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This paper explores the multifaceted world of IoT, exploring its historical evolution, core definitions, enabling technologies, architectural considerations, communication models and future predictions. Moreover, the paper critically scrutinises the potential benefits and challenges associated with IoT adoption. This study employed a systematic literature review (SLR) methodology to review existing literature between January 2020 and January 2024 on IoT technology. 20 relevant studies out of an initial pool of 45,312 studies were reviewed. The study followed the established Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework to ensure a rigorous and unbiased selection process. Findings revealed the lack of a universally accepted definition for IoT and significant variations in architectural models across scholarly works. Moreover, the study acknowledges the complex and ever-evolving nature of IoT technology, recognising its early stages of development. This dynamic technological landscape calls for continuous exploration of future research. Findings will provide valuable insights for academics, researchers, and industry professionals interested in the understanding the intricate technical landscape of IoT and its development. Moreover, by providing insights into growth predictions, benefits, and existing challenges, this will empower stakeholders to navigate the evolving IoT landscape and contribute to its future progress.

The usability and security of information system applications significantly affect the users’ willingness to adopt the applications; online banking is one such service. The emergence of innovative technologies in all facets of our daily activities makes usable security critical to protect users’ privacy and personal information. The paper aims to investigate the usability and security of the online banking authentication process. The study is based on users’ perceptions of the login system of their respective banks' online banking services, using the attitude questionnaire statements related to usability and security aspects of the authentication process. The paper presents the results of 1190 survey responses in South Africa. The findings show that younger and inexperienced users are not satisfied with the usability of online banking authentication systems as they scored the system very low compared to the older and experienced users. Given the prevalence of online security breaches, improving the authentication process' usability will help create a secure online environment.

There is inadequate knowledge on change management model (CMM) for post-acceptance phase of information systems. This paper intends to determine and model change management factors (CMFs) that explain effective use of Examination Administration System (EAS) in South Africa. A survey was used to collect data from 215 EAS users in Technical and Vocational Education and Training colleges and was analyzed quantitatively. Empirical results evince existence of casual nexuses between user involvement and change recognition, user satisfaction, performance measurement with use of EAS which through EAS adaptive use (EAU) in turn together with EAS verification, user learning representational fidelity and informed actions influence effective use of EAS. This study results add to literature by explaining the indirect mechanisms (i.e., EAS and EAU) through which the CMFs can influence effective use. This paper also offers a novel integration of Effective Use Theory and Unified Theory of Acceptance and Use of Technology with three CMFs which provides a holistic model. This study recommends that various communities endeavour to use CMM as a guide to influence user communities to effectively use information systems in diverse contexts. The model also has potential to drive improved return on investments and enhance employees’ productivity.

The rise of portable electronic devices and social media has led to new criminal activities, necessitating advancements in digital forensics. This paper introduces Small-Scale Digital Device Forensics (SSDDF), focusing on the forensic examination of miniature digital devices often used in crimes. SSDDF addresses the challenges posed by these devices, particularly in extracting and analyzing data from them. A key aspect of this research is exploring ontology in social media forensics, particularly within the Android operating system. This involves extracting digital evidence like user accounts, messages, and images from social media platforms. While the paper primarily focuses on social media data, it acknowledges the importance of the devices used in crimes. The integration of SSDDF and the analysis of Android system structures are highlighted as key advancements in digital forensic methodologies. These enhancements are expected to improve the process of collecting and analyzing digital evidence from both compact electronic devices and social media platforms. The study offers significant contributions to the field of digital forensics. It provides new strategies for more efficient and effective forensic investigations, especially in the context of extracting and analyzing digital evidence from small electronic devices and social media, thus paving the way for more robust digital evidence handling in future forensic inquiries.

Cloud-based online learning is the electronic learning activity that supports teaching and learning (T&L) that could be done from anywhere and in the world. Some of its benefits are scalability and affordability that could in a decision-making support on the mechanisms of material selection. Cloud computing has been adopted by most universities around the world. In this regard, lecturers and students will use it to facilitate T&L; however, due to concerns of information technology or systems security, cloud-based online learning users are also not immune. In this regard, the users could be affected by different cybersecurity attacks. In this paper, a systematic literature review method was used to sift the different models and solutions used to address the cybersecurity concerns surrounding cloud-based online learning. A brief Likert-scale questionnaire was used to obtained data that could corroborate the systematic literature findings. In this regard, a group of 20 online learning designers were sampled as participants. It was found that the confidentiality, integrity, and availability issues are a concern. This led to issues of security awareness, authentication and blended attacks being issues. In this regard, a cloud-based online learning model is not immune from security issues. In this paper, a conceptual framework as the line-of-defense is proposed as a solution towards having a cybersecure cloud based online learning.

