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All Journal Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Informatika dan Rekayasa Elektronik Journal of Artificial Intelligence and Innovative Applications (JOAIIA) INFORMATIKA KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi Innovative: Journal Of Social Science Research Journal of Research and Publication Innovation SATIN - Sains dan Teknologi Informasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Jurnal Informatika: Jurnal Pengembangan IT Jurihum
Niki Ratama
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation Other

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ETIKA DAN KEAMANAN DATA PADA AI GENERATIF UNTUK TUGAS SEKOLAH Kahfi Heryandi Suradiraja; Mardiyanto; Niki Ratama
Abdi Jurnal Publikasi Vol. 4 No. 5 (2026): Mei
Publisher : Abdi Jurnal Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of generative Artificial Intelligence (AI) technology has had a significant impact on education, particularly in assisting students with school assignments. However, the use of AI without proper understanding of ethics and data security may lead to various issues, such as plagiarism, technology dependency, and risks of personal data leakage. This Community Service activity aimed to improve students’ understanding of ethical AI usage and the importance of digital data security. The activity was conducted over three days at SMA Al Wafi Islamic Boarding School using lecture, discussion, demonstration, and hands-on practice methods. The results showed that participants gained better understanding regarding responsible AI usage. In addition, this activity successfully developed a web-based application called “AI Ethics Checker for Assignments” to help students independently evaluate the ethical use of AI in school assignments. The application includes text checking, AI ethics checklists, paraphrasing suggestions, and digital data security education features. In the future, the application can be further developed using Fuzzy Logic, Machine Learning, and Deep Learning methods to improve the intelligence and accuracy of analysis. This activity is expected to encourage ethical, secure, and responsible AI usage culture in educational environments.
KLASIFIKASI PENYAKIT DAUN PADI BERBASIS ANDROID MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN TRANSFER LEARNING MOBILENETV3 Fajri Pramana Putra; Niki Ratama
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1622

Abstract

Early identification of Rice leaf diseases remains a challenge in agricultural practices, as detection is commonly performed through manual visual observation that is time-consuming and prone to misclassification. Diseases such as blast, Bacterial Leaf Blight, tungro, and Brown Spot often exhibit similar visual characteristics, particularly at early stages. To address this problem, an Android-based application was developed to classify Rice leaf diseases using a Convolutional Neural Network (CNN) with a transfer learning approach based on the MobileNetV3 architecture. The model was trained using a labeled Rice leaf image Dataset obtained from Hugging Face, with preprocessing and data augmentation applied to improve generalization performance. The trained model was deployed through Hugging Face Space using an API-based architecture, allowing image classification to be performed without heavy computational requirements on mobile devices. Experimental results demonstrate that the proposed model achieved an accuracy of approximately 90% on the testing Dataset, exceeding the predefined minimum target accuracy of 85%, with precision and recall values above 80% across all disease classes based on confusion matrix evaluation. These results indicate that the MobileNetV3-based transfer learning approach provides reliable classification performance with good computational efficiency, making it suitable for mobile-based Rice leaf disease detection applications.
Co-Authors Ade Kurniawan Ade Yuliova Balok Adhie Nugraha Sutan Pratama Adinda Nurkhalifah Akbal Adinda Nurkhalifah Akbal Afrizal Dwi Candra Ahmad Fahrezi Ahmad Haitami Ahmad Juantoro Ahmad Permana Ahmad Tazkiarni R Aldi Husadif Andreansyah Anggi Rinaldi Annisa Bulan Nahdlatul Firdaus Anugrah Gusti Putra Ardieka Fahmi Radhitya Ardika, Juli Bima Ardli Maulana Aries Saifudin Arif Rahman Ayu Anggraini BADYA PRISAI NUGRAHA Bagus Budi Pangestu Bambang Suteja Bambang Bimas febriana putra Bintang Fahrezi Citra Septiyani Deny Priyantoko Derry Bagus Muharto Dicky Wahyudi Diki Purnomo Aji Djulianto, Teddy Donny Rahardika Dwi Bayu Aji Elna Tsalatsah, Ibnu Endar Nirmala Eri Fathoni Alamsyah Fajri Pramana Putra Faroeq Althof Nuru Fatah Ridho Perdana Fathurrahman Febby Alviandi Kurniawan Fian Kurniawan Guntur Geni Saka Heppy Azizzul Khakim Hermawan Saputra Himmatul Ulya Al Makassari Ichsan Kurniawan Ikhsan Aprijal Ikrar Bhakti, Bima Ilham Balit Luxfanza Indika Hanjalah Indrawan, Dicky Irman Gantira Irpan Andika Saputra Iwank Ravi Eka Sakristi Juli Bima Ardika Julieta Cahya Mestika Kahfi Heryandi Suradiraja Laurensius Novian Bagas Setyawan M. Syukur Mardianto Triaji Mardiyanto Mas Taufik Maulana Arvyantomo Mochamad Hasbani Mohamad Fathur Janwar Setiadie Wiriaatmadja Mohamad Sopiyan Mubarok Ali Makhzumi Muhajar, Aldy Muhamad Ikhwan S Muhamad Ilham Qardowi Muhamad Royhan A Muhammad Al Fatih Muhammad Deva Ditgara Muhammad Rendy Nurhakiki Muhammad Rifkan Mukhamat Rifqi Ali Munawaroh Munawaroh Munawaroh - Munawaroh - Munawaroh Munawaroh Nasrullah Nikri Aria Pratama Nova Ardiansyah Nuari, Rivian Nugraha, Rifaldi Setia Nurhidayat, Rizki Octavian Tri Hartanto Opa Fauzi Purwantoro Rachmat Julianto Rachmat Julianto Rakhazona Pamungkas, Rangga Ramadhan, Hafiz Ramdani, Muhammad Rifky Arisandi Putra Rio Azril Rohman Abdul Azis Septa Setiadi Pamungkas Sevhia Khoirun Nisa Sevhia Khoirun Nisa Shalhan Taris Agusti Siregar, Salman Slameto, Ilham Ngudi Sri Mulyati Sri Mulyati Sri Mulyati Syawali, Fabian Tabah Yulistio Teti Desyani Visnu Rahman Wahyu Muhamad Sidik Wike Rahayu Yanto, Galih Ferdhi Yulianti Yulianti Yulianti Zaelani, Rizal Zakaria, Hadi Zunhamsah Ismanu