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 292 Documents
 26   27   28   29   30 
Application Of Double Exponential Smoothing Holt’s Method For Poverty Line Forecasting (Study Case: East Kalimantan Province) Ariantika Putri Maharani; Akhmad Irsyad; Muhammad Rivani Ibrahim
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4349

Abstract

Poverty is a multidimensional problem that remains a challenge in Indonesia. The poverty line is used as an indicator to determine whether someone is poor based on the average expenditure per capita per month. In East Kalimantan Province, the poverty line has increased from Rp796,193 in 2023 to Rp853,997 in 2024. This study aims to forecast the poverty line for the next ten periods using Holt's Double Exponential Smoothing method. This method was chosen because the historical data shows an increasing trend from 2011 to 2024. The forecasting results show that this method is effective with a Mean Absolute Percentage Error (MAPE) value of 4.56%, and optimal parameters α = 0.98 and β = 0.01. The findings are expected to serve as a reference in decision-making regarding poverty alleviation policies in the future.
CUSTOMER SERVICE CHAT APPLICATION DESIGN RADEN INTEN II AIRPORT, LAMPUNG Kesuma, Mezan El-Khaeri; Gunawan, Indra; Ridho Setiawan, M. Alif
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4357

Abstract

This study aims to design a web-based customer service application with live chat and chatbot features that implement Retrieval-Augmented Generation (RAG) technology at Raden Inten II Airport, Lampung. The main problems encountered are the slow response of conventional services and limited access to information by users located far from the airport. The system development uses the Waterfall model which includes requirements analysis, system design, implementation, testing, and maintenance. The main features in the application include user authentication, live chat, RAG chatbot, and data management through the admin dashboard. System testing was conducted using the black-box method, indicating that all features function according to specifications. The results of the study indicate that this system can significantly improve the efficiency and quality of customer service. For further development, it is recommended to use WebSocket to improve real-time communication performance. Index Terms— Customer Service; Live Chat; Chatbot; RAG; Web-based Application.
SALES PREDICTION AT PT. WORLD INFINITE NETWORK USING NAÏVE BAYES AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM METHODS sundari, jenie; Irman, Aden
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4472

Abstract

In the process of analyzing sales transaction data at PT. World Infinite Network, existing information has not yet optimized the sales of offered products. The purpose of this optimization is to obtain purchasing patterns of frequently bought items by customers. Every year, IT products are increasingly needed, even showing growing demand. One data processing technique that can help is data mining. Based on this informational relationship, decisions can be made through processes such as description, estimation, prediction, classification, clustering, and association. Previous studies indicate that the Apriori method is more intuitive and interpretable, while the Naïve Bayes method provides fast, simple, and precise computation, making it one of the most widely used techniques in classification tasks. This study employs both Adaptive neuro fuzzy inference system and Naïve Bayes algorithms to analyze sales data and predict trends. The results show that the Naïve Bayes Algorithm achieved an accuracy of 19.05%, demonstrating its potential application in supporting strategic sales predictions for PT. World Infinite Network.
Application of the Dempster-Shafer Method in Developing a Web-Based Expert System for Diagnosing Dental and Oral Diseases Kelen, Yoseph Pius Kurniawan
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4478

Abstract

Abstract— An expert system is a problem-solving system based on incorporating human expertise into computational models that emulate the analytical approach of experts. Expert systems make it easier for individuals to solve complex problems, even without direct consultation with domain experts. This study aims to develop an expert system that diagnoses dental and oral diseases using the Dempster-Shafer method, which calculates probable diagnoses based on the most prevalent symptoms experienced. The implementation utilizes a web-based framework with PHP and a MySQL database. The expected outcomes include efficient consultations and accurate diagnostic results, providing suitable treatments based on the Dempster-Shafer algorithm.. Index Terms— Expert System; Dempster-Shafer; Dental Disease; Oral Disease
Application of the ANFIS Model in Predicting Diabetes Mellitus Disease Nurfazila, Aprilia; Rohayani, Hetty
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4479

Abstract

This study presents the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predicting Diabetes Mellitus using two primary input features, namely glucose level and body mass index (BMI). The research employs a quantitative experimental approach using the public diabetes dataset obtained from Kaggle. The data underwent preprocessing steps, including cleaning, normalization, and splitting into training and testing subsets. The ANFIS model was designed with fuzzification, rule-based inference, and a hybrid learning algorithm to optimize membership function parameters. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results show that the ANFIS model achieved an accuracy of 69.70% on the test dataset, demonstrating strong sensitivity in detecting diabetic cases but generating a notable number of false positives. These findings indicate that ANFIS has potential as an early-screening decision support tool, although further optimization and additional features are required to enhance predictive performance.
An Explainable Hybrid Machine Learning Framework for Financial and Tax Fraud Analytics in Emerging Economies Mupenzi, Julien
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4483

Abstract

Financial and tax fraud remains a major challenge in emerging economies where digital transformation outpaces regulatory oversight. This study presents an explainable hybrid machine learning framework designed to enhance fraud analytics and tax governance in Indonesia. The model integrates unsupervised anomaly detection (Isolation Forest, DBSCAN) and supervised learning (Random Forest, Logistic Regression) to identify irregularities in financial transactions. Model explainability is achieved through SHAP (SHapley Additive Explanations), enabling transparency in high-risk classifications. The proposed Streamlit-based dashboard supports real-time data visualization and interactive model evaluation by policymakers. Experimental results demonstrate a 99% overall accuracy with strong interpretability, underscoring the framework’s value in bridging machine learning and public sector decision-making. The findings contribute to the growing field of explainable AI for digital governance, offering a scalable and ethical solution to fraud detection in developing economies.
Multimodal Wearable-Based Stress Detection Using Machine Learning: A Systematic Review of Validation Protocols and Generalization Gaps (2021 – 2025) Pannavira; Hermawan, Aditiya
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4488

Abstract

Stress is a major determinant of mental health and productivity. Consequently, continuous, unobtrusive stress detection using wearable sensors and machine learning (ML) has become a key priority in digital health. This paper presents a Systematic Literature Review (SLR) of 19 peer-reviewed articles, selected from 36 initial papers via structured inclusion/exclusion criteria focusing on studies from 2021-2025 that report quantitative ML performance. We employed a quantitative and qualitative synthesis to analyze and map five key dimensions: sensing modalities, ML/DL algorithms, datasets, validation protocols, and societal feasibility. Findings reveal a clear state-of-the-art: multimodal physiological fusion (notably PPG, EDA, and ACC) paired with hybrid deep models (CNN-LSTM) consistently achieves the highest accuracy (85–96%) on benchmark datasets. Our research reveals a significant lab-to-field gap. Most studies utilize intra-subject or k-fold cross-validation, whereas the more robust Leave-One-Subject-Out (LOSO) validation is hardly employed, constraining model applicability. Furthermore, fewer than 15% of studies explicitly address vital practical constraints such as privacy, computational efficiency (Edge AI), or power consumption. This review methodically quantifies the gap, emphasizing that current models, despite their accuracy, are not yet suitable for real-world implementation. We conclude with actionable directions toward generalizable, lightweight, and privacy-aware stress-aware systems.
Comparative Modeling of Naïve Bayes and LSTM with Monte Carlo Forecasting for Silver Prices Muhammad Azmi Alauddin; Firda Fadri; Muhammad Amjad Munjid
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4493

