1 10 37 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/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/97e32c7612d3eaf04647c98c44c6abf6.pdf 5b671268ca402c53062dbaee47ec8d91 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 Analisis Spasial Untuk Klasifikasi Pengembangan Tempat Penampungan Sementara Menggunakan Metode Jaringan Syaraf Tiruan Subject The topic of the resource Spatial Analysis, Web-GIS, Garbage Shelters, Artificial Neural Network, Backpropagation Description An account of the resource Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of waste. The distribution of temporary waste shelters (TPS) in each area is still insufficient to accommodate the volume of waste, and its availability is inadequate. The purpose of this study is to model spatial data through spatial analysis using artificial intelligence methods in classifying the development of integrated temporary shelter locations (TPST) and regional integrated temporary shelters (TPST Regions) by utilizing Web-based technology (Geographical Information System (Web�GIS). The Artificial Neural Network method with the Backpropagation algorithm is used for the spatial analysis process based on the parameters of the population, the amount of organic and inorganic waste, the amount of industrial waste, and the volume of the TPST and Regional TPST capacity. The spatial analysis results using the Artificial Neural Network method obtained an accuracy value of 7171.02%. The results of this study can be the basis for Department of Environment and Cleanliness policies for the development of TPST and TPST areas with information coverage at the village level Creator An entity primarily responsible for making the resource Luqman Hakim1 , Anik Vega Vitianingsih2 , Gita Indah Marthasari3 , Kresna Arief Nugraha4 , Anastasia Lidya Maukar5 Publisher An entity responsible for making the resource available Informatika, Teknik, Universitas Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 27-02-2022 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/a9f1c373d624af56d9b2466c1c9d187a.pdf f3370b671b5f5eabb33fcc06c0000c69 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 Classification Based on Machine Learning Methods for Identification of Image Matching Achievements Subject The topic of the resource : image matching, logo, machine learning, kNN, RF, MLP Description An account of the resource Classification is one method in image processing. Image processing to search for similar images or with similarity ownership is called image matching or image matching. In the measurement of image matching, the original and fake logo objects are used. Identification of similarity manually with the help of human vision is not necessarily precise and it is difficult to obtain accurate results. Based on this, the identification of image matching of the original and fake logos automatically requires an application, in order to obtain precise and more accurate results. Identification of image suitability is determined through the image segmentation process, and feature extraction is based on the statistics of Red-Green-Blue (RGB), Hue-Saturation-Value (HSV), feature extraction of area, perimeter, eccentricity, and tangent distance measurements. The purpose of this study includes the identification of the achievement of image-matching logo images with comparisons of accuracy between various machine learning methods. The use of machine learning methods in this study includes the k-Nearest Neighbor (kNN), Random Forest (RF), and Multilayer Perceptron (MLP) methods. The use of the dataset includes eighteen training data and eight logo image testing data, divided into genuine and fake classes. The results of the measurement of the accuracy value obtained a value of seventy-five percent with the kNN method or the RF method, while the MLP method obtained an accuracy value of eighty-seven point five percent. Based on these results, it can be concluded that the MLP method with the highest accuracy value was chosen as a classification model from machine learning to identify the achievement of image matching on the original and fake logos. For further development, the system can be developed using other methods or a combination of different methods, in order to obtain better accurate results Creator An entity primarily responsible for making the resource Rusydi Umar1 , Imam Riadi2 , Dewi Astria Faroek3 Publisher An entity responsible for making the resource available Ahmad Dahlan University 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/88d7e7d70bdbf3b6d2322afa648a2298.pdf 5a4c6361d2e5575c9bac3712e7180279 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 Classification of Rupiah to Help Blind with The Convolutional Neural Network Method Subject The topic of the resource Blind, Currency, Deep Learning, CNN, Epoch Description An account of the resource Currency is an item humans require as a medium of exchange in transactions, including for those with vision impairments. It can be challenging for certain blind people to identify currencies. This research aimed to help blind people identify nominal currency when in the transaction. Deep Learning with the CNN algorithm and preprocessing with a sequential model were the methods used in this research. This algorithm is modeled as neurons in the human brain that communicate and learn patterns. Data collecting, preprocessing, testing, and evaluation are the stages in this research. 