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The pursuit of success in one's chosen profession is a universal aspiration that requires individuals to engage in competition, not just domestically but also internationally, particularly in today's era of globalization. The rapid advancement of technology presents both opportunities and challenges, necessitating proactive management to achieve professional success. In the field of Informatics Engineering, there is a wide range of professional pathways, each offering unique opportunities and requiring distinct skill sets. For university graduates, it is crucial to have a comprehensive understanding of the specific requirements and demands associated with various job paths to make informed decisions. This knowledge enables them to select a professional route that aligns with their individual aspirations and goals, thereby avoiding potential discontent in their chosen career. Lack of knowledge or awareness during the decision-making process can negatively impact productivity and overall performance. There is a growing demand among university graduates, especially at the undergraduate level, for career-related information. This has led to heightened competition among graduates in terms of skills, knowledge, and availability. To effectively navigate this competitive landscape and engage in meaningful competition with fellow graduates, individuals must possess a comprehensive understanding of their desired career trajectory. To address these challenges, the Decision Support Model (MPK) can be utilized, employing the Fuzzy Analytical Hierarchy Process methodology. This approach considers four primary criteria—Financial Compensation, Non-Financial Compensation, Soft Skills, and Hard Skills—each with several sub-criteria. These criteria are evaluated based on the perspectives of certified graduates of Informatics Engineering and industry specialists. The study successfully identified the primary criteria influencing career decisions, such as job conditions, incentives, and prospects. It also highlighted the most favorable career path, with the role of a Data Analyst being identified as particularly promising. This career path involves roles within the field of Informatics Engineering that focus on processing data according to specific requirements, highlighting the importance of understanding the evolving landscape of technology and its impact on professional opportunities. Doi: 10.28991/ESJ-2024-08-01-011 Full Text: PDF

Personality classification has garnered significant interest in psychology, computational social science, and Machine Learning (ML) due to its wide-ranging applications. This paper presents PersoNet, an innovative framework developed to identify personality types using the Myers-Briggs Type Indicator (MBTI), aimed at enhancing customer service experiences by matching customers with suitable support agents. PersoNet employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network architecture and has achieved an impressive classification accuracy of over 93.98%. Our extensive experiments with the MBTI dataset reveal that the BiLSTM architecture effectively captures both temporal dependencies and semantic subtleties in textual data, contributing to this high level of accuracy. Consequently, PersoNet can accurately select customer service agents who match customer personalities, achieving a Customer Satisfaction Rate (CSR) of over 97.82%–a notable improvement of 20.25% in CSR based on our experimental data. These results establish PersoNet as a cutting-edge tool in personality classification, surpassing existing methods in both accuracy and computational efficiency and markedly enhancing customer service quality.

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics.

Nowadays, most of our daily activities are assisted electronically due to not only time effectiveness but also activity efficiency. Processes that once could only be completed at the office are now able to be done anywhere and anytime, as long as there is internet access. One obvious example is the COVID-19 pandemic which leads to the work-from-home system. Therefore, this spurred the author on to innovation by adapting to the current phenomenon. Most of our daily activities are currently converted to digital, in which these digital users can reap abundant results. This manual-to-digital conversion is known as ‘digital transformation’. The purpose of digitalization in this organization is to transform the disposition distribution of the leaders from manual to digital. The result of the study is the adoption of e-Disposisi application (paperless office) in the organization, which causes the business processes to be more effective and more efficient concerning time, human resources, document storage, and paper use. This implementation results in changes in the business processes, the reduction of time needed to distribute documents, the lower purchase of paper and tint, and the minimized employment of human resources. Generally, the research contributes to the more effortless business processes in the organization, allowing operations to continue without direct contact. Data collection was done through various methods, namely observation, interview, questionnaire, focus group discussion (FGD), and literature. The methodologies used were Soft System Management, qualitative and quantitative referring to the IT Information Library framework. Doi: 10.28991/ESJ-2023-07-06-07 Full Text: PDF

