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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
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Lightweight Deep Learning Approach for Sugarcane Leaf Disease Classification Using MobileNetV2 Paramita, Cinantya; Pradana, Rifky Bintang; Winarsih, Nurul Anisa Sri; Pramunendar, Ricardus Anggi
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.456

Abstract

Sugarcane is one of Indonesia’s strategic crops, yet its productivity is frequently disrupted by leaf diseases such as yellow leaf, rust, and red rot. Previous studies have shown that deep learning models are promising for plant disease detection, but many of them rely on heavy architectures that limit deployment in real-world agricultural settings. To address this gap, this study applies MobileNetV2, a lightweight Convolutional Neural Network, for the classification of sugarcane leaf diseases. Using the publicly available Kaggle dataset, the model was trained and evaluated on four classes: healthy, yellow leaf, rust, and red rot. The results demonstrate that MobileNetV2 achieved 97.0% test accuracy, with strong precision, recall, and F1-scores across all categories. These findings highlight that efficient deep learning architectures can deliver reliable disease classification while remaining practical for implementation on mobile or edge devices. Compared with previous approaches, this study contributes by demonstrating that lightweight model like MobileNetV2 can provide a balance of accuracy and efficiency, making them suitable for supporting precision agriculture practices in resource-limited environments
Innovation in Crop Nutrition Planning Based on Rainfall Prediction Using Singular Spectrum Analysis and Boosting to Optimize Agricultural Management Yuslena Sari; Mambang Mambang; Muhammad Zulfadhilah; Subhan Panji Cipta; Muhammad Nursandi; Finki Dona Marleny; Ricardus Anggi Pramunendar; Sunardi Sunardi; Eka Setya Wijaya; Aurelia Monica Sari; Muhammad Alkaff
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/z3mgdv08

Abstract

The high variability of rainfall in tropical climates presents a major challenge for agricultural management, as weather uncertainty often leads to inefficient fertilization practices due to nutrient loss. This study aims to develop a robust framework for rainfall prediction, which can inform a flexible and precise crop nutrient scheduling system. Utilizing an hourly rainfall dataset (n=6,624) obtained from IoT sensors, the research proposes an approach that integrates Singular Spectrum Analysis (SSA) for signal decomposition and noise reduction with Gradient Boosting algorithms (LightGBM and XGBoost). Spline interpolation was employed to handle missing data, while SSA served to disentangle deterministic trends from random noise, enabling the models to perform more effectively on the refined dataset. Empirical evaluation demonstrates that the SSA-XGBoost hybrid model achieves superior performance, with an RMSE of 0.0057 and an R² of 0.8278, significantly outperforming the SSA-LightGBM model (R² 0.2879), which struggled to capture non-linear patterns within this dataset. The high predictive accuracy of the SSA-XGBoost model facilitates the implementation of responsive nutrient management strategies, wherein fertilizer application can be deferred during forecasted periods of high rainfall to prevent runoff and environmental pollution. This research contributes to the field of hydroinformatics by demonstrating the effectiveness of combining SSA and XGBoost as a cost-efficient yet high-performance solution for mitigating climate-related risks in tropical wetland agriculture.
Pelatihan Implementasi Artificial Intelligence Menggunakan Teachable Machine berbasis Project-Based Learning bagi Siswa SMA/SMK Dibyo Adi Wibowo; Moch. Sjamsul Hidajat; Ricardus Anggi Pramunendar; Muhammad Syaifur Rohman; Danny Oka Ratmana; Rama Aria Megantara
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3226

Abstract

Artificial Intelligence (AI) merupakan teknologi yang berkembang pesat dan penting untuk dikenalkan sejak jenjang pendidikan menengah. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA/SMK di Kota dan Kabupaten Kediri terhadap konsep dasar Artificial Intelligence dan machine learning melalui pelatihan implementasi AI menggunakan Teachable Machine berbasis Project-Based Learning (PjBL). Metode pelaksanaan kegiatan mengombinasikan pendekatan PjBL dan experiential learning, di mana peserta dilibatkan secara aktif dalam pengembangan proyek AI sederhana berbasis gambar, suara, dan pose tubuh. Evaluasi pembelajaran dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman peserta. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada seluruh kategori materi, termasuk konsep dasar AI, computational thinking, machine learning, penggunaan Teachable Machine, serta implementasi dan evaluasi model AI. Temuan ini menunjukkan bahwa penggunaan Teachable Machine yang dipadukan dengan pendekatan PjBL efektif dalam meningkatkan literasi Artificial Intelligence siswa SMA/SMK serta membantu peserta memahami konsep AI secara lebih konkret dan aplikatif.
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Arya Rezagama Sudrajat Aurelia Monica Sari Azzahra, Tarissa Aura Baroroh, Nurul Bastiaans, Jessica Carmelita Brilianto, Rivaldo Mersis Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Danny Oka Ratmana Darmawan, Aditya Aqil De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Finki Dona Marleny Firmansyah, Muhammad Ilham Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Harun Al Azies Hasan Asari Haydar, Muhammad Rifqi Fajrul Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Khoirur Rizky, Muhammad Ivan Kristhina Evandari Kurnia Prayoga Wicaksono Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Mambang Maulana, Isa Iant Megantara, Rama Aria Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Moch. Sjamsul Hidajat Mochamad Arief Soeleman Mochamad Hariadi Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Mohammad Syaifur Rohman Muhammad Alkaff Muhammad Naufal Muhammad Nursandi Muhammad Syaifur Rohman Muhammad Zulfadhilah Muljono, - Muslih Muslih Muslih Muslih Nabila, Mira Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Paramita, Cinantya Pergiwati, Dewi Prabowo, D.P. Pradana, Rifky Bintang Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ramdan, Hendri Ratmana, Danny Oka Riadi, Muhammad Fatah Abiyyu Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Rozada, Akfi Ruri Suko Basuki Sambasri, Fikri Diva Santoso, Siane Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Soeleman, Moh. Arief Sri Winarno Stefanus Santosa Subhan Panji Cipta Sulistyowati, Tinuk Sunardi, Ph.D., Sunardi Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wijaya, Eka Setya Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan