Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma Bayesian Regulation untuk Estimasi Posisi Cadangan Devisa Indonesia

Foreign exchange reserves need to be predicted because it is a significant monetary indicator to show the strength or weakness of a country's economic fundamentals. Therefore, the purpose of this study is to estimate the position of Indonesia's foreign exchange reserves at the end of 2022 and 2023 so that the government has benchmarks and information in determining the right economic policy so that the position of foreign exchange reserves remains stable. The estimation algorithm used in this study is the Bayesian Regulation algorithm, one of the Artificial Neural Network algorithms. The research data used is data on the position of Indonesia's foreign exchange reserves (US$ million) obtained from the economic reports of Bank Indonesia. This research will be analyzed using three network architecture models 4-9-1, 4-18-1, and 4-27-1. Based on the analysis of the three models used, the results show that the 4-27-1 model is the best because it has a lower Mean Square Error (MSE) value of 0.00203297. Another benchmark is seen from more minor epochs (iterations) and faster times than the other two models, even though they both produce a 100% accuracy rate. Thus, it can be concluded that the Bayesian Regulation algorithm is good enough to estimate the position of foreign exchange reserves using the 4-27-1 model. Based on the prediction results, the part of Indonesia's foreign exchange reserves at the end of 2022 and 2023 slightly decreased compared to 2021.
Investigating the cello position, bow motion and cellist posture using motion capture

Cello position and playing posture are of great importance in the cello learning process as well as for the players’ technique. To gain more information on this topic, a study is conducted in which advanced cellists are recorded using optical motion capture technology to examine their posture, cello position and bow movement. In addition, this study aims to find a position of the cello in which it can be played comfortably by both human players and an instructed robotic arm. Therefore, a cello is mounted on a custom-made holding structure in which it can be held immobile during playing. Participants are invited to play several bowing exercises on open-strings and music excerpts from cello literature. The bowing actions and the sitting parameters are compared between the cellists. While the exercises contribute to the data collection of bowing motions to apply to the robotic arm, the music excerpts are used to analyze the playing posture and to allow participants to evaluate their comfort during playing. These investigations will gather information regarding the cello position and the bowing action to inform artificial playing setups as well as introduce systematic methodologies for artistic and educational purposes on the topic of cello posture.
Penerapan Algoritma FP-Growth dalam Penentuan Promosi Kampus

Campus or college is an institution as well as an institution that is included in the education sector. Now the campus is included in a business sector that is engaged in education. Therefore, the campus is also inseparable from the promotion process. The promotion carried out by the campus has the aim of making prospective new students register on the campus. For private campuses, students are input that can be useful to continue the operational process for the campus. The problem that is still widely found at this time is the process of determining campus promotions is still based on assumptions. Where the assumptions used are not based on a fact or special reference. Determination of promotion on campus can be seen based on the data of students who have registered actively on the campus. From the student data, it can be processed based on Regional Origin, School Origin, Department and Study Program taken. Data mining is a technique used to process large amounts of data. Frequent Pattern Growth Algorithm (FP-Growth) is an algorithm that is included in the data mining association technique. The results obtained in the study that for determining the promotion strategy are if you take the Study Program = Informatics Engineering (PS1) then the Department = Science (J1) and Regional Origin = Deli Serdang (AD1) with a Support value = 30% and a Confidence value = 60% and Department = Science (J1), Regional Origin = Deli Serdang (AD1) with Support = 30% and Confidence = 75%. Based on the results of the process that was carried out, it was found that the promotion target was Deli Serdang, the Department was Science and the Study Program of interest was Informatics Engineering.
Sistem Pakar Untuk Diagnosa Penyakit Sapi Menggunakan Metode Bayes

Cow disease can attack animals, both young and adult, in this case it is necessary to do a quick diagnosis to provide basic knowledge about cow disease. 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 it can help farmers to diagnose cattle disease. The purpose of this research is to build an expert system for diagnosing cow disease based on web. The application of the expert system in this decision making uses the Bayes method, in probability theory and statistics, the Bayes theorem is a theorem with two different interpretations. In Bayes' interpretation, this theorem states that one of the decision-making methods, this method was developed to solve decision-making problems by determining the probability value of the event and the value of evidence obtained from the facts about the object under study. What kind of cow did he experience in order to get a solution with treatment. From the results of testing and implementation of this expert system, it has been able to produce Brucellosis (Transmitted Keluron) disease with a weight = 2 which is higher than the weight of other cattle diseases
Rancang Bangun Aplikasi Pemeliharaan Alat Menggunakan QR-Code (Studi Kasus Telkom Property Surabaya Utara)

