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Jurnal Teknologi Informasi dan Pendidikan
Published by Universitas Negeri Padang
ISSN : 20864981     EISSN : 26206390     DOI : https://doi.org/10.24036/jtip
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Engineering
Jurnal Teknologi Informasi dan Pendidikan (JTIP) is a scientific journal managed by Universitas Negeri Padang and in collaboration with APTEKINDO, born from 2008. JTIP publishes scientific research articles that discuss all fields of computer science and all related to computers. JTIP is published twice a year. The editorial board comes from the lecturer board in the Department of Electronics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 365 Documents
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Design of Mobile Learning Application Interface Using Kansei Engineering Method (Case Study: Majelis Daur Ulang) Imam Maruf Nugroho; Yudhi Raymond Ramadhan; Ibrahim Aljaedi
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1119

Abstract

Kansei Engineering is a method for realizing certain product designs based on a systematic exploration of human feelings and sensations (sight, touch, smell, hearing, taste). In mobile applications, the most important factor apart from the technical aspects of the system is the design. Design is an important factor in a mobile application because it becomes a liaison between the user and the existing system. The Mobile Learning application is not only enough to run the application and there are no errors, but the application must be built according to the wishes and interests of the user, this study aims to determine the emotional factors of the user, apply Kansei Engineering in designing and make recommendations for application display design according to Kansei Engineering. This methodology refers to Kansei Engineering Type I. This study uses Kansei Word to detect the user's feelings when looking at the specimen design. The list of Kansei Words used is 10 words related to the Majelis Daur Ulang mobile learning application. There are 5 specimens of similar Mobile Learning applications used. This study involved 32 participants, using multivariate statistical analysis, namely Cronbach's Alpha (CA), Correlation Coefficient Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Partial Least Square (PLS). This research resulted in 2 recommendations for the design Majelis Daur Ulang Mobile Learning display, namely "Professional" and "Unique”.
Strategi Penyetelan Hyperparameter untuk YOLOv8n dalam Pemantauan Lalu Lintas Pasca-Kecelakaan Real-Time I Nyoman Eddy Indrayana; Made Sudarma; I Ketut Gede Darma Putra; Anak Agung Kompiang Oka Sudana
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1132

Abstract

Traffic accidents continue to provide a considerable difficulty in contemporary transportation systems, frequently leading to vehicle damage and heightened risks for pedestrians on streets. Precise and instantaneous identification of post-accident scenarios is thus crucial for facilitating swift response and sophisticated traffic management. This research introduces a streamlined object detection methodology utilizing YOLOv8n to recognize six essential traffic-related categories: bus, automobile, damaged vehicle, motorbike, pedestrian, and truck. The main aim is to examine the impact of hyperparameter modification on detection efficacy, specifically in recognizing damaged automobiles as signs of post-accident situations. Twelve model configurations were created by systematically altering three hyperparameters: learning rate (0.01, 0.001, and 0.0001), batch size (32 and 64), and optimizer type (Adam and MuSGD). All models underwent training for 200 epochs with a dataset derived from actual traffic situations, augmented by techniques such as grayscale transformation, blurring, and rotation. The performance evaluation utilized precision, recall, F1-score, mAP50, and mAP50:95. The findings indicate that hyperparameter selection substantially influences convergence stability and detection accuracy. The optimal model attained a mAP50 of 0.905 and a mAP50:95 of 0.751, utilizing a learning rate of 0.01, a batch size of 64, and the Adam optimizer. Moreover, substantial items like cars, buses, and trucks were identified with high precision, whereas damaged vehicles and pedestrians necessitated more meticulous calibration due to increased visual variability.The findings indicate that optimized lightweight models can attain competitive performance, rendering them appropriate for real-time intelligent traffic monitoring applications.
Hyperparameter Tuning of YOLOv8n for Real-Time Material Truck Detection I Gede Angga Saputra; I Nyoman Eddy Indrayana; Ida Bagus Adisimakrisna Peling
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1138

