2 10 26 https://repository.horizon.ac.id/files/original/d2530f333f747a607b2cb2d597115c1e.pdf 593dbba8287be815569e103d17b0fd5d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prototype of Body Temperature and Oxygen Saturation Monitoring System Using DS18B20 and MAX30100 Sensors based on IOT Subject The topic of the resource COVID-19, Happy hypoxia, Monitoring, Internet of Things Description An account of the resource After people died from infections during COVID-19 epidemic, this attracted a lot of attention. COVID-19 causes symptoms such as fever, headache, sore throat, shortness of breath, and others. Many deaths are asymptomatic, which it makes the problem even bigger. Therefore, real-time system monitoring is needed. Monitoring will be carried out about measuring body temperature and oxygen saturation in patients. Monitoring body temperature is necessary because it can detect symptoms of COVID-19 in patients earlier. Concept of Internet of Things (IoT) is to enable devices to send and receive data over internet network. Monitoring system to be built uses NodeMCU ESP8266, DS18B20 sensor, and MAX30100 sensor. Data communication is used in exchange of information using WiFi. Applications made using MIT App Inventor are used to view body temperature and oxygen saturation data. This system is expected to reduce number of deaths due to COVID-19. DS18B20 sensor has a sensor accuracy of 99,73% and an average error of 0,27%. MAX30100 sensor has an accuracy rate of 99,18% and an average error of 0,82%. Delay test results show an average of 155,57 ms, and packet loss test results show an average of 0%. Result of system that has been tested said the both sensors can read well Creator An entity primarily responsible for making the resource Izhangghani1 , Irmayatul Hikmah2 , Slamet Indriyanto3 Publisher An entity responsible for making the resource available Izhangghani1 , Irmayatul Hikmah2 , Slamet Indriyanto3 Date A point or period of time associated with an event in the lifecycle of the resource Izhangghani1 , Irmayatul Hikmah2 , Slamet Indriyanto3 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/faf29a06dee646b9e1f9f97e73261a90.pdf 51633612796843c612a2ab6283cf21ae Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Topic Classification of Quranic Verses in English Translation Using Word Centrality Measurement Subject The topic of the resource Quran, Topic classification, Multilabel, Word Centrality, SVM, Naïve Bayes, KNN, Decision Tree Description An account of the resource Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term weighting in feature extraction can improve the performance of the classification system Creator An entity primarily responsible for making the resource Achmad Salim Aiman1 , Kemas Muslim Lhaksmana2 , Jondri3 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/ffc31da89b8d18ba91d47c6fcbe31416.pdf 1952537ff9ddf822ee0c95af5cf5f68f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Application of Neural Network Variations for Determining the Best Architecture for Data Prediction Subject The topic of the resource Data Prediction, Backpropagation, Resilent Backpropagation, Conjugate Gradient, Fletcher Reeves, Powell Beal. Description An account of the resource This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667 Creator An entity primarily responsible for making the resource Mochamad Wahyudi1 , Firmansyah2 , Lise Pujiastuti3 , Solikhun4 Publisher An entity responsible for making the resource available Universitas Nusa Mandiri Contributor An entity responsible for making contributions to the resource Universitas Nusa Mandiri Format The file format, physical medium, or dimensions of the resource PDG Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/7ac14cd4cccf53ee89875656a0d986a5.pdf 13e1123b83273dfbdff9c81b7444a27a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of the Quality of Natural Dyes in Weaving Exposed to Sunlight Using MSE and PSNR Parameters Subject The topic of the resource Natural Dyes; MSE; PSNR, RGB; Description An account of the resource It is widely assumed that natural dyes in weaving degrade in quality when exposed to sunlight for an extended period of time. This indication is clearly visible to the naked eye. There is currently no standard for evaluating the quality of natural dyes. The Boti tribe's weaving on Timor Island, East Nusa Tenggara Province, is one type of weaving that uses natural dyes. The dye is made from corn flour and a combination of "nobah" leaves and the bark of the "bauk ulu" tree (from the local language). White (from corn flour) and blue-black are the colors produced by dyeing the yarn. The purpose of this research is to examine the image quality of the Boti tribe's woven fabric. The parameters used were Means Square Error (MSE), Peak Signal to Noise (PSNR), and RGB values. The image of the weaving used as a reference is compared to the image of the sun-dried weaving. The image capture distance was 30 cm, and the cropped RGB image size was 423x623x3. The experimental method was used in the research. The drying time was one hour, and it was repeated every one hour between 10:00 and 15:00 local time. The sun-dried images were photographed, and parameter comparisons were performed for analysis. The results demonstrated that the MSE and PSNR methods were effective in measuring the image quality of weaving dyed with natural dyes. The average value has changed by 8.42% for the R value, 8.58% for the G value, and 9.68% for the B value. The average PSNR for RGB images is 9.44288 dB, and the MSE is 7477.52. For grayscale images, the average PSNR is 10.52 dB and the average MSE is 5832.06. Creator An entity primarily responsible for making the resource Patrisius Batarius1 , Alfry Aristo Sinlae2 , Elisabeth F. Fahik3 Publisher An entity responsible for making the resource available Widya Mandira Catholic University Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/11d41eb1ea1e45de5269016b0233f024.pdf 3804d868c52ebda3f9263a6e38b9dd78 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of Face Mask Detection Using Transfer Learning Model DenseNet169 Subject The topic of the resource classification, transfer learning, COVID-19, DenseNet169, face