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PRE-ECLAMPSIA DIAGNOSIS EXPERT SYSTEM USING FUZZY INFERENCE SYSTEM MAMDANI
Get PDFVarious institutions utilize computer information systems to analyze and process data. An expert system is an information system that is used to help analyze and determine decisions on a problem based on rules determined by experts. This research focuses on creating a prototype expert system for diagnosing pre-eclampsia or pregnancy poisoning in pregnant women based on measuring blood pressure and checking proteinuria. The existing data is then analyzed using the Mamdani system's fuzzy inference method. Supporting theory regarding the fuzzy inference system of Mamdani, pre-eclampsia and its examination indicators will be used as a basis for creating this expert system prototype. The data used were secondary data on preeclampsia patients in the form of medical records of blood pressure measurements, proteinuria examinations and doctor diagnoses of preeclampsia patients at two Regional General Hospitals (RSUD), namely Atambua and Kefamenanu, totaling 20 samples. The interface or user interface of this prototype system is made as simple as possible so that it can be operated by all ordinary people. The programming language used is Visual Basic (VB) with the Visual Studio 2010 developer application. The initial prototype of this system will continue to be developed until it can become a Information systems or real applications used in hospitals. The results of this research are that the expert system for diagnosing preeclampsia can be used well and easily by hospital staff and show congruence between the system diagnosis results and the diagnosis results from obstetricians or experts in the 20 processed data
CLASSIFICATION OF RICE TEXTURE BASED ON RICE IMAGE USED THE CONVOLUTIONAL NEURAL NETWORK METHOD
Get PDFThere are several types of rice that are commonly sold in rice stores. Many people, especially millennials, are not familiar with the different types of rice such as IR42 rice, Pera rice, sticky rice, and Pandan Wangi rice. Therefore, digital image processing techniques are needed to help analyze the types of rice to help people know what kind of rice they are going to buy at the market. The method commonly used in image processing for image classification is the convolutional neural network (CNN) method. Currently, CNN has shown the most significant results in image classification. This research used a dataset of 1560 rice images. The data was divided into two sets (training data and validation data) with an 80:20 ratio. The accuracy obtained by the CNN model using InceptionV3 for the rice data was 95.7% with a loss of 0.123. The Android application developed in this research achieved an accuracy of 83,4% based on the testing results calculated using the confusion matrix
STRATEGI PENGEMBANGAN EKOWISATA MANGROVE DI TAMAN WISATA ALAM TELUK YOUTEFA KOTA JAYAPURA
Get PDFEcotourism is an alternative and sustainable tourism activity. What distinguishes ecotourism from other types of tourism is that ecotourism itself applies tourism activities with nature as the main object that must be managed with a great sense of responsibility, prioritizing aspects of empowering local communities, aspects of nature conservation and paying attention to educational elements. Based on the Decree of the Minister of Forestry of the Republic of Indonesia Number: 714/Kpts-II/1996, regarding the Designation of the Youtefa Bay Area as a Conservation Area Designated as a Natural Tourism Park with an area of 1,675 hectares. Not only is it rich in natural tourism, but with the existence of three villages in the Youtefa Bay Natural Tourism Park area, this area has cultural tourism and alternative religious tourism that can be an option for visiting tourists. However, inadequate facilities and infrastructure have made this Nature Tourism Park (TWA) area not often the choice of Jayapura City residents for sightseeing. This study aims to determine strategies that can be used in developing ecotourism in mangrove areas and improving the welfare of local communities. The method is carried out through a qualitative approach with primary and secondary data types and using a SWOT matrix analysis. From these methods generate strategies that can be used to develop TWA Teluk Youtefa including: tourism product development strategies (attractions, amenities, accessibility, ancillary), marketing development strategy and community involvement development strategy.Ekowisata merupakan kegiatan wisata alternatif dan berkelanjutan. Yang membedakan ekowisata dengan wisata jenis lain dikarenakan ekowisata sendiri menerapkan kegiatan berwisata dengan alam sebagai objek utama yang harus dikelola dengan rasa tanggung jawab yang besar, mengutamakan aspek pemberdayaan masyarakat sekitar, aspek konservasi alam dan memperhatikan unsur pendidikan. Berdasarkan Surat Keputusan Menteri Kehutanan RI Nomor: 714/Kpts-II/1996, tentang Penetapan Kawasan Teluk Youtefa sebagai kawasan konservasi dengan peruntukan sebagai taman wisata alam seluas 1.675 hektar. Tidak hanya kaya akan wisata alamnya saja, tetapi dengan adanya tiga kampung pada kawasan Taman Wisata Alam Teluk Youtefa menjadikan Kawasan ini memiliki wisata budaya dan wisata alternatif religious yang dapat menjadi pilihan wisatawan berkunjung. Akan tetapi fasilitas sarana dan prasarana yang kurang memadai membuat kawasan Taman Wisata Alam (TWA) ini tidak sering menjadi pilihan warga Kota Jayapura dalam berwisata. Penelitian ini bertujuan untuk mengetahui strategi yang dapat digunakan dalam pengembangan ekowisata di kawasan mangrove dan meningkatkan kesejahteraan masyarakat setempat. Adapun metode yang dilakukan melalui pendekatan kuantitatif dengan jenis data primer dan sekunder serta menggunakan analisis matriks SWOT. Dari metode tersebut menghasilkan strategi yang dapat dipakai guna mengembangkan TWA diantaranya yaitu: strategi pengembangan produk wisata (atraksi, amenitas, aksesibilitas, ancillary), strategi pengembangan pemasaran, strategi pengembangan kebijakan dan strategi pengembangan pelibatan masyarakat
