Reviewerly/exHarmony huggingface dataset:
PEMODELAN KASUS TUBERKULOSIS (TB) DI NUSA TENGGARA BARAT MENGGUNAKAN MODEL REGRESI BINOMIAL NEGATIF

ABSTRACTTuberculosis is a direct infectious disease caused by bacteria (Mycobacterium tuberculosis). The number of TB cases in NTB in 2020 decreased by 16.58% from 2019. This needs to be analyzed to find out what factors influence tuberculosis so that the number of tuberculosis cases can be minimized. Data on the number of TB cases is count data, so the analysis used to model is Poisson regresi regression. In Poisson regression analysis, the phenomenon of overdispersion often occurs. If there is overdispersion, Poisson regression is not suitable for modeling the data because it will produce biased parameter estimates. One of the methods used to overcome overdispersion in Poisson regression is Negative Binomial regression. The results of the analysis show that there are four variables that are significant to the number of TB cases, namely population density (), number of health centers (), number of nursing staff (), and the percentage of households that have access to proper sanitation () with the model . The results of selecting the best model show that the Negative Binomial Regression model is better than the Poisson regression model based on the criteria for the goodness of the Deviance and AIC models.
Optimizing Sentiment Big Data Classification Using Multilayer Perceptron

Internet-based platforms such as social media have a great deal of big data that is available in the shape of text, audio, video, and image.Sentiment Analysis (SA) of this big data has become a field of computational studies.Therefore, SA is necessary in texts in the form of messages or posts to determine whether a sentiment is negative or positive.SA is also crucial for the development of opinion mining systems.SA combines techniques of Natural Language Processing (NLP) with data mining approaches for developing inelegant systems.Therefore, an approach that can classify sentiments into two classes, namely, positive sentiment and negative sentiment is proposed.A Multilayer Perceptron (MLP) classifier has been used in this document classification system.The present research aims to provide an effective approach to improving the accuracy of SA systems.The proposed approach is applied to and tested on two datasets, namely, a Twitter dataset and a movie review dataset; the accuracies achieved reach 85% and 99% respectively.
The Implication of Conversational Implicature Used by Teachers in Offline Classroom Interaction

The purpose of this study was to determine the variation or types of Conversational Implicatures used by teacher interactions in the classroom. This study uses qualitative or descriptive methods and uses them according to the theory of Yule. Data were taken from junior high schools and several teachers as research subjects. The data were analyzed by recording and transcribing and also the data was collected using selection, description, and verification techniques. The author categorizes several dialogues spoken by the teacher which show the types of conversational implicatures in-class interaction. The results showed that there are variation or types of conversational implications in classroom interactions that are used by teachers, including (1) specific implicatures, (2) general implicatures, and (3) scaled implicatures. conversational implications identified by the teacher using specific conversational implicatures of the dominant type.
A Review in Applications of Control Engineering Based on Genetic Algorithm

The most popular evolutionary search techniques are genetic algorithms (GAs).Even though they are frequently used to solve control engineering problems, they are currently not a common tool in the control engineer's toolbox.This may be due in part to the fact that there are currently few general overviews of the employment of GAs for control engineering problems, and that they are often reported on at computer science conferences rather than conferences for control engineers.This review study is intended to assist researchers and practitioners in identifying prospective research issues, potential solutions, as well as advantages and disadvantages of each technique.This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems.Additionally, offers a number of control techniques used with the GA that have undergone extensive research.The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn't used till the time of preparing this review.
The role of media culture in today's information wars

The article is devoted to the socio-philosophical analysis of the phenomenon of media culture in the context of the globalization information challenges of today. The formation of the main threats of the modern information society is revealed through the prism of the close relationship of violence, wealth and knowledge as means of implementing power relations in the geopolitical context. It was noted that at each historical stage of the global struggle for power, the most effective means of achieving power goals were strategies of information struggle aimed at manipulating public consciousness. The essential signs of information war from the standpoint of psychological, information and communication approaches are revealed. It is noted that in the society of knowledge, the information and propaganda war acquires the features of a conscious (semantic) one, therefore, in the conditions of the formation of the mass media as key producers of information products marked by certain political or economic interests, it is possible to talk about the media war as a separate technology of modern information wars. In the modern philosophical discourse, to define a special type of culture of the information age, the concept of "media culture" has become widespread, as a set of information and communication tools produced by mankind in the process of cultural and historical development, which contribute to the formation of public consciousness and the socialization of the individual. The work reveals the main functions of media culture - informational, communicative, ideological, normative and legal, motivational, integrative, culture-creating, myth-creating. The fundamental difference between media culture and traditional cultures is emphasized - the level of its technical equipment, which allows the reproduction of media culture products for almost all of humanity, acting as a regulator of its mass consciousness. The specificity of media culture is determined by its semiotic nature and the technical capabilities of the means of its implementation: high information capacity, ease and persuasiveness of perception, speed of replication of information messages, mass and accessibility. The conclusions highlight the need to introduce state programs for the development of digital and media education as an effective mechanism for countering today's informational challenges.
Exploring Non-Linear Programming Formulations in QuantumCircuitOpt for Optimal Circuit Design

