Journal of Information Systems and Informatics (Journal-ISI)
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Modified Genetic Algorithm and Association Rule Mining for the Retail Sector
Get PDFThis paper concentrates on the optimization of elementary association rule mining. The basic approach of association rule mining generates the positive association rule but focusing on both positive and negative association rule mining to find out efficient results is lacking. Thus our aim is to provide an approach to optimize all positive and negative association rules with the help of a modified genetic algorithm. A genetic algorithm is an optimization technique that provides the best possible solutions that are stronger than the other solutions. The present approach focuses on the importance of population through mean fitness value for further genetic algorithm operation. This paper also shows a comparison between normal Apriori, the Genetic Algorithm, and our proposed algorithm. Where in as a result the proposed approach worked better than others. We believe that the proposed methodology would increase the efficiency of the Decision support system of retail stores
Spatial Analysis of Changes in Normalization Differences Vegetation Index in Protected Forest Areas of South Lore District, Poso Regency
Get PDFDetection of changes in vegetation density generally uses the vegetation index parameter. The value of the vegetation index can provide information on the proportion of vegetation cover, live plant index, plant biomass, cooling capacity, and estimation of carbon dioxide absorption. This study aims to analyze changes in the level of vegetation density using Sentinel 2-A imagery in the protected forest area of South Lore District. This study used the method of calculating the Normalized Difference Vegetation Index (NDVI) to identify changes in density over 5 years. The results of the NDVI analysis are the largest in the range of -0.92960 to 0.871725. The vegetation density class in the Protected Forest Area of South Lore District in 2017 is in the dense class with an area of 15,322.24 Ha or around 47.66%, while the smallest in the non-vegetation class, which is 103.11 Ha or 0.32%, while the largest vegetation density class is in the Protected Forest Area of South Lore District in 2022, namely in the medium/quite dense class with an area of 19,948.18 Ha or 62.01% while the smallest in the non-vegetation class of 219.17 Ha or 0.68%. The largest increase in area was in the moderate/quite dense class of 4,892.33 Ha or 15.20% while the largest decrease in area was in the dense class with an area of 6,651.16 Ha or 20.67% of the total area of the Protected Forest Area of South Lore District
Analysis of Frequently Appearing Words in the Titles of 2023 Research Grant Winners in Indonesia Using the TF-IDF Method
Get PDFResearch activities are an obligation to be carried out by a lecturer, each year the Government of Indonesia through the Ministry of Education, Culture, Research and Technology encourages the improvement of research through a large amount of research funding aid through several schemes of grant competition. By 2023, the percentage of proposals funded was only 22.7% of the total of research proposals submitted as 28.404. One of the problems that arises for the lecturer who follows the research grant is to determine the title of the research. The research aims to identify the words that often appear on the research titles that escape funding from each grant scheme by performing word grinding using the TF-IDF method. The results of this research indicate that in the novice lecturer research grant scheme (PDP) the word that often appears is the word "based" with a total of 434 proposals, in the regular fundamental research (PFR) the word that often appears is "development" of 374 proposals , domestic cooperation research (PKDN) the word that often appears is "based" with 117 proposals, post-graduate research doctoral dissertation research (PPS-PDD) the word that often appears is "model" with 154 proposals, in post-graduate research master's thesis research (PTS-PTM) words that often appear "based" are 191 proposals and in the downstream applied research scheme (PT-JH) words that often appear "based" are 82 proposals. This research can provide an overview of the names of titles funded based on the highest number of occurrences of a word from all titles funded. The words "based", "development" and "model" are the 3 largest words that appear in the titles of proposals funded in the PFR, PKDN, PPS-PDD, PPS-PTM, and PT-JH schemes. For the PDP scheme, the order of the 3 largest words that appear in the title of the proposal is "based", "regency", and "development"
User Experience in Cloud Computing Services-Based LMS: a Case Study
Get PDFThis research explores user experiences with Learning Management Systems (LMS) in the context of online education, focusing on the Google Classroom application. Conducted at SDIT Salsabila 4 Jetis, Indonesia, the study navigates challenges faced during the transition to remote learning, particularly amid the COVID-19 pandemic. Evaluating features such as media upload, assignment scheduling, quizzes, and grading, the research identifies key areas of user engagement that require optimization. Effective teacher-student communication emerged as pivotal, underlining the need for tailored training programs to enhance teachers' proficiency with LMS features. Network stability also significantly influenced user experience. The study's insights emphasize the LMS's potential as a powerful educational tool and highlight specific areas for improvement. Recommendations include broader data collection across institutions, facilitating a nuanced understanding of LMS adoption curves. Moreover, targeted training initiatives are crucial, ensuring educators' comprehensive grasp of LMS functionalities. These findings provide a foundational framework for refining online education practices, promoting a more seamless and effective learning environment for educators and students alike
Analysis of Travel Agent Online Marketing Strategies on Social Media Content Using Sentiment Analysis and Social Network Analysis