CLOTH BAG OBJECT DETECTION USING THE YOLO ALGORITHM (YOU ONLY SEE ONCE) V5
Get PDFThe use of plastic in modern life is increasing rapidly, causing the number of people who use plastic to increase, one of which is when shopping. The function of plastic bags as packaging for luggage is not comparable to the impact caused by plastic waste in the years to come. Plastic bags take a long time, even hundreds to thousands of years, to completely decompose. In order to support the government's program to reduce the use of plastic bags, this study will discuss how to detect cloth bags as a substitute for plastic bags. In this research, a system will be implemented to detect the use of cloth bags with Roboflow and Yolo v5. After carrying out all stages of the research, it can be concluded that the goodie bag detection model has been successfully created. The detection model was created using the YOLOV5 algorithm. The dataset used consists of 102 goodie bag images. The process model uses 100 epochs with the training result [email protected] is 89.8%. So, in other words, it can be said that YOLO v5 can detect goodie bags very well
PERANCANGAN UI/UX APLIKASI JOBHUB LAYANAN APLIKASI FREELANCE MENGGUNAKAN FIGMA
Get PDFPerkembangan ilmu pengetahuan dan teknologi informasi saat ini memberikan pengaruh yang kuat terhadap perkembangan hampir di semua bidang kehidupan. Minimnya informasi lowongan kerja juga menjadi salah satu masalah yang membuat pencari kerja dan pencari kerja sulit untuk bertemu, sehingga internet bisa menjadi salah satu alternatif pencarian kerja, oleh karena itu dibuatlah JobHub. Metode yang digunakan pada penelitian ini yakni prototyping. Metode prototyping merupakan salah satu metode yang menggunakan pendekatan secara bertahap, sehingga dapat dengan mudah dievaluasi oleh user maupun pemangku kepentingan. Pada dunia digital saat ini sangat mudah membuat prototyp
Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based
Get PDFThe current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.
 
MODEL RAPID APPLICATION DEVELOPMENT UNTUK RANCANG BANGUN SISTEM PENGELOLAAN TRANSAKSI PERDAGANGAN INDOSURRATI SUKSES MAKMUR
Get PDFThe problem of manual recording is one of the problems that is often experienced by small and medium-scale businesses such as MSMEs, this occurs along with the increasing quantity of transactions, a large number of items of goods, and the growth and development of businesses, including PT. Surrati Sukses Makmur is engaged in the retail sale of perfume products. This problem can have negative impacts, such as difficulties in monitoring stock so that discrepancies easily occur, transactions not recorded so that data is lost, preparation of reports taking more time, fraud, and the need for a place to store documents. To overcome this problem, it is necessary to analyze and design a digital-based sales information system that can help the process of recording sales to be more efficient, accurate, safe, and easy. By using a digital information system, there is no need for a special place for document storage, loss of transaction data can be minimized so that it has an impact, PT. Surrati Sukses Makmur can improve performance and quality of service to customers, as well as operational costs can be reduced and inventory management can be easier. The design of this system uses the Rapid application Development (RAD) model with the aim that applications can be completed immediately and reduce the cost of making application systems so that reports can be made in a comprehensive and effective and efficient manner, , as well as with the black-box testing method in testing the application can be used more effectively and efficiently, and as a whole is in accordance with system requirement
OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER
Get PDFAt present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text
Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning
Get PDFPhysical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment
ELECTRICITY MANAGEMENT SYSTEM WITH TECHNOLOGY INTERNET OF THINGS
Get PDFThe increasing global demand for electricity has put a strain on energy resources and raised concerns about environmental sustainability. To address these challenges, the integration of modern technologies is crucial. This research presents a study on the implementation of an Electrical Energy Management System (EMS) using Internet of Things (IoT) technology. The proposed system aims to optimize electrical energy usage, enhance efficiency, and reduce the environmental impact. The EMS employs IoT devices and sensors to monitor and collect real-time data on electricity consumption in office buildings. Through data, the system can identify patterns and anomalies in energy consumption, allowing for informed decision-making and proactive energy management strategies