Monitoring the movement of boats in shallow waters requires a real-time monitoring system. However, for small-size wooden boats, they are still monitored manually, and data is unavailable in real time, which makes it difficult to effectively monitor them. The integration of IoT platforms with the boat monitoring system is a challenging task, especially in the transport system. This paper has the objective of developing an architectural model of a modified LoRaWAN-based boat monitoring system that is connected to a GPS-based mobile device and base station. The proposed architectural model is an integration of Bluetooth Low Energy (BLE) and LoRaWAN networks, which are also tested in real time to solve the boat traffic monitoring issues. The field tests with parameters of signal transmission, location coordinates, and position of the boats are also presented. The analysis result shows the proposed model is suitable for waters with high noise levels, especially in shallow water and delta rivers. The signal noise can be reduced by extracting the real-time data. In addition, signal interference can be minimized. The performance of this system is also compared to the reference system in real conditions, which shows an adequate correlation result. This proof of concept forms an important basis for deploying it for large-scale applications and commercialization capabilities. Doi: 10.28991/ESJ-2023-07-04-011 Full Text: PDF

Customer reviews are valuable resources containing customer opinions and sentiments toward the product. The reviews are informative but can be quite lengthy or may contain repetitive information calling for opinion summarization systems that retain only the significant opinion information from the review. Abstractive summarization is a form of text summarization that generates a summary mimicking a human-written summary [1]. When pretrained language models are finetuned for abstractive review summarization, there usually occurs a problem known as the ‘domain shift’, because the source and target domains exhibit data from varying distributions [2]. This issue results in performance degradation of the model at the target end. This paper contributes a data package comprising of an annotated abstractive summarization dataset (annotated_abs_summ) of airline reviews having 500 reviews and abstractive summary pairs, a dataset (review_titles_data) consisting of 7079 reviews and review title pairs for review title generatioon or domain adaptive training [3] to address the domain shift problem for abstractive opinion summarization and, an annotated reviews dataset (annotated_sentiment) for rating-based sentiment classification. All datasets have been collected from the Skytrax Review Portal via web scraping using Python programming language. The datasets have several potential use cases. The abstractive summarization dataset can serve as a benchmark dataset for airline review summarization. The dataset for domain adaptive training can be used as a standalone dataset for review title generation. The dataset for sentiment analysis is multipurpose having columns like user rating and recommendation value, that can be used for statistical analysis like finding correlation between these data items as well as for other Natural Language Processing (NLP) tasks like predicting rating or recommendation value from the customer reviews. The datasets can be extended using various data augmentation techniques [4,5]. Moreover, the datasets are related and can be collectively used to develop a multi-task learning model [6] for better learning efficiency and improved performance.

In order to gain a competitive edge in the banking industry, direct face-to-face interactions between customers and banks are being gradually replaced by virtual interactions. Instead, they communicate using electronic devices such as smartphones, tablets, and web applications. Transaction banking, which provides commercial services for banking products to small, medium, and large corporations, is a highly typical work unit in banking. Shipping and international payments, risk management for international trade, and other services are provided. We use the Service Oriented Modelling Architecture (SOMA) methodology to solve the problem of the transaction banking unit by relocating it from a system with general dependencies to a system that is independent of the entire service and operates on a small function scale. The study found that a decision as a service (DAAS) model with an Enterprise Service Bus (ESB), Business Process Management (BPM), and Business Rule Management (BRM) solution can provide guidelines for the design of decision rules pertaining to integrated or separate business modeling, thereby aiding business unit delivery.

Objectives: The purpose of this research is to conduct an examination of intention factors for using social commerce in Indonesia. Methods/Analysis: This research is a quantitative study that applies the customer analysis model to four big social commerce sites in Indonesia. This study uses the multivariate regression method and IBM SPSS 25 software to prove the relationship between research variables. Findings: Variables will include performance expectations, effort expectations, societal effects, supportive circumstances, and cost savings. Data from 210 online respondents in Indonesia were collected. Novelty and Improvements:Positive outcomes are provided by the model as a result of changes in the use of social commerce. Doi: 10.28991/HIJ-2022-03-04-01 Full Text: PDF

The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration.