Telkom Property North Surabaya has a responsibility in terms of maintaining and maintaining equipment for all assets of the Telkom Indonesia company in the North Surabaya and Madura areas. Previously, tool maintenance records were written manually, but the Mei-V application has an impact because now tool maintenance recording can be done through an android application by scanning a QR-Code. The Mei-V application was created with the aim of providing a solution so that equipment maintenance records can be carried out through an application that is connected to a direct database. The application uses the Spiral Model method so that it can carry out continuous development in the form of adding functions or changes to suit existing needs. The result of implementing the Spiral Model that can be felt is the flexibility of application development because it can always be monitored and improved at any time. The Mei-V application can provide various useful information for maintenance activities, including detailed information on equipment maintenance, maintenance reports that can be downloaded by supervisors. The Mei-V application testing was carried out with two test methods. Functionality testing is carried out with details of 4 test scenarios and shows a 100% success percentage. While the Usage Test conducted on 5 respondents showed positive results with details of the average score scale of 3.8 out of 5 points.
Pemetaan Produksi Tanaman Tomat di Indonesia Berdasarkan Provinsi Menggunakan Algoritma K-Means Clustering

Tomato is one of the essential horticultural commodity vegetables because it has high economic value. The need for this plant continues to increase along with the increase in population, income levels, and heightened public awareness of the importance of nutritional value. Therefore, this research aims to see and map the production of tomato plants in Indonesia by the province in the form of clusters (grouping). The research data used in this paper is data on tomato production in Indonesia by the province in the last five years (2017-2021) obtained from the District/City Agriculture Service of each province and the Indonesian Central Statistics Agency. The algorithm proposed in this study is K-Means Clustering with the help of RapidMiner. The results of the proposed paper are grouping and mapping of tomato production in Indonesia, which is divided into 5 (five) zones, including the Black Zone (areas with very high tomato production), which consists of 1 province, Green Zone (areas with high production of tomatoes). Which consists of 2 provinces, the Blue Zone (areas with moderate production), which consists of 4 provinces. The Light Blue Zone (areas with low production), which consists of 8 provinces, and the Orange Zone (areas with moderately low production), which consists of 18 provinces.
Kinerja Algoritma Yamamoto’s Recursive Code dan Algoritma Fixed Length Binary Encoding pada Kompresi File PDF

The use of files in digital form is currently growing and requires media to store them. The more files that store, the more storage space need. This encourages the development of file reduction techniques or data compression techniques with the aim of reducing the required storage space. The compression technique has several algorithms that can be used to compress files such as Yamamoto's recursive code algorithm and Fixed Length Binary Encoding (FLBE). These algorithms have different performance to produce quality compressed results, so they need to be compared. This study describes the analysis and comparison of the performance of the two algorithms based on the exponential method as measured by the quality of the compressed pdf file. The exponential comparison method is one of the methods of the Decision Support System (DSS) to determine the priority order of decision alternatives with multiple criteria. Compression quality parameters that are measured as an alternative comparison are the value of the ratio of compression, compression ratio, space saving. Based on the results of the comparison with Exponential Comparison Method (MPE), it was found that the fixed length binary encoding algorithm has a better performance with a value of 2.55% compared to the Yamomoto's recursive code algorithm with a smaller value of 2.22%.
Sistem Pakar Untuk Mendeteksi Penyakit Mata Menerapkan Metode Case Based Reasoning

