cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ni Made Satvika Iswari
Contact Email
satvika@umn.ac.id
Phone
-
Journal Mail Official
ultimatics@umn.ac.id
Editorial Address
-
Location
Kota tangerang,
Banten
INDONESIA
Jurnal ULTIMATICS
Published by Universitas Multimedia Nusantara
ISSN : 20854552     EISSN : 2581186X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Jurnal ULTIMATICS merupakan Jurnal Program Studi Teknik Informatika Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang analisis dan desain sistem, programming, algoritma, rekayasa perangkat lunak, serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, kecerdasan buatan, pemrograman sistem mobile, serta topik lainnya di bidang Teknik Informatika. Jurnal ULTIMATICS terbit secara berkala dua kali dalam setahun (Juni dan Desember) dan dikelola oleh Program Studi Teknik Informatika Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 304 Documents
 27   28   29   30   31 
An Integrating GIS, IoT, and AI for Climate-Resilient Urban Forestry: A Smart Framework for Tree Planting Strategies. Julien Mupenzi
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4470

Abstract

Tree planting is a proven strategy for climate resilience, contributing to carbon sequestration, air quality improvement, and urban heat mitigation. However, conventional approaches often lack precision and scalability. This study proposes a novel integrated framework that combines Geographic Information Systems (GIS), Internet of Things (IoT) sensors, remote sensing (Sentinel-1 and Sentinel-2), and artificial intelligence (AI) to optimize urban forestry. Unlike previous research that treats these technologies separately, our approach fuses multi-source spatial data, real-time IoT monitoring, and AI-driven predictive analytics for evidence-based decision-making. Pilot projects in Sukabumi and Bogor (Indonesia) validate the framework: tree survival rates improved by 20%, urban heat islands were reduced by up to 2°C, and maintenance costs decreased by 10%. These findings demonstrate that geospatial-IoT-AI integration not only strengthens environmental resilience but also delivers measurable economic and social benefits for sustainable urban ecosystems.
Android-Based Chili Leaf Disease Detection System Using Deep Learning For Harvest Loss Mitigation wawang adi darma; Tia Ernawati; Ridwan; Ariya Fawaz
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4492

Abstract

Bird's eye chili productivity in Indonesia faces persistent decline due to leaf disease infections. Conventional visual inspection methods by farmers show limited accuracy (65-70%) and high subjectivity, causing delayed identification and yield losses. This study develops an Android-based detection system optimized for low-end mobile devices using deep learning with MobileNetV3-Large architecture enhanced through transfer learning. The dataset contains 5,000 annotated chili leaf images across healthy leaves and three disease types (anthracnose, bacterial spot, mosaic virus). Implementation includes quantization-aware training and TensorFlow Lite conversion for mobile optimization. Model evaluation uses 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The model achieved 94.34% classification accuracy with 94.5% precision. Quantization reduced model size by 92.2% (75.6 MB to 5.9 MB) with only 0.3% accuracy loss. The Android application operates in real-time on 3GB RAM devices with inference latencies below 100ms. This system provides an effective solution combining high accuracy with computational efficiency for early chili leaf disease detection, supporting sustainable farming in Indonesia. Index Terms---Deep learning; Mobile application; Plant disease detection; Chili leaf;
Implementation of ECDSA and MLDSA Digital Signature Schemes for Transaction Authentication on Blockchain Ridwan Muhammad Raihan; Alam Rahmatulloh
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4516

Abstract

Blockchain technology serves as a core foundation for decentralized systems that demand high levels of security, transparency, and data integrity. However, the advancement of quantum computing introduces substantial risks to classical cryptographic algorithms such as the Elliptic Curve Digital Signature Algorithm (ECDSA), which is vulnerable to attacks targeting the discrete logarithm problem. This study proposes a hybrid digital signature scheme that integrates ECDSA with the Module Lattice-based Digital Signature Algorithm (MLDSA) to strengthen blockchain transaction authentication against both classical and quantum threats. Experimental results shown that the hybrid scheme achieves efficient performance, with an average verification time of 0.647 ms and a signing time of 0.806 ms. Although the resulting signature size reaches 6761 bytes due to the concatenation of two signatures, the hybrid approach successfully provides dual-layered cryptographic protection capable of maintaining transaction authenticity and integrity in a post-quantum environment. These findings highlight the feasibility of adopting hybrid digital signatures in blockchain systems. Future work may focus on optimizing signature size through compression techniques to improve scalability and reduce payload overhead.
Automated Vader Lexicon Labeling on Sentiment Analysis Against The E-Wallet Febri Liantoni; Muhammad Hardiansyah; Elham Syahrian Putra; Erna Piantari
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4536

Abstract

In Indonesia, e-wallets have gained immense popularity due to their user-friendly interfaces and attractive features. Despite their convenience, user opinions about e-wallet applications remain polarized, with sentiments ranging from highly positive to critically negative. This study seeks to analyze these diverse user sentiments by leveraging the VADER Lexicon model, a powerful tool for sentiment analysis. The Naïve Bayes Classifier, a well-established probabilistic model renowned for its efficacy in text-based classification tasks, is employed to categorize user reviews. The sentiment analysis yielded promising results, with the model achieving an impressive accuracy rate of 92.02%. Additionally, the precision of the model, indicating the ratio of correctly predicted positive sentiments to all predicted positive sentiments, stood at 83.23%. The recall, representing the ratio of correctly predicted positive sentiments to all actual positive sentiments, was recorded at 86.80%. These metrics underscore the model's robustness in accurately classifying user sentiments. The insights gained from this analysis provide a deeper understanding of user perspectives, aiding in the evaluation and enhancement of e-wallet applications.

Page 31 of 31 | Total Record : 304

 27   28   29   30   31 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika Vol 17 No 1 (2025): Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika Vol 15 No 1 (2023): Ultimatics : Jurnal Teknik Informatika Vol 14 No 2 (2022): Ultimatics : Jurnal Teknik Informatika Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika Vol 11 No 2 (2019): Ultimatics : Jurnal Teknik Informatika Vol 11 No 1 (2019): Ultimatics : Jurnal Teknik Informatika Vol 10 No 2 (2018): Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika Vol 9 No 2 (2017): Ultimatics : Jurnal Teknik Informatika Vol 9 No 1 (2017): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 8 No 2 (2016): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 8 No 1 (2016): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 7 No 2 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 7 No 1 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 6 No 2 (2014): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 6 No 1 (2014): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 5 No 2 (2013): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 5 No 1 (2013): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 4 No 2 (2012): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 4 No 1 (2012): Ultimatics: Jurnal Ilmu Teknik Informatika Vol 3 No 2 (2011): Ultimatics: Jurnal Ilmu Teknik Informatika More Issue