mask Description An account of the resource COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks in order to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, so as to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images. Creator An entity primarily responsible for making the resource Lidya Fankky Oktavia Putri1 , Ahmad Junjung Sudrajad2 , Vinna Rahmayanti Setyaning Nastiti3 Publisher An entity responsible for making the resource available Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource : 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar Bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/e6b6fda4de3fd557f3e8774c0ba985f4.pdf 63925314381bdad6e2718c9645c3d4e8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation Subject The topic of the resource Malaria, Image Classification, Convolutional Neural Network, Fine-Tuning, ResNet50 Description An account of the resource Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%. Creator An entity primarily responsible for making the resource Aris Muhandisin1 , Yufis Azhar2 Publisher An entity responsible for making the resource available University of Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 02-11-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/e4b6965bf87619cc5927f4c0df67bef2.pdf bcabb28808643685fdf871450c931875 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods Subject The topic of the resource Diabetes, Diacylglycerol Acyltransferase-1, Particle Swarm Optimization, QSAR, Support Vector Machine https://repository.horizon.ac.id/files/original/fe50aebf1f008bfc796559f08973f1c5.pdf 9f381e4c0003bd163c341ffc5066011f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Improving AI Text Recognition Accuracy with Enhanced OCR For Automated Guided Vehicle Subject The topic of the resource : OCR, AGV, AI, Image Processing, Computer Vision Description An account of the resource AGV (Automated Guided Vehicle) with artificial intelligence (AI) is expected to change the industry's development in Indonesia, this artificial intelligence robot uses a mini-computer to operate it and uses mechanical movement like a four-wheeled vehicle with a 2WD drive system. In this article, a control strategy of the AGV robot will be shown and implemented to detect the location. This research Uses OCR (Optical Character Recognition) for the OpenCV library itself which has been enhanced/modified. This enhanced OCR is the main library used in text recognition. This research produces very accurate text detection compared to the default OCR that was previously used on the AGV robot in our university. After the process of reading this text is passed, it will produce text previously read through the camera which will then provide output in the form of text where the AGV robot is located. After the reading is validated, the AGV robot will move to the next point until it returns to its starting point. Based on hardware implementation through testing in the AGV laboratory with artificial intelligence, it can work according to the algorithm and minimize reading errors with a 95% success rate. Creator An entity primarily responsible for making the resource Florentinus Budi Setiawan*, 2Farrel Adriantama, 3Leonardus Heru Pratomo, 4Slamet Riyadi Publisher An entity responsible for making the resource available Universitas Katolik Soegijapranata, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource : 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/d0c4f4d6bff31f9c31f2bcc08b50576b.pdf 0ac934f06523bb562fea67be3b889696 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification Subject The topic of the resource Recommender System, User-based, Item-based, Collaborative Filtering, Support Vector Machine Description An account of the resource Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95% Creator An entity primarily responsible for making the resource Anang Furkon Rifai1 , Erwin Budi Setiawan2 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource : 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/68cce57805b87bb0384bb22d2b371ddb.pdf 741cb95a7613cd9b56614ef386a4cdae Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Surrogate Model-based Multi-Objective Optimization in Early Stages of Ship Design Subject The topic of the resource ship design, multi-objective optimization, surrogate model, neural network, particle swarm optimizer Description An account of the resource The abstract isthe early stages of ship design, the decision of the ship's main dimensions significantly impacts the ship's performance and the total cost of ownership. This paper focuses on an optimization approach based on surrogate models at the early stages of ship design. The objectives are to minimize power requirements and building costs while still satisfying the constraints. We compare three approaches of surrogate models: Kriging, BPNN-PSO (Backpropagation Neural Network�Particle Swarm Optimizer), and MLP (Multi-Layer Perceptron) in two multi-objective optimization algorithms: MOEA/D (Multi-Objective Evolutionary Algorithm Decomposition) and NSGA-II (Non-Dominated Sorting Genetic Algorithm II). The experimental results show that MLP surrogate models get the best performance with MAE 6.03, and BPNN-PSO gets the second position with MAE 7.2. BPNN-PSO and MLP with MOEA/D and NSGA-II improve the design with around 58% smaller adequate power and 6% less steel weight than the original design. However, BPNN-PSO and MLP have lower hypervolume than Kriging for both optimization algorithms MOEA/D and NSGA-II. On the other hand, Kriging has the most inadequate model accuracy performance, with an MAE of 22.2, but produces the highest hypervolume, lowest computational time, and far lower objective values than BPNN-PSO and MLP for both optimization algorithms, MOEA/D and NSGA-II. Nevertheless, the three surrogate model approaches can significantly improve ship design solutions and reduce work time in the early stages of design Creator An entity primarily responsible for making the resource Nanda Yustina1 , Ari Saptawijaya2 Publisher An entity responsible for making the resource available Universitas Indonesia, Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text