2 10 17 https://repository.horizon.ac.id/files/original/3977089e2766f934be2737eb9fde3d9b.pdf 3a18f1efdec291324c11f60d89f21fd1 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 4 (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 Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term Subject The topic of the resource kernel, photovoltaic, prediction, support vector regression Description An account of the resource Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output. Creator An entity primarily responsible for making the resource Kharisma Bani Adam1 , Desri Kristina Silalahi2 , Bandiyah Sri Aprillia3 , Husayn Aththar Adhari4 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 22-08-2022 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/828bd49f106e70dfee1b75aa882c585c.pdf 127ca950284c0340c01d58c11f194ff2 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 4 (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 Forecasting Pneumonia Toddler Mortality Using Comparative Model ARIMA and Multilayer Perceptron Subject The topic of the resource Pneumonia, Forecasting, ARIMA, Multilayer Perceptron Description An account of the resource Pneumonia is an inflammatory lung disease that causes the second largest number of deaths in Indonesia after Dengue Hemorrhagic Fever (DHF). In 2021, there was an increase in cases of 7.8% compared to the previous year, and was exacerbated by the Covid-19 pandemic. Predictive methods were needed to predict and compare the ARIMA and MLP methods, where the results of the best methods were selected for long-term forecasting. The research data used was from January 2014 – December 2021, with a total of 96 data. In choosing the best method, the basic error calculations used were Mean Absolute Deviation, Mean Squared Error, and Mean Absolute Percentage Error. This study aims to build a predictive model for the next period of pneumonia under-five mortality. These results can be used for government policy-making related to mortality prevention for the next period. The results showed that the MLP method was superior to ARIMA. Testing 28 mortality rate data using the final test result showed that the best method was MLP, with a hidden layer value of 2.2, a learning rate of 0.3, and an error percentage of 1.27%. The prediction results of the overall mortality rate of pneumonia under five in 2022 was predicted to be 136 people. Creator An entity primarily responsible for making the resource Ni Kadek Ary Indah Suryani1 , Oka Sudana2 , Ayu Wirdian Publisher An entity responsible for making the resource available Universitas Udayana Date A point or period of time associated with an event in the lifecycle of the resource 22-08-2022 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/325c3c66270748d31b9bcdf9b9194916.pdf 96e7ac41e23dd049e2cd014178a87d5c 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 4 (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 System Dynamic Modeling: A Case Study of a Hotel Food Supply Chain Subject The topic of the resource Hotel Food Supply Chain, Inventory, Sales, System Dynamic Description An account of the resource Supply chain management (SCM) has become an integral and indispensable part of the tourism and hospitality industry. However, a closer examination reveals that many firms are battling inventory bottleneck as the covid pandemic spike in demand converges, which results in unpredictability and unstable sale fluctuation. This situation forced the hotel industry to find a balance between fulfilling demand and inventory turnover, which is impossible to predict accurately. Therefore, this research aims to combine system dynamics modelling with a hotel food supply chain system to solve the unpredictability of supply chain dynamics. In addition, The Causal Loop Diagram (CLD) and Stock and Flow Diagram (SFD) are used in this study to model the complexity of the case study supply chain, with an objective model to minimize the instability of inventory turnover and to stabilize the sales movement. This study proposes five policy scenario simulations where standard deviation (SD) and Standard of Error (SE) are used as the decision-making parameters. The simulation result suggests that the fifth scenario provides the highest SD (49.484) and the lowest SE (6.336). Therefore, controlling the customer response time variable to a maximum of 25 minutes and the menu unavailability variable to 15 occurrences per week will result in higher stability of the case study inventory turnover. Creator An entity primarily responsible for making the resource System Dynamic Modeling: A Case Study of a Hotel Food Supply Chain Publisher An entity responsible for making the resource available Universitas Syiah Kuala Date A point or period of time associated with an event in the lifecycle of the resource 22-08-2022 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/10999bcf67f4cc00f433795500c297ab.pdf 2cf47c039e13d399c53959732a4586c6 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 4 (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 Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM Subject The topic of the resource : COVID-19, Deep Learning, Bidirectional LSTM, CNN Description An account of the resource COVID-19 is a new disease that has a negatively impacts in Indonesia, so the government is taking several measures to suppress the spread of COVID-19, such as new normal, social distancing, health protocols fines, and COVID-19 vaccination. The government's handling efforts have reaped a variety of negative to positive responses from the public on social media, so this study aims to determine the effectiveness of the government's efforts by analyzing public sentiment using the Deep Learning method with 1,875 training datasets consisting of four types government efforts and taken from various media social. The use of Deep Learning begins with testing several Deep Learning architectures to determine the best architecture for predicting data. The architectures tested include CNN and Bi-LSTM, where from these tests, Bi-LSTM outperforms CNN with the best performance achieving the accuracy of 97.34% and 97.33% for precision, recall, and F1-score. The results of public sentiment analysis show that social distancing efforts are considered the most effective by obtaining the most positive sentiments by 33.93%, while the effort to health protocol fines is considered