The era of the industrial revolution 4.0 was an era marked by the transition of information and communication technology that was able to create new technology-based investments. Internet of things (IoT), Big Data, and Cloud Computing are the foundations that underlie this 4.0 industrial revolution. Cloud Computing is a service that provides network storage space and computer resources using an internet connection as a medium of access. Cloud Service Providers (CSP) offer attractive services, making more and more companies want to migrate to the cloud. Sometimes the migration process to cloud computing faces problems or even failures, and this is certainly a risk for cloud service users. This study will identify the types of risks and risk components in cloud migration using the Systematic Literature Review (SLR) method. The databases used in selecting articles that match the criteria include: Emerald Online, IEEE Xplore, ScienceDirect, SpringerLink, and between 2015 and 2020. The results of this study, there were 74 articles selected according to the criteria and reviewed. The output of this study shows that there are 7 types of risk in cloud migration, namely information security risk, risk of losing data access, risk of using virtual machines, errors in choosing CSPs, risk of compliance with various laws and regulations, financial risk, and management failure, the weights of 25%, 21%, 18%, 14%, 11%, 7%, and 4% respectively, as well as 5 risk components, namely threats, impacts, risk factors, vulnerabilities, and damage with a weight of 33%, 27%, 20%, 13%, and 7%.

Abstract This paper presented the implementation of Bluetooth low energy (BLE) devices implanted on 7 small wooden boats. The BLE implantation is aimed at boat identification and certification. We use four parameters to measure the performance of BLE signal strength, e.g., solar panel usage (with/without solar panel usage), weather effect (sunny/rain), sea wave form (shallow/tidal waters), and distance. The specification of BLE and boat identification system is also presented. From the experiment, we found that BLE signal strength performance is impacted by four parameters including its combined parameters: (a) the highest hour of the detected signal is generated when the boats are connected to the solar panel; (b) the boats transmitting the signal in sunny weather; (c) the boats are in shallow water sea wave; (d) the boats are in sunny weather and connected to solar panel will have a detectable distance for up to 30 m compared to without solar panel connection, it will only have a detectable distance for up to 15 m. It is suggested for future recommendations to expand this study with the newest BLE version and better solar panel equipment to get higher signal strength performance.

Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.

The development of news media and social media has radically changed the way the public consumes information. This study explores the structure of online news media networks in three countries, namely Indonesia, Malaysia and Singapore, to investigate the phenomenon of fragmentation in the news consumption pattern on social media. Based on the results of the three network indicators used in this study, it can be concluded that the structure of online news media networks in Indonesia and Malaysia shows a tendency of fragmentation. In contrast, this study did not find sufficient evidence that the phenomenon of fragmentation was occurring in the Singapore media network. In-depth analysis on each formed media cluster shows that online news media in Indonesia and Malaysia tend to group based on similarity in market segments, regions or political alignments.

The objective of this research was to discover the behavior of museum visitors on Facebook fan page in Indonesia based on the user motivation, user expectation, online community involvement, and Facebook fan page of the museum. This research used a quantitative approach to descriptive analysis. The population was the Facebook users who had followed the Facebook fan page of the museum in Indonesia. The samples used were 270 respondents. The researchers distributed the questionnaire to a Facebook group managed by museums or communities. Based on the demographic profile of respondent, the researchers discover that the respondents are highly educated, work as employees or student, and allocate more than Rp500.000,00 per month for traveling expense. Based on social media behavior of the respondents, the respondents are active using Facebook and not aware of the presence of museum in social media. The respondents require museum information, social interaction, and entertainment on Facebook fan page of the museum. Therefore, museum managers must maintain the content quality and perceived usefulness in delivering the information through Facebook. The involvement of cultural community can help people to get honest information about museum through credible opinion from the respondents.

P systems with mitochondria enzymes layer computing is a recent version of P systems; it integrates a new feature inspired from the enzymes gate of a natural cell to the cell-like P systems. The model of a computational layer is well known as a problem of Non-Deterministic (NP-complete) in polynomial time. In this paper, we propose a P systems with enzymes to solve one of the most NP-hard problems, which is the determination of the maximal clique in a given undirected graph. In this context, the evolution strategy proposed is based on using objects under the control of enzymes placed on layers.

