Media Pembelajaran Perilaku Hidup Bersih dan Sehat menggunakan Metode Gamifikasi berbasis Website

Students have difficulty in understanding the learning of clean and healthy lifestyle behavior (PHBS) using the lecture method because it is considered boring and less interactive. They also hesitate to ask questions when they don't understand the material. This research aims to create Learning Media for clean and healthy lifestyle behavior using a website-based gamification method, in order to increase student learning motivation by providing interactive learning media and evaluating the feasibility and usefulness of learning media. The research used a Research and Development approach with the ADDIE model, and data collection techniques through observation. This research involved 1 media expert, 2 material experts, teachers, and 30 respondents of 4th and 5th grade students at SD Negeri 3 Purwokerto Kulon as research subjects. Usability evaluation is carried out using the SUS (System usability scale) method. The result of this research is PHBS learning media using website-based gamification method, consisting of 10 pages. Based on the Validation Test by media experts getting a final score of 88.88%, material experts getting a final score of 82.77% and usability evaluation of website-based PHBS learning media by 30 respondents, resulting in a final score of 78.41. Based on the results of the validation and evaluation tests that have been carried out, it can be concluded that this learning media is suitable for use and falls into the acceptable category

IDENTIFICATION OF MENTAL ILNESS FROM PATIENT DISEASES USING KNN AND LEVENSHTEIN DISTANCE ALGORITHM

According to WHO, in 2017, the estimated number of people with mental disorders worldwide was around 450 million people, including schizophrenia. Globally, for the condition of Southeast Asia alone, the number of people affected by mental disorders is 13.5%. Meanwhile, 13.4% of cases in Indonesia are affected by mental illness. The Association of Mental Medicine Specialists (PDSKJ) during October 2020 noted that 5661 people who did self-examination through the PDSKJ website came from 31 provinces and found that 32% of the population had psychological problems and 68% had no psychological issues. Seeing that the level of mental illness in Indonesia is increasing, it is necessary to have a system to help the community with early prevention and treatment. With the growth of technology at its peak, Machine Learning technology can overcome the problem which is part of artificial intelligence. Furthermore, machine learning has an important role in improving the quality of health services because it is able to provide a medical diagnosis to predict disease. Therefore, the authors conducted a study to create a system to identify mental illness using the TF-IDF method. This method calculates the word weighting from a collection of complaints that the user gives. Then, these complaints will be classified using the KNN algorithm classification method and the Levenshtein Distance method to find the distance between the word inputted by the user and the word in the database and then calculate the number of differences between the two strings in the form of a matrix. The accuracy result of this machine learning classification is 0.928 or 93%, and will be visualized through web-based software using the Flask framework

CLASSIFICATION OF CAT SOUNDS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND LONG SHORT-TERM MEMORY (LSTM) METHODS

Cats become pets who are very close to humans, and they convey messages by producing identical sounds. Therefore, analysis of pet voices is important for a better relationship between cats and human. Animal communication through sound, especially in cats, depends on the situation or context in which the sound is made such as in a state of danger. Based on these problems, a classification method is needed to classify the similarity of characteristics in the resulting sound pattern. The classification methods used are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) which can remember information for a long time and are used for a long time period. This study aimed to determine feelings or moods based on the sound produced into 4 categories: The Purr, The Meow, The Mating Call, and The Howl. The result of this study is that the best architectural model is to use 4 CNN convolution layers measuring 8-8-8-8 and 2 LSTM layers measuring 8-8. The precision value in this architecture is 0.68, the recall value is 1.00, the accurary value is 0.5625 and the f1-score value is 0.77. The small value of the confusion matrix is ​​caused by the lack of dataset duration in the training process, resulting in underfitting

CLASSIFICATION OF BATIK MOTIF USING TRANSFER LEARNING ON CONVOLUTIONAL NEURAL NETWORK (CNN)