lacking because it obtains the most negative sentiment of 35.64%, so the government must continue to enforce social distancing and optimize other efforts that are still considered ineffective Creator An entity primarily responsible for making the resource Gusti Agung Mayun Kukuh Jaluwana1 , Gusti Made Arya Sasmita2 , I Made Agus Dwi Suarjaya3 Publisher An entity responsible for making the resource available Universitas Udayana Date A point or period of time associated with an event in the lifecycle of the resource : 22-08-2022 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/872c712c0232602c9c15eb45a6bbcdfe.pdf 245a1b967e79df8ad4a4d5d8975d85a3 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 4 (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 Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means Subject The topic of the resource K-Means, GLCM, Image Processing, Clove Plants, Diagnosis. Description An account of the resource The detection of disease in clove plant leaves is generally carried out by diagnosing the symptoms that appear on clove plants. This diagnosis is conducted by clove farmers only by relying on their experience or even having to seek information from other clove farmers. This is because the agricultural sector has no disease detection system for clove leaves by utilizing digital image processing technology to detect diseases in clove leaves. In this study, the researchers applied two methods to make it easier for clove farmers to diagnose diseases in their clove plants. Those methods were the imaging system using Gray Level Co�Occurrence Matrix (GLCM) and disease clustering using the K-Means algorithm. The objective of this study was to design and build image pattern recognition by utilizing 4 features of the Gray Level Co-Occurrence Matrix (GLCM): energy, entropy, homogeneity, and contrast. These 4 features were used to obtain the extraction value from an image. The outcomes were then used to cluster the clove plant diseases using the K-Means method. In making the software, the researchers used Javascript, HTML, CSS, PHP, and MySql to create a database. The output in this study was an information system application that provides disease-type clustering using the K-Means algorithm. The results of the Gray Level Co-occurrence Matrix (GLCM) concerning extracting images of clove plant leaves affected by disease indicated that the created system can be used to help clove farmers in diagnosing what diseases are infecting their plants by only uploading photos from affected leaves of the clove plant. Furthermore, the results of the K-Means calculation on the examined data showed several categories of Anthracnose leaf spot diseases. In addition, sample number #40 was included in cluster 2 status, in which the average values for energy, entropy, homogeneity, and contrast were 0.583, 0.175, 0.939, and 0.175, respectively Creator An entity primarily responsible for making the resource Mila Jumarlis1 , Mirfan2 Publisher An entity responsible for making the resource available STAIN Majene Date A point or period of time associated with an event in the lifecycle of the resource 30-08-2022 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/065140cc4edd99a1702fc008e41e8ffd.pdf 5732e35fe17bc1f53a769348af44b099 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 4 (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 Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document Subject The topic of the resource Deep Learning, BiLSTM, Text classification, Word Embedding, Clinical Trials Description An account of the resource In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4. meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5 Creator An entity primarily responsible for making the resource Jasmir Jasmir1 , Willy Riyadi2 , Silvia Rianti Agustini3 , Yulia Arvita4 , Despita Meisak5 , Lies Aryani6 Publisher An entity responsible for making the resource available Universitas Dinamika Bangsa Jambi Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-08-2022 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/b5b6013cb5e4381d87ddb670b5414060.pdf acbd63a3c7ae8a84b55427a8ec9547f7 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 4 (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 Chatbot-based Information Service using RASA Open-Source Framework in Prambanan Temple Tourism Object Subject The topic of the resource Covid-19, Chatbot, Machine Learning, RASA Open Source, Prambanan Temple Description An account of the resource The pandemic has caused a shift in the tourism industry's drive towards comprehensive digitization. This approach is used to prevent the spread of the Covid-19 virus. The impact of Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) limiting the mobility of tourists who will vacation in Indonesia causes losses and foreign exchange earnings of the state in the tourism industry sector of 20.7 billion. So, to survive in the current situation, industry players must be able to adapt and rise by providing more effective innovations. This study aims to develop a Question Answering System or a digital question and answer system using a chatbot (ChatterBot). The chatbot is used as an information service provider that can make it easier for tourists who are looking for information about tourist attractions. Chatbot-based information service systems can work 24 hours or all day, reducing the intensity of direct physical contact with officers and saving operational costs. The chatbot implementation is built on the Machine Learning Framework using RASA Open Source with the Python programming language. The knowledge base of the chatbot system is trained based on the FAQ (Frequently Asking Question) dataset with a case study of the Prambanan Temple tourist attraction as a sample of Indonesian tourism. The results of the evaluation and system performance based on data testing obtained the level of model accuracy is 0.91. Furthermore, the weighted average value in the Confusion Matrix produces a precision of 0.97, a recall of 0.94, and an F1-score of 0.95. The training and testing model processes locally using the Visual Studio Code software Creator An entity primarily responsible for making the resource Zein Hanni Pradana1 , Hanin Nafi’ah2 ,Raditya Artha Rochmanto3 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 : 31-08-2022 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