This 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.

This 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.

The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to make the best decisions for the situation at any given time. The relentless collection of data from user interactions have introduced both a high level of complexity, as well as a great opportunity for businesses. In addition to connecting not just people, but also machines to the internet, and then collecting data from these machines via sensors would result in an unimaginable repository of data. This ever-increasing collection of data is known as Big Data. Integrating this with existing Business intelligence systems and deep analysis using Machine Learning algorithms, Big Data can give useful insights into business problems and perhaps even to make suggestions as to when and where future problems will occur (Predictive Analysis) so that problems can be avoided or at least mitigated. This paper targets at developing a system capable of optimizing a business intelligence using big data and machine learning approach. The design of a system to optimize the Business Intelligence System using Machine Learning and Big Data at NNPC was successfully carried out. The System was able to automatically analyze the sample report under NNPC permission to use and it generated expected predictive outputs which serves as a better guide to managers. When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures.

This paper presents a novel approach for cyberbullying detection and classification in social media text using an ensemble model that combines BERT (Bidirectional Encoder Representations from Transformers) and Support Vector Machine (SVM) with grid search for multiclass classification. We have also compared the performance of our proposed with various machine and deep learning models and the results show that our proposed model outperforms other models achieving an accuracy of 90% on testing data. Further, we have used to used SHapley Additive exPlanations (SHAP) an Explainable (XAI) technique to interpret the predictions of the BERT-SVM ensemble model.

Internship programs are essential for tertiary students to gain relevant experience before entering the workforce. Managing these programs, especially in institutions such as the East Java Province Marine and Fisheries Agency (DKP), poses challenges due to the reliance on paper-based processes and the need for centralized management across 17 branches. Therefore, this research developed a "Centralized Internship Information System" (CIIS) program designed with the aim of simplifying internship registration, student placement, document management, and program monitoring at DKP and its branches. This research focuses on designing a user-friendly interface for CIIS to address these issues. The design process follows the Design Thinking method, which emphasizes deeply understanding user needs and creating innovative solutions. The effectiveness of CIIS was evaluated using the Short User Experience Questionnaire (UEQ), which assesses user experience across key aspects. The UEQ results showed an average pragmatic quality score of 1.954 (excellent) and an average hedonic quality score of 1.856, with an overall average of 1.91. These results indicate that CIIS provides a high-quality user experience, facilitating easier access to internship opportunities for students, efficient candidate selection for companies, and effective monitoring for internship managers at DKP.

This study is unearthing the localizing process of SDG in Bangladesh in meeting the Sustainable Development Goals (SDGs) targets. Though nations bear the main responsibility for the SDGs, these goals specifically call for involvement from local authorities. Around 12 of the 17 SDGs require comprehensive strategies at the grassroots level to tackle interconnected issues like poverty, poor health, social challenges, and environmental degradation, with exceptions being Goals 9, 12, 13, 14, and 17. Bangladesh's constitution wisely allocates significant responsibilities for social and economic development, including the formulation and execution of plans concerning public services and economic progress, to the local government bodies, particularly the union parishad (UP), which serves as the primary interface with the community [Article 59(2)(c)(Constitution, 2004)]. Local Government Institutions in Bangladesh are positioned to have a significant influence on the localization of the SDGs, given their proximity to the most marginalized and frequently vulnerable rural communities, allowing for direct impact. Despite this potential, they have yet to make a substantial contribution to SDG localization, and with the deadline approaching, there is a pressing need to address this issue. This paper seeks to examine the obstacles encountered by local government in Bangladesh in their efforts to localize the SDGs, aiming to gain insight into the challenges hindering effective policy implementation.

