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Edo Yonatan Koentjoro
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edo@dinamika.ac.id
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+6281252457234
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joti@dinamika.ac.id
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Jalan Raya Kedung Baruk No. 98, Surabaya 60298
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INDONESIA
Journal of Technology and Informatics (JoTI)
Published by Universitas Dinamika
ISSN : 27214842     EISSN : 26866102     DOI : https://doi.org/10.37802/joti
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering
1. Teknologi Informasi : Rekayasaperangkat lunak, Pengetahuan data maining, Mobile Computing, Parallel/Distributed Computing, Kecerdasan Buatan, Tata Kelola dan Manajemen Sistem Informasi, User Interface/ User Experience, Process Management, IT Security, IS Adoption and Evaluation. 2. Sistem Komunikasi : Jaringan Protokol dan Manajemen, Sistem Telekomunikasi, Komunikasi Nirkabel, Jaringan Sensor.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 102 Documents
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Implementation of The Topsis Algorithm In A Car Purchase Decision-Making System Viki Julian Avinda Nur Ependi; Dedi Gunawan
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1272

Abstract

Private vehicles such as cars and motorcycles are crucial modes of transportation for the movement of goods and people. With technological advancements, car manufacturers offer a wide range of vehicles. Therefore, prospective buyers face challenges in selecting a vehicle that best suits their preferences and criteria. To tackle the issue, this study develops a practical decision support system (DSS) as a user-friendly tool for buyers, with theoretical contributions in the form of a more adaptive TOPSIS application and systematic analysis in car selection. This study focuses on collecting car-related data using 12 criteria, such as price, fuel consumption, safety, and design. The TOPSIS method is then normalized to ensure a fair and objective comparison between criteria. The results show the top alternative ranking, Suzuki 2002 (closeness score of 0.7089 in position 1), and the SUS test result of 85.6, indicating that the system is easy to use and capable of providing recommendations that align with user preferences. Therefore, this study highlights that the TOPSIS method can be an effective tool in supporting car purchase decision-making and making it easier for prospective buyers to choose the car that best suits their needs.
Semantic Knowledge Fusion in Healthcare: A Hybrid Approach for Connected Medicine Muhala Luhepa, Blaise; Bukasa Kakamba, John; Munduku Munduku, Deo; Mazono Magubu, Daniel; Ntumba Nkongolo, Albert; Matondo Mananga, Herman; Munene Asidi, Djonive
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1182

Abstract

In a context where connected medicine requires increasingly explainable, accurate, and responsive systems, this paper presents an applied experimental research focusing on the development and evaluation of a hybrid intelligent assistant for healthcare data fusion. The study is based on the parallel combination of two data paradigms: classical tabular structures and their ontological equivalent. Using an intelligent assistant, we simultaneously query a medical dataset on diabetes in tabular form and the same dataset translated into an OWL ontology that can be queried using SPARQL. The aim is to demonstrate that the synchronised combination of these two models not only provides a more complete response but also one that is better contextualised and clinically exploitable. The research follows an experimental methodology, involving the implementation, testing, and comparative evaluation of both models on 300 questions classified by increasing complexity (simple, complex, and very complex). The results reveal a relevance rate above 99%, with an average response time suited to medical use. This work highlights the potential of hybrid architectures in connected health and paves the way for new decision-making assistants that fully exploit the semantic richness of medical knowledge.

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