Reviewerly/exHarmony huggingface dataset:
Pengaruh Peningkatan Kualitas Citra Menggunakan Modifikasi Kontras Pada Kompresi Data RLE

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.
Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm

Systematic keyword is needed in improving the quality of higher education, one of which is the needed to increase the competence of graduates every year. In increasing student graduation, it is necessary to classify student graduation to find out whether the student is said to be on time (TW) or possibility on time (KTW) using the BPNN and PNN methods. The data used is the Alumni data of the 2013-2020 Information System study program with 7 criteria use, namely GPA, Total Credits, Number of Repetitive Courses, Taking TA Curse in Semester 7, Procrastination, Self-Confidence, and Discipline. The data obtained is then carried out in the process of sharing training data and testing data using K-Means Clustering with the aim; of getting the best accuracy results. Furthermore, the classification stage using BPNN and PNN resulted in an accuracy of 98% and 95% with learning rate of 0.125 and a spread value of 0.1
Penerapan Metode TOPSIS Dalam Pemilihan Media Pembelajaran Berbasis Komputer

Information technology is one of the needs and has an important role in the field of education. One form of utilizing technological developments in the field of education is through computer-based learning media. Computer-based learning media is a tool that helps the learning process between teachers and students in the digital era. Various types of computer-based learning media are available, but the similarities in the types of variations become obstacles in the selection process. The selection of learning media has several important elements that will determine the suitability of the science and material to be studied. The selection process with a decision support system using several criteria will help in making decisions. The TOPSIS method can identify solutions from available alternatives based on criteria. Elements of learning media which are the criteria for each alternative, namely, sound, images, video, text, animation, graphics, graphics, tutorials, interactive, visual and practical. The media element is an indicator in this research. Based on the application of the method, the results of implementation and testing in the selection process have a more spesific value between each alternative, namely the PPT alternative has a preference value of 0, the CD alternative gas a preference value of 0.4299, the video alternative has a preference value of 1 and the internet alternative has a result preference value 0.6205. Then the TOPSIS methid in selecting computer-based learning media can assist teachers in directing students to choose the type of learning media that is suitable for learning interests. The overall appearance of the system has been adapted to the needs of students to make it easier to understand and use.
Implementasi Algoritma Data Mining J48 Untuk Klasifikasi Mahasiswa Yang Layak Mendapat Beasiswa PPA

The PPA Scholarship is a support for educational costs that is given to students who have taken at least semester 2 in Higher Education with consideration of limited economic capacity and have academic and non-academic achievements. In classifying students who are eligible for PPA scholarships, the management has difficulties because the limited PPA scholarships are not proportional to the number of students. One solution in classifying students who are eligible for PPA scholarships is to utilize the data of students who have received PPA scholarships in the previous year. However, in the classification process, data research is needed, one of the data research techniques is data mining. Where data mining is an interesting pattern extraction of large amounts of data [1]. One of the algorithms in data mining used in classification is the J48 algorithm. The J48 algorithm is the implementation of the C4.5 algorithm in the WEKA tools, besides the J48 algorithm is the decision tree implementation in the rapidminer tools. This algorithm is expected to help the management of the PPA scholarship at STMIK Budi Darma in classifying students who really deserve to receive the PPA scholarship. As a result, one of the criteria for a student to be classified as receiving a PPA scholarship is to have achievement
Prediksi Tingkat Kesembuhan Pasien Covid-19 Berdasarkan Riwayat Vaksin Menggunakan Metode Naïve Bayes

Covid-19 has shocked the world since it first appeared at the end of December 2019. At the beginning of 2022, the global community is more prepared to face the COVID-19 pandemic, especially with the mass vaccination program in countries around the world, including Indonesia. The next issue is how effective the vaccine is in dealing with the COVID-19 virus. The main parameter used is to see the recovery rate of patients affected by COVID-19 based on the history of vaccine doses that have been received by the patient. In this study using data mining techniques, namely using the Naïve Bayes algorithm. The test results show the accuracy of the Naïve Bayes algorithm is 98.14%. The prediction results show that the recovery rate of patients who have received the vaccine, either dose 1, dose 2, or dose 3 (booster) is higher than those who have not been vaccinated at all (dose 0). The results of this study are expected to provide an overview to the public and the government about the benefits of vaccination in dealing with the Covid-19 virus.
Supervised Learning Approaches for Nested People Entity Extraction in Indonesian Translated Quran