The problem that is often faced by the general public is the difficulty of knowing the occurrence of eye disease in humans because it is caused by different symptoms, so if the eye is disturbed and we ignore it, it could be an early symptom of eye disease that can be fatal. Given that experts and practice hours are limited, so that patients cannot consult experts anytime and anywhere, an expert system is needed. The existence of an expert system can solve problems that occur because the expert system uses an appropriate method. In the expert system, the method used to diagnose an eye disease is the Case Based Reasoning (CBR) method, which is a reasoning process for a previous similar case. In the experiment, it is proven by looking for the level or value of the new case data approach with the old case data data for reference in making decisions on new cases. Therefore, the authors build an expert system application to diagnose disease. In this system the inference used is Case Based Reasoning because the process is carried out to recognize the disease and its symptoms and early characteristics of the disease. The development of this web-based system uses the PHP and Mysql programming languages ​​as the database. This expert system can represent an expert in the field of eye disease to find out eye diseases based on existing characteristics and symptoms and can provide solutions if there are diseases suffered by patients
Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging

This research provides information about land prices in Jakarta by classifying using the Random Forest method. Where Random Forest is a data mining technique that is usually used to perform classification and regression. Random Forest is one of the best classification methods. It is found that classification accuracy will increase dramatically as a result of voting to select class types and ensemble tree growth. The method helps in providing information about the classification of land prices with the class of land prices per meter less than IDR 15 million, land prices per meter with a price range of IDR 15 to 25 million and land prices per meter more than IDR 25 million. With a fairly good accuracy of 82%, this method can classify where the permeter land price data that is tested will match the predicted classification accurately. Classification is performed on unbalanced data which is then oversampled using the ADASYN method. Assisted by doing spatial interpolation with the Ordinary Kriging method using Semivariogram, information about the classification of land prices can be seen on the distribution of the Jakarta area map. Ordinary Kriging can predict the estimated price per meter of land around the area of land that has a known price. The Root Mean Square Error (RMSE) results of the best Semivariogram model are obtained from the lowest RMSE value, namely the Spherical model with a value of 1.014896e7. The contribution of this research is to provide information about a reliable classification method, namely Random Forest and Ordinary Kriging performance as a spatial analysis method that can predict land prices per meter at unknown points so as to provide information about the distribution of land prices in Jakarta with each price class.
Analisis Sentimen Dalam Pengkategorian Komentar Youtube Terhadap Layanan Akademik dan Non-Akademik Universitas Terbuka Untuk Prediksi Kepuasan

The key to the success of an educational organization in achieving its goals of course cannot be separated from the quality of service both in academic and non-academic forms. Where in achieving these goals of course by giving satisfaction to the academics. The case study was carried out to predict service satisfaction at the Open University by using comments on social media Youtube as data processing. The text mining approach is a good alternative in terms of interpreting the meaning in the comments given. This study aims to analyze the predictions of service satisfaction from several categories as a benchmark. The categories are: Module, Tutorial, Scholarship, Lecturer, Exam, Application, Non-Academic and Others. The research method used is comparative, by applying 4 algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) for Prediction Accuracy. The total initial dataset is 7776 data and after cleansing and preprocessing is 6920 data. And then evaluated for the 7 categories after being accured to produce: Module category with the highest accuracy of 99.37% using the DT algorithm, Application Category with the highest accuracy of 100% using the DT algorithm, Teaching Category the highest accuracy of 99.42% using the algorithm DT. The tutorial category has the highest accuracy 92.4% using the SVM algorithm, the exam category has the highest accuracy 99.7% using the RF algorithm, the non-academic category has the highest accuracy 99.90% using the DT algorithm. And for the Others category the highest accuracy is 96.58% using the DT algorithm
Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Bagging Methods

COVID-19 is an infectious disease caused by a newly discovered type of coronavirus. Based on recommendations from the Technical Advisory Group on Virus Evolution, WHO established a new variant called Omicron. Due to the rapid spread of COVID-19, a booster vaccine was created to deal with the new virus variant. However, the strategy of giving vaccines that never ends is considered controversial by the community, and this is shown by the number of people who express their opinions, both positive and negative opinions on social media, one of which is Twitter. This research was conducted by collecting data with the help of the Twitter API. The classification method uses ensemble bagging with three basic lessons, namely Naive Bayes, K-Nearest Neighbor, and Decision Tree. Meanwhile, the feature extraction used in this research is TF-IDF (Term Frequency-Inverse Document Frequency). The performance of the ensemble bagging method by applying Hyperparameter Tuning is a precision of 0.72, recall of 0.71, F1-Score of 0.72, and accuracy of 0.72.
Sistem Pakar Untuk Diagnosa Retardasi Mental Anak dengan Metode Bayes Berbasis Web

