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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
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Integration of Internet of Things Technology in Digital-Based Residential Security Application Rajib Ghaniy; Binanda Wicaksana; Fahmi Arnes; Laras Melati; Helena Septiana
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.v17i2.4518

Abstract

This study evaluates the usability of an IoT-based residential security application designed to improve guest registration and access control in housing environments. The system integrates multiple user roles, including administrators, residents, guests, and security officers, and utilizes QR Code verification to streamline entry procedures. Usability testing was conducted using the System Usability Scale (SUS) with 30 respondents. The results show an average SUS score of 74.83, indicating that the application falls within the “Good” usability category. Most users reported that the interface is intuitive, the functions are well integrated, and system navigation is easy to understand. Although minor improvements are still required—such as notification speed and icon clarity—the system is considered acceptable for public use. These findings demonstrate that IoT integration can enhance residential security operations while maintaining positive user experience.
Multiclass Emotion Detection on YouTube Comments Using IndoBERT: A Web-Based Incremental Learning System with Multiple Data Split Evaluation Naufal; Nurirwan Saputra
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.v17i2.4558

Abstract

YouTube comments contain rich emotional expressions, but their large volume makes manual analysis inefficient. This study proposes a multiclass emotion classification approach for Indonesian YouTube comments using the IndoBERT model integrated with a database-driven incremental learning system. Comment data were collected through the YouTube Data API and labeled into six emotion categories: anger, sadness, happiness, fear, surprise, and neutral. Text preprocessing included lowercasing, text cleaning, and normalization of informal Indonesian words. The model was fine-tuned using three training–testing split scenarios (60:40, 70:30, and 80:20). The results show that the 80:20 split achieved the highest accuracy of 68%, influenced by an imbalanced class distribution with underrepresented minority classes. In addition, the system supports continuous data storage and incremental retraining, allowing the model to learn from new data without retraining from scratch. This adaptive mechanism makes the proposed system suitable for long-term emotion analysis on YouTube comments.
Sentiment Analysis of User Satisfaction with Access by KAI Application Using Support Vector Machine and Random Forest Algorithms Kurnia Gusti Ayu
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.v18i1.3878

Abstract

PT Kereta Api Indonesia (Persero), a State-Owned Enterprise (SOE) in the railway sector, holds an important responsibility in providing efficient services. To enhance their services, they introduced the application "Access by KAI.” Although this application represents a technological innovation, it often falls short of users' expectations due to existing shortcomings. Therefore, this study proposes the use of sentiment analysis to gauge users' perspectives on the "Access by KAI” application. By applying Support Vector Machine (SVM) and Random Forest algorithms to user review datasets, this study classifies user sentiments into three categories: positive, neutral and negative, and compares the performance of these two algorithms. The results of this study offer valuable insights into users' attitudes towards the application, which can assist PT Kereta Api Indonesia in improving service quality. The classification results using the Random Forest and Support Vector Machine (SVM) algorithms, based on the factors used in this study, show varying performance. Random Forest achieved an accuracy of 86% for an 80:20 and 90:10 data ratios, and 85% for a 70:30 ratio, while SVM achieved 83% accuracy across different data ratios
Speech Emotion Recognition through Acoustic Data Augmentation and Attention-Driven CRNN-BiGRU Fitra Kacamarga; Kresna Andika Aprianto
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.v18i1.4250

