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All Journal Nuansa Informatika JEJARING JOIN (Jurnal Online Informatika) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Buffer Informatika Empowerment : Jurnal Pengabdian Masyarakat Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Computer Science and Information Technology (CoSciTech) Algoritme Jurnal Mahasiswa Teknik Informatika Buffer Informatika Indonesian Journal of Accounting and Governance
Iwan Lesmana
FKOM UNIKU
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Nursing Social Sciences

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Classification of Bougainvillea Plant Types Using Convolutional Neural Network Algorithm Fauzi Rachman; Iwan Lesmana; Nugraha, Nunu
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.15354

Abstract

Bougainvillea is one of the most popular ornamental plants, featuring a variety of types with morphological characteristics that often appear very similar. This resemblance frequently complicates the conventional identification process, particularly for sellers and buyers at Rabiku Florist. This study aims to develop an Android application capable of automatically classifying different bougainvillea types using a Convolutional Neural Network (CNN) algorithm. The system is developed using the Rapid Application Development (RAD) methodology, leveraging the MobileNetV2 architecture and integrating it with the TensorFlow Lite framework to ensure compatibility with mobile devices. The application is designed to identify five types of bougainvillea using digital images captured via the device’s camera or selected from the user’s gallery. Based on implementation results, the system demonstrates strong classification performance and delivers accurate information to users. This application is intended to serve as a practical and user-friendly tool for both the general public and businesses in accurately identifying bougainvillea species.Keywords: Image Classification, Bougainvillea, Convolutional Neural Network, MobileNetV2, Android.
Pemberdayaan dan Penerapan Teknologi Pengenalan Emosi Anak Autisme Berbasis Deep Learning di SLBN Taruna Mandiri Kuningan Sugiharto, Tito; Priantama, Rio; Lesmana, Iwan; Cahyawiguna, Bagas; Apriah, Lilis; Yusuf, Muhamad Faizal
Abdimas Galuh Vol 8, No 1 (2026): Maret 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v8i1.23418

Abstract

Anak dengan spektrum autisme seringkali memiliki hambatan dalam mengekspresikan emosi, sehingga guru di SLBN Taruna Mandiri Kuningan kesulitan memberikan respons pembelajaran yang tepat secara real time. Kondisi ini jika dibiarkan dapat menghambat efektivitas transfer materi dan perkembangan psikologis anak. Pengabdian masyarakat ini bertujuan untuk memberdayakan guru melalui peningkatan literasi digital dalam penerapan teknologi pengenalan emosi berbasis deep learning. Bahan yang digunakan mencakup perangkat lunak deteksi emosi dan modul panduan literasi digital yang dirancang secara inklusif. Metode pelaksanaan kegiatan meliputi tahap sosialisasi, pelatihan penggunaan aplikasi berbasis model MobileNetV2, implementasi langsung dalam proses belajar mengajar, serta pendampingan dan evaluasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengoperasikan teknologi kecerdasan buatan untuk mengenali emosi siswa (senang, sedih, marah, takut, netral, dan terkejut) secara otomatis melalui kamera dengan tingkat akurasi yang tinggi. Pembahasan menekankan bahwa integrasi teknologi ini mampu mengurangi hambatan komunikasi antara guru dan siswa serta menciptakan lingkungan belajar yang lebih adaptif dan berpusat pada kebutuhan khusus anak. Kesimpulannya, penerapan teknologi deep learning efektif membantu guru dalam memantau kondisi emosional anak autisme secara objektif. Program ini berhasil menjembatani kesenjangan antara teknologi AI dengan kebutuhan pendidikan luar biasa. Disarankan agar pihak sekolah melakukan pembaruan data secara berkala dan memperluas penggunaan aplikasi pada jenjang kelas yang berbeda untuk menjaga akurasi deteksi emosi pada berbagai karakteristik siswa.
Comparison of MobilenetV2 and NASNetMobile for Lavender Flower Analysis using Convolutional Neural Network Sugiharto, Tito; Lesmana, Iwan; Priantama, Rio; Saleh Ba Matraf, Munya
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1654

Abstract

The identification of lavender flower varieties is a critical challenge in botany and agriculture, primarily due to the high morphological similarity among different varieties and the influence of environmental conditions on their appearance. Traditional methods of identifying lavender varieties, which often rely on manual observation, face significant limitations. These methods are time-consuming, prone to subjective error, and may not account for subtle environmental variations that affect flower morphology. The specific goal of this research is to develop an automated classification model using Deep Learning techniques, specifically Convolutional Neural Networks (CNN), to improve the accuracy and efficiency of lavender variety identification. The study leverages a dataset from Kaggle, which contains images of three lavender varieties—Lavandula angustifolia, Lavandula viridis, and Lavandula multifida. By applying data augmentation techniques to address dataset variability, the research compares two advanced CNN architectures, MobileNetV2 and NASNetMobile, for their classification performance. The key contribution of this work is demonstrating that NASNetMobile achieves superior performance, with 91.87% accuracy and a lower loss value, compared to MobileNetV2, which reaches 81.67% accuracy. This study highlights the novelty of using CNN models for lavender classification, offering a significant advancement over traditional methods by enhancing the identification process's accuracy and reducing reliance on manual and inefficient approaches. The findings have broad implications for botanical research, agricultural practices, and plant conservation efforts, showing that CNNs can significantly improve the efficiency of plant species identification.  
Co-Authors Alif, Muhammad Alif Fathan Apriah, Lilis Bambang Setiono Biantara, Dheny Bintara, dheny Cahyawiguna, Bagas Dede Irawan Deni . Didin Lukmanudin Fahmi Yusuf Fauzan, Farhan Fauzi Rachman Gumelar, Gilang Handayani, Sri Hegar Paningkat Bagja Margaretha, Viona Nunu Nugraha Purnama, Agung Andri Tri Rasul, Muh Abi RIO ANDRIYAT KRISDIAWAN Rio Priantama Rumondang, Safrida Saleh Ba Matraf, Munya Savira, Winnie Sitardja, Meco Sugeng Supriyadi Sugiarto, Lawberto Sutanto, Shelby Tito Sugiharto Virginia, Chyntia Augustia Yati Nurhayati Yulyanto Yulyanto Yusuf, Muhamad Faizal