The learning and mastering of programming language syntax pose a significant challenge for non-computing students. Most teaching approaches and existing educational tools often fail to address this issue. Therefore, this paper introduces an interactive learning environment called PyLe, specifically designed for introductory programming in Python programming courses. We evaluated the effectiveness of PyLe on first-year students at North-West University in South Africa and the University of Yaoundé 1, Cameroon. Firstly, the study conducts an experiment to assess the effect of PyLe on the time taken to solve a problem and the response quality. Secondly, PyLe’s usability and its instructional value were evaluated by the students and the instructors, respectively. The results from post-test method and a quantitative survey indicate that PyLe improves students’ ability to learn and master program syntax and has a high usability rate. Moreover, feedback from students and teachers affirms PyLe’s potential to address programming syntax challenges for non-computing students. However, the analyses revealed no real relationship between the time taken to complete a task in PyLe and the quality of the solution. This study contributes to improving the teaching and learning of computer programming, which has been considered difficult for both computing and non-computing students.

Waste, encompassing both domestic and industrial materials, presents a significant environmental challenge. Effectively managing waste requires accurate identification and classification. Convolutional Neural Networks (CNNs), particularly the Residual Network (ResNet) architecture, have shown promise in image classification tasks. This research aims to utilize ResNet to identify types of waste, contributing to more efficient waste management practices. The ResNet101 architecture, comprising 101 layers, is employed in this study for waste classification. The dataset consists of 2527 images categorized into six classes: Cardboard, Glass, Metal, Paper, Plastic, and Trash. The ResNet model is pre-trained, leveraging existing knowledge to enhance classification accuracy. The dataset is divided into training and testing sets to evaluate the model's performance. The testing results, evaluated using a Confusion Matrix, demonstrate strong performance in waste classification. The ResNet101 model achieves 92% accuracy in detecting inorganic waste objects within the training dataset and maintains a high accuracy of 90% on the testing dataset. This indicates the effectiveness of the ResNet architecture in accurately identifying various types of waste, contributing to improved waste management efforts. he utilization of ResNet101 for waste classification yields promising results, with high accuracy rates observed across both training and testing datasets. By effectively identifying types of waste, this approach facilitates more efficient waste management practices, enabling better resource allocation and environmental conservation. Further research and application of CNN architectures in waste management could lead to enhanced sustainability efforts and improved waste-handling strategies.

The 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.

In the evolving landscape of Software-Defined Networking (SDN), the strategic placement of controllers poses a critical challenge that necessitate a precise balance between network performance and security. This paper presents an integrated framework for enhancing security and performance in SDN by combining controller placement and intrusion detection systems (IDS). Unlike existing solutions which were implemented disjointedly, we propose a holistic approach that leverages the proximity of controllers to network traffic for real-time threat detection, rapid response, and mitigation of security attacks. We employ an advanced clustering model for optimal controller placement, reducing costs and latency while ensuring reliability and balanced loads. In addition, we utilize k-nearest neighbour (KNN) for efficient anomaly detection in our IDS for improved network security. Experimental results confirm the framework’s effectiveness in strengthening SDN security and resilience. The enhanced-DBSCAN-based CPP model significantly minimized the cost, and latency, and ensured continuous operation in dynamic SDN environments while the KNN-based IDS shows effectiveness in improving threat detection capabilities, achieving high detection accuracy of 100% on the LAN dataset, outperforming other machine learning models such as Random Forest and Naïve Bayes. The indication is that strategic controller deployment, in conjunction with IDS, can significantly bolster threat detection, response times, and the overall security stance of the SDN environment.

The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, Web of Science and Scopus databases, were consulted. The research articles published before the beginning of 2017 to 2023 were considered as part of the analysis. The results showed that the prevalent data analytics techniques used to address the cloud computing resources allocation challenge are Support Vector Machine, Spatio-temporal and edge-cloud collaborative scheme. Failure to effectively and efficiently provide cloud computing management resource allocation will lead to system bottlenecks especially during peak periods. In this regard, such a failure could lead to dissatisfied clients.