Given the limitations of current hardware, the theoretical gains promised by quantum computing remain unre-alized across practical applications. But the gap between theory and hardware is closing, assisted by developments in quantum algorithmic modeling. One such recent development is Quan-tumCircuitOpt (QCOpt), an open-source software framework that leverages state-of-the-art optimization-based solvers to find provably optimal compact circuit decompositions, which are exact up to global phase and machine precision. The quantum circuit design problem can be modeled using non-linear, non-convex constraints. However, QCOpt reformulates these non-linear constraints using well-known linearization techniques such that the resulting design problem is solved as a mixed-integer linear programming model. In this work, we instead explore whether the QCOpt's model could also be effective with a continuous Non-Linear Programming (NLP) model obtained via relaxation of the integer variables in the non-linear constraints. We are able to present not only multiple significant enhancements to QCOpt, with up to 11.3x speed up in run times on an average, but also opportunities for more generally exploring the behavior of gradient-based NLP solvers.
OPTIMALISASI RUTE WISATA DI YOGYAKARTA MENGGUNAKAN METODE TRAVELLING SALESMAN PERSON DAN ALGORITMA BRUTE FORCE

Yogyakarta is one of the popular tourist destinations in Indonesia. There are many tourist sites that can be visited in the city of Yogyakarta, including Prambanan Temple, Breksi Cliff, Van den Berg Museum, and Malioboro. For tourists who are not familiar with these tourist sites, determining the route to be visited is important because it will affect travel costs, especially transportation costs. To solve this problem, the Traveling Salesman Person method and the Brute Force Algorithm are used. The Brute Force algorithm was chosen because of its simple characteristics so that it is easy for all people to understand. The results of the research resulted in the most optimal route with a total cost of Rp. 100,300.00 and the least optimal route with a total cost of Rp. 131,000.00.
ANALISIS MANAGEMENT BANDWIDTH DAN FIREWALL DENGAN ROUTER MIKROTIK PADA PT. BCA MULTIFINANCE

The internet network is something that is familiar to mankind in this current century, where the need for internet access is currently very high, both to find information, articles and the latest knowledge. In general, many agencies have integrated the internet network into their daily activities in the work environment. PT. BCA MULTIFINANCE (BCAMF), is an institution that is engaged in vehicle and multipurpose loan financing. In order for computer network performance to be optimized, it is necessary to have management on the network that is able to handle congested and slow connection problems. The solutions that will be used include the addition of switches and bandwidth management and firewalls using a mikrotik router. The mechanism is that the proxy router regulates bandwidth sharing and IP blocking for each user. The choice of bandwidth management and firewall methods is because these methods meet the criteria for managing access speed for each client or network.
A powerful Transfer learning technique for multiclass classification of lung cancer CT images

Lung cancer is a lethal disease caused by unusual cell growth in the lungs. Early cancer detection leads to potent treatment planning. Precise identification of different types of nodules in CT images through naked eyes becomes arduous for radiologists. Transfer learning-based computer-aided detection system has shown effectual results in providing a second opinion to the radiologist. This paper proposes an EfficientNet-based transfer learning model for multi-class classification of benign, normal and malignant CT images. Experimental results revealed that the proposed model obtains accuracy, precision, recall, the area under curve and F1-score of 100% each. The classification model excelled over the different variants of EfficientNet and other pre-trained networks. Thus, the proposed multi-class EfficientNet model is felicitous for early lung cancer detection.
Analisis Pengaruh Data Augmentasi Pada Klasifikasi Bumbu Dapur Menggunakan Convolutional Neural Network

