Journal of Information Systems and Informatics (Journal-ISI)
Not a member yet
580 research outputs found
Sort by
Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers
Get PDFNutritional status is a crucial foundation for human health and development. Global facts indicate serious challenges in ensuring adequate nutrition, and the situation is no different in Indonesia. This research collected data from the Kelapa Dua Tangerang community health center and utilized data mining techniques with the k-means clustering algorithm to delve deeper into the nutritional status of toddlers. The research findings revealed that nearly 37.3% of toddlers experience issues with abnormal height or weight, as well as poor nutritional conditions, highlighting the importance of careful and timely intervention. With regular health monitoring by community health centers and active parental involvement, actions can be taken to support the optimal growth and development of these children. The results of this research provide a strong understanding to address malnutrition issues, which will ultimately support the formation of a healthier and more promising future generation in Indonesia
Recurrent Neural Network-Gated Recurrent Unit for Indonesia-Sentani Papua Machine Translation
Get PDFThe Papuan Sentani language is spoken in the city of Jayapura, Papua. The law states the need to preserve regional languages. One of them is by building an Indonesian-Sentani Papua translation machine. The problem is how to build a translation machine and what model to choose in doing so. The model chosen is Recurrent Neural Network – Gated Recurrent Units (RNN-GRU) which has been widely used to build regional languages in Indonesia. The method used is an experiment starting from creating a parallel corpus, followed by corpus training using the RNN-GRU model, and the final step is conducting an evaluation using Bilingual Evaluation Understudy (BLEU) to find out the score. The parallel corpus used contains 281 sentences, each sentence has an average length of 8 words. The training time required is 3 hours without using a GPU. The result of this research was that a fairly good BLEU score was obtained, namely 35.3, which means that the RNN-GRU model and parallel corpus produced sufficient translation quality and could still be improved
Prediction of Forex Prices on USD/NGN Using Deep Learning (LSTM and GRU) Techniques
Get PDFThe goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized. The results obtained showed that, GRU performed better than LSTM with a 0.950 Test R2 score and an adjusted R2 score of 0.122. The RMSE is way lower than LSTMs at 0.105 and MAE is even lower at 0.950
Web and Mobile Data Management System for Garongan Asri Garbage Bank: A Case Study
Get PDFThis research addresses the inefficiencies of manual garbage data management by developing a dual-platform system: a website-based application for Garongan Asri Garbage Bank staff and a mobile application for customers. Utilizing the waterfall method for systematic development, the project involved stages of analysis, design, coding, testing, support, and maintenance. Key technologies used include the Bootstrap framework, Visual Studio Code, Android Studio, and MySQL database. The resulting website application enables staff to efficiently manage garbage data, while the mobile app allows customers to access their disposal history. The effectiveness of these applications was confirmed through black box testing, demonstrating their functionality and suitability for improving garbage data management and customer service
Towards Sustainable Smart Living: Cloud-Based IoT Solutions for Home Automation
Get PDFIn recent times, the realm of home automation systems has garnered significant attention, thanks to the ever-evolving landscape of communication technology. The concept of a smart home, essentially an application of the Internet of Things (IoT), leverages the power of the internet to oversee and employ household appliances through a sophisticated automation infrastructure. Nevertheless, challenges persist within the existing home automation systems, such as constrained wireless transmission reach, a deficiency in backup power management, and the substantial financial outlay involved. Addressing these limitations, our study introduces an economical and resilient solution that combines cloud based IoT with an uninterrupted power management system, making a cutting-edge home automation prototype. This system relies on a microcontroller unit, specifically the ESP-32, which functions as a Wi-Fi-enabled gateway for connecting a variety of sensors and transmitting their data to the Blynk IoT cloud server. The data assembled from a multitude of sensors, including vibration sensors and voltage detectors, becomes readily accessible on users' devices, be it smartphones or laptops, irrespective of their geographical location. The system is further strengthened by a set of relays that link the ESP-32 with household appliances, allowing for centralized control. Structurally, the design uses a control box that can be seamlessly integrated into a real home environment, offering the means to both monitor and govern an array of household devices. This IoT-based home automation solution not only efficiently manages internet-connected appliances but also provides an effective emergency power management system, enabling remote initiation and deactivation of backup generators. It represents a innovative leap in the evolution of home automation systems, steering in convenience, efficiency, and cost-effectiveness
Risk Assessment and Recommendation Strategy Based on COBIT 5 For Risk - A Case Study of an Internet Service Provider Company
