cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuliansyah
Contact Email
admin@goodwoodpub.com
Phone
-
Journal Mail Official
admin@penerbitgoodwood.com
Editorial Address
Z.A. Pagar Alam Street No. 57, Rajabasa, Bandar Lampung City - Indonesia Email: admin@goodwoodpub.com
Location
Kota bandar lampung,
Lampung
INDONESIA
Jurnal Teknologi Riset Terapan
Published by Goodwood Publishing
ISSN : -     EISSN : 29867169     DOI : https://doi.org/10.35912/jatra
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering
Jurnal Teknologi Riset Terapan (JATRA) adalah jurnal peer-review yang menerbitkan artikel penelitian asli dan berkualitas di bidang teknologi riset terapan. Jurnal ini bertujuan menjembatani kesenjangan antara teori dan praktik dalam ilmu pengetahuan dan teknologi agar dapat diterapkan dalam kehidupan sehari-hari. Ruang lingkup JATRA mencakup, namun tidak terbatas pada, bidang-bidang seperti Teknik Industri, Teknik Informatika, Ilmu dan Teknik Material, Manufaktur, Mikroelektronika, Teknik Elektro, Teknik Mesin, Teknik Kelautan, Arsitektur Kapal, Teknik Dirgantara dan Pemeliharaan Pesawat, Teknik Kimia, serta berbagai aplikasi mekanikal, elektrikal, elektronika, dan informatika dalam bidang rekayasa dan teknologi terapan.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 24 Documents
 1   2   3 
Pengaruh Digital Marketing melalui TikTok terhadap Pemasaran Produk UMKM di Cicarimanah Husnulmar’ati, Ghina; Firmansyah, Esa; Helmiawan, Muhammad Agreindra
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5008

Abstract

Purpose: The purpose of this study is to understand how using TikTok as a digital marketing platform can help Micro, Small, and Medium Enterprises (MSMEs) in Cicarimanah Village, Situraja District, Sumedang Regency to improve their product marketing. The study aims to see whether TikTok can increase brand awareness, customer interaction, and product sales. Methodology/approach: This research uses a quantitative approach. Data were collected through surveys, interviews, and direct observations of MSME owners who actively use TikTok for marketing their products. The study was conducted specifically in Cicarimanah Village, Situraja District, Sumedang Regency, and focused on local MSMEs that utilize TikTok as part of their marketing efforts. No specific software or brand tools were mentioned, but the TikTok platform was central in the data collection and analysis process. Results/findings: The findings show that using TikTok for digital marketing has a positive effect on MSMEs. It helps in increasing brand awareness, improving customer engagement, and boosting product sales. TikTok allows MSMEs to promote their products in a creative, interactive, and effective way. Conclusion: TikTok has strong potential as a marketing tool for UMKM in Cicarimanah. Its effective use requires digital skills, creative content, platform integration, and data-driven strategies to support brand growth and business development. Limitations: The study is limited to MSMEs in one village only, so the results may not represent MSMEs in other regions or sectors. Also, the study does not explore long-term impacts or compare TikTok with other digital marketing platforms Contribution: This research contributes to the field of digital marketing and entrepreneurship development, especially for rural MSMEs. It provides practical insights for small business owners, marketing practitioners, and policymakers on how social media particularly TikTok can be used as a low-cost and high-impact marketing tool to help MSMEs grow and reach wider audiences.
Classification of Rare Mussaenda Species in Indonesia's Tropical Forests Using the CNN Algorithm Raja, H. F. Muchammad; Muhammad, Meizano Ardhi; Martinus, Martinus; Pandu, W.; Muhkito, A.; Muhammad, A.
Jurnal Teknologi Riset Terapan Vol. 2 No. 2 (2024): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v2i2.5011

Abstract

Purpose: Mussaenda frondosa is a rare plant species native to Indonesia’s tropical forests, with limited research focused on its classification and identification, particularly using machine learning. This study aims to develop a classification model for Mussaenda species using a Convolutional Neural Network (CNN) approach to support the advancement of automated plant identification systems. Methodology/approach: The dataset used consists of 650 labeled images, categorized into six primary parts of the plant: leaves, stems, twigs, fruits, flowers, and trees. A CNN model was developed and trained over 200 epochs to classify the images according to these categories. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to enhance model performance. Results/findings: The trained CNN model achieved an accuracy of 80%, demonstrating its ability to classify Mussaenda frondosa components despite the relatively small dataset. Visual inspection of prediction outputs showed consistent identification across several categories, particularly leaves and flowers. Conclusion: The results suggest that CNN can be effectively used to classify rare plant species like Mussaenda frondosa. The model's performance also indicates that even a limited dataset, when properly processed, can yield promising classification results. Limitations: The main limitation of this research is the small dataset size, which may restrict the model's generalizability to broader plant species or more diverse environmental conditions.. Contribution: This study contributes to the field of plant classification by providing a foundation dataset and a validated CNN model for rare tropical species. It opens pathways for further research in biodiversity monitoring and conservation using AI.
Pemodelan AI dengan CNN Untuk Klasifikasi Tanaman Uvaria Grandiflora di Hutan Tropis Indonesia Martinus, Martinus; Ferbangkara, Sony; Annisa, Resty; Hidayatullah, Vezan; Pratama, Rama Wahyu Ajie; Makarim, Alvin Reihansyah
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5012

Abstract

Purpose: This research aims to develop an artificial intelligence (AI) model based on the Convolutional Neural Network (CNN) to classify Uvaria plant species, a tropical genus native to Indonesia. The study addresses the challenge of limited datasets for automatic classification in tropical plant identification. Methodology/approach: Images of Uvaria plants were collected directly from their natural habitat and categorized into four primary classes: leaves, stems, twigs, and trees. The dataset comprises 400 labeled images, split into training (279 images, 70%), validation (40 images, 10%), and testing (81 images, 20%). The CNN model was trained for 200 epochs, using data preprocessing techniques such as normalization and augmentation to improve performance. Results/findings: The CNN model achieved an accuracy of 90% on the test set, indicating strong performance in classifying the four categories of Uvaria plant components. The model showed particularly consistent results in distinguishing between leaves and twigs. Conclusion: Despite the relatively small dataset, the results demonstrate that the CNN algorithm is capable of accurately classifying images of Uvaria species. The dataset is considered sufficient to build an effective classification model. Limitations: The main limitation of this study is the limited number of images, which may restrict the model’s ability to generalize to broader or more varied data in real-world conditions. Contribution: This research contributes to the development of AI-based tools for identifying tropical plant species. It offers a practical model and dataset that can support biodiversity monitoring, environmental research, and conservation efforts in Indonesia and similar tropical regions.
Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN Ferbangkara, Sony; Mulyani, Yessi; Mardiana, Mardiana; Pratama, Rama Wahyu Ajie; Putri, Renatha Amelia Manggala; Rafi'syaiim, Muhammad Afif
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5014

Abstract

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.

Page 3 of 3 | Total Record : 24

 1   2   3 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2025): Januari Vol. 2 No. 2 (2024): Juli Vol. 2 No. 1 (2024): Januari Vol. 1 No. 2 (2023): Juli Vol. 1 No. 1 (2023): Januari