Analysis of Tuberculosis (TB) on X-ray Image Using SURF Feature Extraction and the K-Nearest Neighbor (KNN) Classification Method

With current technological developments, machine learning has become one of the most popular methods, one of the popular machine learning algorithms is k-nearest neighbors (KNN). Machine learning has been widely used in the medical field to analyze medical datasets, in this study the k-nearest neighbors (KNN) machine learning algorithm will be used because of its good level of accuracy in recognition and is included in the supervised learning algorithm group. The results showed the k-nearest neighbors (KNN) method in recognizing x-ray images of tuberculosis (TB) using SURF feature extraction with an average accuracy of 73%

Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification

Online reviews are critical in supporting purchasing decisions because, with the development of e-commerce, there are more and more fake reviews, so more and more consumers are worried about being deceived in online shopping. Sentiment analysis can be applied to Marketplace product reviews. This study aims to compare the two categories of Naïve Bayes and XGBoost by using the two vector spaces wod2vec and TFIDF. The methods used in this research are data collection, data cleaning, data labelling, data pre-processing, classification and evaluation. The data scraping process produced 25,581 data which was divided into 80% training data and 20% test data. The data is divided into two classes, namely good sentiment and bad sentiment. Based on the research that has been done, the combination of Word2vec + XGBoost F1 scores higher by 0.941, followed by TF-IDF + XGBoost by 0.940. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification using XGBoost proved to be able to classify unbalanced data better than Naïve Bayes

CREATIVE FUNDRAISING AS A DISASTER FUNDRAISING EFFORT BY STUDENTS

Abstrak: Fundraising adalah sebuah seni yang bisa dipelajari oleh siapapun termasuk seorang pemula. Dalam kerangka fundraising, substansi fundraising berupa program yaitu kegiatan dari implementasi visi dan misi lembaga yang jelas sehingga masyarakat mampu tergerak untuk melakukan perbuatan filantropinya.secara kreatif. Kegiatan pengabdian pada masyarakat yang dilakukan tim dosen Prodi PBI FKIP UMMAT dengan melibatkan mahasisiwa Prodi PBI atau HMPS PBI atau ESA sebanyak 15 orang dengan menggunakan metode observasi dan metode tindakan dalam pelaksanaannya di lapangan. Adapun hasil dan temuan dari kegiatan ini adalah a) meningkatkan pengetahuan dan pemahaman tentang creative fundraising dan contoh metode kreatif yang telah dilaksanakan untuk menumbuhkan jiwa kreatif dan inovatif dalam diri mahasiswa umumnya dan jiwa kewirausahaan pada khususnya, b) mengembangkan kreasi, daya cipta dan pengalaman mereka untuk menggunakan creative fundraising sebagai cara atau metode dalam penggalangan dana bencana, c) dapat mengaplikasikan metode lainnya untuk kegiatan berikutnya untuk mengembangkan semua potensi yang dimiliki mahasiswa yang lebih melekatkan dasar kearah perkembangan sikap, pengetahuan, dan ketrampilan untuk menyesuaikan diri dan peka dengan kondisi lingkungan bencana yang sedang berlangsung di seluruh wilayah Indonesia, dan d) tumbuhnya sikap tepo seliro dan awareness atau sikap peduli sesama sehingga menimbulkan dampak psikologis yang bermanfaat bagi para mahasiswa selama masa pandemi ini.Abstract: Fundraising is an art that can be learned by anyone, including a beginner. In the framework of fundraising, the substance of fundraising is in the form of programs, namely activities of implementing a clear vision and mission of the institution so that the community can be motivated to do creative philanthropic deeds. Community service activities carried out involve 15 students of PBI or HMPS PBI or ESA study programs using observation and action methods in their implementation. The results and findings of this activity are a) increasing knowledge and understanding of creative fundraising and the examples of creative methods that have been implemented to foster a creative and innovative spirit in general and entrepreneurial spirit in particular, b) develop their creations, creativity and experience in raising funds for disasters, c) can develop all the potential of students who are more attached to the basis for the development of attitudes, knowledge, and skills to adapt and be sensitive to the conditions of the ongoing disaster environment in all regions of Indonesia, and d) the growing attitude of tepo seliro and awareness or a caring attitude towards others, which will have beneficial psychological effects for students during this pandemic

Analysis Of Right And Wrong Use Of Mask Based On Deep Learning

Pandemic COVID-19 makes it important to apply the proper and correct use of masks. The correct use of a mask where its use can cover the nose mouth and chin. One of the problems in using masks is that there are still many people who have not used masks properly and correctly. The importance of the correct use of masks because the transmission of the Covid-19 itself does not only occur through splashes when sneezing or coughing between humans but can also occur when talking or breathing by spreading through fluid particles less than 0.0002 inches (5 microns) in diameter called aerosols that are emitted when people talk. From these problems  it is necessary to have a computational-based analysis system to be able to identify patterns and make decisions and perform certain tasks automatically so that the results obtained are more efficient and objective. In this study, a deep learning method with a resnet  50 will be used to obtain the correct and incorrect results of using masks. The results of this study indicate that the deep learning method with resnet 50 is able to achieve 98.41% accuracy in classifying the correct and incorrect use of masks

