Label achieves high- quality labeled data at low human labeling cost. The proposed method provides an effective way to obtain sizable and high- quality annotated datasets with minimal manual effort, which can strongly support downstream natural language processing tasks.