1 10 37 https://repository.horizon.ac.id/files/original/1996cd3f90c70d47298afb78e12c1c74.pdf d39785f0957a9fb8874f09f588bdfa92 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 2 (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 Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling Subject The topic of the resource pneumonia, image classification, Convolutional Neural Network Description An account of the resource Pneumonia is still a frequent cause of death in hundreds of thousands of children in most developing countries and generally detected clinically through chest radiographs. This method still difficult to detect the disease and requires a long time to produce a diagnosis. To simplify and shorten the detection process, we need a faster method and more precise in diagnosing pneumonia. This study aims to classify chest x-ray images using the CNN method to diagnose pneumonia. The proposed CNN model will be tested using max & average pooling. The proposed model is a development of the model in previous studies by adding batch normalization, dropout layer, and the number of epochs used. To maximize model performance, the dataset used will be optimized with oversampling & data augmentation techniques. The dataset used in this study is "Chest X-Ray Images (Pneumonia)" with a total of 5,856 data divided into two classes, namely Normal and Pneumonia. The proposed model gets 98% results using average pooling where the results increase by 9-13% better than the previous study. This is because overall pixel value of the image is highly considered to classify normal lungs and pneumonia Creator An entity primarily responsible for making the resource Annisa Fitria Nurjannah, 2Andi Shafira Dyah Kurniasari, 3Zamah Sari, 4Yufis Azhar Publisher An entity responsible for making the resource available Informatics, Faculty of Engineering, University of Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 29-04-2022 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/7a72b9b28f2884fa02737eb9d0d26543.pdf ca3a83eae46ed770bb737ae15de7c921 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 2 (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 Model-Based Feature Selection for Developing Network Attack Detection and Alerting System Subject The topic of the resource Machine Learning, Feature Selection, IDS, Snort, ELK Stack Description An account of the resource The use of the Intrusion Detection Systems (IDS) still has unresolved problems, namely the lack of accuracy in attack detection, resulting in false-positive problems and many false alarms. Machine learning is one way that is often utilized to overcome challenges that arise during the implementation of IDS.. We present a system that uses a machine learning approach to detect network attacks and send attack alerts in this study. The CSE-CICIDS2018 Dataset and Model-Based Feature Selection technique are used to assess the performance of eight classifier algorithms in identifying network attacks in order to determine the best algorithm. The resulting XGBoost Model is chosen as the model that provides the highest performance results in this comparison of machine learning models, with an accuracy rate of 99 percent for two-class classification and 98.4 percent for multi-class classification Creator An entity primarily responsible for making the resource Yuri Prihantono1 , Kalamullah Ramli2 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 29-04-2022 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 Indnesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/1e6d5f7f230b0083b7639e2546d416fb.pdf d831b8595ffee0439fb45deb3fde3cc7 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 2 (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 The Implementation of MQTT Protocol using PT-100 for Monitoring the Vaccine Temperature Subject The topic of the resource : ESP 32, MQTT, Quality of Service, PT-100 sensor, vaccine Description An account of the resource Fajar bagus W Creator An entity primarily responsible for making the resource Sigit Pramono1 , Slamet Indriyanto2 , Wahyu Junianto3 Publisher An entity responsible for making the resource available o2 , Wahyu Junianto3 1,2,3Telekomunikasi, Teknik Elektro dan Telekomunikasi, Institusi Teknologi Telkom Purwokerto Date A point or period of time associated with an event in the lifecycle of the resource 29-04-2022 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/56d4867e740d789af35729bc0259f096.pdf b4ec39264a99617d21b5af8e73afc87e 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 2 (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 A Model of Non-ASN Employee Performance Assessment Based on the ROC and MOORA Methods Subject The topic of the resource : Non ASN employees, ROC, MOORA, Performance Appraisal, Confusion Matrix, Ranking Description An account of the resource This study aims to assess the performance of non-ASN employees at the Human Resources Development Agency (BPSDM), East Kalimantan Province, Indonesia in order to assist organizers in determining the feasibility of extending work contracts. The performance of 37 non-ASN employees has been assessed based on 12 criteria including honesty, discipline, loyalty, responsibility, courtesy, commitment, ability and skills, neatness, communication, achievement, absence, and violations. In this study, the Rank Order Centroid (ROC) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods have been implemented to obtain rankings. Meanwhile, the confusion matrix (CM) method has also been used