Abstract

Volatilitas harga perak dalam beberapa tahun terakhir, terutama sejak 2023, yang didorong oleh lonjakan permintaan di sektor energi terbarukan, telah meningkatkan kompleksitas prediksi menggunakan pendekatan konvensional. Studi ini menguji dua pendekatan yang berbeda secara filosofis: Naïve Bayes (NB), yang mengandalkan asumsi independensi fitur, dan Long Short-Term Memory (LSTM), yang secara eksplisit dirancang untuk menangkap dependensi temporal. Menggunakan data harga perak harian (USD/troy ons) dari Investing.com untuk periode Januari 1989–Oktober 2025, NB diimplementasikan dengan tiga fitur lag (t−1 hingga t−3), sementara LSTM menggunakan arsitektur dua lapis (50 unit), 0,2 dropout, dan jendela sekuensial 60 hari. Hasil menunjukkan bahwa LSTM menghasilkan prediksi yang lebih responsif terhadap titik balik, meskipun RMSE-nya (1,0222) sedikit lebih tinggi daripada NB (0,9888). Fenomena ini sebenarnya mencerminkan sensitivitas LSTM terhadap volatilitas ekstrem di akhir tahun 2025 (>USD 53/oz), bukan kegagalan model. Sebaliknya, NB cenderung terlalu halus (over-smooth), sehingga mengakibatkan deviasi sistematis ketika tren berbalik. Dengan MAE 0,7002 (vs. 0,7354) dan stabilitas pola prediksi yang lebih realistis, LSTM direkomendasikan sebagai kerangka kerja utama, terutama jika dikombinasikan dengan estimasi ketidakpastian melalui simulasi Monte Carlo.
Hybrid V-Net And Swin Transformer Deep Learning Model For Brain Tumor Segmentation in Low-Quality MRI Scan Hermawati, Fajar Astuti; Pramudya, Andre
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4494

Abstract

Brain tumor segmentation from low-quality magnetic resonance imaging (MRI) remains a challenging task due to noise, resolution variation, and low contrast between tumor and healthy tissues. Improving segmentation accuracy is essential to support more precise diagnosis and treatment planning. This study proposes a hybrid deep learning model that integrates V-Net and Swin Transformer architectures for automatic brain tumor segmentation in multimodal MRI images. The MICCAI BraTS 2020 dataset was used, consisting of T1, T1c, T2, and FLAIR sequences with corresponding segmentation labels. The preprocessing pipeline includes resampling, skull stripping, intensity normalization, and data augmentation. V-Net is employed to extract local spatial features from 3D volumetric data, while the Swin Transformer captures global spatial relationships through a self-attention mechanism. Postprocessing procedures such as thresholding, morphological refinement, and false-positive removal are applied to enhance segmentation quality. The proposed hybrid model achieves Dice scores of 0.8635 for Whole Tumor (WT), 0.7179 for Tumor Core (TC), and 0.8073 for Enhancing Tumor (ET), with additional evaluation using precision, recall, and IoU further confirming its effectiveness. These results highlight the model’s potential to improve automated brain tumor segmentation in low-quality MRI images and support its applicability as an efficient AI-assisted diagnostic tool in clinical practice.
Design and Evaluation of an AI-Driven Gamified Intelligent Tutoring System for Fundamental Programming Using the Octalysis Framework Dzaky Fatur Rahman; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4514

Abstract

This research aims to address the challenges of student motivation and engagement in fundamental programming education by implementing the Octalysis Gamification Framework within an Intelligent Learning System. Traditional learning methods often fail to visualize abstract concepts or provide personalized feedback, leading to student demotivation. To overcome this, a platform named "Starcoder" was designed and built, integrating two conceptual pillars: the eight core drives of the Octalysis Framework and an AI-supported Intelligent Tutoring System (ITS). The system employs the Next.js framework and integrates the Gemini AI API (M.E.C.H.A.) to provide real-time, adaptive feedback and remedial learning paths. The effectiveness of the platform was evaluated using the Hedonic-Motivation System Adoption Model (HMSAM) with 54 respondents, comparing the gamified platform against traditional classroom methods. Evaluation results demonstrate that the platform significantly outperforms traditional methods, achieving an 86.44% score in Perceived Usefulness and an 85.56% score in Curiosity. Notably, Behavioral Intention to Use increased by 15.56% compared to the baseline. These findings demonstrate that the comprehensive integration of gamification frameworks with generative AI agents effectively enhances student motivation and immersion in technical education. Future work should focus on expanding the AI's capability to dynamically adjust gamification elements in real-time based on student performance.

Page 29 of 30 | Total Record : 292

 26   27   28   29   30 

Filter by Year

2011 2025


Reset
Filter By Issues
All Issue 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