Tag: digital entrepreneurial university

  • Application of Agglomerative Hierarchical Clustering (AHC) for Grouping Prospective Scholarship Recipients

    Authors: Fakhrian Fadlia Adiwijaya; Arisza Zufar Fathurrahman; Chrismikha Hardyanto

    DOI: 10.1109/INCITEST59455.2023.10395926

    Abstract

    Scholarships are a form of financial aid provided to individuals to support their education. Scholarships can be granted by educational institutions, governments, organizations, or individuals as a means of assisting with educational expenses. Scholarships can take various forms, including cash grants, tuition waivers, assistance in purchasing books or educational supplies, or a combination of these forms of aid. Scholarship recipients are typically selected based on specific criteria such as academic excellence, financial need, special talents or interests, or achievements in a particular field. The current method of selecting scholarship recipients involves evaluating the characteristics of students who are deemed outstanding and actively engaged in the learning process. Due to the large number of students and limited scholarship quotas, this process often consumes a significant amount of time, and the selection of scholarship recipients may not be entirely accurate. To aid in the selection of scholarship recipients, data mining techniques are employed, specifically the clustering method. Agglomerative Hierarchical Clustering (AHC) is one of the clustering algorithms capable of grouping a set of data into specific data clusters. The data analyzed for the determination of scholarship recipients include academic performance data or grades, extracurricular activity data, economic data, and student achievement data. By using data mining and clustering methods, educational institutions or organizations offering scholarships can analyze the data of potential scholarship recipients to identify groups with similarities based on specific criteria. This approach can make the selection and grouping of scholarship candidates more efficient and objective. © 2023 IEEE.

    Author keywords

    AHC; Clustering; Data Mining; Scholarship Management

    Indexed keywords

    Engineering controlled terms

    Cluster analysis; Clustering algorithms; Data mining; Economics; Learning systems

    Engineering uncontrolled terms

    ‘current; Academic excellence; Agglomerative hierarchical clustering; Clusterings; Educational institutions; Financial aids; Government organizations; Prospectives; Scholarship management; Special talents

    Engineering main heading

    Students

    This article can be accessed at https://www.scopus.com/pages/publications/85185202061

  • Big Data Algorithms as Enablers of Sustainable Marketing Practices: A Preliminary Research

    Authors: Rahma Wahdiniwaty; Neng Susi Susilawati Sugiana; M Yani Syafei; Yeffry Handoko Putra; Lili Adi Wibowo

    DOI: 10.1109/INCITEST59455.2023.10396871

    Abstract

    This study presents a systematic literature review on the role of Big Data algorithms in supporting sustainable marketing practices. Sustainable marketing practices have garnered increasing attention in an era where environmental concerns and consumer awareness of environmental issues are on the rise. In this context, Big Data algorithms have gained a progressively crucial role in analyzing large datasets to understand consumer behaviors, predict market trends, and design effective marketing campaigns. The literature review is conducted using a systematic approach to identify, collect, and analyze relevant articles pertaining to the utilization of Big Data algorithms in sustainable marketing practices. The primary findings of this literature review encompass the application of Big Data algorithms in sustainable market needs analysis, the utilization of these algorithms to predict environmentally friendly consumer patterns, and their employment in tailoring sustainable marketing campaigns. Through an in-depth analysis of the existing literature, this research identifies the significant contributions of Big Data algorithms in supporting sustainable marketing practices. However, challenges related to data privacy, analysis integrity, and technological complexity are also revealed in the literature. The practical and theoretical implications of these findings illustrate the potential of Big Data algorithms as valuable tools for marketing practitioners seeking to adopt more sustainable approaches. © 2023 IEEE.

    Author keywords

    Algorithms; Big Data; Marketing Practices; Sustainable Marketing

    This article can be accessed at: https://www.scopus.com/pages/publications/85185195922

  • Travel Route Recommendation System Based on 

    Weather Prediction and Geolocation Technology

    Authors: Eko Budi Setiawan; Gradiyanto Putera Husein; Angga Setiyadi

    DOI: 10.1109/INCITEST59455.2023.10395920

    Abstract

    Information regarding weather conditions is quite challenging to obtain and predict. People who in their daily lives have to travel to a destination, such as workers or traveling salesman, cannot predict the weather they will encounter during their journey. The impact is that people often experience some losses when traveling. This research produces an Android-based application used by the general public to avoid travel losses caused by weather. The application provides travel recommendation information in the form of routes obtained from the Google Directions API and weather predictions obtained from the Dark Sky API. This application helps users predict weather predictions on their path to reduce the risk of rain loss. The results of black-box testing and user acceptance found that Android-based applications managed to get a level of 84% in providing travel recommendations, achieving 80.6% success in predicting the weather and obtaining 76.6% in reducing rain risk. © 2023 IEEE.

