1 10 26 https://repository.horizon.ac.id/files/original/f823a46015c06ab14c833a53927a5eec.pdf cac32b0588ee58a7e575707be4f6b40e 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace Subject The topic of the resource N-gram; sentiment analysis; shopee; support vector machine Description An account of the resource Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E�marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace Creator An entity primarily responsible for making the resource M. Eka Purbaya, Diovianto Putra Rakhmadani, Maliana Puspa Arum, Luthfi Zian Nasifah Source A related resource from which the described resource is derived : http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text N-gram sentiment analysis shopee Support Vector Machine https://repository.horizon.ac.id/files/original/e91032d62f06e6eb1e4095b7551607d0.pdf 1a85aed0808cc4de2ed50d153c821513 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points Subject The topic of the resource NDV; drone; agriculture; random forest Description An account of the resource Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m Creator An entity primarily responsible for making the resource Try Kusuma Wardana, Yandra Arkeman, Karlisa Priandana, Farohaji Kurniawan Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text agriculture drone NDVI Random Forest https://repository.horizon.ac.id/files/original/a2a451abbacc5ff353c6adbcdd87e49b.pdf 1e8389df404802a139a7a8bf52764244 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction Subject The topic of the resource malware; machine learning; ensemble algorithm; important features Description An account of the resource By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system (Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has infected a user's device. This study uses the opposite approach, which is to predict the potential for malware infection on the user's device before the infection occurs. Similar studies still use single algorithms, while this study uses ensemble algorithms that are more resistant to bias-variance trade-off. This study builds models from data on computer features that affect the possibility of malware infection on computer devices with Microsoft Windows operating system using ensemble algoritms, such as Bagging Classifier, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Machine, Category Boosting, and Stacking Classifier. The best model is Stacking Classifier, which is a combination of Light Gradient Boosting Machine and Category Boosting Classifier, with training and test results of 0.70665 and 0.64694. Important features have also been identified as a reference for taking policies to protect user devices from malware infections. Creator An entity primarily responsible for making the resource Yusuf Bayu Wicaksono, Christina Juliane Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text ensemble algorithm important features machine learning malware https://repository.horizon.ac.id/files/original/e9365d1352310d0ef275c48ac57e4ec8.pdf 6ed105ff5221e3a1866ef5ed968a81bf 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts Subject The topic of the resource data mining; SVM; perceptron; classification; fuel Description An account of the resource Currently, fuel oil is one of the important factors for the community and even a country on this earth to utilize this natural gas fuel for daily use as the main use and also by increasing the community's need for fuel oil, starting from the type of pertalite, Pertamax, to diesel for various types of private vehicles to tourists who use it for vehicles and even daily staples used. But there are several factors that cause this fuel problem, there is a factor of time and usage time, which is certain that one day it will expire and its capacity in a country, even if the country runs out of fuel, will make requests to other countries and also obstacles to supplying this fuel oil to the public. which is the main fuel from the Pertamina government agency which has begun to limit purchases for this fuel oil to certain circles by marking the types of subsidies or not subsidies that must be controlled by the government in limiting purchases for the public. In dealing with solving problems from the perspective of ownership or even utilization, there are limits to owning fuel, and not everyone has to have a lot or even too much. That way, to get everyone in the community who deserves to receive this fuel limit to the maximum by designing subsidized fuel oil revenues. In solving the problem of dividing fuel revenue, which is good for filling revenue, it can be solved by using machine learning, namely data mining itself can help in completing subsidized fuel receipts without being excessive for the community so that they can be controlled and managed for their purchases. In building a fuel oil reception design, it can be grouped into a classification model that uses SVM and perceptron which uses the activation function of the sigmoid to get the final result of accuracy where getting the average value of 5-fold, 10-fold, 20-fold is accuracy. is 90.0%, the F1 value is 85.6%, the precision value is 87.6%, and the recall value is 90.0%. Creator An