Criminal intention is a critical aspect of human interaction in the 21st-century digital age where insecurity is on the high side as a major global threat. Kidnapping, killings, molestation of all sorts, gender-based violence, terrorism, and banditry are the trends of criminality in our nation, as such, there is a need to effectively explore innovative means to identify and cope with this evil menace in our society. The facial positioning of humans can tell their evil intention even if they pretended to smile with the evil in their minds. In normal instances, it may be very difficult to predict the heart of man, but with the trending information technology like image processing, the state of a human face could be used as a means to read their tendencies. This paper proposes a deep learning model based on the FER2013 dataset through the implementation of a CNN model that predicts criminal tendencies with the help of facial expressions. With this goal in mind, we explore a new level of image processing to infer criminal tendency from facial images through a convolutional neural network (CNN) deep learning algorithm in other to discriminate between criminal and non-criminal facial images. It was observed that CNN was more consistent in learning to reach its best test accuracy of 90.6%, which contained 8 convolutional layers. To increase the accuracy of this model, several procedures were explored using Random Search from the Keras tuner library, testing out various numbers of convolutional layers and Adam optimizer. It was also noticed that applying the dissection and visualization of the convolutional layers in CNN reveals that the shape of the face, eyebrows, eyeball, pupils, nostrils, and lips are taken advantage of by CNN to classify the images.

Security 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.

This paper presents the development of the Archipelago Cultural Insight Education Application, an Android-based platform designed to enhance the learning of Nusantara’s rich cultural heritage. Utilizing Extreme Programming (XP), an agile software development methodology, the application was developed to accommodate the dynamic requirements of educational content and interface design. The XP approach facilitated rapid iterations and continuous feedback, ensuring the application remained aligned with educational goals and user needs. The application features a user-friendly interface with dedicated sections for Traditional Houses, Local Attractions, Regional Foods, Folk Songs, and other cultural elements. Each section provides comprehensive data and detailed descriptions that aim to educate and engage users. The design prioritizes intuitive navigation and ease of content management, which is critical for the educational effectiveness and sustainability of the app. Moreover, the integration of multisensory learning elements, such as auditory content in the Folk Songs section, enhances the educational experience by providing a more immersive understanding of the cultural context. The application's development process and its features illustrate the benefits of applying agile methodologies in educational technology, highlighting how they can be used to produce a robust, engaging, and informative educational platform. This study contributes to the field by demonstrating the practical application of agile principles in the design and implementation of educational technology that effectively bridges cultural education and digital innovation.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized numerous industries and sectors, offering transformative potential for Results-Based Management (RBM). RBM is a management paradigm wherein organizations and government entities plan and assess the effectiveness of their projects, policies, or programs in achieving outcomes. Integrating AI and ML into RBM can significantly enhance outcomes, fostering data-driven and informed decision-making. AI and ML integration into RBM practices facilitates improved decision-making, resource optimization, accountability, and transparency. These technologies enhance RBM by enabling predictive analytics, real-time monitoring, task automation, customization, and scalability. The dynamic synergy of AI and ML extends beyond RBM into sectors like agriculture, public health, academia, and public administration. Despite their immense potential, AI and ML tools face challenges such as perpetuating inaccuracies and biases due to inherent biases or low data quality. Nevertheless, their application in RBM empowers organizations to plan better, monitor, evaluate, and refine projects and programs, optimizing resource allocation and performance. Ongoing research, ethical considerations, data quality, and accountability are essential priorities for harnessing the full benefits of AI and ML in RBM. Therefore, this research paper investigates the potential of AI and ML tools and technologies in improving results-based management. It comprehensively reviews existing literature, practical applications, and case studies to elucidate how AI and ML can enhance results-based management practices and contribute to better decision-making.

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data.

Pay-per-click is one of common and important ways in online advertising through Google; but some keywords are usually expensive and this issue is visible through Google cost-per-click in Google keyword planner tool. It has done many research work in Google advertising formats, but place of studies in field of relationship between Google cost-per-click and keyword search-volume, especially in tourism area is blank. This paper tries to answer of this main question that is there a significant relationship between cost-per-click and keyword search-volume and the number of keyword's word? In other words, does cost-per-click increase or decrease, based on keyword search-volume and the number of keyword's word (multi-word keyword or hyper focused keyword phrases)? Chicago auspicates a mighty increase in tourism 2022 as its announcement an 86 percent growth in visitors in 2021 compared to 2020 and choosing the right keyword in Google search engine by travel agencies can lead to more sales. In this research, I sampled 100 search result through combination of ″chicago+tours″ as a search keyword on google.com (Google USA) and Google keyword planner daily data (December 26, 2022) based on the last 7-10 days, and via regression analysis with least squares and loess model, I tried to investigate the relationship between search-volume and cost-per-click and it will help scholars in future research in this area. This study was carried out within the scope of selected keyword in tourism area in form of cross-sectional. I find that there is no significant relationship between short-tail keywords and cost-per-click. The result of this research shows that some medium or long-tail keywords are more expensive than short-tail keywords with more search results. Another result that was observed is the lack of significant relationship between keyword search-volume and its cost-per-click, so that in some cases, high-search keywords are cheaper than keywords with a low-search.