    Author keywords

    Geolocation; Recommendation; Route; Travel; Weather

    Indexed keywords

    Engineering controlled terms

    Acceptance tests; Android (operating system); Application programming interfaces (API); Black-box testing; Weather forecasting

    Engineering uncontrolled terms

    Condition; Daily lives; Geolocations; Recommendation; Route; Travel; Travel routes; Weather; Weather prediction; Workers’

    Engineering main heading

    Rain

    This article can be accessed at https://www.scopus.com/pages/publications/85185198321

  • Optimizing Bankruptcy Prediction Through Filterbased Feature Selection

    Authors: Nelly Indriani Widiastuti, Ednawati Rainarli, Kania Evita Dewi

    DOI: 10.1109/INCITEST64888.2024.11121476

    Abstract

    Predicting corporate bankruptcy is a critical task in the financial world. Artificial intelligence techniques have become a promising solution for predicting a company’s condition. One of the crucial phases in the bankruptcy prediction process is feature selection. It is the procedure for choosing from a data set the variables that will be most useful in a prediction model. This study examines and evaluates the important feature selection for machine learning-based bankruptcy prediction. A Taiwan Stock Exchange public dataset, which includes 95 financial ratio features, was used in the study. Several filtering feature selection methods, such as Analysis of Variance (ANOVA), Kendall Tau correlation coefficient (KT), and Mutual Information (MI), were used to identify relevant features. The methods of bankruptcy classification applied were Logistic Regression and Random Forest. According to the test results, ANOVA produces the highest recall value for the Taiwan Stock Exchange dataset, both with the Logistic Regression (LR) and Random Forest (RF). Using 61% of the available features or 58 features, the bankruptcy prediction model with Random Forest performed well without compromising accuracy, precision, recall, or F1 score. © 2024 IEEE.

    Author keywords

    ANOVA; bankruptcy prediction; feature selection; filtering methods; financial ratios

    This article can be accessed at https://www.scopus.com/pages/publications/105015829493

  • Optimizing Bankruptcy Prediction Through Filterbased Feature Selection

    Authors: Nelly Indriani Widiastuti, Ednawati Rainarli, Kania Evita Dewi

    DOI: 10.1109/INCITEST64888.2024.11121476

    Abstract

    Predicting corporate bankruptcy is a critical task in the financial world. Artificial intelligence techniques have become a promising solution for predicting a company’s condition. One of the crucial phases in the bankruptcy prediction process is feature selection. It is the procedure for choosing from a data set the variables that will be most useful in a prediction model. This study examines and evaluates the important feature selection for machine learning-based bankruptcy prediction. A Taiwan Stock Exchange public dataset, which includes 95 financial ratio features, was used in the study. Several filtering feature selection methods, such as Analysis of Variance (ANOVA), Kendall Tau correlation coefficient (KT), and Mutual Information (MI), were used to identify relevant features. The methods of bankruptcy classification applied were Logistic Regression and Random Forest. According to the test results, ANOVA produces the highest recall value for the Taiwan Stock Exchange dataset, both with the Logistic Regression (LR) and Random Forest (RF). Using 61% of the available features or 58 features, the bankruptcy prediction model with Random Forest performed well without compromising accuracy, precision, recall, or F1 score. © 2024 IEEE.

    Author keywords

    ANOVA; bankruptcy prediction; feature selection; filtering methods; financial ratios

    This article can be accessed at https://www.scopus.com/pages/publications/105015829493

  • Design of Public Service Administrative Information System

    Authors: Herry Saputra, Nizar Rabbi Radliya, Eddy Soeryanto Soegoto, Fadhil Fadhilah Ilham

    DOI: 10.1109/INCITEST64888.2024.11121522

    Abstract

    The sub-district office is one of the government agencies in charge of carrying out population administration services at the village level. However, in the implementation of these administrative services, there are still obstacles that must be addressed, such as the process of making certificates and filing documents, which are still too complicated and timeconsuming. To help solve these problems, we need an information system website that can help speed up the administrative service process. This study aims at designing an administrative information system called SIAPIK, which is based on the website at the Tagaraja Sub-district office, Riau, Indonesia. Descriptive analysis method with a qualitative approach was used in this study to determine the needs of the system to be built. In the system development process, we used the concept of an object-oriented approach with a prototype development method. The results showed that the development of this information system could provide convenience in supporting the role and function of the sub-district office in carrying out the population service administration process. In conclusion, the existence of this online administration system will help solve various problems at the sub-district office. This information system used an online system that allows residents to make certificate via desktop or smartphone devices regardless of time and place without having to come directly to the sub-district office. © 2024 IEEE.

