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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Prosiding Simposium Nasional Manajemen dan Bisnis
Akbar, Fadhilah Aditya
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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ANALISIS STRATEGI PEMASARAN UNTUK MENINGKATKAN DAYA SAING PADA HARA CHICKEN DENGAN METODE SWOT Akbar, Fadhilah Aditya; Zubair, Wa Ode Nurlita; Aliansyah, Feri; Limantara, Arthur Daniel
Prosiding Simposium Nasional Manajemen dan Bisnis Vol. 4 (2025): Simposium Manajemen dan Bisnis
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/jmy9rw45

Abstract

Penelitian ini bertujuan untuk menganalisis strategi pemasaran dalam meningkatkan daya saing Hara Chicken menggunakan metode SWOT. Data dikumpulkan melalui observasi dan wawancara, kemudian dianalisis menggunakan matriks IFAS, EFAS, dan QSPM untuk mengidentifikasi posisi strategis serta merumuskan rekomendasi. Hasil penelitian menunjukkan Hara Chicken berada pada Kuadran I (Strategi Agresif), mengindikasikan kekuatan internal yang solid di tengah peluang eksternal yang besar. Rekomendasi strategi prioritas adalah penguatan promosi digital melalui media sosial yang lebih interaktif, konten kreatif, dan pemanfaatan influencer lokal, mengingat rendahnya engagement media sosial Hara Chicken saat ini. Diharapkan strategi ini mampu meningkatkan visibilitas, penjualan, dan daya saing Hara Chicken di pasar kuliner Kediri.
Orchid Species Classification Using the DenseNet121 Deep Learning Model with a Data Imbalance Handling Approach Akbar, Fadhilah Aditya; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

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Abstract

For conservation, commercial cultivation, and scientific research, accurate identification of orchid species often requires specialized expertise. In this study, the DenseNet121 deep learning architecture was employed to develop an automated classification system for four popular orchid species. DenseNet121 was selected for its ability to extract complex hierarchical features and its strong performance on limited-scale datasets. The initial dataset comprised 1,935 images of Phalaenopsis, Cattleya, Dendrobium, and Vanda orchids. However, after manual removal of duplicate images, only 1,658 images remained, revealing significant class imbalance. The undersampling method was applied to balance each class to 248 samples. The dataset was then split into 75% training, 15% validation, and 10% testing, and enhanced through data augmentation techniques such as rotation, flipping, brightness variation, width shift, height shift, and zoom. The final model achieved 97.00% accuracy with class-specific performance ranging from 92.59% to 100% accuracy across different orchid species. This research can serve as a foundation for developing mobile or web applications to assist researchers, farmers, and orchid enthusiasts in accurately identifying orchid species, while supporting conservation efforts for orchid biodiversity in Indonesia.
Co-Authors Aliansyah, Feri Arthur Daniel Limantara, Arthur Daniel Christy Atika Sari Zubair, Wa Ode Nurlita