Abstract

Speech emotion recognition (SER) systems have transformed human-computer interactions by enabling machines to identify emotional cues in speech. This study presents a comprehensive approach that combines robust data augmentation techniques with an advanced neural architecture to address these limitations. The proposed methodology employs four key data augmentation strategies to enhance model generalization and prevent overfitting: background noise injection, time stretching (both up and down), and pitch shifting. This augmented dataset is fed into a novel Convolutional Recurrent Neural Network (CRNN) architecture integrated with a Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism, designed to capture both local and temporal emotional features effectively. The model processes input through log-Mel spectrograms, enabling precise detection of emotional speech patterns. Experimental validation on the RAVDESS database demonstrated the superiority of this combined approach, achieving state-of-the-art performance with a weighted accuracy (WA) of 90.53% and an unweighted accuracy (UA) of 90.19%—representing an 11% improvement over CNN with Multi-Head method. These results validated the effectiveness of integrating data augmentation with advanced neural architectures for SER applications.
Evaluating the Capability of VGG16 Trained on Kaggle Dataset for Detecting Tomato Diseases in Indonesian Tegar Satriya Wiguna; Febri Liantoni; Yudianto Sujana
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.v18i1.4252

Abstract

Tomato leaf diseases pose a significant threat to agricultural productivity in Indonesia, often leading to severe yield losses. This study evaluates the effectiveness of the VGG16 convolutional neural network in detecting tomato diseases, particularly when trained on the standardized PlantVillage dataset and applied to local agricultural conditions in Sragen, Central Java. The research involved data preprocessing using background removal and resizing techniques, model training via transfer learning, and deployment through a FastAPI backend and React Native frontend. The VGG16 model achieved high accuracy 82% on the PlantVillage test set but exhibited a sharp decline 25% accuracy when tested on locally sourced images, highlighting limited generalization capabilities. These findings emphasize the necessity of incorporating local datasets and domain adaptation strategies to develop AI-based plant disease detection tools that are effective in real-world settings. The study underscores the importance of contextualizing AI solutions for local agricultural environments to ensure their practical applicability and reliability.
Web-Based Information System for Monitoring Soil Conditions (Moisture and Temperature) Integrated with IoT Ahmad Farzi Anwar; M. Husaini; Khairun Nita Aulia
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.v18i1.4356

Abstract

Indonesia's agricultural sector faces challenges due to climate change and limited access to accurate, real-time information on soil conditions. Traditional methods relying on visual observation are inefficient and prone to error, which can reduce crop productivity. This research aims to develop a web-based information system integrated with the Internet of Things (IoT) for real-time monitoring of soil moisture and temperature. The system was developed using a Research and Development (R&D) approach with prototyping methodology. The hardware components include an ESP32 microcontroller, a capacitive soil moisture sensor v1.2, and a DS18B20 digital temperature sensor. Sensor data is transmitted to Firebase Realtime Database and displayed via a web interface built with HTML, CSS, and JavaScript. Field trials conducted in Talang Padang village (OKU Selatan) over 7 days demonstrated the system's ability to provide accurate, real-time monitoring. The system includes real-time gauge visualizations, historical graphs, notification features, and exportable sensor logs. Positive responses from farmers indicate the system's potential to support better agricultural decision-making. However, internet dependency and the need for sensor recalibration remain as limitations.
Fuzzy Expert System for Early Heart Disease Diagnosis Using Mamdani Method in Web-Based System ANANDA DWI RIZKYTA; Muhamad Bahrul Ulum; Fenina Adline Twince Tobing
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.v18i1.4367

Abstract

Heart disease is one of the leading causes of death and is known as a silent killer because it often does not show clear symptoms in the early stages. Limited medical personnel and access to health services are obstacles to early detection. This study proposes the development of a web-based expert system that utilizes the Fuzzy Mamdani method to perform rapid and accurate early diagnosis of heart disease. The system is designed using variables such as Body Mass Index (BMI), blood pressure, medical history, smoking habits, psychological aspects, and common symptoms that have been validated by medical professionals. The Fuzzy Mamdani method was chosen for its ability to handle data uncertainty and produce decisions that resemble human reasoning. Development was carried out using the Extreme Programming method, which includes the stages of planning, design, coding, and testing. Testing results show that the system can provide accurate risk estimates and is easily accessible to the public via computers or mobile devices. This system serves as an early detection tool to raise awareness of heart health and encourage further medical examinations, not as a replacement for the role of a doctor.
The Implementation Of Agile Methods In Designing A Web-Based Information System In The Geomin Laboratory Of Pt Antam Tbk Dennis Bramastha; Waeisul Bismi
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.v18i1.4426

