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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 292 Documents
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Clustering Student Competencies Using the K-Means Algorithm Andini, Ratih Friska Dwi; Liantoni, Febri; Budianto, Aris
ULTIMATICS Vol 17 No 1 (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.v17i1.4071

Abstract

This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement.
A Comparative Study : Predicting Customer Churn in Banking Using Logistic Regression & Random Forest Basri, Mhd.
ULTIMATICS Vol 17 No 1 (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.v17i1.4075

Abstract

This research explores the prediction of bank customer churn using machine learning techniques. The dataset used includes various customer features such as demographics, transaction history, and interactions with the bank. After performing exploratory data analysis (EDA) and pre-processing, two machine learning models were applied: Logistic Regression and Random Forest. The EDA results showed that factors such as number of transactions, total transaction value, and credit utilization rate were correlated with the likelihood of churn. Pre-processing included handling categorical data, removing irrelevant features, and dividing the data into training and testing sets. The Logistic Regression model achieved 84% accuracy on training data and 83.9% on testing data, but showed poor performance in terms of recall and F1-score for the "Attracted Customer” class. In contrast, the Random Forest model showed excellent performance with 100% accuracy on both datasets, as well as perfect precision, recall, and F1-score values for both classes. In conclusion, the Random Forest model was selected as the best model to predict bank customer churn. These findings can help banks identify customers at risk of churn and develop effective retention strategies.
Implementation of YOLOv8 in Object Recognition Systems for Public Area Security in Kebun Raya Bogor Prihandoko, Prihandoko; Rumapea, Sri Agustina; Fawwaz, Muhamad Faishal
ULTIMATICS Vol 17 No 1 (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.v17i1.4133

Abstract

Pedestrian areas often serve as centers of high public activity, requiring intelligent monitoring systems to ensure the safety and comfort of their users. The application of computer vision technology, particularly object detection, offers a promising approach for identifying and estimating the number of individuals in open public spaces. This study implements the YOLOv8 algorithm to develop a human detection and crowd counting model within the pedestrian zones of the Bogor Botanical Garden. Data were collected in the form of images and videos from three strategic locations and annotated using Roboflow with a single object class labeled "person.” The model was trained on the Google Colab platform using a Region of Interest (ROI)-based approach and evaluated through confusion matrix, precision, recall, F1-score, and mean Average Precision (mAP). Results indicate a precision of 0.846, recall of 0.858, F1-score of 0.85, and mAP@50 of 0.951, although a performance drop was observed at mAP@50-95 with a score of 0.586. These findings suggest that YOLOv8 demonstrates strong real-time performance in pedestrian human detection, while challenges remain in enhancing precision under complex and varied conditions.
Calculus Calculus Scores and Sleep Quality: A Study of UMN Informatics 2022 Wibawa, Bonifacius Martin; Revian, Revan; Channiko, William Lo; Halim, Fransiscus Ati
ULTIMATICS Vol 17 No 1 (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.v17i1.4135

Abstract

This study explores the interplay between academic performance and sleep quality among UMN Informatics students from the 2022 class. Examining calculus scores and Pittsburgh Sleep Quality Index (PSQI) data, the analysis reveals a diverse range of performance in calculus, with a notable concentration of students reporting good sleep quality. Covariance and correlation matrices suggest an inverse relationship between PSQI scores and exam performance, indicating that better sleep quality may be associated with higher exam scores. Shape measure analysis further emphasizes the prevalence of good sleep quality among students. However, residual tests unveil challenges such as heteroscedasticity and autocorrelation, cautioning against overinterpretation of the regression model. The GLS model highlights a significant negative relationship between PSQI scores and exam performance, providing valuable insights into the potential impact of sleep quality on academic outcomes. This study contributes to understanding the complex dynamics between sleep quality and academic achievement, acknowledging the need for nuanced interpretation and consideration of underlying statistical assumptions.
Enhancing Intelligent Tutoring Systems through SVM-Based Academic Performance Classification and Rule-Based Question Recommendation Tobing, Fenina Adline Twince; Haryanto, Toto
ULTIMATICS Vol 17 No 1 (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.v17i1.4178

Abstract

The aims to automatically classify students' academic performance levels using Support Vector Machine (SVM) algorithm and automatically recommend questions based on classification results. Dataset consists of six assignment scores per student, averaging students into three performance levels: Beginner, Intermediate, and Advanced. Before training, the data undergoes preprocessing involving normalization with Standard Scaler and splitting into training and testing sets. The model is trained using Radial Basis Function (RBF) kernel with hyperparameter tuning to optimize its performance. The evaluation results show that the model achieved an accuracy of 91.67%, with a precision of 93.06%, a recall of 91.67%, and an F1-score of 91.89%. The best performance was found in the Intermediate class, the dominant category in the dataset, while performance in the Advanced category was relatively lower due to limited sample size. Following classification, a rule-based recommendation system is used to suggest questions that match the student's predicted level of competence. This approach supports a more adaptive and personalized learning environment. The findings demonstrate that the SVM algorithm effectively supports intelligent learning systems such as the Intelligent Tutoring System (ITS). Future work should include data balancing techniques, expansion of dataset size, and comparative analysis with other algorithms to enhance model generalization.
Bacteria Recognition Application Model Using Marker-Based Augmented Reality for Android Mobile Devices Mulyani, Asri; Kurniadi, Dede; Fadillah, Hadi Bagus
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3278