Occupational safety and health hold significant importance for both agencies and individuals alike. A crucial aspect of assessing driver readiness involves examining their physical well-being. Truck drivers who possess unhealthy physical conditions face a four-fold higher risk of work accidents compared to those with sound physical health. PT. Toyota Motor Manufacturing Indonesia (TMMIN), being a manufacturing company, must prioritize occupational safety and health, particularly within the logistics domain. Presently, some logistics partners collaborating with TMMIN adhere to their own standards when conducting checks, relying on paper records for documenting the results. Unfortunately, this approach hinders proper archiving of inspection records and fails to establish a direct link with logistics partner customers in cases involving drivers with health issues. Hence, PT. Toyota Motor Manufacturing Indonesia is dedicated to enhancing the quality of driver health checks through logistics partners by implementing an integrated recording system called the Logistics Driver Tenko System Application (ORIENT). The development of ORIENT is based on the Scrum framework. This research aims to offer insights into the direct implementation of the Scrum method in project development.

Micro, Small, and Medium Enterprises (MSMEs) are vital for a country's economy, fostering job creation and economic growth. This research focuses on MSME water distribution agents engaged in selling and distributing various brands and types of bottled drinking water. With a customer base exceeding a thousand and an average of 30 daily transactions, these agents encounter specific challenges in inventory and sales reporting. Presently, MSMEs rely on traditional methods, using books to record stock and sales reports, manually calculating transactions with calculators, and preparing handwritten paper invoices. These practices often result in writing errors, inaccuracies in product information, and pricing inconsistencies. Furthermore, the manual nature of these processes consumes significant time and effort on a daily basis. This study highlights the applicability of the Rapid Application Development (RAD) system design method in creating an efficient and time-sensitive solution. By leveraging RAD, the system design was swiftly accomplished with a limited team. The traceability matrix test validates the successful alignment of the designed information system with the needs of MSME agents in the drinking water distribution sector. The findings of this study provide compelling evidence that the RAD system design approach significantly enhances inventory and sales management practices for MSMEs involved in drinking water distribution. By implementing the developed information system, MSME agents can streamline their operations, eliminate manual errors, and improve overall efficiency, thereby fostering their growth and success in the market.

The unstable climatic conditions are promoting the adoption of smart agriculture. The introduction of IoT technology in the cultivation process allows for the monitoring and control of plant growth through automation. While the traditional greenhouse system is already an upgrade to the traditional cultivation system, it still has room for improvement with the integration of technology. The traditional greenhouse system requires close monitoring of growth rates through manual intervention and can be prone to various hazards. Incorrect interpretations of plant requirements can result in wrong interventions that may severely affect plant growth. It is imperative to have accurate data to maintain the health of plants throughout the cultivation process. IoT plays a predominant role in providing accurate data for constant monitoring of plant growth. The aim of this paper is to present a fully functional greenhouse automation model using microcontrollers, sensors, fans, pumps, and appropriate networking routing technology capable of providing instant data for proper monitoring of plant growth. The presented model includes safety features and can issue alerts in case security measures are breached. The system may even allow users to perform remote monitoring using a web application. Therefore, the proposed model will contribute to enhancing the horticultural industry by increasing output and reducing the need for human intervention.

Client-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.

The 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.

