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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 346 Documents
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A Aplikasi Web untuk Klasifikasi dan Deteksi Penyakit Daun Tomat Menggunakan Model CNN dan YOLO Novaliendry, Dony; Rizal, Fachri; Anwar, Muhammad; Irfan, Dedy
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1073

Abstract

This study developed a web-based application for the classification and detection of tomato leaf diseases using Convolutional Neural Network (CNN) and You Only Look Once (YOLO) models. The research followed a Research and Development approach that consisted of requirement analysis, system design, implementation, model training, and testing. The CNN model was trained to classify tomato leaf images into specific disease categories, while the YOLO model was designed to detect and localize diseased areas in real time. Both models were integrated into a Flask-based web system to provide accessible and interactive functionality through standard web browsers. Testing results showed that the CNN model achieved an accuracy of 96.1%, effectively identifying disease types such as Early Blight and Bacterial Spot. The YOLO model reached a mean Average Precision (mAP) of 87.3% for real-time detection, successfully locating and labeling infected regions on tomato leaves. The integration of CNN and YOLO models demonstrated strong classification and detection performance, offering an efficient and scalable solution to support early disease diagnosis and digital transformation in precision agriculture.
Workflow Automation in Digital Medication Reminder Systems to Enhance Patient Adherence Bestari, Salsabila Intania; Syafrijon; Randi Proska Sandra; Ahmaddul Hadi
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1096

Abstract

Pharmacotherapy is a form of treatment that utilizes pharmaceutical products as a medium for healing. The effectiveness of this therapy largely depends on patient adherence and consistency in taking medication as prescribed by physicians. Unfortunately, in practice, many cases of non-adherence occur, leading to medication errors and reduced treatment outcomes. This study proposes a solution by employing workflow automation technology combined with several API integrations, such as WhatsApp API and Google Calendar API, to provide a broader and more accessible user experience. The proposed system is a web-based platform connected to n8n, a workflow automation tool that supports integration with third-party applications like WhatsApp and Google Calendar. First-party users, such as doctors and hospital operators, can input patients’ medication schedules into the system. The system then sends reminders and confirmations to second-party users (patients) via WhatsApp, while also synchronizing schedules through Google Calendar. WhatsApp was selected due to its popularity and accessibility in Indonesia, while Google Calendar was chosen as one of the most widely used calendar applications worldwide. This research is expected to contribute to pharmacotherapy support by providing a simple yet practical technological approach to improve medication adherence.
Development of Augmented Reality (AR) Learning Media with the Project-Based Learning Model to Improve Motivation and Learning Outcomes in Projection Drawing Romi Hartono; Warpala, I Wayan Sukra; Suartama, Kadek
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1099

Abstract

The low level of students’ spatial understanding and the limited availability of dynamic visual media capable of facilitating three-dimensional object representation in projection drawing learning constitute a strategic educational problem. Students often experience difficulties in visualizing spatial relationships, which affects both their motivation and learning outcomes. To address this issue, this study developed augmented reality (AR) learning media using the 4D development model, consisting of Define, Design, Develop, and Disseminate stages. The purpose of this research was to improve students’ learning motivation and learning outcomes in projection drawing instruction. This research was conducted in the Visual Arts Education Study Program at Universitas Pendidikan Ganesha, involving 22 students as research subjects. Data were collected through interviews, observations, questionnaires, tests, and documentation. The research instruments included expert validation sheets, motivation questionnaires, and learning outcome tests. All instruments were validated by content experts, media experts, and instructional design experts. Data analysis techniques comprised descriptive analysis, normality testing, Wilcoxon tests, paired-sample t-tests, and N-gain analysis to determine the effectiveness of the developed media. The results showed that the AR learning media was highly feasible, with a validation score of 91% from content experts and 88% from media experts. Individual, small-group, and field trials indicated positive student responses, with acceptance rates between 81% and 91%. The mean pretest score increased from 41.00 to 72.41 in the posttest, with an N-gain value of 0.52 (moderate category). Students’ learning motivation also increased significantly (p = 0.000). Therefore, the developed AR media is feasible and effective for projection drawing learning.
Implementasi indoBERT dalam Analisis Sentimen Program Makan Bergizi Gratis pada Platform Media Sosial X Muhammad Mursyid; Setiawan, Arif; Arifin, Muhammad
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1104

