2 10 37 https://repository.horizon.ac.id/files/original/9bd0098c5e0de95c650ff8de748d6a50.pdf 112199554747b72bbc08f6f857aebb7f 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 Platform Digital and Content Innovation to Increase Youth Interest in the Agricultural Sector Subject The topic of the resource Digital Platforms, Internet of Things, Youth, Agriculture Description An account of the resource The use of digital technology is essential in increasing the younger generation's interest in the agricultural sector. Deficient awareness of youth in the agricultural sector, even though the agricultural sector has great potential and has a crucial role in handling anything. The methodology carried out in this study uses data collection, initial processing of data, analysis using python, evaluation, and validation of results. Content with agricultural topics, the use of the Internet of things on agriculture that contains the content of the role of the younger generation in the agricultural sector is then used as a dataset. Variables analyzed in these contents include the year of content creation, how many subscribers, number of viewers, number of videos. In-person interviews with the younger generation were also conducted in this study to explore information with variables in knowledge levels, family environment factors, land availability, social practice, risk factors, and income. The results and discussions of the analysis of content related to agriculture and the Internet of things showed the younger generation's interest in farming with the help of digital platforms. Of the 30 respondents who were used as a sample, prestige social has the highest value compared to other variables with 0,59. The results obtained from the analysis showed that the number of impressions on content related to the younger generation in the agricultural sector reached 248,882,953 impressions and the number of impressions related to Internet of Things content as many as 23. 969 impressions. The use of technology with the digital Youtube platform is an excellent opportunity in giving birth to various kinds of innovations by utilizing digital technology to support the sustainability of the agricultural sector in Indonesia Creator An entity primarily responsible for making the resource Mambang1 , Finki Dona Marleny2 Publisher An entity responsible for making the resource available Information Technology, Faculty of Science and Technology, Sari Mulia University, Banjarmasin 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/dc56217366c596079ba3075ae60f69f2.pdf 71efd7ea3aafa987cb214e79385976bf 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 Prediction of Water Levels on Peatland using Deep Learning Subject The topic of the resource water level, peatlands, prediction, deep learning, LSTM, CRISP-DM Description An account of the resource The water level on peatlands is one of the causes of peatland fires, so water levels must be maintained at a safe standard value. Government Regulation No. 71/2014 stipulates water level standard value is 0.4 meters. The forest and land fires in 2015 caused huge losses of 220 trillion Rupiah. However, fires still occur frequently. BRGM (Peatland and Mangrove Restoration Agency) installed sensors measuring peatland water levels to obtain real-time water level data. These data can be used to predict water levels. Several previous studies used drought indices, regression models, and artificial neural networks to predict water levels. In this study, it is proposed to use deep learning Long Short-Term Memory (LSTM), and apply the CRISP-DM methodology. The dataset in this study contains water level data from 15 measurement stations in Central Kalimantan from 2018 through 2021. It was concluded that the LSTM model was able to predict water level well, as indicated by the average RMSE of 0.07 m, the average R2 of 0.85, and the average MAE of 0.04 m. The optimal LSTM model parameters are 50 epochs, a 70%:30% ratio of training data to testing data, and 2 hidden layers Creator An entity primarily responsible for making the resource Namora1 , Jan Everhard Riwurohi2 Publisher An entity responsible for making the resource available Universitas Budi Luhur 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/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 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/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/2e951efad001e640e4c43e87c3c26d7a.pdf ab0b131de409040051f44354388c868a 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 LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient Subject The topic of the resource : LRDDoS, SD-IoT, Random Forest, Logistic-Regression, Machine Learning Description An account of the resource Software Defined Internet of Things (SD-IoT) is currently developed extensively. The architecture of the Software Defined Network (SDN) allows Internet of Things (IoT) networks to separate control and data delivery areas into different abstraction layers. However, Low-Rate Distributed Denial of Service (LRDDoS) attacks are a major problem in SD-IoT networks, because they can overwhelm centralized control systems or controllers. Therefore, a system is needed that can identify and detect these attacks comprehensively. In this paper, the authors built an LRDDoS detection system using the Random Forest (RF) algorithm as the classification method. The dataset used during the experiment was considered as a new dataset schema that had 21 features. The dataset was selected using feature importance - logistic regression with the aim of increasing the classification accuracy results as well as reducing the computational burden of the controller during the attack prediction process. The results of the RF classification with the LRDDoS packet delivery speed of 200 packets per second (pps) had the highest accuracy