Tahfidz Quran Monitoring System in Islamic Boarding Schools

Purpose: development a good tahfidz quran monitoring system, in presenting the data to quran teachers and parents. Presentation of data in the proposed monitoring system is in the form of tables, text, a graph of the Tahfidz progression and a dashboard for the achievement of the Tahfidz target.Design/methodology/approach: waterfallFindings/result: the tahfidz monitoring system that presents data in the form of graphs, charts, tables and text, thus providing monitoring functions that are easy to read and quickly understood.Originality/value/state of the art: dashboard display and chart on the tahfidz quran monitoring system

Prediction Of Drug Sales Using Methods Forecasting Double Exponential Smoothing (Case Study : Hospital Pharmacy of Condong Catur)

Purpose: Knowing the best alpha value from the data for each type of drug with various alpha parameters in the Double Exponential Smoothing Method and knowing the prediction results on each type of drug data at the Condong Catur Hospital pharmacy.Design/methodology/approach: Applying the Double Exponential Smoothing method with alpha parameters 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9Findings/result: The test results on a system built using test data show that the double exponential smoothing method provides accuracy below 20% by producing a different Alpha (α) for each type of drug because the trend patterns in each drug sale are different at the Pharmacy at the Condong Catur Hospital. .Originality/value/state of the art: Based on previous research, this study has similar characteristics such as themes, parameters and methods used. Previous researchers used smoothing methods such as Double Exponential Smoothing in predicting stock / sales of goods

Pencak Silat Tournament Information System

Purpose:This research was conducted to help manage the implementation of the pencak silat championship. So that the championship can run in an orderly and professional manner.Design/methodology/approach:This research went through several stages, starting from data collection, system requirements analysis, design, implementation, and system testing.Findings/result:Website-based information system for pencak silat tournament.Originality/value/state of the art:Pencak silat is a martial arts rich in techniques, benefits, and carries noble values that should be preserved as the Indonesian nation's successor. To preserve the existence of pencak silat in Indonesia, various pencak silat competitions were held in several cities in Indonesia. In the championship implementation, several things can disrupt the course of the matches. Of course, it will make the championship unprofessional. For this reason, along with the development of science and technology, a system was created that would help manage the implementation of the pencak silat championship so that the championship can run in an orderly and professional manner.

Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.

Development of Applications for Simplification of Boolean Functions using Quine-McCluskey Method

Purpose: This research makes an application to simplify the Boolean function using Quine-McCluskey, because length of the Boolean function complicates the digital circuit, so that it can be simplified by finding other functions that are equivalent and more efficient, making digital circuits easier, and less cost.Design/methodology/approach: The canonical form is Sum-of-Product/Product-of-Sum and is in the form of a file, while the output is in the form of a raw and in the form of a file. Applications can receive the same minterm/maksterm input and do not have to be sequential. The method has been applied by Idempoten, Petrick, Selection Sort, and classification, so that simplification is maximized.Findings/result: As a result, the application can simplify more optimally than previous studies, can receive the same minterm/maksterm input, Product-of-Sum canonical form, and has been verified by simplifying and calculating manually.Originality/value/state of the art: Research that applies the petrick method to applications combined with being able to receive the same minterm/maksterm input has never been done before. The calculation is only up to the intermediate stage of the Quine-McCluskey method or has not been able to receive the same minterm/maksterm input.