Indonesia is a country that produces the best spices and herbs. The types of kitchen spices and spices are indeed very diverse and almost similar, so many people cannot distinguish between the types. So it is necessary to create a digital image processing model to sort out the types of spices and herbs. Due to the minimal data in making the classification model, the use of augmented data is used to make the data more diverse. This study aims to compare architecture by applying augmentation and not applying augmentation from photo data of kitchen spices. From the two models, it will be known which level of accuracy of each model is higher than the two models. This study uses the Convolutional Neural Networks method using a mapped architecture. Then use the confusion matrix method for the results after testing is carried out on data testing. The test was carried out with 80 testing data images consisting of own photo images and internet search images. The result of this research is obtained a model for the classification of kitchen spices. The test results show that CNNs that do not apply augmentation from the model only get an accuracy of 54%, while CNNs that apply augmentations from the model get an accuracy of 80%. The application of augmentation to the model gets higher accuracy because the process of using augmentation is to increase the amount of data by creating new data from existing data so as to make more image data. For internet search data and own photos, higher accuracy is data from internet searches. The model that has the highest accuracy is then implemented into a web-based application.
Implementasi Algoritma Haar Cascade Classifier Dalam Mendeteksi Robot Sepak Bola Beroda

In soccer robot contests, generally soccer robots can recognize their own team robots through color detection using the HSV model. Robots that use color detection to identify their own team robot can detect objects that have the same color value, so objects that have the same color will be considered as their own team robot. However, if you only rely on this method, it is still lacking when viewed in terms of object tracking. Haar like feature or known as Haar Cascade Classifier is a rectangular (square) feature method, which gives a specific indication of an image. This method is able to detect quickly and in real time. Therefore, the researchers tried to unite these two detections to be able to produce a better detection system than before. It is hoped that hoped that in this study, researchers can provide input for the Robotics team of Dinamika Bangsa University in improving the accuracy of robot detection against robots in the same team. From the results of the study it was found that the detection of objects with a distance from the camera as far as 100 cm to 150 cm was successfully carried out, but with a distance of more than 150 cm the object was not detected.
Data Mining Dalam Penentuan Pemesanan Buku Perpustakaan UAD dengan Menggunakan Metode Naïve Bayes

Library Universitas Ahmad Dahlan (UAD) has not utilized technology in the book ordering process. The process of ordering books from distributors requires many considerations such as the number of requests, recommendations for study programs, location, year and language. This consideration made the UAD Library take more than 2 weeks in the book selection process. This study aims to apply data mining in determining book orders using the Naïve Bayes method. This study uses 1106 book procurement data for the past year with criteria, namely the number of requests, study program recommendations, location, year, and language. Implementation of data mining using the Naïve Bayes algorithm is carried out in stages including data cleaning, data selection, data transformation, sharing of training data and data testing, implementation and results of the Nave Bayes algorithm and system testing. System testing using the Confusion Matrix method. Based on the Confussion Matrix calculation on the testing data, the accuracy is 90.24%, the precision is 89.69%, the recall is 93.54%, the specificity is 91.04%, and the F1 score is 91.57%. It was concluded that the system test results were said to be good.
Penerapan Metode Dempster Shafer Pada Sistem Pakar Diagnosa Penyakit Rabies

Rabies is a disease caused by the bite of animals such as dogs, cats and bats, if not treated quickly it can cause death. Patients can make a diagnosis for easy treatment of the disease. The number of patients who died because of the bites of animals that transmit Rabies. Animal owners still do not understand the consequences of the bites of the animals they maintain. Advances in expert systems can overcome this problem, namely by designing a web-based computer system that is integrated with databases and programming languages such as PHP-MySQL so that can help people with Rabies to diagnose the symptoms and types of the disease. The purpose of this research is to build an expert system to diagnose web-based Rabies. The application of the expert system in making this decision uses the Dempher Shaper method by generating true and false values on the new and old knowledge bases and comparing them with the weight values in each frame so that the percentage of the disease type is obtained. The result of the system implementation is that the system provides questions in the form of symptoms that must be answered by the patient based on the symptoms experienced by the patient and the results of the process the system will provide information on the type of Rabies Rabies (Prodromal Stage) = with a confidence value of 80.4% and can also obtain a solution.
Deteksi Tingkat Kemiripan Judul Menggunakan Algoritma Oliver Pada Sistem Informasi Pengajuan Skripsi

