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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of International and Local Studies
Tenriola, Andi
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Humanities Computer Science & IT Education Engineering Social Sciences

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Strategi Ekspor Tekstil dan Produk Tekstil PT. Sri Rejeki Isman ke Amerika Serikat di Masa Pandemi COVID-19 Tenriola, Andi; Tas'an, Ayu Kartika Julianingsih
JILS (Journal of International and Local Studies) Vol. 8 No. 2 (2024): July
Publisher : Universitas Bosowa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56326/jils.v8i2.4405

Abstract

This research aims to analyze various export strategies of Indonesian textile and textile products (TPT) to the United States carried out by PT. Sri Rejeki Isman Tbk (Sritex) during the Covid-19 pandemic. This study uses a literature research method by analyzing various literature related to the export of textile and textile products by PT. Sritex. The results of this research show that Sritex utilizes various strategies to maintain and increase its product exports to the United States amidst the pandemic. Strategies used include product differentiation, low costs, product line focus, fiscal incentives from the government, textile 4.0 road map, production capacity enhancement, production process planning system improvement, approach to target countries, commodity determination, and increased engagement through E-commerce. Despite facing significant challenges due to the pandemic, Sritex successfully maintained its market share of TPT in the United States. This research refers to the theory of competitive advantage by Michael E. Porter to ensure the company's success in facing changing global market challenges during the Covid-19 pandemic. Additionally, this study provides recommendations for other TPT companies planning to expand their export markets to the United States and other countries during uncertain times due to global uncertainty challenges.
Optimizing Sentiment Analysis of Electric Vehicles Through Oversampling Techniques on YouTube Comments Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Tenriola, Andi; Surianto, Dewi Fatmarani; Sidin, Udin Sidik
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.88205

Abstract

Air pollution from motorized fuel vehicles causes adverse impacts on the environment and human health, driving the need for more sustainable alternatives such as electric vehicles. However, the transition to electric vehicles is often met with mixed responses from the public, reflected by sentiments that are split between positive and negative. This research investigates such sentiments through analyzing comments on the YouTube platform, which are classified using two algorithms, SVM and Naïve Bayes, and three oversampling techniques: Random Oversampling, SMOTE, and ADASYN. A comparative evaluation is conducted to determine the most effective algorithm and oversampling strategy for handling imbalanced sentiment data, where negative comments dominate. Initial experiments showed that Naïve Bayes with SMOTE achieved the best result among baseline models, with 64% accuracy. However, traditional oversampling methods alone were not sufficient to significantly improve classification quality. To address this, the study proposes a hybrid method that combines Easy Data Augmentation (EDA), specifically Synonym Replacement (SR), with oversampling techniques. The proposed method substantially improved performance. Naïve Bayes combined with SR and SMOTE or Random Oversampling achieved 88% accuracy, with F1-scores of 0.84–0.85 for the positive class. The best result was obtained using SVM with SR and Random Oversampling, reaching 97% accuracy and F1-scores of 0.97 (negative) and 0.96 (positive). These findings demonstrate the effectiveness of combining augmentation and oversampling in improving sentiment classification and provide insights for stakeholders in promoting EV adoption.
Identifying Rice Plant Damage Due to Pest Attacks Using Convolutional Neural Networks Tenriola, Andi; Azis, Putri Alysia; Kaswar, Andi Baso; Adiba, Fhatiah; Andayani, Dyah Darma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6125

Abstract

Rice (Oryza Sativa) is an important crop for meeting global food needs; however, one of the main challenges in its cultivation is the attack of stem borer pests, which can cause significant damage. This study aims to identify the damage caused by these pest attacks using Convolutional Neural Networks (CNN) methods. We developed and trained several CNN architectures, including the proposed architecture, MobileNet, and EfficientNetB0, to detect pest attacks on rice. The dataset used consists of 700 images per class taken directly from the field, where the images depict rice plants that have been peeled or opened to inspect for the presence of pests, specifically stem borer pests. To enhance the quality and diversity of the dataset, we applied a rigorous selection process, ensuring that only high-quality images were used. Additionally, augmentation techniques such as rotation were employed to expand the dataset to 2000 images per class. Labeling was carried out carefully to ensure that each image accurately reflected the condition of the pest attack. The results of the study indicate that the proposed CNN model can identify damage with high accuracy, thereby contributing to efforts to increase rice production through early detection of pest attacks using computer vision technology.
Co-Authors Adiba, Fhatiah Andi Baso Kaswar Andi Baso Kaswar Azis, Putri Alysia Dewi Fatmarani Surianto Lapendy, Jessica Crisfin Resky, Andi Aulia Cahyana Tas'an, Ayu Kartika Julianingsih Udin Sidik Sidin