    Author keywords

    information system; public service administrative; SIAPIK

    This article can be accessed at https://www.scopus.com/pages/publications/105015848206

  • Faculty Of Law Unikom And Bani Bandung Strengthen Their Partnership To Prepare Adaptive Legal Professionals For The Era Of Ai And Digital Transformation

    Faculty Of Law Unikom And Bani Bandung Strengthen Their Partnership To Prepare Adaptive Legal Professionals For The Era Of Ai And Digital Transformation

    BANDUNG, UNIKOM – The Faculty of Law at the Universitas Komputer Indonesia (UNIKOM) continues to strengthen its commitment to providing legal education relevant to technological developments through the signing of a Cooperation Agreement with the Indonesian National Arbitration Board (BANI) Bandung on Tuesday, July 21, 2026. Held in the 4th-floor auditorium of UNIKOM Miracle Building, the event was combined with the 2026 Business Law Public Lecture titled “BANI in the Digital Age: Challenges and Opportunities for the Reconstruction of AI-Based Arbitration Law and Cybertribunals.”

    On this occasion, the cooperation agreement was signed by the Dean of the UNIKOM Faculty of Law, Assoc. Prof. Dr. Hetty Hassanah, S.H., M.H., together with the Vice Chair of BANI Bandung, Dr. H. Asep Rozali, S.H., M.H., MIIArb., CIM., CMED., CPM., C4M., as a form of strategic synergy in the implementation of the Tri Dharma of Higher Education, encompassing education, research, community service, as well as various other forms of academic and professional collaboration.

    As an initial implementation of this collaboration, the UNIKOM Faculty of Law organized a public lecture featuring practitioners and arbitrators from BANI Bandung. The event was attended by all students of the UNIKOM Faculty of Law, as well as student representatives from various study programs within UNIKOM. Through this academic forum, participants gained an understanding of the mechanisms for resolving business disputes through arbitration as an alternative to court-based dispute resolution an approach increasingly adopted by the business community.

    In an interview, the Dean of UNIKOM’s Faculty of Law, Assoc. Prof. Dr. Hetty Hassanah, S.H., M.H., explained that the choice of the public lecture’s theme was driven by the business world’s growing need for dispute resolution that is more effective, efficient, and capable of keeping pace with developments in digital technology.  “Currently, arbitration has become one of the increasingly relevant mechanisms for resolving business disputes, particularly for business practitioners. Nearly every field of study is interconnected with the business world, making an understanding of arbitration essential for students. Through this public lecture, we aim to provide insights into the arbitration process, its legal aspects, and how digital transformation and advancements in Artificial Intelligence are beginning to influence out-of-court dispute resolution,” she said.

    Furthermore, he added that advancements in information technology including the implementation of Artificial Intelligence (AI) at UNIKOM represent a significant milestone in driving the modernization of arbitration practices, which are now moving toward the use of digital systems and the concept of cyber tribunals in dispute resolution processes.

    According to her, this event also has a direct impact on the development of the UNIKOM Faculty of Law’s curriculum. The material presented by BANI practitioners will serve as a reference for updating course materials, the Semester Learning Plan (SLP), and strengthening the Alternative Dispute Resolution Methods course, ensuring that students gain insights aligned with developments in both national and global legal practices.

    In addition, Assoc. Prof. Dr. Hetty stated, “This collaboration not only enriches students’ learning experiences through public lectures and interactive discussions but also provides invaluable input for curriculum refinement. We want UNIKOM Law School graduates to possess competencies that meet the needs of the professional world, particularly in the field of technology-based business dispute resolution,” she added.

    Moving forward, the collaboration between the UNIKOM Faculty of Law and BANI Bandung will be expanded through various strategic programs, including joint research, applications for national and international research grants, and the implementation of community service programs. One area of focus will be raising awareness about arbitration among the public and business operators including the Micro, Small, and Medium Enterprises (MSME) sector to help them understand alternative dispute resolution methods that avoid litigation in court.

    Assoc. Prof. Dr. Hetty also emphasized that the application of AI within the Faculty of Law is intended to support the learning process, not to replace students’ critical thinking skills. “We utilize Artificial Intelligence to assist faculty and students in exploring references, generating ideas, and expediting the search for legal sources. However, AI cannot be relied upon as the sole reference. All information must still undergo verification against applicable laws and regulations and legitimate legal sources. At the Faculty of Law, what we are building is not merely the ability to memorize, but the ability to understand, reason, and critically analyze every legal issue,” she concluded. Through this collaboration, the UNIKOM Faculty of Law hopes to continue providing legal education that adapts to technological advancements, expand its network with professional organizations, and produce graduates who possess academic and professional competencies and are capable of addressing the challenges of dispute resolution in the digital age. (Directorate of Hms & Pro)

  • Analysis and Monitoring System of Train Generator Sets Based on Internet of Things (IoT)

    Authors: Henny; Agus Heri Setya Budi; Arjuni Budi Pantjawati; Mochamad Ilham Alwi Rifa; Iyan Andriana

    DOI: 10.1109/INCITEST59455.2023.10396896

    Abstract

    This research aims to create a reliable and monitoring system for train generator sets based on the Internet of Things (IoT) using the Research and Development (R&D) method. The main objective of this research is to obtain a system that can provide real-time information about power factor changes in the generator set and improve the power factor through reactive power compensation by determining the value of capacitors that should be installed to achieve a target power factor of 0,9. The research stages include designing and developing the system using relevant hardware and software components. The system is designed based on components such as PZEM-004T, CT, LM2596 SIM 800L, and ESP32 microcontroller to collect data from the sensors and send it to the server through the internet. Next, the system stores parameter data in the Firebase database. Then, an algorithm is created to recommend capacitor values to improve the power factor with a target of 0.9. Finally, a website is built using ReactJS to display all parameter data and capacitor recommendations. Testing the PZEM-004T sensor showed an error rate of 0.8% to 1.5% in the generator set parameters. From this research, it can be concluded that the reliability and monitoring system for train generator sets based on the Internet of Things (IoT) have been successfully developed. The system can provide real-time information about the power factor and offer capacitor value recommendations with a target power factor of 0,9. © 2023 IEEE.

    Author keywords

    capacitor; generator sets; internet of things (IoT); power factor

    This article can be accessed at: https://www.scopus.com/pages/publications/85185196384

  • Computational Calculation on the Shell and Tube-Type Heat Exchanger for Lanthanum Oxide (La2O3) Nanoparticle Production Process for Energy-Related Material Application

    Authors: Asep Bayu Dani Nandiyanto ; Irine Sofianty ; Adani Gina Puspita Sari ; Rofi Fadilah Madani ; Fitri Febrianti ; Risti Ragadhita ; Teguh Kurniawan ; Rizky Jumansyah ; Eddy Soeryanto Soegoto ; Senny Luckyardi ; Muhammad Aziz

    DOI: 10.18280/mmep.100243

    Abstract

    This paper introduced a design of a heat exchanger to get an optimum heat transfer enhancement technique that can be used for the production of lanthanum oxide (La2O3). The shell and tube type was selected since this type is one of the effective types for making excellent heat transfer, which was then compared to the Tubular Exchanger Manufacturers Association (TEMA) standard to obtain the dimensional specifications of the heat exchanger device. Several parameters were calculated to evaluate the performance of the designed heat exchanger. Numerical calculation obtained from the heat exchanger containing 138 tubes can be used to maintain the temperature in the reactor (prospective temperature can change from 40 to 60°C with rapid heating) using heating liquid (controlling by transferring heat from 100 to 70°C) with the effective value of 96%. This study can be used as a reference for supporting information in the current issue of the need for the large production of La2O3 particles © 2023, Mathematical Modelling of Engineering Problems.All Rights Reserved.

    Author keywords

    heat exchanger; La2O3; nanoparticles; shell and tube

  • Integration of Failure Mode and Effect Analysis with Fuzzy Analytical Hierarchy Process for Risk Prioritization in Machining Processes

    Authors: Gabriel Sianturi, Agus Riyanto, M. Yani Syafei, Julian Robecca, Alam Santosa, Adi Lukman Nurhakim

    DOI: 10.1109/INCITEST64888.2024.11121517

    Abstract

    This paper develops a Failure Mode and Effect Analysis (FMEA) method integrated with the Fuzzy Analytical Hierarchy (FAHP) method to prioritize the risk in the autoclave bolt machining processes. FMEA is applied to identify and evaluate the failure modes, while FAHP is utilized to determine the weights of risk factors so that the weights are not taken the same as in the traditional FMEA approach, but it is based on the decision maker’s judgment. The incorporation of fuzzy set theory into FAHP can overcome the problems of uncertainty and vagueness faced by decision makers during the evaluation process. The result of the research shows that the severity has the highest weight, followed by detection and occurrence. In addition, the diameter is not accurate in the failure mode with the highest Risk Priority Number (RPN) among all failure modes. © 2024 IEEE.

    Author keywords

    FMEA; Fuzzy AHP; machining process

    This article can be accessed at https://www.scopus.com/pages/publications/105015745069