Jurnal Teknologi Riset Terapan

Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN

Wed, 02 Jul 2025 00:00:00 +0700

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.

Pengaruh Digital Marketing melalui TikTok terhadap Pemasaran Produk UMKM di Cicarimanah

Ghina Husnulmar’ati, Esa Firmansyah, Muhammad Agreindra Helmiawan — Wed, 02 Jul 2025 00:00:00 +0700

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.

Pemodelan AI dengan CNN Untuk Klasifikasi Tanaman Uvaria Grandiflora di Hutan Tropis Indonesia

Wed, 02 Jul 2025 00:00:00 +0700

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.

Unjuk Kerja Teknologi Akses Jamak TD-CDMA dan TD-SCDMA pada Infrastruktur Jaringan High Altitude Platform Stations

Thu, 18 Jan 2024 00:00:00 +0700

Purpose: This study evaluates the performance of TD-CDMA and TD-SCDMA access technologies for High Altitude Platform Stations (HAPS) to meet growing demands for high-speed access and larger bandwidth in telecommunications.

Methodology/approach: A performance comparison was conducted between TD-CDMA and TD-SCDMA based on three key parameters: access speed, data transfer capacity, and power consumption levels. Analytical calculations were carried out to assess data capacity and transmitted power at varying distances from the HAPS.

Results/findings: The analysis showed that TD-CDMA provides higher data capacity compared to TD-SCDMA. Additionally, transmitted power requirements increase with greater distances between HAPS and ground receivers, with a maximum transmitted power of 19.423 W observed at 50,000 meters.

Conclutions: TD-CDMA offers better overall performance than TD-SCDMA for HAPS applications, providing higher data capacity while maintaining comparable power efficiency, making it a preferable choice for high-speed communication services.

Limitations: The study is limited to theoretical calculations without real-world field testing. Factors such as environmental interference and atmospheric conditions were not included in the analysis.

Contribution: This research provides insights into selecting efficient access methods for HAPS technology, highlighting TD-CDMA as the more suitable option to support high-speed, large-bandwidth telecommunication services with optimized resource utilization.