Since the Quran is the primary holy book for Muslims, information extraction research on Quranic texts, especially in a form of People Entity Extraction, is an important task for further Quran and Tafseer understanding. The challenges in extracting people entities from the Quranic text is that many verses have a complex structure, such as nested entities, making it crucial to build a system that can extract the entity automatically, accurately, and quickly. People Entity Extraction on Quran itself is a task that aims to extract people entities in a sentence or verse, such as the name of a person, the name of a group, etc. on the Quranic texts. Example of input taken from snippet Surah Al-Baqarah verse 46 which reads “Those who believe that they will meet their Lord and that they will return to him” from that input the people entity extraction system is expected can identify people entities i.e. “Those who believe that they will meet their Lord”. Currently, People Entity Extraction research for the Quran has not been widely carried out, only a few algorithms with scattered results have been conducted. In this research, we will use several supervised models which are Conditional Random Field (CRF), BiLSTM-CRF, and a pre-trained deep learning model based on IndoBERT transformers. We apply and perform a comparative analysis for the performance of those several models. We found out that deep learning based model, namely BiLSTM-CRF perform best at extracting people entities, whilst probabilistic based model, namely CRF, had difficulty in extracting people entities, specifically nested people entities.
Analisis Perbandingan Algoritma NGG dan GGHN pada Frekuensi Hasil Enkripsi

Data and information in the development of digital technology have an important role. Every activity or activity that uses digital technology is related to data and information, so information security and data confidentiality are very important. To maintain information security and data confidentiality, protection with cryptographic techniques is needed. Cryptographic techniques are related to encryption, which is where the process of scrambling data is carried out and hiding data with a key system, while decryption is the process of changing the condition of the data to its original form so that it is easy to understand. There are obstacles and problems ini digital communication, so cryptographic techniques are needed that have higher level of security and can be applied in digital communications. To determine the level of security in cryptographic techniques required frequency anlysis. Frequency analysis on the NGG and GGHN algorithms is carried out to determine the level of information security based on the results of data encryption. BAse on the testing process on the NGG and GGHN algorithms, it will be known that the frequency of characters in the text varies. The more characters used in the key will affect the level of information security. The NGG algorithm has a higher level of security than the GGHN algorithm with a precentage difference of 0.000299967%. If the frequency of occurrence of characters in the message text that has been encrypted is more frequent or higher, then the level of information security in the message is lower and the password is easier to crack.
Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine

Mushrooms are plants that have high nutritional content and have various benefits for the health of the human body. However, not everyone knows the types of mushrooms that are suitable for consumption. The types of mushrooms have their own characteristics when viewed from the image. For this reason, a system is needed by utilizing digital image processing to classify types of mushrooms suitable for consumption, so that people can find out which types of mushrooms are suitable for consumption. This research is to classify types of mushrooms suitable for consumption using the Multiclass SVM algorithm with first-order feature extraction, which performs feature extraction based on the characteristics of the image histogram. The result of feature extraction is used as input for classification in Multiclass SVM. Multiclass SVM can map data points to dimensionless space to obtain hyperplane linear separation between each class. The developed method is implemented in Matlab, in order to produce a system in the form of a GUI so that it can be used by general users easily. Based on the test results, the average accuracy is 83%.
Sistem Klasifikasi Pengaduan Masyarakat Pada BPJS Ketenagakerjaan Menggunakan Algoritma Naïve Bayes Berbasis Mobile

Public complaints are needed so that the performance of an agency can be known whether it is running well or vice versa. Improving the quality of public services can be done through the quick resolution of complaints from service providers. Naive Bayes Classifier is an approach that refers to Bayes theorem by combining previous knowledge with new knowledge. So it is called a classification algorithm that is simple but has high accuracy. For this reason, this study will prove the ability of Naive Bayes to classify public complaints against BPJS Employment which contain information on services or social conditions in Pekanbaru City. There are 3 types of complaint classification that can be submitted, namely disbursement problems, BPJS Employment service problems and problems with social security administration. The mobile-based system is implemented with the programming language used, namely Java, while PHP is for administrators. Administrators in this system are employees who work in the BPJS Employment office. This system is tested using whitebox testing for unit testing and integration testing, blackbox testing for validation testing and usability testing. The results of this study are the classification system for public complaints at BPJS Employment using the mobile-based Naive Bayes algorithm. The classification accuracy using the Naïve Bayes algorithm is 0.9, the average precision is 0.93, the recall is 0.91, and the F1-score is 0.9.
Optimasi Website Duniagoogle.com Untuk Meningkatkan Pengunjung Halaman Menggunakan Metode Search Engine Optimization (SEO)