Fuzzy Cognitive Maps (FCM) is a soft computing technique whose vertices and edges are fuzzy values with an inference mechanism for solving modelling problems; it has been used in modelling complex systems like industrial and process control. The concept was first introduced in 1986, with an initial learning algorithm in 1996; several works have been published on FCM methodology, learnings and applications. Fuzzy cognitive maps continue to evolve both in theory, learning algorithms and application. Many theories like intuitionistic theory, hesitancy theory, grey system theory, wavelet theory, etc., are integrated with the conventional FCM. These extensions have improved Fuzzy cognitive Maps to handle problems of uncertainty, incomplete information, hesitancy, dynamic systems and probabilistic fuzzy events. They also strengthen fuzzy cognitive Maps’ modelling power for application in almost any domain. However, the compilation of the development in methodology and adaptation of FCM are either old or omitted some of the recent advances or focused on specific applications of FCM in some areas. This paper reports extension, learning and applications of FCM from the initial conventional FCM to recent extensions and some of the important features of those extensions and learning.

Government infrastructure development is growing rapidly, starting from the central and provincial levels. One of the infrastructures currently being pushed by the government is road infrastructure. Both toll roads, regional roads, and district/city roads. Road infrastructure that has been built requires maintenance and repair. Road damage maintenance and repair in urban districts, especially Madiun Regency, Existing Road damage data is still recorded manually using paper, where the government needs help knowing data on road damage that has been recorded, which road conditions have not been repaired, and which have been fixed immediately. Because manual data is prone to wear and loss, tucked in with other manual files. So, the road improvement priority in Madiun Regency still needs to be put on target. For this reason, an information system is required to record the damage to road infrastructure in Madiun Regency. This information system was built using the web development life cycle (WDLC) method. We chose this method because it can be used in all web application development processes and can be modified or added to prototyping and looping to maximize results. The information system for road damage in Madiun Regency was developed using the PHP & MySQL programming languages. The result of this study is a road damage information system that contains updated data on road damage conditions in the Madiun district. Apart from being paperless, digitized data has the advantage of minimal damage and expected data loss and can help decision-makers prioritize repairs by looking at the data in the road damage information system.

From remote control and surveillance to energy and health monitoring, IoT devices provide cutting-edge services to improve our lives. Nevertheless, IoT technologies raise complicated, innovative privacy-related issues that might limit their widespread adoption. Due to the passive nature of many IoT devices, it may be challenging for users to understand that their personal information is being gathered. Therefore, this paper aims to examine the Privacy by Design IoT application idea. We adapted the seven principles of Ann Cavoukian's Privacy by Design, which were first developed. In this study's qualitative descriptive methodology with interview sections are used. The results indicate that the majority of IoT users agree with and have high expectations for the Privacy by Design concept to be one of the remedies to the imbalance between concerns about the risks and benefits of using IoT.

A project cannot be separated with the name of the risk, not the exception to the abstract information technology projects, and can only be perceived benefits in the long term. So, it becomes a very important thing to manage risk properly so helpful to the success of the project. In this paper, we contribute to the model in analyzing the priorities of risk to the project E-Government to adopt the model of risk assessment using RIPC4 and Analytic Hierarchy Process (AHP) in assessing the risk, so it can know which risks a priority to perform the procedure changes in the implementation of e-government based on the strategy used in the process of change management.

Konsep green supply chain (GSC) banyak diterapkan dalam mengatasi permasalahan limbah industri, seperti pengerajin tekstil tradisional Jumputan Palembang. Proses pembuatan kain jumputan menggunakan air dan menghasilkan limbah cair yang berpotensi membahayakan lingkungan. Penerapan konsep teknologi ramah lingkungan dalam pengelolaan rantai pasokan bisnis dapat mengatasi hal tersebut jika dikelola dengan optimal. Paper ini menyajikan hasil analisis jaringan GSC dalam bentuk model proses dan prototipe sistem informasi yang dapat digunakan oleh pengerajin untuk mengolah data pengadaan, produksi, dan penjualan produk jumputan mereka, serta yang tidak kalah pentingnya adalah pengelolaan data limbah cair. Limbah cair ini akan diproses menjadi air bersih kembali oleh pengerajin secara bersama-sama. Dihasilkan beberapa model GSC yang dapat digunakan oleh peneliti, pengerajin maupun pemerintah untuk mengatasi permasalahan dampak lingkungan dari limbah proses pembuatan kain jumputan. Model analisis yang dihasilkan dapat digunakan untk mengoptimalkan proses produksi dan penjualan kain jumputan oleh pengerajin. Di saat yang sama juga dapat dihasilkan model bisnis baru terkait pengolahan limbah produksi ini, yaitu dengan menghadirkan atau membuat unit bank limbah. Konsep model bank limbah ini dapat mengoptimalkan pengendalian dampak lingkungan akibat limbah cair produksi jumputan di kawasan industri pengerajin jumputan, dan hasil ini dapat direplikasi di kawasan industri yang lain dalam bentuk yang sejenis.