Thesis is the final requirement of academic education. The procedural system for the title checking process is based on the memory of the huma, thus allowing for the similarity of the thesis title. Therefore, a system can detect the degree of similarity of titles automatically in order to prevent redundancy of thesis titles. This study applies the Oliver algorithm which is a function in the PHP programming language that produces a similarity value of two strings. Oliver's algorithm has not been widely applied in plagiarism detection techniques. Oliver algorithm testing using test data as much as 10 test title data and 217 titles for training data. The test results show that Oliver's algorithm produces a percentage of the title similarity level in accordance with the accepted provisions, namely 60%. It was found that 2 out of 10 thesis titles were rejected with a similarity percentage of 70.06% and 62.89% and 8 thesis titles were accepted with a similarity percentage of 31.94%, 30.48%, 26.11%, 31.86%, 29.47%, 31.13%, 35.01% and 37.08%.
Penerapan Metode SMARTER Pada Penentuan Media Literasi Pembelajaran Anak Berkebutuhan Khusus

Children with Special Needs (ABK) are part of society that must be empowered, both from their physical and mental limitations. In learning activities, especially for abk, they need learning media tools. There are 4 types of abk, namely Deaf, Visually Impaired, Deaf and Deaf. There are many learning media that can be used to increase the potential of abk students at SLB, but the school does not know the learning literacy media that suits the needs of students. This study aims to provide recommendations for learning media for website-based ABK students by considering three aspects of the criteria, namely academic assessment, non-academic assessment, and developmental assessment. The method used in decision making is the Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER). Weighting in the SMARTER method uses the Rank Order Centroid (ROC) formula. The results of determining abk learning literacy media using the SMARTER method from 52 ABK students obtained media recommendations that can be given by students based on the severity level determined based on the final grade range. Meanwhile, based on the results of the questionnaire assessment to 14 respondents consisting of 1 principal and 13 teachers, it showed that the results of testing the system with the blackbox testing method obtained data for interface aspects as much as 88%, application performance 91%, database 90%, missing / damaged functions 46% and initialization 94%. Thus, the overall average application produced an index of 82% which is included in the good criteria.
Analisis dan Penerapan Algoritma C4.5 Untuk Memprediksi Kualitas Penelitian dan Publikasi Ilmiah

Research is an obligation for every lecturer in Indonesia. In the midst of increasing publications in Indonesia, however, there is a problem, namely the quality of Indonesian publications which is still low when compared to those countries in Southeast Asia. In this case the C4.5 algorithm is a method that can help predict problems that have been carried out by research on the quality of research and scientific publications. This research was conducted in order to assist the study program to assess the quality of research and scientific publications. From this research, it can be said that by using the C4.5 algorithm, the quality of research and scientific publications can be predicted with a fairly high accuracy, the results of measuring the accuracy of the data obtained from data training with Confusion Matrix with dataTraining testing 81.65%.
Penerapan Metode Double Moving Average dalam Memprediksi Permintaan Kayu

UD. Tunas Meranti Kuala Tanjung is a business engaged in the manufacture of furniture. Problems faced by UD. Tunas Meranti is difficult to predict the demand for the type of wood that customers want for furniture making, so it is difficult to determine which type of wood should be in stock every month. If the supply of wood exceeds demand then storage costs will increase. The risk is that wood that is stored for too long will become dry, hard and difficult to process. To maintain business continuity, a wood supply strategy is needed, namely by making predictions (forecasting). The purpose of this research is to be able to predict the demand for wood types so that business owners can provide wood types according to customer needs. The method used in this study is the Double Moving Average method in order to minimize the chance of errors between predictions and actual data. To be more efficient, the author designed a web-based forecasting system and uses a MySQL database. The conclusion from the prediction results of meranti wood demand for the next period is 43 sticks with a MAPE value of 8% and mahogany wood is 19 sticks with a MAPE value of 11%, which means that the prediction using the DMA method is very effective.
Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

The purpose of this study is to cluster the efficiency and performance of students. This is because the academic community is currently faced with several challenges in terms of analyzing and evaluating the progress of a student's academic achievement. In the real world, classifying student performance is a scientifically challenging task. Recently, several studies have applied cluster analysis to evaluate student outcomes and used statistical techniques to divide their scores in relation to student performance. This approach, however, is not efficient. In this study, we combined two techniques, namely k-mean and elbow clustering algorithm to evaluate student performance. Based on this combination, the performance results will be more accurate in analyzing and evaluating the progress of student performance, the application of the Elbow method according to this study gives the best number of clusters to 3, and when the K-Means method is applied, data is generated that the number of students is 73 students, from 4 repetitions. There are 3 clusters, namely the category of "Achievable", "Potential for Achievement", and "Less Achievement", with the results of the "Achievable" cluster as many as 34 students with a percentage of 47.22%, the cluster "Potential for Achievement" as many as 24 students with a percentage of 33.33 %, and the "Less Achievement" cluster as many as 15 students with a percentage of 19.45%.