Sentiment Analysis On YouTube Comments Using Word2Vec and Random Forest

Purpose: This study aims to determine the accuracy of sentiment classification using the Random-Forest, and Word2Vec Skip-gram used for features extraction. Word2Vec is one of the effective methods that represent aspects of word meaning and, it helps to improve sentiment classification accuracy.Methodology: The research data consists of 31947 comments downloaded from the YouTube channel for the 2019 presidential election debate. The dataset consists of 23612 positive comments and 8335 negative comments. To avoid bias, we balance the amount of positive and negative data using oversampling. We use Skip-gram to extract features word. The Skip-gram will produce several features around the word the context (input word). Each of these features contains a weight. The feature weight of each comment is calculated by an average-based approach. Random Forest is used to building a sentiment classification model. Experiments were carried out several times with different epoch and window parameters. The performance of each model experiment was measured by cross-validation.Result: Experiments using epochs 1, 5, and 20 and window sizes of 3, 5, and 10, obtain the average accuracy of the model is 90.1% to 91%. However, the results of testing reach an accuracy between 88.77% and 89.05%. But accuracy of the model little bit lower than the accuracy model also was not significant. In the next experiment, it recommended using the number of epochs and the window size greater than twenty epochs and ten windows, so that accuracy increasing significantly.Value: The number of epoch and window sizes on the Skip-Gram affect accuracy. More and more epoch and window sizes affect increasing the accuracy.

Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)

Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion from time to time are interesting to analyze, especially about the patterns of public sentiment and emotions contained in these opinions. Sentiment and emotional conditions can illustrate the public's response to the Covid-19 pandemic in Indonesia. This research has two objectives, first to reveal the types of public emotions that emerged during the Covid-19 pandemic in Indonesia. Second, reveal the topics or words that appear most frequently in each emotion class. There are seven types of emotions to be detected, namely anger, fear, disgust, sadness, surprise, joy, and trust. The dataset used is Indonesian-language tweets, which were downloaded from April to August 2020. The method used for the extraction of emotional features is the lexicon-based method using the EmoLex dictionary. The result obtained is a monthly graph of public emotional conditions related to the Covid-19 pandemic in the dataset.

The Determinant Analysis of the Utilization of Google Classroom as the E-Learning Facility in Yogyakarta Nahdlatul Ulama University

Purpose: to find out what factors cause lecturers and students to adopt and refuse to adopt Google Classroom as a means of E-Learning at the Yogyakarta Nahdlatul Ulama University.Design/methodology/approach: This research was conducted using a qualitative approach to get the meaning of a phenomenon. The Innovation Diffusion Theory is used as the basis for this research to find out how the role of Google Classroom as a means of E-Learning and how the suitability of Google Classroom as a means of E-Learning at Nahdlatul Ulama University Yogyakarta.Findings/result: the factors of adoption consisted of synchronizing the students and lecturers’ email with Google, integrating other Google features, making an efficiency of fund, time and place, finding an alternative way for e-learning, evaluating the facilities, filling the teaching and learning process, communicating between the lecturers and students, and knowing the lateness of submitting assignment. Besides, there were some factors of rejection such as the limited ownership of electronic media, limited knowledge, Internet connection, and no attendance facilityOriginality/value/state of the art: The factors of lecturers and students are adopt and refuse to adopt Google Classroom as a means of E-Learning at Nahdlatul Ulama University Yogyakarta.

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.

Decision Support System For Determining The Type Of Workout Using The Fuzzy Analythical Hierarchy Process (F-AHP) In GYM STIKI

Purpose: Decision Support System for Determining the Type of Workout Using the Fuzzy Analythical Hierarchy Process (F-AHP) Method The STIKI GYM was created to make it easier for trainers to provide training for STIKI GYM participants who carry out workouts at STIKI GYM. Meanwhile, for STIKI GYM participants, the system can make it easier to carry out workout activities according to their respective body loads.Design/methodology/approach: Fuzzy Analythical Hierarchy Process (F-AHP) Method and being tested with black box testingFindings/result: Users can find out workout activities by entering the criteria for body weight, height, and exercise intensity into the system and helping trainers provide training in accordance with the recommendations for workout activities from the Decision Support System for Determining the Types of Workout Using the Fuzzy Analythical Hierarchy Process (F-AHP) Method at STIKI GYM.Originality/value/state of the art: The Decision Support System for determining the Type of Workout is indeed implemented at STIKI GYM by using data support in the form of interview results and participant data from STIKI GYM.