No full textThe growth and development of the internet users have given Indonesia an opportunity to develop internet-based services, such as online travel agents (OTA). Along with this OTA development, conventional travel agents were declining. Many conventional travel agents have decided to switch to online travel agents. The emergence of new OTAs has also made OTAs competition more challenging. Thus, a lesson learned from the market leader OTA is expected to help new OTAs surviving the competition. This research uses the sentiment analysis method to understand consumers' perceptions towards OTA and uses the social network analysis method to recognize actors who play significant roles in the travel agent business network. Lastly, the marketing strategies of the major and well-known OTAs perceived by online consumers was analyzed. Using the data collected from three major OTAs social media network (i.e., Traveloka, Tiket, and Booking), it was found that the general impression of consumers towards OTA is a positive sentiment. Furthermore, each key actor for each OTAs can be recognized. Lastly, marketing strategies can be proposed, namely by providing the complete product offerings, provide competitive price, creating special promos for consumers, promotion to be carried out on all social media using Bahasa Indonesia, and make the products offered available throughout Indonesia and can be used by everyone, especially travelers
Design of Information Technology Governance in Educational Institutions Using COBIT 2019 Framework
Get PDFIn the contemporary era, the role of information technology in the performance of organizations, institutions, and companies is of utmost importance. The implementation of information technology can effectively enhance decision-making efficiency and effectiveness. Educational institutions have been utilizing information technology to support their academic activities and business processes; however, the governance of this technology has never been audited before. This study aims to conduct an audit and analysis of information technology governance that employs the COBIT 2019 framework. The study's results suggest that educational institutions must improve risk and security management in the use of information technology and evaluate their business processes' implementation in accordance with the established regulations and requirements
IoT and QRIS Payment System Integration in Entrepreneurship Lockers to Improve Culinary Business in Schools
Get PDFThe Covid-19 pandemic has disrupted various activities, including the operation of school and campus canteens. In response, innovative solutions are needed to ensure the continuation of the culinary business while keeping all stakeholders safe during the pandemic. This study proposes the development of IoT-integrated entrepreneurial lockers that enable cashless payments through QRIS. These lockers can be used to revive the culinary business in school and campus canteens that had to close due to the pandemic. The results of the study show that the use of these lockers can reduce physical contact during transactions, thus minimizing the risk of virus transmission. Overall, this research offers a practical solution for sustaining the canteen business in the face of the pandemic
Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization
Get PDFThe demand for brown sugar products often falls below the level of production, resulting in unsold goods when market demand surpasses the production capacity. This paper addresses the challenge faced by many brown sugar businesses in estimating production yields. Another issue, apart from production uncertainty, is the presence of a dataset with a significant nominal range. The study focuses on a specific brown sugar producing company in Indonesia. To address the production estimation problem, this research proposes the use of k-NN supervised learning as a forecasting method. However, instead of relying solely on k-NN, the study suggests employing z-score normalization to handle the dataset's large nominal range. The production data used for analysis spans from March 2019 to February 2022, comprising 144 weekly records. The dataset is divided into training and testing data, employing an 8:2 split validation ratio. The proposed method consists of several steps, including data normalization using z-score, processing k-NN based on the Minkowski distance, and concluding with the de-normalization process. The results demonstrate the successful implementation of the proposed method in predicting production levels. The evaluation indicates an average error margin of 3.34%, which is below the 5% threshold. The evaluation of predictive data for k-NN with z-score normalization proves effective in forecasting brown sugar production uncertainty and addressing the challenge of a large nominal range
Classification of Explicit Songs Based on Lyrics Using Random Forest Algorithm
Get PDFThis study focuses on the potential negative impact of explicit songs on children and adolescents. Although an explicit song labeling program is currently in place, its coverage is limited to songs released by artists affiliated with the Recording Industry Association of America (RIAA). Consequently, songs falling outside the program's scope remain inadequately labeled. To address this issue, a machine learning model was developed to effectively classify explicit songs and mitigate mislabeling challenges. A comprehensive dataset of song lyrics was collected using web scraping techniques for the purpose of constructing the classification model. The model was trained using the TF-IDF vectorization method and the random forest algorithm. A meticulous comparison of distribution parameters was conducted between the training and testing data sets to determine the optimal model. This superior model achieved a training-testing data distribution ratio of 90:10, with an impressive accuracy of 96.3%, precision of 99.3%, recall of 93.5%, and an f1-score of 96.3%. The classification results revealed that explicit songs accounted for 39.22% of the dataset, and the visual representation highlighted the fluctuating prevalence of explicit songs over time. Additionally, the hip-hop/rap genre exhibited the highest proportion of explicit songs, reaching a staggering 92%
Blockchain-Enabled Vaccination Registration and Verification System in Healthcare Management
Get PDFClient-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure