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All Journal Proceedings of KNASTIK The IJICS (International Journal of Informatics and Computer Science) Jurnal Informatika JTIM : Jurnal Teknologi Informasi dan Multimedia
Ryan Peterzon Hadjon
Citra Bangsa University, Kupang City
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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DESIGNING E-CRM SYSTEM FOR BANKING INDUSTRY BASED ON WEB 2.0 TECHNOLOGY: A PROPOSAL Hadjon, Ryan Peterzon; Wisnubhadra, Irya; Julianto, Eddy
Proceedings of KNASTIK 2013
Publisher : Duta Wacana Christian University

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Abstract

Use of Customer Relationship Management (CRM) as a way to support marketing strategy is an important key to increase company’s profitability. Integration of classic CRM concepts and technology becomes new paradigm for success implementation of CRM. One way to accomplished this is by using web 2.0 technology. The innovation of web 2.0 which is based on user-oriented approach is a fit combination for CRM which is based on customer-oriented approach. This research was aimed to combine those two concepts for analyzing and designing an E-CRM system to help improved business marketing strategy in banking industry.
IMPLEMENTASI METODE REST REQUEST PADA YOUTUBE WEB SERVICE UNTUK REPRESENTASI INFORMASI BERBASIS TIMELINE Ryan Peterzon Hadjon; Restyandito Restyandito; Willy Sudiarto Rahardjo
Jurnal Informatika Vol 10, No 1 (2014): Jurnal Teknologi Komputer dan Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (6726.168 KB) | DOI: 10.21460/inf.2014.101.321

Abstract

Developing video search application using the service of a third party data (resources) provider is a complex process. Therefore to obtain the desired data, an efficient communication method is needed. This research explored the use of REST request communication method by using URL to identify video resources through YouTube API data. Video data that were identified will then be processed to get more relevant video search. Relevancy of the search result was determined by applying the mechanism of relevance-based system and user-based system. Based on the experiment analysis, it is concluded that key word has an important role in determining the relevance of the search result, as it is needed for timeline based representation.
Teachable Machine: Real-Time Attendance of Students Based on Open Source System Edwin Ariesto Umbu Malahina; Ryan Peterzon Hadjon; Franki Yusuf Bisilisin
The IJICS (International Journal of Informatics and Computer Science) Vol 6, No 3 (2022): November 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v6i3.4928

Abstract

The utilization of open source-based services will be very useful, simplifying and accelerating the process of object recognition and complex computational processes, one of them uses the Teachable Machine service. Identification of student faces in real-time attendance is a case study that will be applied to students to recognize and identify accurately and clearly the presence of students during online / offline lectures, by applying Teachable Machine services that have good algorithms with a machine learning approach that utilizes the Tensorflow.js library where the training data testing uses Convolutional Neural Network (CNN). Of the objects identified, the average accuracy of all classes ranged from 91-100%, with the number of samples for each object class being 23 objects or more. Number of sample images in one class. Clothing, object background and lighting intensity around the image object are also very influential in determining the accuracy value of student face recognition later, so that the use of the tensorflow.js library that implements Convolutional Neural Network (CNN) will be very helpful in facial recognition and influencing factors so that the data entered later needs to be further corrected and improved again, so that the results obtained in implementing the online attendance system have been very helpful in detecting student faces with an average accuracy rate of 91.8%
Optimalisasi Potensi Wisata NTT dari Perspektif Google Trends dan Big Data Analytics Tantrisna, Ellen; Hadjon, Ryan Peterzon
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.670

Abstract

This study aims to identify and analyze tourism trends in East Nusa Tenggara (NTT) using the K-Means clustering method integrated with Google Trends and Big Data Analytics. By utilizing data that includes the number of tourist attractions, hotel accommodations, tourist visits (Domestic and foreign), and restaurant accomodation, the NTT region is categorized into several clusters based on tourism characteristics. The analysis results reveal three main clusters: areas with low tourist attractions and accommodations, areas with very high tourist attractions, and areas with good accommodation facilities but moderate attractions. These findings provide crucial insights for policymakers and tourism industry stakeholders to formulate more effective development strategies, such as infrastructure enhancement in high-potential areas and targeted promotion for niche markets. Additionally, the analysis results indicate significant fluctuations in tourist interest towards NTT, with peak searches occurring in April and September. This research utilizes data from Google Trends and other sources to analyze trends and tourist attractions in NTT tourism, thereby aiding in the development of more effective promotional strategies. Overall, this study contributes to a deeper understanding of tourism dynamics in NTT and the necessary optimization steps to enhance the competitiveness of these destinations. With this data-driven approach, it is hoped that the tourism sector in NTT can develop sustainably and provide economic benefits to local communities.
Co-Authors Eddy Julianto Edwin Ariesto Umbu Malahina Franki Yusuf Bisilisin Irya Wisnubhadra, Irya Restyandito Tantrisna, Ellen Willy Sudiarto Rahardjo