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All Journal Tadris: Jurnal keguruan dan Ilmu Tarbiyah INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JSFK (Jurnal Sains Farmasi & Klinis) Jurnal Abdimas Madani dan Lestari (JAMALI) Jurnal Bimas Islam IQ (Ilmu Al-qur'an): Jurnal Pendidikan Islam Jurnal Mekanik Terapan Jurnal Ilmiah Wahana Pendidikan Media Jurnal Informatika Jurnal Ekonomi dan Manajemen (JEKMA) JOMPA ABDI: Jurnal Pengabdian Masyarakat Takuana: Jurnal Pendidikan, Sains, dan Humaniora The Journal of City: Branding and Authenticity Jurnal Teknologi Informasi Indonesia Tamaddun: Jurnal Ilmu Sosial, Seni, dan Humaniora Jurnal Abdimas Untag Samarinda Madania: Jurnal Kajian Keislaman Journal of Modern Social and Humanities
Fikri Maulana, Fikri
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Classification of Banana Ripeness Using a VGG16-Based Convolutional Neural Network (CNN) Maulana, Fikri
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5930

Abstract

The ripeness level of bananas is a crucial factor that affects the quality, taste, and selling value of the commodity, but the manual sorting process that is commonly carried out is still subjective, inconsistent, and time-consuming. This study aims to implement and evaluate the performance of a VGG16-based Convolutional Neural Network (CNN) architecture in automatically classifying the ripeness level of bananas. The research dataset consists of 5,616 digital images obtained from the Roboflow Universe platform and grouped into six specific classes: freshripe, freshunripe, overripe, ripe, rotten, and unripe. The system development methodology includes data division using stratified splitting techniques, image pre-processing with data augmentation strategies to prevent overfitting, and the application of transfer learning. The model was trained using the Stochastic Gradient Descent (SGD) optimization algorithm with a learning rate of 0.001 for 25 epochs on GPU-based hardware. Performance evaluation was conducted in depth using a confusion matrix, F1-Score metrics, and Precision-Recall curve analysis. The experimental results showed that the VGG16 model achieved an overall accuracy of 97.13%. Class-by-class analysis shows perfect performance in the freshunripe category, although there is a slight decrease in precision in the ripe class due to the similarity of visual characteristics with the overripe class. The stability of the training and validation accuracy curves also indicates that the model has good generalization capabilities. This study concludes that the VGG16 architecture is a reliable and accurate solution to support the efficiency of smart farming systems.
Pelatihan Perancangan UI/UX Aplikasi Mobile E-Masjid Menggunakan Platform UXPilot.ai: Indonesia Falgenti, Kursehi; Rianto, Yan; Pardede, Hilman Ferdinandus; Maulana, Fikri; Amalia, Chalvina Izumi; Dinnillah, Amir Hamzah
Jurnal Abdimas Madani dan Lestari (JAMALI) Volume 08, Issue 2, September 2026
Publisher : UII

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jamali.vol8.iss2.art18

Abstract

Mosque management in Indonesia is still largely carried out manually and conventionally, resulting in inefficiencies in information dissemination, activity recording, and financial transparency. The development of generative artificial intelligence enables the acceleration of designing more effective and user-friendly e-mosque application interfaces. This community service activity aims to enhance the knowledge and skills of mosque administrators and youth of the Majelis Pemuda dan Remaja Islam (MADARIS) Jakarta Islamic Center (JIC) through mosque digitalization. Community training for mosque youth was conducted with the topic of designing mosque management mobile applications using generative AI technology. The output of this community service activity produced user-friendly UI/UX designs that meet community needs. The training was held offline at the Nusa Mandiri Margonda Campus on May 9, 2026. Evaluation results showed that 64% of participants agreed and 36% strongly agreed that this activity broadened their insights. Meanwhile, 55% of participants agreed and 45% strongly agreed that this activity enhanced their skills. The achievement of this activity is that participants gained knowledge on how to utilize generative AI to design UI/UX and acquired practical skills in creating e-mosque application prototypes to support mosque digitalization.
Co-Authors Abdul Rahman, Mohd Safian Bin Abidin Riam , Zaenal ACHMAD FAUZI Alam, Mufassirul Amalia, Chalvina Izumi Anggeraini, Juliya Anwar, Muhammad Syaiful Bachrie Tanrere, Syamsul Bakrie, Ismail Banuaji, Tristan Benni Iskandar Chairul Anwar Desri, Syuryatman Destiani, Risma Dinnillah, Amir Hamzah Dwiny Meidelfi Erwin Budi Setiawan Fajriani, Fenny Fatahillah, Irfan Fauziah, Erna Firda, Muti'ah Graha, Fager Van Hamid, Ihsan HUSNI, MUHAMMAD FAHD DIYAR Ihsan Ikhtiarudin Irawati Irawati Jumani, Jumani juraidi, ahmad Kaimudin, Kaimudin Kursehi Falgenti Marlius, Farizal Muflihin, Iin Yunus Muhit, Abdul Mulyawati, Husni Muslimin Muslimin Neni Frimayanti Pardede, Hilman Ferdinandus Rahma Dona Ramadhan, Abdillah Riam, Zaenal Abidin Rianto, Yan Rinata, Radisa Rohman, Baeti Saputra, Sahrul Aji Shunhaji, Akhmad Silalahi, Nova Tantri Siskandar Siskandar, Siskandar Sitorus, Yalizar Rahayu Sugeng Mulyono Sunaryo, Silfiyanti Syafitri, Anisa Nur'aini Tagor Rambey Winika, Tiara Resti Zaenal Abidin