entity primarily responsible for making the resource Jaka Tirta Samudra, Rika Rosnelly, Zakarias Situmorang Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text classification data mining fuel perceptron SVM https://repository.horizon.ac.id/files/original/06d3bef8e8c82518fa38838f0118c819.pdf f5d30b43697e6f18fab9b0bdfd449c48 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 LSTM and IndoBERT Method in Identifying Hoax on Twitter Subject The topic of the resource hoax detection; social media; twitter; deep learning; LSTM; IndoBERT Description An account of the resource In recent years, social media users have been increasing significantly, in January 2022 social media users in Indonesia reached 191 million people which has an increase of 12.35% from the previous year as many as 170 million people, With this massive increase every year, more and more people tend to seek and consume information through social media. Despite the many advantages provided by social media, However, the quality of information on social media is lower than in traditional news media there is a lot of hoax information spreading. With many disadvantages felt by hoax information, it has led to many research to detect hoax information on social media, especially information that is widely spread on Twitter. There are several previous researches that use various models using machine learning and also using deep learning to detect hoax. deep learning is very well used to perform several text classification tasks, especially in detecting hoax. The aim of this paper is to compare the LSTM and IndoBERT methods in detecting hoax using datasets taken from Twitter. In this study, two experiments work are conducted, LSTM and IndoBERT methods. The experimental results is average value obtained from experiments using 10-fold cross-validation. The IndoBERT model shows good performance with an average accuracy value of 92.07%, and the LSTM model provides an average accuracy value of 87.54%. The IndoBERT model can show good performance in hoax detection tasks and is shown to outperform the LSTM model which can provide the best average accuracy results in this study Creator An entity primarily responsible for making the resource Muhammad Ikram Kaer Sinapoy, Yuliant Sibaroni, Sri Suryani Prasetyowati Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text deep learning hoax detection IndoBERT LSTM Social media Twitter https://repository.horizon.ac.id/files/original/c9dcdbc99429547a5d95fbc2bcda8d10.pdf 563fd0769c4a9d936ee6ff1813684c1c 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Secure Cybersecurity Information Sharing for Sectoral Organizations Using Ethereum Blockchain and IPFS Subject The topic of the resource cybersecurity; information; sharing; blockchain; IPFS. Description An account of the resource The COVID-19 pandemic has resulted in increased cross-sector cyber-attacks. Passive and reactive cybersecurity techniques relying solely on technology are insufficient to combat sophisticated attacks, necessitating proactive and collaborative security measures to minimize attacks. Cybersecurity Information Sharing (CIS) enhances security via proactive and collaborative cybersecurity information exchange, but its implementation via cloud services faces threats from man in the middle (MITM) and distributed denial of service (DDoS) attacks, as well as a vulnerability in cloud storage involving centralized data control. These threats and vulnerabilities result in a lack of user confidence in the confidentiality, integrity, and availability of information. This paper proposes Secure Cybersecurity Information Sharing (SCIS) to secure Cybersecurity Information in sectoral organizations using the private interplanetary file system (IPFS) network and the private Ethereum Blockchain network. Private Ethereum Blockchain enables secure and transparent transaction logging, while Private IPFS network provides decentralized storage, addressing vulnerabilities in centralized storage systems. The outcomes of the tests reveal that the suggested SCIS system offers cybersecurity information availability, confidentiality, and integrity. SCIS provides a high level of security to protect cybersecurity information exchanged between sectoral organizations using the Private Ethereum Blockchain network and the Private IPFS network so that organizations can safely share and utilize information. Creator An entity primarily responsible for making the resource Tony Haryanto, Kalamullah Ramli Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Blockchain cybersecurity information IPFS sharing https://repository.horizon.ac.id/files/original/8e073d23297d605ecba03e43b0a5e03c.pdf 079c274030753dbd20feda0323f5670a 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Game Design for Mobile App-Based IoT Introduction Education in STEM Learning Subject The topic of the resource game education; IoT; STEM; game-based learning; aplikasi mobile Description An account of the resource STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users Creator An entity primarily responsible for making the resource Indra Puja Laksana, Evi Dwi Wahyuni, Christian Sri Kusuma Aditya Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Aplikasi