Analysis Of Deep Learning Architecture In Classifying SNI Masks

In preparing for the new normal for COVID-19, every government agency, school and university will be required to comply with new regulations by the government, in which the government will oblige everyone who does activities outside the home to wear masks and practice physical distancing. This is one of the new habits that the government will familiarize with starting in 2020. Due to the ease with which the Covid-19 virus spreads. So the selection of a good mask is recommended good mask, namely a mask that follows the recommendations of the WHO at least 3 layers. The purpose of this study was to classify the types of SNI and non-SNI masks so that the presence of this SNI mask cluster monitoring system could improve security at locations that apply the use of masks and the masks used can function effectively to prevent the spread and spread of Covid-19, classification of research models it uses the InceptionV3, Resnet50, InceptionV2, AlexNet and DenseNet architectures. The results of trials that have been carried out by the InceptionV3 architecture have the most optimal accuracy with loss values of 3.4889 and 0.9894 (98.94%)

ANALYSIS OF GOODS MANAGEMENT SYSTEM IN THE COMMUNICATION AND INFORMATICS DEPARTMENT OF SERDANG BEDAGAI DISTRICT

The current problem is that the Serdang Bedagai district communication and informatics office is experiencing difficulties in managing incoming and outgoing goods. Management is currently carried out manually and cannot be viewed online so that many errors occur, one of which is a mismatch between the stock of goods recorded and the physical stock of existing goods. The goods management system that will be built in this study uses the waterfall model and PHP and HTML programming languages as well as CSS to design the web appearance. The system has 9 entities, namely: officer, user, sender, person in charge, goods, recipient, detail_recipient, expenditure and data_dinas. The results of the implementation of the proposed system are more optimal management of goods and help the performance of the warehouse admin in making real-time stock update reports and borrowing good

REAL TIME DETECTION OF CHICKEN EGG QUANTITY

A common problem currently being faced in the chicken egg production home industry is difficulty in counting the number of eggs. Currently, calculating the number of eggs is still done manually, which is less than optimal and prone to errors, so many entrepreneurs often experience losses. The manual system currently used also has the potential for this to happen. The use of technology on an MSME scale among laying hen breeders has not been widely adopted, this is due to limited access and understanding of technology. One alternative solution to deal with this problem is to build a real-time computerized system. The system that will currently be built in this research uses GLCM feature extraction and the SVM classification method. This system will detect egg production via CCTV cameras and will be stored in a database to be displayed on the website. The advantage of this system is that egg entrepreneurs can monitor chicken egg yields in real time. The results of trials that have been carried out using GLCM feature extraction and the SVM classification method in calculating the number of eggs using the SVM method with a polynomial kernel are highly recommended for use in this research because it can achieve 95% accuracy

COMPARISON ANALYSIS OF BANDWIDTH SPEED USING MIKROTIK ROUTERBOARD 750

The purpose of this study is to analyze and optimize bandwidth management at aminet cafes which often experience bandwidth leakage problems where users can access the internet without any bandwidth restrictions. Bandwidth management in this study uses a proxy and Queue Tree with the Network Development Life Cycle (NDLC) system development method. The results of the study show that bandwidth management runs optimally with balanced upload and download speeds for each user because the bandwidth distribution process has been carried out. The average upload speed is 121 kbps and the download speed is 732kbps

APPLICATION OF THE FUZZY TIME SERIES MODEL IN CLOTHING MATERIAL STOCK FORECASTING

The application of fuzzy time series is used to view the stock of clothing materials. As for the problem so far, CV Duta Express does not have a model for seeing the stock of complete materials in the warehouse, so the process is not optimal. This will have an impact on orders that come in at the same time and in large quantities. to avoid stock shortages, which resulted in the company experiencing losses. The purpose of this study is to make it easier to predict the stock of clothing materials and to be able to analyze every stock management at CV Duta Express with a fuzzy time series model. The variables used are stock needs, the amount of stock of school clothes, batik clothes, and pants. The research methodology in data collection consists of product type data from 2018–2021 at CV Duta Express Aceh Utara. Data analysis needs consist of school clothes, school pants, and clothes. Next, the fuzzy time series process determines the actual data is the type of school clothes, and what is forecast is sales for January 2018–2021 at the end of December. For a value of 1108 A2 fuzzification value, 172 A4 fuzzification value for batik clothes, and 894 pants with an A1 fuzzification value, then the value of the universe set used is U = [26, 323]. The value of forming a linguistic set is based on the length of the interval U3 = [111,153], U4 = [153,196], and U5 = [196,238]. The result of the fuzzification value from historical data for the value of 172 fuzzification is A4, for data of 133 fuzzification is A3. The formation of Fuzzy Logic Relationship (FLR) values for the period 1/7/2021 to 1/13/2021 is obtained from the data range A4=174 and range A3=125 in each period to be related. The results of forecasting with fuzzy time series testing at the end of December 2021 are 196 stocks of clothes that must be optimized in the following month. The test results in this study are to see if the error value using the AFER model is 0.4511% while the RMSE test value has an error value of 5.0199. After being calculated for the forecast every month, the average obtained for AFER is 0.50154 % and the RMSE is 9.86518.. Keywords : Forecasting, Fuzzy Time Series, stoc

Deep Learning Model for Sentiment Analysis on Short Informal Texts

This paper proposes a classification model to classify short informal texts. Those short informal texts were texts that were noisy, typos, irregular, and could consist of a very small number of words or even only a single word. The proposed model was trained using a dataset collected from student comments from an application called Evaluasi Dosen Oleh Mahasiswa (EDOM). This application assesses the lecturers using questionnaires filled out by students. It also records the student's comments but is not part of the evaluation calculation, therefore this work makes the data possible to be part of the assessment through sentiment analysis. This work focuses on building suitable preprocessing algorithm and building a simple deep learning network. The preprocessing algorithm was based on multiple word n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, and the network was built with a relatively shallow network. To evaluate the model in real usage, an application was built. The results were very convincing, reaching 0.979 in accuracy and 0.63 in F1-Score. Nonetheless, the imbalanced dataset was the only factor that needed to be investigated further for better overall performance