Abstract

The management of human resource administration at the Geomin Laboratory Unit of PT Antam Tbk, which includes recording attendance, overtime requests, and leave requests, faces various obstacles due to the continued implementation of manual procedures. This process, which relies on physical forms, is not only time-consuming in recapitulation, but is also prone to recording errors and document loss, as well as the lack of accurate quota monitoring. This study aims to design and build a web-based information system that can overcome these problems by integrating all administrative processes into a single digital platform. The development method used in this study is the Agile method, which allows for a flexible and iterative development process. This system is built using the Laravel framework and utilizes geolocation technology for attendance validation. The result of this study is a functional information system with key features such as dare attendance, a multi-level approval flow, and real-time leave quota management. Based on the results of the User Acceptance Test involving 15 respondents from various roles, this system achieved an acceptance rate of 93.1%, which falls into the “Highly Acceptable” category.
Machine Learning for Chili Pepper Price Forecasting Using Exogenous Public-Attention Signals and Bayesian Hyperparameter Optimization Wresti Andriani; Gunawan Gunawan; Naella Nabila Putri Wahyuning Naja
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.v18i1.4439

Abstract

Chili prices in Indonesia are highly volatile due to seasonal production, fragile supply chains, and shocks in public perception. This study improves short-run forecast accuracy by adding public-attention signals (Google Trends and news volume) as exogenous features summarized in a Shock Index. Evaluation metrics are sMAPE (primary), RMSE, and MASE; hyperparameters are tuned via Bayesian HPO. Empirically, the attention-augmented configuration (S4: +Trends +News +Shock) is best. Post-HPO (average across horizons), S4 attains sMAPE 12.47%, RMSE 3,433 IDR/kg, and MASE 0.87. By horizon, S4’s sMAPE is 9.8% (H=1), 12.0% (H=2), 15.6% (H=4); RMSE 2,550/3,350/4,400 IDR/kg; MASE 0.78/0.86/0.96. Compared with the price-only (S1) baseline, S4 is already better pre-tuning and becomes even stronger after HPO (average sMAPE reduction ≈ −6.2% relative). These findings show that incorporating the intensity of public issues enhances predictive value—especially at longer horizons when uncertainty rises—and that the approach is ready for operational use in nowcasting and early-warning.
Implementation of Artificial Int Implementation of Artificial Intelligence in Anemia Screening for Adolescent Girls in Pontianak City: Development of a Machine Learning-Based Early Detection System hermanto
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.v18i1.4469

Abstract

Deficiency anemia is a major public health issue among adolescent girls in Indonesia, with a national prevalence of 32% and a higher rate in Pontianak (42.3%). Factors such as tropical climate, ethnic diversity, and limited healthcare access contribute to this condition. Conventional screening methods face challenges, including uneven laboratory availability, high costs, and low sensitivity. This study aimed to develop and evaluate an AI-based anemia screening system for adolescent girls in Pontianak, focusing on diagnostic performance, cost-effectiveness, and user acceptance. A cross-sectional, mixed-methods design was applied to 1,134 girls aged 15–18 years from 20 high schools across six districts (March–July 2025). The AI system used computer vision to analyze conjunctiva, nail bed, and facial images, combined with clinical and demographic data. Models were built using Random Forest, SVM, Neural Network, and ensemble approaches, validated against laboratory standards (hemoglobin, ferritin, transferrin saturation). Random Forest achieved 91.8% accuracy, 88.2% sensitivity, and 94.1% specificity. AI detected 52.3% more anemia cases than routine screening. Significant risk factors included low fish intake, prolonged menstruation, and underweight status. The system reduced screening costs by 79.9% and showed high user acceptance (4.2/5), proving effective and affordable for early anemia detection in Pontianak.

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