The number of batik motifs in Indonesia is not comparable to the knowledge possessed by the Indonesian people about batik motifs. The diversity of batik motifs can be a problem because classifying them can only be done by those who are familiar with batik in depth, both the pattern and the philosophy behind the motif, most of which are elderly people. To classify batik accurately and quickly is to use image classification technology. In this study, data were obtained from the previous researchers' GitHub repository, google images, and camera shots with a total dataset of 3,534 images. The data only focused on five batik motifs, namely Ceplok, Kawung, Parang, Megamendung, and Sidomukti. Before the batik motif is processed, preprocessing is carried out to obtain various quality data. Then the dataset was trained using the CNN model then the results were retrained using the VGG-16 and Xception Transfer Learning models. The researcher made several model scenarios, namely the CNN model without Transfer Learning and the model with Transfer Learning which took into account the effect of the learning rate values ​​of 0.0004 and 0.0001. Therefore, the results of the CNN model without Transfer Learning (M0) obtained training accuracy results of 89.64%. While the results of the model with the best Transfer Learning is the M2 model (CNN + VGG-16, learning rate = 0.0001) with an accuracy of 91.23%, a loss of 24.48%, and the test results obtained an accuracy of 89%. Based on the results of the classification method, it can be concluded that the CNN model with Transfer Learning performs classification better in terms of accuracy and computation time than the CNN model

SINERGITAS BUMDES DAN UMKM DENGAN OPTIMALISASI DATA HASIL KOMODITI UNTUK PEMAKSIMALAN SISTEM INVENTORI HASIL USAHA DI DESA SAWANGAN, KEBASEN

Covid 19 telah membawa dampak yang buruk terhadap perekonomian yang ada di Desa Sawangan. Keterbatasan SDM yang memiliki kepedulian dan pengetahuan terhadap teknologi sehingga organisasi dari BUMDes menjadi salah satu masalah krusial yang sedang dihadapi. Padahal jika dilihat dari teori ekonomi, bahwa kecakapan manajerial sebuah unit usaha menjadi nafas untuk sebuah bisnis bisa berkembang dengan baik. Disisi yang lain BUMDes Desa Sawangan juga mengalami banyak keterbatasan selain SDM yang mumpuni, juga kesulitan menemukan potensi SDM yang aware terhadap teknologi yang bisa dijadikan core SDM dari unit usaha BUMDes Desa Sawangan tersebut.  Berlatar belakang permasalahan BUMDes yang ada di Desa Sawangan tersebut maka pengabdian masyarakat ini terfokus dan menitikberatkan pada peningkatan keahlian pada strategi pemasaran dengan menggunakan e-commerce selama masa new normal ini

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model

Scalability of mobile cloud storage

Today, there are high demands on Mobile Cloud Storage (MCS) services that need to manage the increasing number of works with stable performance. This situation brings a challenge for data management systems because when the number of works increased MCS needs to manage the data wisely to avoid latency occur. If latency occurs it will slow down the data performance and it should avoid that problem when using MCS. Moreover, MCS should provide users access to data faster and correctly. Hence, the research focuses on the scalability of mobile cloud data storage management, which is study the scalable on how deep the data folder itself that increase the number of works

Mobile analytics database summarization using rough set

The mobile device is a device that supports the mobility activities and more portable. However, mobile devices have the limited resources and storage capacity. This deficiency should be considered in order to maximize the functionality of this mobile device. Hence, this study provides a formulation in data management to support a process of storing data with large scale by using Rough Set approach to select the data with relevant and useful information. Additionally, the features are combining analytics method to complete analysis of the data storage processing, making users more easily understand how to read the analysis results. Testing is done by utilizing data from the Malaysia’s Open Government Data about Air Pollutant Index (API) to determine the condition of the air pollution level to the health and safety of the population. The testing has successfully created a summary of the API data with the Rough Set approach to select significant data from the main database based on generated rule. The analysis results of the selected API data are stored as a mobile database and presented in the chart intended to make the data meaningful and easier to understand the analysis results of API conditions using the mobile device