Journal of Information Systems and Informatics (Journal-ISI)
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Improving Junk Sale and Purchase Transactions Using a Spiral Model-Based System
Get PDFTraditionally, individuals looking to sell junk or recyclable materials often rely on waiting for roaming junk collectors, a process that is inefficient and lacks transparency. Furthermore, the fluctuating prices of junk goods frequently leave sellers uninformed, creating uncertainty in transactions. To address these issues, this research developed a smartphone-accessible system designed to facilitate junk goods transactions. The system was developed using the Spiral Model, ensuring iterative refinement and reliability. Key features of the system include real-time price updates for junk items, enabling customers to stay informed about the latest market values, and a Location-Based Service (LBS) feature that allows customers to share their location with collectors. This feature enhances the efficiency of junk collection by providing real-time location tracking, enabling collectors to locate and reach customers seamlessly. The implementation of this system aims to make junk buying and selling transactions more effective, transparent, and satisfying for customers. The results of this study demonstrate that the developed system significantly streamlines the transaction process, ensuring improved service delivery and customer satisfaction
Selecting KB Villages Using the VIKOR Method: A Case Study DPPKB Labuhanbatu
Get PDFTechnological advancements have taken place in various fields. Health, education, business, and decision-making are no exception. The KB program was created in order to echo the KB program as an effort to strengthen the KKBPK program and span the birth rate. Therefore, a decision support system using the vikor method was created. The research process was carried out at DPPKB Labuhanbatu. Data collection was carried out by conducting direct interviews to the object of research, namely DPPKB Labuhanbatu. The criteria in this study are the number of pre-prosperous households, the number of family planning participants, regional criteria, the existence of family data and maps, the number of elementary school age population, the number of MKJP, the number of unmet needs, family participation in the family resilience development program, family participation in the family economic improvement empowerment program, youth participation in planning generation activities. This research uses the R&D research method and uses the waterfall method in its system development method. The final result of this study found that the selected KB village was Perbaungan village with an index of 0, second place Bandar Kumbul village with index 0.0588 where the alternative with the minimum Q value was the highest rank
Rule-Based Transliteration of Ulu Kaganga Script using Character Mapping
Get PDFUlu Kaganga script is a historical writing tradition that developed in the southern region of Sumatra. With the widespread use of Latin script, the Ulu Kaganga script has become rare, and very few people can read and write in this script. To preserve the Ulu script, a tool is needed to assist in transliterating Latin text into the Ulu script. This research aims to preserve the Ulu script with the help of technology. In this study, a mobile and web-based application has been developed to transliterate the Ulu Kaganga script from Latin text. The technique used for this script conversion is rule-based, which is employed to break words into syllables and map those syllables into Ulu script characters. Through the rule-based technique and character mapping, adding Indonesian syllables and writing Ulu Kaganga script characters, consisting of 1139 primary characters, becomes easy. This application has been repeatedly tested to improve the mapping of Ulu script characters. The results of testing the application to transliterate 1746 words from Latin script were successful in transliterating. The tests conducted show that the approach used is very effective, with a transliteration accuracy from Latin to Ulu script of 99.98% The testing results show that the application can transcribe text accurately and conveniently, allowing non-expert users to write in Ulu script characters
Today's Academic Research: The Role of ChatGPT Writing
Get PDFThe purpose of this study is to examine the place of ChatGPT writing in the current academic environment. Significant attention has been drawn to the amazing capacity of ChatGPT, a sophisticated language model created by OpenAI, to produce text answers that nearly mimic human speech. The current study examines ChatGPT's effects on a number of academic areas, including writing support, data analysis, literature reviews, and scientific cooperation. The paper looks at the benefits and drawbacks of using ChatGPT in academic research and offers some insight into prospective uses for this technology in the future. To efficiently respond to the research questions and accomplish the stated goals, the present study used a quick review of the literature technique. The study has discovered several ChatGPT uses in academic writing, including data gathering, teamwork, implications, and restrictions. The study also looked at how to prevent plagiarism in written work produced using ChatGPT. In conclusion, if ChatGPT is used wisely and responsibly, it has the potential to dramatically enhance and revolutionize academic research, enabling multidisciplinary cooperation
Analysis and Design of Natural Spring Water Preservation and Monitoring System Using Rapid Application Development
Get PDFClimate change in Indonesia has resulted in droughts in several regions, affecting both ecological conditions and the livelihoods of local communities in accessing clean water sources. Therefore, natural spring water preservation and monitoring are needed to identify and analyze the factors contributing to ecological changes. This research utilizes the Rapid Application Development method through the Oracle Apex instrument in designing the database and information system for natural spring water preservation and monitoring. The stages of designing this application are requirements planning, user design, construction, and cutover. Each stage has its challenges, namely the relevance of data to user needs in identifying and analyzing the sustainability of natural spring waters. The findings of this study demonstrate that information on natural spring water location, water quality, local community activities, and observer data play a crucial role in the decision-making process. Through this application, natural spring waters can be identified and protected through appropriate policies, thus contributing to Indonesia's sustainable water resource management and environmental conservation efforts. Additionally, the analysis and design results reveal insights into the structural framework and functionalities of the developed system, highlighting its potential to effectively address the complexities of natural spring water preservation and monitoring
Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm
Get PDFPipeline leaks are a frequent occurrence in oil and gas infrastructure worldwide. Though leak detection systems are expected to be installed on all pipelines in the near future, relying on human efforts to physically monitor these pipelines is and will continue to be challenging. Though today's leak detection techniques are not able to completely stop leaks from occurring or to detect most leaks, they are essential in lessening their effects. Despite recent developments toward solving this problem, the solution still falls short of expectations. This research presents an approach to pipeline leak detection by leveraging on the exceptional abilities of Convolutional Neural Network (CNN) and Internet of Things (IoT). A comprehensive dataset on oil and pipeline leakage is collected, and the CNN model is developed and trained with the collected dataset. Thereafter, the trained model is integrated into the monitoring system to provide notifications of leaks. The model is adaptable and scalable and its performance, as evaluated, shows an improvement over existing systems with an accuracy of 97% hence well suited for deployment in various pipeline networks for the overall improvement of safety environment in the oil and gas sector
Measuring the Level of HRIS Governance Capability in the Automotive Financing Company Using COBIT 2019
Get PDFIn response to the imperative advancements in information technology, companies strive to leverage it for a competitive edge. An automotive financing entity with over 4,000 employees encounters challenges in governing its Human Resources Information System (HRIS). Difficulties include employee service being negatively impacted by delayed HRIS computations, budgets growing faster than regulations, and branch employees not understanding HRIS. The organization intends to use the COBIT 2019 framework to assess its IT governance to address these issues. Based on qualitative interviews and literature reviews, data collection will identify relevant domain processes—APO03, APO06, APO011, APO07, and DSS06—to address the issues. The research reveals that APO03, APO06, APO011, and APO07 are "Largely Achieved" but with identified gaps, while DSS06 is "Fully Achieved." These findings, derived from audit document analysis, will inform recommendations to address process gaps. The company will be presented with these recommendations to enhance its IT governance and management in alignment with COBIT 2019
Comparative Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction: A Machine Learning Approach
Get PDFStroke is the second most deadly disease in the world and the third leading cause of disability. However, most deaths due to stroke can be prevented by recognizing the symptoms of stroke and taking preventive measures using information technology. Therefore, this research utilizes the role of information technology using a machine learning approach to predict stroke in a person using the K-Nearest Neighbor and Decision Tree classification methods. The two algorithms were compared to determine which algorithm was more effective in predicting stroke. Data analysis using the CRISP-DM approach was carried out using a dataset containing 5110 observations with 12 relevant attributes. Implementation of Exploratory Data Analysis (EDA) was also carried out for preprocessing, and oversampling techniques were applied to overcome the problem of unbalanced classes. The research results show that the predictive model with the highest level of accuracy was obtained at around 97.1845% using the K-Nearest Neighbor algorithm. This research makes a significant contribution to stroke prevention efforts through the use of information technology and machine learning algorithms for early identification of stroke risk
The Cyber Kill Chain Model and Its Applicability on The Protection of Students Academic Information Systems (SAIS) in Tanzanian HEIs
Get PDFSecurity threats are constantly evolving in various computerized systems. As in many other systems, security threats and attacks have been directed to Students Academic Information System (SAIS) in Higher Education Institutions (HEIs). The seven steps cyber kill chain model offers preventive defense against such security threats. Little is known, however, on how well the model is applicable in the protection of SAIS. This study was therefore carried out to investigate the applicability of the cyber kill chain model on the protection of SAIS. The study was qualitative in which empirical evidence from literature was employed to gather data which were then analysed thematically through content analysis. Results showed that the cyber kill chain model is very relevant and applicable in the protection of SAIS. Each of the seven steps of the model practically applies differently in SAIS which entails for distinct protective measures as detailed in the paper. The study calls upon HEIs stakeholders to leverage the proposed preventive measures against security threats in SAIS
An Artificial Neural Network Model for Predicting Children at Risk of Defaulting from Routine Immunization in Nigeria
Get PDFIt has been widely recognized that immunization remains one of the most successful for decreasing child mortality rates and preventing several serious childhood diseases globally. This study proposed a prediction model for accurate identification of routine immunization defaulters in Nigeria. The proposed framework classified defaulters at five different risk stages: insignificant risk, minor risk, moderate risk, major risk and severe risk to reinforce targeted interventions by accurately predicting children at risk of defaulting from the immunization schedule. Data from Nigerian Demographic and Health Survey 2018 was obtained for this study and thirty-four (34) demographic and socio-economic factors were used to predict children at risk of defaulting from routine immunization in Nigeria by using Artificial Neural Network (ANN) to train the dataset. The results indicated that ANN model produced an accuracy of 99.16% for correctly identifying children who are likely to default from immunization series at different risk stages. Other performance measures include Precision of 99%, Recall of 99% and F1 Score of 99%. The model was further validated using one thousand (1000) dataset, out of which nine hundred and seventy four (974) were correctly predicted