In children's mental retardation, sometimes the child's parents cannot directly consult with a psychiatric specialist because of the limited time in carrying out activities in the hospital and the high cost. 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 it can help patients with mental retardation to diagnose the disorder. The purpose of this research is to build a web-based expert system for diagnosing mental disorders. The application of the expert system in this decision making uses the Bayes method, in probability theory and statistics, the Bayes theorem is a theorem with two different interpretations. In Bayes' interpretation, this theorem states how far the degree of subjective belief must change rationally when there are new clues. to get a diagnosis. The result of the implementation of the system is that the system provides choices in the form of symptoms that must be answered by the patient's parents based on the symptoms experienced by the patient and the results of the process the system will provide information on the mental retardation he experienced in order to get a solution with the handler
Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Stacking Methods

The outbreak of the COVID-19 virus in Indonesia has not ended until the government has made various efforts to reduce this outbreak, such as the Large-Scale Social Restriction (PSBB) policy and the obligation of the entire community to vaccinate against COVID-19. The government has made a new policy for the community: booster vaccination for people who have already been vaccinated against COVID-19 1 and vaccinated against COVID-19 2. With this new policy, many people have given opinions on social media. One of them is Twitter social media. Positive and negative opinions given by Twitter users can be used as a source of information data. Because of these problems, researchers conducted a sentiment analysis of the booster vaccine using the Ensemble Stacking method. The dataset that has collected as many as 6,500 data from Twitter will be grouped into positive and negative class sentiments. The best results from this study using ensemble stacking and oversampling have an accuracy value of 80%.
Performance Analysis of Bandung City Traffic Flow Classification with Machine Learning and Kriging Interpolation

This research focuses on making classification maps using the Classification And Regression Trees (CART), Random Forest and Ordinary Kriging methods. The dataset used is data from the Area Traffic Control System (ATCS) of the Bandung City Transportation Agency and the Google Maps application in April 2022. After the dataset is obtained, then the data pre-processing process will be carried out then the CART and Random Forest classification learning models will be made, after the CART and Random Forest classification learning is complete. From the CART and Random Forest classification models, traffic congestion classification map will then be made using the ArcMap application with the Ordinary Kriging interpolation method. The results of the comparison of classification maps made with Ordinary Kriging interpolation with the Gaussian Model semivariogram in both methods, namely CART and Random Forest. With the CART method has an accuracy of up to 88% while the classification map made with the Random Forest method has an accuracy of up to 90%. This proves that in this study the Random Forest method is far superior in building classification maps compared to the CART method
Penerapan Metode Hybrid Case Base Pada Sistem Pakar Diagnosa Penyakit Jantung

Many people have died of sudden heart attacks. The role of a specialist doctor is very necessary to overcome this, but the role of the doctor is also bumped due to limitations in terms of numbers and hours worked. In addition, in general, people with heart disease often ignore and do not understand the causes and symptoms of heart disease. Most patients are reluctant to have their heart health checked because they are constrained by expensive medical expenses, as well as a lack of service to patients. In the process of consulting for heart disease, the patient must come to the hospital and do an examination in person which of course must go through a series of procedures starting from registration onwards to get results. To overcome this problem, an expert system application is needed for the early diagnosis of heart disease. The purpose of this study was to build a web-based system of heart disease diagnosis expert applications. This expert system application uses the Hybrid Case Base method which is a combination of the Rule-Based Reasoning (RBR) and Case-Based Reasoning (CBR) systems. The RBR system and the CBR system have their advantages and disadvantages. The analysis and design of the systems used are UML and Entity Relationship Diagrams. In this expert system application, patients will consult by answering several questions about the symptoms of heart disease displayed by the system by checking the symptoms they experience. Then the system will provide diagnostic results based on the symptoms experienced by the patient. The results of this study can diagnose the type of heart disease experienced by patients and their treatment solutions and what percentage of the patient is likely to suffer from the disease quickly
The Organization Entity Extraction Telkom University Affiliated using Recurrent Neural Network (RNN)

In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%