to measure the accuracy of both methods. Based on the experiment, the ROC method has been used to achieve the criteria weight and the MOORA method has been utilized to rank all non-ASN employees based on the highest score. Where the CM suitability level of 81.1% has been gained so that the ranking of 37 non-ASN employees can be revealed. The study indicates that both methods can be implemented as alternative models in assessing the performance of non-ASN employees. Therefore, these methods are quite effective, efficient, and relatively easy to use. Creator An entity primarily responsible for making the resource Haviluddin1 , Edy Budiman2 , Nurfaizi Amin3 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Engineering, Universitas Mulawarman. East Kalimantan, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-04-2022 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/4d9a9bbeceba62454d8260d082e0def0.pdf 7560b3562eb7b8f038972a7ee818f397 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 2 (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 Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features Subject The topic of the resource image identification, learning vector quantization, morphological features Description An account of the resource The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle, so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. In order for LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, major axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall and accuracy, it is obtained that the precision value is 85%, recall is 82% and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by a single layer network architecture, this makes the computational process faster and does not burden the computational process. Creator An entity primarily responsible for making the resource Rohmat Indra Borman1 , Yusra Fernando2 , Yohanes Egi Pratama Yudoutomo3 Publisher An entity responsible for making the resource available Universitas Teknokrat Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 30-04-2022 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/c3c88b974484f4d87c4a1e6df3983ef2.pdf 0d0dec2a852a53772f77d50eb668edff 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 2 (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 Towards Generating Unit Test Codes Using Generative Adversarial Networks Subject The topic of the resource unit test, code generation, generative adversarial network Description An account of the resource Unit testing is one of the important software development steps to ensure the software’s quality. Despite its importance, unit testing is often neglected since it requires a significant amount of time and effort from the software developers to write them. Existing automated testing generating systems from past research still have shortcomings due to the Genetic Algorithm (GA) limitations to generate the appropriate unit test codes. This study explores the feasibility of using Generative Adversarial Networks (GAN) models to generate unit test code with the ability of GAN to cover GA’s drawbacks. We perform experimentations using four state-of-the-art GAN models to generate basic unit test codes and compare the results by analyzing the generated output codes using novel metrics proposed from past studies as well as performing qualitative evaluation on the generated outputs. The results show that the generated codes have satisfactory quality scores (BLEU-2 of around 99%) from the models and adequate diversity score (NLL-Div and NLL-Gen) in most models. Our study shows positive indications and potential in the use of GAN for automatic unit test code generation and suggests recommendations for future studies in GAN�based unit test code generation system Creator An entity primarily responsible for making the resource Muhammad Johan Alibasa1 , Rizka Widyarini Purwanto2 , Yudi Priyadi3 , Rosa Reska Riskiana4 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 : 29-04-2022 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/dca0461f15844327fde107e432774816.pdf 82579017486029c054f7a7f917674459 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 2 (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 Food Fraud Prevention using a Blockchain-Based System: Case Study Slaughterhouse in Sidoarjo Subject The topic of the resource : livestock, food fraud, blockchain, trust, supply chain Description An account of the resource In the supply chain of foodstuffs derived from livestock, data consistency and security are the most important thing to pay attention to. However, the length of the existing supply chain and the lack of facilities to store and maintain the correctness of livestock data are problems in ensuring the security of the data. The use of blockchain in livestock has existed in Indonesia, but its scope is only to agriculture or only a small part of the livestock supply chain. Therefore, this research was conducted to develop the application of a blockchain-based livestock supply chain system in Indonesia. This research begins with conducting a literature study, analyzing the slaughterhouses. Then the development of a blockchain system for the food supply chain was carried out. System validation was carried out through interviews with the head of the slaughterhouses, breeders, and wholesalers. The results state that the system helps in ensuring the security of food supply chain data from livestock. However, the problem that has not been resolved is the validation process of data on the