of 98.7%. The greater the delivery rates of the attack pattern, the accuracy results increased. Creator An entity primarily responsible for making the resource Wahyuli Dwiki Nanda1 , Fauzi Dwi Setiawan Sumadi2 Publisher An entity responsible for making the resource available , University of Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource : 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/9f86fc927ecf53f281b2803fe0d00e2e.pdf fce844534d32bf99e2d6b61afc144d01 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 Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method Subject The topic of the resource classification, support vector machine, text mining, data mining Description An account of the resource One of the important aspects of e-commerce is product quality, such as the quality of the trade and the application itself. Customers who buy goods will provide an assessment in the form of a review. If an item is dominated by negative reviews, other customers will be reluctant to buy at that store, so customers look for other stores and this affects the store's revenue. Therefore, the purpose of this study is to classify e-commerce beauty product reviews using the Support Vector Machine to create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000 datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried out which is labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed in the feature extraction step. The feature extraction step aims to explore potential information that represents words. Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to use. The results showed that the Support Vector Machine can classify beauty product reviews well with an accuracy of 80.06% Creator An entity primarily responsible for making the resource Muhammad Rio Pratama1 , Faza Abdillah Gunawan S.2 , Rafdi Reyhan Zhafari3 , Rendy4 , Helena Nurramdhani Irmanda Publisher An entity responsible for making the resource available University of Pembangunan Nasional Veteran Jakarta 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/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/e923dfa0f4fb7c1349bc04e1afbded7f.pdf 64ad21c5e93a6a0a22135884709b9804 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 Sentiment Analysis of Spider-Man: No Way Home Film Based on IMDb Reviews Subject The topic of the resource sentiment analysis, movie reviews, spider-man, classification, IMDb Description An account of the resource Sentiment analysis is used to determine the overall sentiment in a movie review. The goal of this paper is to investigate the sentiment analysis using multiple classification methods from Spider-Man: No Way Home movie reviews. The review dataset is procured from the IMDb website. Preprocessing methods are used and compared to determine the difference in accuracy score. The methods proposed for this study include Naïve-Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Decision Tree to find the best accuracy possible. The sentiment analysis of the movie review resulted in 94 positive reviews and 65 negative reviews. The highest accuracy and f1 score for this study are obtained from the SVM and the SGD classifier with an accuracy of 82% and an F1 score of 81% respectively Creator An entity primarily responsible for making the resource Tb Dzulfiqar Alhafidh1 , P. H. Gunawan2* , Bambang Ari Wahyudi3 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 : 28-02-2022 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/be1a5c616556abbd62f0b688a7d9d89a.pdf 95c17161d15cab46eb791b174cc561ca 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 Live Forensic to Identify the Digital Evidence on the Desktop-based WhatsApp Subject The topic of the resource Live Forensics, Data Acquisition, Digital Artifact, Volatile Data, Dekstop-based WhatsApp Description An account of the resource The live forensics method was used to acquire lawful digital evidence data from device memory in the WhatsApp application, particularly for desktop-based WhatsApp. There has been little research on live forensics on desktop-based WhatsApp applications. These studies involve mimicking crime cases in cyberspace using the Instant Messenger application. Much of the acquisition process is completed only once, even though many possible conditions may arise during the purchase process. Investigators or experts can employ digital evidence data discovery to identify crimes that have occurred. The stages of research carried out in detecting digital evidence are data collecting, the examination process, and the acquisition of analysis and reporting outcomes. During the data-gathering phase, a case simulation dataset was obtained. The examination process stage results in the integrity of the duplicated data; data reduction is performed on data related to fundamental operating system components, influential application features, and incomplete data. According to the investigation findings, there are difficulties in looking for digital evidence, and the features of each digital evidence vary. The simulation file contained many reports on the finds of digital evidence. As a data acquisition method, the characteristics of live forensics are limited to the data retrieval process in RAM. Based on these findings, it is possible to conclude that the data collection and examination processing were completed effectively. The analysis results were acquired, and the report was presented with the indicated digital evidence. Further study can be paired with chip-off procedures on RAM devices for data recovery. Creator An entity primarily responsible for making the resource Triawan Adi Cahyanto1 , Muhammad Ainul Rizal2 , Ari Eko Wardoyo3 , Taufiq Timur Warisaji4 , Daryanto5 Publisher An entity responsible for making the resource available Universitas Muhammadiyah Jember 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