Get PDFInformation technology governance is part of organizational management that includes leadership and ensuring that information technology has a broad scope to meet needs. The concept of IT Governance is a method of managing technology users in. The company, in this case, provides IT needs and solutions to customers ranging from hardware, software, and services. A good business case will include the problem in information technology governance, especially when it comes to maintenance related to POP or Post Office Protocol which is the internet protocol used on TCP/IP networks such as the internet. In this study, the framework used as a reference in IT development is COBIT 5. The domain processes studied are EDM02 – Ensure Benefits Delivery, APO07 – Manage Human Resources, and APO10 – Manage Suppliers, who can evaluate IT governance at this Company. The research method used is Gallego's Theory starting from planning, field inspection, reporting, and follow-up. The results of this study were obtained from the evaluation of information technology governance in the Company got to level 1 capability with fully achieved achievement but could not move up to the next stage, that is, level 2, resulting in a gap analysis of 1 level from the target level expected by the Company
Information Technology Risk Management in Educational Institutions Using ISO 31000 Framework
Get PDFAs information technology becomes increasingly integrated into daily business processes within educational institutions in Indonesia, the need to address potential risks associated with this integration has become crucial. This research focuses on analyzing information technology risk management in Indonesian educational institutions using the ISO 31000 framework. The study aims to minimize the occurrence and impact of various information technology risks that can disrupt organizational processes. Through a comprehensive examination encompassing risk assessment, analysis, evaluation, and treatment, this research provides valuable insights into identifying potential risks within educational institutions. Furthermore, the findings serve as a reference for formulating risk management policies, enabling institutions to proactively mitigate the possibility of information technology risks and their future impact. By adopting the ISO 31000 framework, educational institutions can enhance their efficiency, effectiveness, and accessibility while safeguarding against potential disruptions
Machine Learning Approach for Credit Score Predictions
Get PDFThis paper addresses the problem of managing the significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble credit model that integrates the XGBoost and Support Vector Machine models to improve the accuracy and reliability of risk assessment credit scoring models. The method employs machine learning techniques to recognise patterns and trends from past data to anticipate future occurrences. The proposed approach is compared with existing credit score models to validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall and F1_Score. The paper highlights credit scoring models’ challenges, such as class imbalance, verification latency and concept drift. The results show that the proposed approach outperforms the existing models regarding the evaluation metrics, achieving a balance between predictive accuracy and computational cost. The conclusion emphasises the significance of the proposed approach for the banking and financial sector in developing robust and reliable credit scoring models to evaluate the creditworthiness of their clients
Fake News Detection Using Optimized CNN and LSTM Techniques
Get PDFConcerns have been raised about the social consequences of fake news as it has spread rapidly on online platforms. It is critical to detect and mitigate the spread of fake news in order to maintain a healthy community conversation. There is a need to put more effort into the identification of fake news as more people use the internet, especially as more internet-enabled gadgets become more widely available and inexpensive. With the help of two Neural Network techniques: long-short-term memory (LSTM) and Convolutional Neural Network (CNN). This research proposes novel deep-learning methods for identifying fake news using two datasets. These methods were considered for this research because they had proven to be successful in earlier studies that had been looked at. Finding the best-performing optimal models is the goal of this study. HyperOpt Technique was used for Neural Network model. The performance of the optimized models was compared with the performance of the models without optimization. The results obtained showed that for both datasets, CNN and LSTM performed better when training the models with the optimal values with an average difference of 12.7% for Accuracy, 11.9% for Precision, 12.3% for Recall and 15.4% for F1-Score
Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection
Get PDFSocial media has become an important web technology for creating and sharing information plus enhancing business reputations worldwide. However, the anonymity accorded by social media platforms has been cryptically vituperated to spread horrendous content such as hate speech. Recently, researchers have been progressively gravitating towards the use of deep learning techniques to address the problem of social media hate speech detection. This study provides bibliometric analysis and mapping of the existing literature on hate speech detection using deep learning algorithms. The study used articles published between 2016 and 2022 from the Scopus database, while Vos Viewer, Biblioshiny, and Panda’s software tools were employed for the bibliometric analysis. The research explored the yearly trajectory of recent publications, dominant countries, collaborative institutions, sources of primary studies that have employed deep learning for hate speech detection, and the intellectual and social structures of the research constituents. It has been observed that the literature on hate speech detection is rapidly growing, but research output and collaborations from the developing countries of the world are still limited. The findings of this study provide insights into the intellectual structure and advancements in deep learning applications for hate speech detection while identifying research gaps for future work