681 datasets are used, consisting of IDR 50.000, IDR 75.000, and IDR 100.000. Model testing was carried out with different iterations of 5, 10, 15, and 20 epochs. Different epoch values will affect the time it takes the model to learn, but the longer of learning process will result more accurate models. The highest result obtained from all epoch tests is 100%. The class prediction results for the 69 test data show that they can be predicted based on the actual class, indicating that the model is adequate. The results of this classification might be used to construct a smartphone app that would assist visually challenged people in recognizing the nominals Source A related resource from which the described resource is derived Octavian Ery Pamungkas1 , Puspa Rahmawati2 , Dhany Maulana Supriadi3 , Natasya Nur Khalika4 , Thofan Maliyano5 , Dicky Revan Pangestu6 , Eka Setia Nugraha7 , Mas Aly Afandi8 , Nurcahyani Wulandari9 , Petrus Kerowe Goran10, Agung Wicaksono11 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 Creator An entity primarily responsible for making the resource Octavian Ery Pamungkas1 , Puspa Rahmawati2 , Dhany Maulana Supriadi3 , Natasya Nur Khalika4 , Thofan Maliyano5 , Dicky Revan Pangestu6 , Eka Setia Nugraha7 , Mas Aly Afandi8 , Nurcahyani Wulandari9 , Petrus Kerowe Goran10, Agung Wicaksono11 https://repository.horizon.ac.id/files/original/94f48e957ff1abdb6549527b4633022d.pdf 64333cd849051e1cb688b0d74ddde87b 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 Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter Subject The topic of the resource PPKM, Support Vector Machine, One Against One, One Against Rest, Polynomial, RBF Description An account of the resource PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the model that uses polynomial kernel produces higher accuracy, both applied to the One Against One and One Against Rest methods. In contrast, the model that uses the RBF kernel produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus. Creator An entity primarily responsible for making the resource Andi Nurkholis1 , Debby Alita2 , Aris Munandar3 Publisher An entity responsible for making the resource available Teknokrat Indonesia University 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/bdfb5bef88fda0357f78a9b51adc8eb6.pdf 2392e74dd7564f2ac12049e53269bb5e 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 Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm Subject The topic of the resource Classification, Convolutional Neural Network, COVID, Machine Learning, Image Description An account of the resource Corona Virus or also known as COVID-19 is one of the new viruses in 2019. Viruses caused by animal or human disease are called coronaviruses. Coronavirus will direct respiration in humans. Humans who are exposed to the corona virus will experience a respiratory infection. The research that will be made is useful for classifying X-rays of the lungs of patients affected by the coronavirus. In this study, the classification of coronaviruses focuses on three classes, namely Covid, Normal, and Viral Pneumonia. This study uses a lung X-ray image dataset. In this study there are 4 folders in it, namely Scenario 1, Scenario 2, Scenario 3, and Scenario 4. This study will use the Convolutional Neural Network (CNN) method by using an architectural model including Convolutional 2D, activation layers, max pooling layer, dropout layer , flatten, and finally dense layer. After building the model, in each scenario, the results of accuracy, precision, recall, and f1-score will be obtained. The result of accuracy of Scenario 1 is 97.87%, in Scenario 2 the accuracy is 94.84%, in Scenario 3 is 91.66%, and Scenario 4 is 91.41% Creator An entity primarily responsible for making the resource Melly Damara Chaniago, Amellia Amanullah Sugiharto, Qhistina Dyah Khatulistiwa, Zamah Sari, Agus Eko Publisher An entity responsible for making the resource available Universitas Muhammadiyah Malang 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/ce2e2e84c1de6a62f37bce92e42b5b82.pdf e0fe43d01df7190676176c5e415cca94 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 Critical Section Overhead Reduction for OpenMP Program by Nesting a Serial Loop to Increase Task Granularity of Parallel Loop Subject The topic of the resource : critical section, overhead reduction, OpenMP, task granularity, Parallel loop Description An account of the resource This paper presents a simple method to reduce performance