Abstract

This article aims to develop a multimedia application model for bacterial recognition using Marker-Based Augmented Reality (Marker-Based AR) technology for Android mobile devices. Marker-based tracking creates mobile augmented reality markers to increase user interaction in Marker-Based Augmented Reality systems. The software development method uses the Multimedia Development Life Cycle, which consists of 6 phases: concept, design, material collecting, assembly, testing, and distribution. The results of this study are a model of a multimedia application for bacterial recognition using Marker-Based AR, which has a marker-based tracking feature and displays bacterial objects in 3 dimensions along with their explanations and exercise questions. Based on user tests, it shows that the application model developed helps and makes it easier to learn about bacteria independently using Android mobile devices more easily and interestingly. This is proven based on beta testing towards users who got a score of 73.68% is obtained which agrees.
A Comparative Study of Body Motion Recognition Methods for Elderly Fall Detection: A Review Apriantoro, Roni; Setyawan, Muhammad Adriano Khairur Rizky; Lavindi, Eri Eli
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3293

Abstract

To maintain the welfare of the elderly, intensive and effective monitoring is needed to ensure their safety. Conventional elderly activity monitoring has several limitations (i.e., space and time) due to human abilities. This problem can be overcome by applying real-time monitoring methods using Wireless Body Area Networks (WBAN) and Artificial Intelligence (AI). Several methods have been used and tested, including artificial intelligence implementations from sensor data-based to computer vision-based pattern recognition for body motion classification. Several methods that have been studied show accurate results in classifying elderly body motions/gestures. However, the Human Activity Recognition (HAR) method performs better for elderly activity monitoring applications and makes fall classification more accurate.
Design and Development of a Learning Style Identification Application for JPTK Students using the K-Nearest Neighbor Ramadhan, Firdaus Ditio; Liantoni, Febri; Prakisya, Nurcahya Pradana Taufik
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3299

Abstract

Learning styles are crucial for all students, as the chosen learning style can greatly assist them in learning. The data source for this research originates from questionnaire results distributed to JPTK students of the 2019-2021 cohorts, which were used to assess the effectiveness of a learning style product on the students' JPTK website. This study employs the K-Nearest Neighbor approach, which utilizes the principle of nearest neighbors to categorize students' learning styles based on provided features. The data used in this research is derived from the website that students use to input information about their preferred learning styles. Various elements, including visual, auditory, and kinesthetic preferences, are present in the questionnaire on the website. Subsequently, the data is processed and fed into a Python K Nearest Neighbor model to predict students' learning styles and nearest neighbors. The evaluation results indicate that the developed classification model achieves a reasonably high accuracy level of 93%, making it a useful tool for effectively and efficiently identifying students' learning styles. It is hoped that implementing this learning style classification model will benefit the field of education. By understanding students' learning styles, educators can create more tailored lesson plans, enhance learning outcomes, and reduce the likelihood of knowledge loss.
Application of Convolutional Neural Network Using TensorFlow as a Learning Medium for Spice Classification Saputro, Muhammad Naufal Adi; Liantoni, Febri; Maryono, Dwi
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3304

Abstract

The purpose of this research are: (1) To determine the accuracy of the CNN method in the development of a website for classifying spices, (2) To assess the feasibility of the spice classification website as a learning medium, (3) To ascertain user responses to the spice classification website as a learning medium. The method employed in this research is research and development. This study utilizes the ADDIE development method, which comprises 5 stages: (1) Analysis, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. The research yielded a significantly high accuracy rate. This is demonstrated by the results showing an accuracy of 96%, precision of 97%, and recall of 96%. Moreover, the research found the developed website to be feasible. This is supported by the evaluation using the Learning Object Review Instrument (LORI), resulting in a score of 88% from media experts and a score of 90% from subject matter experts. Additionally, user response was positive. This is evidenced by testing the learning media on 10th-grade culinary students from SMK N 4 Surakarta, which yielded a score of 76% using the System Usability Scale (SUS), indicating a favorable usability assessment. In conclusion, the spice classification website, as a learning medium, can be employed as a suitable educational tool.
REAL-TIME TOMATO QUALITY DETECTION SYSTEM USING YOU ONLY LOOK ONCE (YOLOv7) ALGORITHM: Sistem Deteksi Mutu Tomat Secara Real-time Menggunakan Algoritma You Only Look Once (YOLOv7) Muarofah, Isna Ayu; Vikri, Muhammad Jauhar; Sa'ida, Ita Aristia
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3337

Abstract

Real-time object detection is a crucial aspect of computer vision. With the increasing prominence of the big data field, it has become easier to gather data from various sources. Over the past few decades, computer vision inspection systems have become essential tools in agricultural operations, and their usage has seen a significant rise. Computer vision automation-based technology in agriculture is increasingly being employed to enhance productivity and efficiency. Tomato is a widely utilized crop commodity, finding applications in food, cosmetics, and pharmaceuticals. Consequently, tomato farming continues to evolve and has become one of the nation's export commodities. YOLO is an algorithm capable of real-time object detection and recognition. In this study, the YOLOv7-tiny architecture, which has lower computational overhead, was utilized. For quality detection of tomatoes, they were categorized into three classes: ripe, unripe, and defective. The trained model yielded a recall score of 0.97, precision of 1.0, a PR-curve of 0.838, and an F1-score of 0.81, indicating that the model learned effectively. The research achieved an accuracy of 90.6% on original images with an average IoU of 0.90 and a detection time of 2.7 seconds. In images with added light disturbance, the average accuracy was 91.2%. Images with reduced light yielded an average accuracy of 92%, while images with blur disturbance had an average accuracy of 78.2%. In real-time testing, ripe tomatoes were detected up to a maximum distance of 90cm, unripe tomatoes at 90cm, and defective tomatoes at 70cm.

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