Abstract

The increasing number of material trucks on arterial roads has posed challenges for traffic surveillance and regulatory compliance. Traditional monitoring techniques that rely on manual observation are often ineffective and susceptible to irregularities, highlighting the need for automated real-time monitoring systems. This study proposes a lightweight object detection approach using YOLOv8n to improve real-time truck detection performance in traffic monitoring applications. A quantitative experimental methodology was employed by performing hyperparameter tuning through adjustments to the number of epochs, batch size, optimizer, and learning rate. The dataset was collected from real traffic environments using smartphone cameras and CCTV (TP-Link Tapo C320WS). A total of 36 experimental configurations were evaluated using Precision, Recall, F1-score, mAP@50, and mAP@50–95 metrics. Experimental results showed that the optimal configuration, consisting of 100 epochs, a batch size of 16, the Adam optimizer, and a learning rate of 0.001, achieved a mean Average Precision (mAP)@50 of 0.9302 and mAP@50–95 of 0.7226. Although the performance improvement over the baseline YOLOv8n model was relatively modest, repeated experiments demonstrated improved model stability and consistency after hyperparameter optimization. Real-time deployment on a local GPU achieved a stable processing speed of 14–23 Frames Per Second, with an average of 19 FPS, enabling real-time monitoring performance aligned with the camera input rate. The integrated system successfully combines object detection, tracking, and license plate recognition for practical traffic monitoring applications. However, smaller objects such as license plates remained more challenging to detect due to localization limitations under occlusion and low-light conditions.
Analysis of Provocative Speech During the 2025 DPR Demonstration on X Using the IndoBERTweet Method Nazhrin Nazarudin Achmad; Yuliant Sibaroni; Sri Suryani Prasetyowati
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1127

Abstract

Social media platforms have become important channels for public discussion during political events. During the DPR demonstrations in August 2025, online discussions on X (formerly Twitter) contained various forms of expressions, including provocative speech that may influence public opinion and collective behavior. Detecting such content automatically is challenging due to the informal language, slang, and contextual nuances commonly found in social media texts. This study aims to analyze provocative speech on the social media platform X using text classification techniques and transformer-based models. A total of 8,899 Indonesian tweets related to the demonstration period from August 25 to August 31, 2025 was collected using the Tweet Harvest crawling tool. The dataset was manually labeled into two categories, namely provocative and non-provocative, using a majority voting approach by three annotators. Several preprocessing steps were applied, including cleaning, normalization, stemming, tokenization, and stopword removal. Several models were evaluated, including Multinomial Naïve Bayes, Linear Support Vector Machine, BiLSTM, IndoBERT, and IndoBERTweet. Experimental results show that transformer-based models outperform traditional machine learning approaches. The best performance was achieved by the IndoBERTweet model with a learning rate of 3×10⁻⁵, achieving an accuracy of 93.07% and an F1-score of 91.56%. These findings indicate that domain-specific language models are effective for detecting provocative speech in Indonesian social media discussions related to political events.
MOOCs Innovation for MICE Education: Strengthening Digital and Sustainable Competence in Tourism Learning Khairunnisa Khairunnisa; Rezki Orientani; Yulita Suryantari
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1135

Abstract

The tourism industry is undergoing rapid transformation driven by digitalization, sustainability imperatives, and evolving learner expectations. Within this context, the MICE sector requires professionals who are digitally competent and sustainability-oriented. Traditional classroom approaches often fail to keep pace with these demands, while Massive Open Online Courses (MOOCs) offer scalable and flexible alternatives. This study investigates the role of MOOCs innovation in enhancing digital literacy and sustainability education in MICE learning. Drawing on Constructivist Learning Theory and Competency-Based Education, a quantitative survey was conducted with 42 undergraduate students who had completed a MICE course at Universitas Terbuka, Indonesia. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The measurement model demonstrated strong reliability and validity, and the structural model showed moderate to substantial explanatory power. Results confirmed that MOOCs innovation significantly influences both digital literacy and sustainability education, with digital literacy emerging as the strongest predictor and a key mediator between innovation and sustainability outcomes. These findings extend theoretical understanding by demonstrating how MOOCs innovation operationalizes constructivist and competency-based approaches in specialized tourism education. Practically, the results emphasize the importance for educators, curriculum designers, and policymakers to prioritize digital literacy as the most effective pathway toward sustainability-oriented learning outcomes. The study concludes that MOOCs innovation offers a strategic tool for preparing future-ready MICE professionals equipped with digital and sustainability competencies.

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