The speed of information and competitors in the internet world makes duniagoogle.com website not included in the search list and searches of the first page of Google, thus causing the website to be quiet and not have a maximum end. To overcome the problem of increasing website page visits, it is necessary to optimize using the Search Engine Optimization (SEO) method. The stages in this study were carried out with 2 stages through website analysis and process analysis. While the website SEO method uses 5 techniques, namely creating page segmentation, content structure, speed optimization, keyword research, and utilizing website backlinks. This technique is able to provide more optimal traffic to compete with other website competitors. The goal is that this website can appear on the first page of Google search, so as to get maximum results in the business world using the website. The results of website optimization based on the analysis and application of this method, the main page of the website with the main keyword duniagoogle.com has been indexed in the search engine and google search the first page. A total of 4 posts based on google search short keywords have been found to also appear on the first page of the google search engine. It is certain that the number of statistical website visits has increased from before. In the future, this website will continue to be optimized so that it gets better results. If there is further research related to this website optimization research based on the development of internet technology, it is likely that there are changes and new opportunities, then it must be further researched to be developed, so that the website can survive and compete in the world of Google search engines.
Perbandingan Metode Linear Regression dan Exponential Smoothing Dalam Peramalan Penerimaan Mahasiswa Baru

In improving the quality of higher education, the Telkom Purwokerto Institute of Technology needs to plan and make decisions about what strategies and policies can support teaching and learning activities or other activities within the institution. To plan these strategies and policies, the institution needs reference data such as data on predictions of the number of students that will be accepted in the next five years. With this predictive data, the institution can also consider various things that must be improved and plan a better marketing strategy so that new student admissions in the following year can increase. This study aims to predict the number of students who will be accepted at the Telkom Purwokerto Institute of Technology in the next five years. The overall comparison of the linear regression and exponential smoothing methods is carried out to test the accuracy of the forecasting results. The forecasting accuracy used is MAE, MSE, and MAPE. Forecasting accuracy testing was carried out using the linear regression method with the MAE, MSE, and MAPE values of 115.28 each; 15238.46; and 0.1216054793, and the exponential smoothing method with = 0.1 has the value of the results of the MAE, MSE, and MAPE forecasting accuracy tests of 327.2938; 137036,2639; and 0.2875524468. The best method for forecasting new student admissions at the Telkom Purwokerto Institute of Technology. namely the linear regression method because it has the smallest MAE, MSE, and MAPE forecasting accuracy values. The results of forecasting new student admissions at the Telkom Purwokerto Institute of Technology for the next 5 years, from 2022 to 2026, respectively, are 1369.7; 1479.4; 1589.1;1698,8; and 1808.5.
Prediksi Perambatan Arus Lalu Lintas Berdasarkan Korelasi Tertinggi Antar Jalan

Over the past few years, many algorithms for traffic flow predictions have been proposed to predict traffic flow. Time series models and neural network models have been widely implemented to predict traffic flow and traffic congestion based on traffic data, vehicle speed, weather, accidents, and special days. However, most of previous studies are used to predict traffic flow, not applied to predict the propagation of traffic flow. Traffic flow propagation is an interesting study. Using this finding the driver can avoid neighboring roads affected by road congestion. We propose the correlation method to find the relationship between the road. To evaluate the relationship between the road, we display the correlation results in the map. The visualization show that the correlation of traffic when congestion occurs shows better in showing the relationship between the road than the correlation at all times
Pengujian Penerapan Layanan Sistem Informasi Pada Dinas Perhubungan Menerapkan Metode DeLone and McLean

