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All Journal Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Tali Jagad Jurnal Ilmiah Manajemen INOVTEK Polbeng - Seri Informatika
Ambarwati, Rizki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Pengaruh Pelayanan Admin E-Commerce dalam Menjawab Pertanyaan Customer Terhadap Kemajuan Usaha Digital Printing: Pengaruh Pelayanan Admin E-Commerce dalam Menjawab Pertanyaan Customer Terhadap Kemajuan Usaha Digital Printing Ambarwati, Rizki; Sholikhah, Afifatus
TALI JAGAD JOURNAL Vol. 3 No. 1 (2025): TALIJAGAD
Publisher : Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/UNU.TJJ.2024.03.01.1

Abstract

E-commerce telah berkembang pesat dan menjadi pilihan utama konsumen, termasuk dalam bisnis digital printing. Pelayanan admin yang cepat, jelas, dan tanggap menjadi faktor penting untuk meningkatkan kepuasan pelanggan dan mendukung pertumbuhan bisnis. Penelitian ini menganalisis pengaruh layanan admin e-commerce terhadap kemajuan bisnis digital printing, dengan fokus pada kecepatan respon, kejelasan informasi, dan sikap pelayanan dalam meningkatkan penjualan, jumlah pelanggan tetap, dan loyalitas pelanggan. Menggunakan pendekatan kuantitatif, sampel diambil melalui teknik simple random sampling dan pengumpulan data dilakukan dengan kuesioner. Analisis dilakukan menggunakan SPSS dengan uji t. Hasil penelitian menunjukkan bahwa layanan admin e-commerce berpengaruh signifikan terhadap kemajuan bisnis digital printing. Hasil uji t menunjukkan t_hitung = 3,955 > t_(tabel) = 1,313 dengan signifikansi 0,000 yang menunjukkan bahwa layanan yang cepat dan informatif dapat mempercepat pertumbuhan bisnis. Kualitas layanan admin juga meningkatkan kepuasan dan loyalitas pelanggan, dengan koefisien regresi sebesar 0,530. Temuan ini menegaskan pentingnya layanan admin dalam mendorong perkembangan bisnis digital printing.
Klasifikasi Jenis Tanaman Obat Herbal Berdasarkan Ciri Daun Menggunakan K-NN Meilani, Cindy; Ambarwati, Rizki; Saputri, Devita; Fujianto
Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Vol. 3 No. 2 (2025): JURNAL PENGEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIAKSI (JUPTIK)
Publisher : Prodi Teknologi Informasi Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/juptik.v3i2.3028

Abstract

Indonesia has a variety of abundant plants, including medicinal plants. However, many people still do not know about the types of herbal medicinal plants that exist. The process of identifying types of herbal medicinal plants generally relies on the knowledge of botanists with manual methods, which rely on morphological characteristics and vision. With advances in technology, leaf image recognition can be done using computer vision methods. This study aims to identify types of herbal medicinal plants based on leaf image patterns using the K-Nearest Neighbors (K-NN) method. The identification process begins with taking leaf images, then feature extraction is carried out to distinguish plant types. The results of the study show that the K-NN method can provide a fairly good level of accuracy in identifying types of medicinal plants. This system is expected to help the public recognize medicinal plants more effectively and expand knowledge about the benefits of herbal plants. Thus, the application of leaf image recognition technology can be a solution in conserving knowledge about medicinal plants in Indonesia.
Classification of Herbal Plant Images Using Transfer Learning EfficientNetV2-B3 Ambarwati, Rizki; Devella, Siska
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fz4jy549

Abstract

Herbal plants are natural resources that have high economic and health value, but the identification process is still done manually, making it prone to errors due to morphological similarities between species. This study aims to develop a leaf image classification model for herbal plants using a Convolutional Neural Network (CNN) with the EfficientNetV2-B3 transfer learning approach and AdamW optimizer. The dataset used is the Indonesian Herb Leaf Dataset 3500, which consists of 3,500 leaf images from 10 types of Indonesian herbal plants, namely Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri, and Sirih. The research stages included preprocessing, dataset division, and augmentation such as flipping, rotation, zooming, contrast and brightness changes, translation, and the addition of Gaussian noise and salt-and-pepper noise to increase data variation and test model robustness. Evaluation based on accuracy, precision, recall, and f1-score shows that the model without augmentation achieved 98.57% accuracy, 98.63% precision, 98.57% recall, and a 98.58% f1-score, while the model with augmentation and noise addition achieved an accuracy of 97.71%, precision of 97.83%, recall of 97.71%, and an f1-score of 97.72%. These results prove that EfficientNetV2-B3 is capable of effectively classifying herbal plant leaves with good performance.  
Co-Authors Devella, Siska Fujianto Meilani, Cindy Saputri, Devita Sholikhah, Afifatus