1 10 37 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/d06eec662b03a7fe65231e27107252fa.pdf d2aec48c3a3988bd57bd261b034b1e3a 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 Management Administration Smartphone Application as a Strategy to Increase Accreditation Score in Primary Healthcare Facilities Subject The topic of the resource administration, management, Puskesmas, accreditation, smartphone application Description An account of the resource Health facility accreditation has been in great demand in recent years. Mobile applications can serve as a tool for community healthcare center’s (Puskesmas) to assess the value of their accreditation, especially regarding administration and management. In the context of preparation for accreditation, it can be used as a self-assessment tool for elements of management administration. This case-based article focuses on smartphone application development using the input-process�output-outcome scheme as a framework. We developed a checklist and assessment for the management administration component by reviewing Permenkes No. 46 the Year 2015 concerning Puskesmas accreditation. This is the first available mobile application on the Google Play Store, targeting Puskesmas staff to improve the quality of administration and management services based on accreditation criteria. This smartphone application functions as an independent assessment tool for Puskesmas to complete the accreditation criteria as stated in the regulation of the minister of health. About 20 Puskesmas staff showed a very good response and positive. All stated that they were very helpful and satisfied with this application, so they will continue to use and distribute it to other Puskesmas staf Creator An entity primarily responsible for making the resource Agus Sugiharto1 , Boy Subirosa Sabarguna2 , Ajeng Pramastuty3 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 : 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/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/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/0ec9d8e14b77ab4547d97eada35875c7.pdf 048f3e4996a05ca0059e294b8d5ffb1a 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 Perbandingan Model Proses Algoritma Alpha dan Alpha++ Pada Aplikasi E-commerce Subject The topic of the resource Alpha, Alpha++, Process Discovery, Process mining, Event log, E-commerce Description An account of the resource Utilization of information technology is currently growing rapidly in helping activities especially in storing an event log. The activity which is behavior of the user can be analyzed using process mining. The process mining purpose to extract information from event logs on business processes that working. Discovery technique is used in this research. The purpose of this study is to compare two algorithms applied by creating an e-commerce application that is aware of the processes. E-commerce applications require event logs to read the behavior of visitor activities against the application. This research method starts from understanding the business processes that working, then designing a website by creating the application used. Furthermore, data collection through applications that are promoted through social media. The application will be recorded user activity and formed an event log. The event log that formed then discovered using alpha and alpha++ algorithms by utilizing the ProM Lite 1.2 tools. The evaluation results show that the alpha algorithm has shortcomings, namely length one loop, length two loop and non-free choice. And the alpha++ algorithm fixed this deficiency Creator An entity primarily responsible for making the resource Bambang Jokonowo1*, Miskah Alfiyyah Kulsum2 , Nita Komala3 , Publisher An entity responsible for making the resource available Universitas Mercu Buana 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/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/b038c4f230f17e7917e3915b90d5bfdf.pdf 93ce4dc41a229d48001d51c540646110 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 E-commerce Recommender System Using PCA and K-Means Clustering Subject The topic of the resource recommender system, collaborative filtering, principal component analysis, k-means, e-commerce Description An account of the resource Recently, recommender system has an important role in e-commerce to market products for users. One of recommender system approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity problems, namely the system is difficult to provide recommendations because of the lack of important information needed. Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed system has a lower RMSE value compared to other methods. For the number of neighbors of 10, 20, 30, and 40, our system obtains values of 0.771806, 0.75747, 0.75304, 0.75304, and 0.75270 Creator An entity primarily responsible for making the resource Dendy Andra A.N1 , Z. K. A. Baizal2 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 Telkom University 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/0cc9e0803ed7e748655c45ac35631671.pdf ee803f3bf84359edbeeb3d15640fadc6 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 Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data Subject The topic of the resource ensemble method, microarray, schizophrenia, disease detection Description An account of the resource Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively. Creator An entity primarily responsible for making the resource Diya Namira Purba1 , Fhira Nhita2 , Isman Kurniawan3 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 01-02-2022 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/1f897a2e1a8862de62522fb2fb3be9e2.pdf 381d5b862df01694c9c75889a2695a6f 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 Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm Subject The topic of the resource Clustering, Fuzzy C-Means, Genetic Algorithm, Image Processing, Rice Plants Diseases Description An account of the resource Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add 5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly Creator An entity primarily responsible for making the resource Faza Adhzima1 , Yandra Arkeman2 , Irman Hermadi Publisher An entity responsible for making the resource available IPB 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 agus 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/c10b1d6e342b36cf8d054789628cfd43.pdf c730c9b7f02c6cd78754d62106ec8cac 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 Klasifikasi Kematangan Tanaman Hidroponik Pakcoy Menggunakan Metode SVM Subject The topic of the resource Classification, Pakcoy, Machine learning, SVM Description An account of the resource Pakcoy is a type of vegetable plant belonging to the Brassica family. Pakcoy plants can be cultivated using hydroponic techniques, namely plant cultivation techniques without soil media. The advantage of cultivating Pakcoy plants using hydroponic techniques is that it does not require a large area of land, so it is easy to apply in the yard. However, cultivation with hydroponic techniques has drawbacks such as farmers need to make regular observations to determine the harvest readiness of each plant. This causes a lack of effectiveness of farmers in cultivating Pakcoy plants. With the development of Machine Learning technology, a model can classify the maturity of Pakcoy plants based on digital image data. By applying the Support Vector Machine (SVM) Algorithm, the Machine Learning model can learn to classify a digital image of Pakcoy plants with the category "Small" to represent immature Pakcoy plants and "Large" to represent mature Pakcoy plants which results in an accuracy level of above 79%. It can be concluded that Machine Learning can be implemented in Pakcoy cultivation activities to support hydroponic farmers. Creator An entity primarily responsible for making the resource Hanin Latif Fuadi1 , Lukman Priyambodo2 , Tasya Enjelika Saputri3 , Naura Nazhifah4 , Angga Bagus Prawira5 , Ibrohim Huzaimi6 , Mas Aly Afandi7 , Eka Setia Nugraha8 , Agung Wicaksono9 , Petrus Kerowe Goran Publisher An entity responsible for making the resource available Institut Teknologi Telkom Purwokerto 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