The Motor Vehicle Testing Information System (SIMPKB) is an Information System (SI) for the implementation of periodic vehicle tests that are used to improve the quality of public transportation. The Brebes Regency Transportation Service has a vehicle periodical testing unit for the Pekalongan residency area. The implementation of the test focuses on providing services to the community by periodically testing the feasibility of motorized vehicles. The problem that occurs is that there are still obstacles in the use of the system and the lack of a document for evaluating the performance of system services whether the system is suitable for use from the organization and user perspective. This study aims to determine the extent to which the success rate of implementing SIMPKB services. This research method uses quantitative methods using the DeLone and McLean model of measurement. Data collection method by analyzing questionnaires from respondents using SmartPLS. The results of this study user satisfaction has a value of 0.765, it can be said that it is influenced by the construct of system quality, service quality and information quality by 76.5%. Meanwhile, having an value of 0.745, it can be interpreted that the validity of the construct of Use (P) can be said to be influenced by the construct of system quality, service quality and information quality is 74.5%. From the 7 hypotheses, only system quality and service quality greatly affect user satisfaction and the use of the Motor Vehicle Testing Information System (SIMPKB) at the Brebes Regency Transportation Service
Penerapan Framework Information Technology Infrastructure Library (ITIL) Versi 3 pada Domain Service Operation untuk menganalisa Manajemen Layanan Teknologi Informasi

Information Technology (IT) in the era of the Industrial revolution 4.0 is increasingly developing and changing the way humans complete all work. Advances in technology have caused automation/computerization in business management to be fast. Computerization helps humans improve performance and save resources in the process of working on it. The use of IT really supports the objectives of government administration so that it must pay attention to the efficiency of resource use and risk management. ATR/ BPN Purbalingga as one of the government agencies has used the use of services through the use of the ATR / BPN Purbalingga website. The role of important websites in the organization includes ATR/BPN Purbalingga. ATR / BPN shows several problems such as tools in the website are lacking and there are often server downs. The purpose of this study is the analysis of IT service management on websites kkp2.atrbpn.go.id using the ITIL Framework V.3 domain service operation. The research data analysis method uses quantitative methods by using SPSS measuring instruments. ITIL model V.3 as a medium of analysis of questionnaire data and to obtain the results of the analysis of the level of maturity of IT service management. The results of this study are recommendations for the maturity level of IT service management of the ATR / BPN website for the progress of improving website performance. The result of the maturity value on the website kkp2.atrbpn.go.id on the subdomain service operation domain service management as a practice by obtaining an average of 3.97. Service operation principles with an average of 3.27. Service operation processes earned an average of 2.95. Common service operation with an average of 4. Organizing service operations with an average of 4.12. Service operation technology consideration with an average of 3.8. Implementing service operations with an average of 3.3. The average total obtained from all domain service operations is 3.1 at level 3 (defined) which means it is important to carry out basic processes and document each process and standardize in order to reach the expected level
Penerapan Metode K-Means Untuk Clustering Desa Rawan Bencana Berdasarkan Data Kejadian Terjadinya Bencana Alam

Located in the Southeast Asian region, the country of Indonesia is one of the areas prone to disasters related to demographic, geological and geographical conditions that trigger disasters both caused by natural factors, non-natural factors and human factors. Purbalingga is an area of Central Java Province which has the potential for disasters to occur when weather conditions are uncertain. The K-Means clustering method is used to make it easier to analyze and group data to identify several disaster-prone areas in the Purbalingga area. In this research, data processing uses rapidminer tools. Based on data processing, there were 5 clusters of disaster-prone areas in Purbalingga Regency with a very high level of vulnerability, a high level of vulnerability, a medium level of vulnerability, a low level of vulnerability and a very low level of vulnerability. With the existence of groups of disaster-prone areas that have been determined, it is hoped that the anticipation made for disasters that may arise can continue to be carried out appropriately so as to minimize the effects of disasters on the community.
Analisis Kualitas Aplikasi Simpel Pol Menggunakan Metode McCall

The Simpel Pol application is a health test service application for submitting a Driving License (SIM). The Purbalingga Police Clinic has implemented the use of the Simpel Pol application for the Purbalingga Regency area since April 2021. After searching in the Google Playstore review column, the Simple Pol application has several problems such as less accurate on hearing tests, applications that run slowly, and difficulty signing in. Based on these problems, this study aims to analyze the quality of the Simple Pol application using the McCall method. Product operation is used as the main perspective in this study with the metrics of completeness, consistency, traceability, error tolerance, accuracy, simplicity, execution efficiency, access control, operability, training, and communicativeness. The subject of this study is a driver's license applicant in the Purbalingga Regency who performs a medical test through the Simpel Pol application. The number of respondents was determined by the Slovin formula and data was collected using a questionnaire. The results of this study will determine the quality of the Simpel Pol application which is categorized as very good, good, good enough, not good, or very bad