Mobile game education game-based learning IoT STEM https://repository.horizon.ac.id/files/original/b04ab82e4e93568c0830637b5c1f868f.pdf def5cb8dc8354c5d1aad0acefb6e7a69 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Remote Penetration Testing with Telegram Bot Subject The topic of the resource mobile penetration tester; penetration testing; telegram bot; web vulnerabilities. Description An account of the resource The widespread of websites and web applications makes them the main target of cyber attacks. One way to increase security is to perform a penetration test. This test is carried out using the attacker's point of view to find out vulnerabilities on a website or web application and then exploit these vulnerabilities. The results of the penetration test can be used as recommendations to close the gaps that have been known through testing. Because penetration testing requires special resources such as tools and operating systems, a solution is needed to make penetration testing possible with low resources. Telegram bots that are open source offer a solution to overcome these problems. Using the SDLC waterfall approach, this bot was built to provide penetration testing services by connecting the Kali Linux server as a tools provider and the Telegram bot as an interface to users. As a result, users can access penetration testing tools anywhere and anytime via the Telegram bot. To ensure that the bot can run well, testing is carried out through black box testing and load testing. Telegram bot is a solution for integrated compact automatic mobile penetration tester with low resources. Based on load testing, the maximum limit of users who can access Telegram bots simultaneously is 35 users with the highest load average of 5.4. Based on the results of the User Acceptance Test, the Telegram bot has an acceptance rate score of 88,457 % and a questionnaire score of 774 which is an agreed area. Creator An entity primarily responsible for making the resource Naufal Hafiz, Obrina Candra Briliyant, Dimas Febriyan Priambodo, Muhammad Hasbi, Sri Siswanti Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text mobile penetration tester penetration testing telegram bot web vulnerabilities https://repository.horizon.ac.id/files/original/a64311d9df48bd9cbdea85ff0a0cdee9.pdf 6dd9ee4196bca736effa0cf2c95f68cb 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short�Term Memory (LSTM) Subject The topic of the resource sentiment analysis; public acceptance; covid-19 vaccine; naïve bayes; support vector machine; lstm. Description An account of the resource The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac, Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax, often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January 2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19, #vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively. Creator An entity primarily responsible for making the resource Dinar Ajeng Kristiyanti, Sri Hardani Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text COVID-19 vaccine LSTM Naïve Bayes public acceptance sentiment analysis Support Vector Machine https://repository.horizon.ac.id/files/original/9fbd18c0a47e11a48ac681daa4408f32.pdf 760e9e8f64a7fc64a4e4cae725e51a09 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 7 NO 3 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Impact of IoT on The Storing Process of Leather Raw Material Subject The topic of the resource internet of things (IoT); proactive monitoring; consistent quality; efficiency; effectiveness Description An account of the resource A critical step in processing leather raw material is the storing that keeps them in good condition and not easily damaged through the right temperature and humidity level, as otherwise the quality of leather raw material would not be consistent and its economic value would be low. This particular study, therefore, is to conduct an experiment that uses the Internet of Things (IoT) which allows proactive monitoring in keeping a specific temperature and humidity level in their storing process. Hence the subsequent leather processing can be done at the optimal level. The experimentation showed positive results as the use of IoT made storing process of leather raw materials became more proactive and run three times more effective, from just 8 hours to 24 hours, and also brought about a positive effect on the economic efficiency and effectiveness as it enable users to produce more consistent quality of leather raw material whilst the total operating costs remained and even lower. Besides its economic impact, IoT has increased the workers' and their relatives' welfare and social life driven by better income and less time consuming. It also brings about some positive environmental effects as it reduced carbon emissions by keeping energy waste at a lower level Creator An entity primarily responsible for making the resource Franciskus Antonius, Asep Saepudin Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource June 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text consistent quality Effectiveness efficiency Internet of things (IoT) proactive monitoring