weight loss of foodstuffs in the course of the supply chain process. In the future, this technique is expected to be used for halal certification. Creator An entity primarily responsible for making the resource Daniel Soesanto1 , Liliana2 , Bambang Prijambodo3 Publisher An entity responsible for making the resource available Universitas Surabaya Date A point or period of time associated with an event in the lifecycle of the resource 29-04-2022 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/b5de37da7d89aa76b6215f36370eb712.pdf e9a09dc8458b2c3581a4dbd2a298eb7f 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 2 (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 The Generating Super Resolution of Thermal Image based on Deep Learning Subject The topic of the resource Super resolution, thermal image, deep learning Description An account of the resource The need of high resolution to thermal image is very urgent and important. The high resolution of thermal image is able to give the accurate information on heat distribution map of the objects. The accurate heat distribution maps is able to give accurate temperature information. This accurate temperature measurement is used for measuring many objects such as electric motors, engines, human body and so on. These information used for detecting the anomalies of the object in order to find the damaged parts. The anomalies are considered as damaged parts find in solar panel, agriculture field, the building, bridges and so on. As the super resolution of thermal image is very important, that the generating of them is a compulsory. Whereas, the camera for obtaining the super resolution thermal images are very rare, not available at common market. Furthermore, this kind of device is very expensive too. Therefore not all the user such farmer or technician are able to have them. In order to handle the problem, the proposed method has the purpose to generate super resolution thermal image economically and easier through deep learning method. The dataset is taken from solar panel. The results show that the proposed method is able to handle the low resolution problem of thermal images. Creator An entity primarily responsible for making the resource Ismail1 , Yefriadi2 , Yuhefizar3 , Fibriyanti4 , Zulka Hendri5 Publisher An entity responsible for making the resource available Politeknik Negeri Padang Contributor An entity responsible for making contributions to the resource 29-04-2022 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/91a98649614dad71224e1dbc828adda6.pdf 8709ab2a3beec66f47f0a616005afe87 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 2 (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 A Comparison of Deep Learning Approach for Underwater Object Detection Subject The topic of the resource : Underwater Object Detection, Faster-RCNN, SSD, RetinaNet, YOLOv3, YOLOv4 Description An account of the resource n recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time used to compare how fast a model can detect object within an image; and mAP also applied to measured detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models. Creator An entity primarily responsible for making the resource Nurcahyani Wulandari1 , Igi Ardiyanto2 , Hanung Adi Nugroho3 Publisher An entity responsible for making the resource available Universitas Gadjah Mada Date A point or period of time associated with an event in the lifecycle of the resource 20-04-2022 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/677361d72d6d3b6e88bb1e18c1a8df7d.pdf 7e59b59c2ac265da8be5fcb45a4fbd8a 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 2 (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 Knowledge Repository Design to Improve Knowledge Management Process Capabilities: A Systematic Literature Review Subject The topic of the resource knowledge repository system, knowledge management system, ontology Description An account of the resource The role of technology in supporting knowledge management is very vital. The knowledge repository system is the foundation of knowledge management and its implementation. Knowledge sharing, discovery, and other knowledge processes will be more accessible through proper knowledge repository system as an organizational knowledge base. This research explored various approaches employed by practitioners and researchers in designing a knowledge repository that allowed people to study and implement the knowledge repository as needed. A Systematic Literature Review (SLR) method proposed by Kitchenham was used to answer three research questions; (1) how is model knowledge storage in an organizational repository developed? (2) what tools can help create a knowledge repository? (3) what are features that should be present in a knowledge repository? It was confirmed that the repository storage model was mainly represented in the ontology model. Furthermore, the technologies for creating knowledge were Protégé and Neo4j graph-based databases. In addition, features that were mostly applied in the knowledge repository system included data catalogs, search, API, knowledge management, visualization, rule-based and case�based reasoning Creator An entity primarily responsible for making the resource Muhammad Farid Fadhlan1 , Dana Indra Sensuse2 Publisher An entity responsible for making the resource available Universitas Indonesia, Depok Date A point or period of time associated with an event in the lifecycle of the resource : 20-04-2022 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