Location Based Service is the ability to find the geographic location of a mobile device and provide services based on the location it gets. The development of location base service applications has been widely used today, one part that needs attention to be applied by this application is the handling of network disturbances in service companies. In the field of handling network disturbances, there are problems, namely where there are still delays in handling disturbances, this is due to the length of time searching for the location of the disturbance. This paper aims to improve the handling of network disturbances using mobile devices. With a mobile device that is integrated with location base service, it can solve the problem of delay in handling network disturbances.

There has been a rapid development of Information and communication Technologies (ICTs) globally, and this includes the Geographic Information Systems (GISs) which has evolved over the years and aimed at solving many geographic problems as fast and efficiently as possible. The importance of GIS to students and lecturers in higher educational institutions is it’s ability to easily analyse locations together with the conventional database systems. This paper assessed the correlation between the availability and use of GIS in Higher Educational Institutions (HEIs). The study is exploratory in nature and employed the use of questionnaires as instrument to collect data. Simple random sampling technique was used to select one hundred and ten (110) lecturers one thousand five hundred and seventy-nine (1579) students from a university. Findings revealed that GIS for analysis is still at its lowest ebb and hence calls for an action to motivate or encourage lecturers and students to use it. Though there are indications that GIS will be of great value in schools, students and lecturers they will be happy to use it for their teaching and learning. The study also revealed availability of GIS was not tied to its usage.

Customer analysis is receiving special attention from both researchers and professionals. The objective of this paper is to identify the trends of techniques used to address customer’s current problems and shed light on future research directions using a literature review. We reviewed the literature for the last five years. The findings revealed that customer purchase was the most popular technique used by the research community followed by customer satisfaction and visit wit. Whereas customer segmentation and customer churn were the least. However, the regression method was commonly used for predicting customer purchase and behavior. But, social media and big data are still in their early stages for customer analytics research.

Information Systems at this time is needed by the company to survive in business competition with competitors. The information system must also be aligned with the vision and mission and business processes that exist in the company now days so company can generate profits (profitable) at the same time. PT. Hostingan Awan Indonesia is a web development product & service provider company with online transaction feature and purchase processes that integrated with mobile payments. To make business process run optimally, an information system is needed to archive every output document that produced by each division in the company. The process of collecting data in this paper through observation and direct interviews with several stakeholders in the company. This research used Ward & Peppard metho with internal value chain analysis, SWOT, PEST, Five Forces Porter and Mc Farlan Strategic Grid. This research also provides application portfolio recommendations with internal business environment analysis, external business environment analysis, internal SI environmental analysis and external SI environmental analysis.

The use of information technology system in managing data of various company activities, especially in the warehouse, is an important part for every company. From the observation that have been made, the information system for recording goods flow in PT Global Infotech Solution is still done manually. During this time, employees carry out the process of recording data of goods at the warehouse using paper, so that the risk of data loss is easily caused by tearing, wet or tucked with other data. The system development method that used is the FAST (Framework for Application System Thinking) Method. This study aims to design an information system for recording goods flow which is expected to make it easier for employees to record goods in the warehouse. Using a computerized system can shorten the time and protect all data items from the risk of theft. The result of this study is designing an information system for recording goods in the form of information systems using the Unified Modeling Language and User Interface. This item recording information system is expected to be able to change the process from a manual recording system to a computerized recording system, so that it can speed up every recording process and minimize the possibility of mistakes in entering the data of goods in the warehouse.

Clustering is a useful technique that organizes a large number of non-sequential text documents into a small number of clusters that are meaningful and coherent. Effective and efficient organization of documents is needed, making it easy for intuitive and informative tracking mechanisms. In this paper, we proposed clustering documents using cosine similarity and k-main. The experimental results show that based on the experimental results the accuracy of our method is 84.3%.