Abstract

The Free Nutritious Meal Program (MBG) is a strategic initiative by the Indonesian government to improve child nutrition and prevent stunting. However, its implementation has sparked diverse public opinions on social media, which are difficult to analyze manually due to the large volume of data. This study aims to identify public sentiment toward the MBG program through social media X by implementing the IndoBERT model. A total of 4,380 tweets were collected using web scraping techniques with relevant keywords between March and May 2025. The research process included preprocessing (data cleaning, stopword removal, stemming, and tokenization), semi-automatic data labeling, and data division into a 71.97% training set, 8.02% validation set, and 20.01% test set. The model used was the Indonesian RoBERTa Base Sentiment Classifier architecture, which underwent a fine-tuning process for 20 epochs. The results showed that the IndoBERT model achieved an accuracy rate of 80.11% and a weighted average F1-score of 0.8000. Negative sentiment was detected most accurately with an F1-score of 0.8301. Although effective, the model still faces challenges in handling linguistic ambiguity in neutral sentiment and the risk of overfitting. Further research is recommended to expand slang language normalization and apply stricter model regulation techniques.
Sentiment Analysis of Free Nutritious Meal Programs Using Naïve Bayes on Platforms X and TikTok Fadila Ullul Azmie; Yudie Irawan; R.Rhoedy Setiawan
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1112

Abstract

This study analyzes public sentiment toward the Free Nutritious Meal Program (MBG) using the Multinomial Naive Bayes algorithm on data from X (Twitter) and TikTok. A total of 5,173 entries were collected through web scraping and processed with cleaning, normalization, tokenization, stopword removal, and stemming. To address class imbalance, SMOTE was applied, and evaluation employed accuracy, precision, recall, F1-score, and AUC-ROC. Results show that without SMOTE, the model tended to be biased toward the majority class, especially on TikTok, while after SMOTE recall increased significantly and a better balance between precision and recall was achieved. On Twitter, performance was more stable with a moderate class distribution, and SMOTE further improved sensitivity to positive sentiment. Word cloud analysis revealed differences across platforms: TikTok leaned more toward negative sentiment with dominant words such as “racun,” “korupsi,” and “dapur,” while Twitter showed a stronger balance with positive terms like “gizi,” “gratis,” and “program.” These findings highlight the importance of cross-platform analysis to comprehensively understand public perceptions.
Optimizing Hotel Room Booking Patterns Using Apriori and FP-Growth Methods: A Case Study at Sapphire Boutique Hotel Putri Handayani; Latifah, Noor; Nugraha, Fajar
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1113

Abstract

The hospitality industry utilizes digital reservation systems that generate large volumes of hotel room booking transaction data. However, these data are often not optimally analyzed to support data-driven decision making. This study aims to analyze and compare the performance of the Apriori and FP-Growth algorithms in discovering association patterns in hotel room bookings. The research employs a quantitative approach using Association Rule Mining (ARM) techniques and the CRISP-DM framework on booking transaction data from Sapphire Boutique Hotel. The dataset consists of booking transaction data from Sapphire Boutique Hotel, including room type, additional facilities, booking time, and length of stay attributes. Algorithm performance is evaluated based on computation time, the number of generated association rules, and rule quality measured using support, confidence, and lift values. The results indicate that both algorithms are capable of generating relevant booking patterns; however, FP-Growth demonstrates superior performance in terms of computational efficiency and the number of patterns produced compared to Apriori. These findings are expected to support the development of recommendation systems and data-driven marketing strategies in the hospitality industry.

Page 35 of 35 | Total Record : 346

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