loss due to a parallel program's massive critical sections of parallel numerical integration. The method is to transform a fine grain parallel loop into a coarse grain parallel loop which is nesting a sequential loop. The coarse grain parallel loop is by nesting a loop block to make task granularities coarser than that naive one. In addition to the overhead reduction, the method makes the parallel work fraction significantly larger than the serial fraction. As a result, nesting a serial loop within a parallel loop improves the parallel program’s performance. Compared to the naïve method, which does not scale performance of parallel program of numerical integration, the nesting serial loop method scales a parallel program up to 3.26 times fold relative to its sequential program on quad-core processor. This result shows that the proposed method makes parallel program much faster compared to the naïve method Creator An entity primarily responsible for making the resource Adnan1 , Intan Sari Areni2 , Zulkifli Tahir3 Publisher An entity responsible for making the resource available Universitas Hasanuddin 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/ba1ddb87c77b3feefa1eab08641b3ded.pdf 0b07afb99f3c93f5d49f22f4003911a9 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 Design of Smart Farm Irrigation Monitoring System Using IoT and LoRA Subject The topic of the resource smart irrigation, monitoring system, internet of things, LoRa Description An account of the resource Agriculture is an essential part of society in Indonesia because most of the population lives off of farming. Water and irrigation are the most critical and central factors in the agricultural system. The uneven distribution of irrigation water can be a problem for farmers. In addition, most of the current irrigation systems are still operated manually, for example, irrigation gates. The gate still works manually and requires human labor to run it. This study aims to design a smart farm irrigation system using internet of things and LoRa communication technology. LoRa can transmit information up to a range of several kilometers without an internet connection. It will be advantageous when the farm's location is deep in the forest, and there is no GSM signal for internet access. The results show that the system can control the farm irrigation automatically without human hands. The water gates in the field are operated based on the water level. The irrigation monitoring process becomes easier because they do not need to come to the farm location. In fact, they can use smartphones to monitor it. Furthermore, the system can be monitored well under distance of 100 meters with LoRa communications Creator An entity primarily responsible for making the resource Kurniawan D. Irianto Publisher An entity responsible for making the resource available , Universitas Islam Indonesia Date A point or period of time associated with an event in the lifecycle of the resource : 01-02-2022 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/e980e890ee8953fa2c52c8dabd7b8b2a.pdf ff9cbe65961483f11ceac017a0e722fd 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 Designing an Ethereum-based Blockchain for Tuition Payment System using Smart Contract Service Subject The topic of the resource Blockchain, Ethereum, Smart Contract, Payment Gateway Description An account of the resource The security and disclosure of information in online transaction data remains a sensitive subject until this day. Whenever a collection of data from a transaction process is accessible over the internet system, certain parties may misuse any one of these data. Blockchain technology, which includes the use of smart contracts, is thought to be capable of overcoming this problem due to the blockchain's decentralized and distributed nature. Blockchain allows transaction data to be accessed openly and transparently while still being securely protected by hashing encryption owned by smart contracts. This enables users to have detailed access privileges to each transaction's data. The development of smart contracts will be carried out in the production of microservices payment gateways based on decentralized apps (DApps) on the Ethereum blockchain in this research, with the payment gateway generated being used in the tuition payment process. The Truffle framework and the Metamask wallet will be used to assist the Ethereum payment process during the DApps development process. The results of testing the functionality of each smart contract feature reveal that the payment system can be utilized effectively and that there are no issues that cause transaction failures Creator An entity primarily responsible for making the resource Moch Sholeh1 , Esther Yolanda Talahaturuson2 , Maulana Rizqi3 , Agustinus Bimo Gumelar4 Publisher An entity responsible for making the resource available Fakultas Ilmu Komputer, Universitas Narotama, 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