Today, most studies of audience networks analyze the landscape of the news media on the web. However, media ecology has been drastically reconfigured by the emergence of social media. In this study, we use Twitter follower data to build an online news media network that represents the pattern of news consumption in Twitter. This study adopted a weighted network model proposed by Mukerjee et al. and implemented the Filter Disparity Method suggested by Majó-Vázquez et al. to identify the most significant overlaps in the network. The implementation result on news media outlets data in three countries, namely Indonesia, Malaysia, and Singapore, shows that network analysis of follower overlap data can offer relevant insights about media diet and the way readers navigate various news sources available on social media.

An integrated information system is needed in an enterprise to support business processes run by an enterprise. Therefore, to develop information system can use enterprise architecture approach which can define strategic planning of enterprise information system. SMP Negeri 1 Jambi can be viewed as an enterprise because there are entities that should be managed through an integrated information system. Since there has been no unification of different elements in a unity yet, enterprise architecture model using Enterprise Architecture Planning (EAP) is needed which will obtain strategic planning of enterprise information system in SMP Negeri 1 Jambi. The goal of strategic planning of information system with Enterprise Architecture Planning (EAP) is to define primary activities run by SMP Negeri 1 Jambi and support activities supporting primary activities. They can be used as a basis for making data architecture which is the entities of application architecture. At last, technology architecture is designed to describe technology needed to provide environment for data application. The plan of implementation is the activity plan made to implemented architectures by enterprise.

The purpose of this study is to identify the determinants of IT operational performance related to IT Service Desk. And to determine the consideration of implementing IT Service Desk in the company business processes. The population in this study is 1500 employees or users of the IT Service Desk service. By using the Slovin, 100 users were obtained in the sample. The variables in this study include success factors of IT Service Desk and Problem Management. Methods of data collection include using questionnaire s. Analysis of data is conducted using multivariate analysis of the factor analysis. The conclusions of this research is obtained from, among others, the factors of success of the IT Service Desk and Problem Management. There are 9 to 37 variables and indicators. From the research, it is found that there is a relationship between factors. And after the reduction process of 37 indicators four new factors was formed which consist of a quick solution factor, excellent IT Service Desk factor, focus support factor, and excellent staff factor. From all the new factors, quick solution is the most dominant factor.

Service Oriented Architecture (SOA) is a framework for integrating business processes and supporting IT infrastructure as secure, standardized components services that can be reused and combined to address changing business priorities. Services are the building blocks of SOA and new applications can be constructed through consuming these services and orchestrating services within a business process. In SOA, services is map to the business functions that are identified during business process analysis. Upon a successful implementation of SOA, the enterprise gain benefit by reducing development time, utilizing flexibility and responsive application structure, and following dynamic connectivity of application logics between business partners. This paper presents SOA reference architecture blueprint as the building blocks of SOA which is services, service components and flows that together support enterprise business processes and the business goals. Layanan Berorientasi Arsitektur (SOA) merupakan framework untuk mengintegrasikan proses bisnis dan mendukung infrastruktur TI secara aman, layanan komponen standar yang dapat digunakan kembali dan dikombinasikan untuk mengatasi perubahan prioritas bisnis. Layanan merupakan blok bangunan SOA, di mana aplikasi baru dapat dibangun melalui konsumsi layanan ini dan merancang layanan dalam proses bisnis. Dalam SOA, layanan dipetakan ke fungsi bisnis yang diidentifikasi selama analisis proses bisnis. Setelah keberhasilan implementasi SOA, perusahaan memperoleh keuntungan diantaranya mengurangi waktu pengembangan, memanfaatkan fleksibilitas dan struktur aplikasi responsif, dan mengikuti konektivitas dinamis dari logika aplikasi antara mitra bisnis. Paper ini menyajikan cetak biru referensi arsitektur SOA sebagai blok bangunan SOA yang merupakan komponen pelayanan jasa, dan arus yang bersama-sama mendukung proses bisnis perusahaan dan tujuan bisnis.