Causal Relationships of Sexual Dysfunction Factors in Women Using S3C-Latent

Women with cancer are at risk for sexual dysfunction characterized by problems with sexual desire, sexual arousal, lubrication, orgasm, sexual satisfaction, and pain during sexual intercourse. The literature review shows that most studies have focused on correlation analysis between factors, and no studies have attempted to identify a causal relationship between factors of sexual dysfunction. This study aims to determine the causal mechanism between factors of sexual dysfunction in cancer patients using a causal algorithm called the Stablespec Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). The causal algorithm has been implemented into the R software package called Stablespec. The computation of the model is done in parallel using the CPU server. The result of this study is that there are a causal relationship and association with a high-reliability score of sexual dysfunction factors. We hope that the causal model obtained can be a scientific reference for doctors and health workers in making decisions so that the quality of life of female cancer patients who experience sexual dysfunction can be improved.

Gamification-based The Kampus Merdeka Learning in 4.0 era

Recently, education has been enlivened by the presence of the Merdeka Campus program initiated by Nadiem Makarim. It uses the Kampus Merdeka concept to learn to follow the development of education in the 4.0 era. This change has become a paradigm for Higher Education to build a Merdeka Campus to learn to face challenges in the 4.0 era. However, the challenge is not easy for universities, so that students join the independent program to learn quickly. This study aims to motivate students to participate in independent learning activities in a collaborative learning system with gamification techniques. Gamification is in the form of reward badges for student achievement in all learning activities carried out. The higher education independent learning system is designed using the library study method and Agile Development with two frameworks, namely Laravel and VueJS. It can be proven from the results of the SUS Score Analysis showing the number 92.5 indicating that the independent learning campus system provides positive benefits by gamification of students being more motivated and ready to face learning challenges in the 4.0 era.

Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning

Machine learning approaches have been used to solve various problems. PKH experienced miss-targeting. This study aims to compare the result of big data by SIKS-NG and machine learning based on the same data and measurement indicators. Obtained algorithms Averaged Neural Network with optimal output compared to others. As for data testing obtained on SIKS-NG and machine learning that uses elevated matrix evaluations with the following 3 indicators: 1) Accuracy obtained by SIKS-NG 72.40% increased to 81.18% for Machine Learning; 2) Precision at the center is getting a high percentage of 91,01%, but it is capable of increasing once the data is given Machine Learning to 95,37%; 3) Recall with the cycle was obtained at 75.49%, while Machine Learning obtained a higher yield of 82.19%. Thus, machine learning has been proven to reduce miss-targeting and can be used as an alternative recommendation in automatic decision making and innovative management practices in government circles.

Indonesian Music Classification on Folk and Dangdut Genre Based on Rolloff Spectral Feature Using Support Vector Machine (SVM) Algorithm

Music Genre Classification is one of the interesting digital music processing topics. Genre is a category of artistry, in this case, especially music, to characterize and categorize music is now available in various forms and sources. One of the applications is in determining the music genre classification on folk songs and dangdut songs.The main problem in the classification music genre is to find a combination of features and classifiers that can provide the best result in classifying music files into music genres. So we need to develop methods and algorithms that can classify genres appropriately. This problem can be solved by using the Support Vector Machine (SVM). The genre classification process begins by selecting the song file that will be classified by the genre, then the preprocessing process, the collection features by utilizing feature extraction, and the last process is Support Vector Machine (SVM) classification process to produce genre types from selected song files. The final result of this research is to classify Indonesian folk music genre and dangdut music genre along with the 83.3% accuracy values that indicate the level of system relevance to the results of music genre classification and to provide genre labels on music files as to facilitate the management and search of music files.

Steganographic Model for encrypted messages based on DNA Encoding

Information has become an inseparable part of human life. Some information that is considered important, such as state or company documents, require more security to ensure its confidentiality. One way of securing information is by hiding the information in certain media using steganography techniques. Steganography is a method of hiding information into other files to make it invisible. One of the most frequently used steganographic methods is Least Significant Bit (LSB).In this study, the LSB method will be modified using DNA Encoding and Chargaff's Rule. Chargaff's Rule or complementary base pairing rule is used to construct a complementary strand. The modification of the LSB method using DNA encoding and Chargaff's Rule is expected to increase the security of the information.The MSE test results show the average value of the LSB method is 0.000236368, while the average value for the DNA Encoding-based Steganography method is 0.000770917. The average PSNR value for the LSB method was 76.82 dB while the DNA Encoding-based Steganography method had an average value of 70.88 dB. The time of inserting and extracting messages using the Steganography method based on DNA Encoding is relatively longer than the LSB method because of its higher algorithmic complexity. The message security of the DNA Encoding-based Steganography method is better because there is encryption in the algorithm compared to the LSB method which does not have encryption.

Recommendation System for Thesis Topics Using Content-based Filtering

When pursuing their bachelor degree, every students are required to pursue a thesis in order to graduate from the major that they take. However, during the process, students got several difficulty regarding chosing their thesis topics. Therefore, a recommendation system is needed to classify thesis topics based on the students’ interest and abilities. This study developed a recommendation system for thesis topics using content-based filtering where the students will be asked to choose the course that they interested in along with their grades. After getting all the required data, the recommendation system will process the data and then it’ll show the title and the abstract of publication that fits the criteria.In this research, there are 2 datasets that is used, there are lecturer publication within 3 years and syllabus data of Computer Science UGM course. After running this research, it was found that the recommendation system has an average 7.46 seconds running time. It was also found that the recommendation system got an average 83% of the recommendation system objectives. The recommendation system objectives consist of relevance, novelty, serendipity, and increasing recommendation diversity.

Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration. This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods. The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance. The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.

Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method

Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods. This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score.

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.