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All Journal Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Deep Learning, Computer Vision and Digital Image Processing Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal MediaTIK
Marwan Ramdhany Edy
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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FOOD COURT UNM: PENGEMBANGAN APLIKASI PEMESANAN MAKANAN BERBASIS ANDROID DENGAN METODE AGILE : UNM FOOD COURT: DEVELOPMENT OF ANDROID-BASED FOOD ORDERING APPLICATION WITH AGILE METHOD Muhammad Argya Yunansyah; Aqsa Mahmud; M. Aflah Ogi Daffa; Ilham Daffa Maulana; Andi Dio Nurul Awalia; Marwan Ramdhany Edy
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 3 Issue 1 Maret 2025
Publisher : CV. Sakura Digital Nusantara

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Abstract

The food ordering system on campus is one example of how advances in information technology encourage the conversion of manual services into digital services. The traditional food ordering procedure at Makassar State University poses a number of problems, including long queues and slow service. The purpose of this project is to create UNM food court android application, an Android-based food ordering software, as a digital way to improve service effectiveness. The approach used is the Agile method which is an incremental and iterative approach, with many sprint cycles that allow for continuous assessment and improvement in response to customer requirements. Using black box and white box methodologies, the development process is run through planning, requirements analysis, interface design, implementation, and testing phases. The app can enable multi-role login functions, food ordering, order progress notifications, and real-time administration of stores and users, according to implementation results. Testing shows that each feature works as intended. This study concludes that the UNM food court app contributes significantly to the digitalisation of food ordering services on campus and the Agile method was successful in creating a mobile app based on real demand. Furthermore, this study contributes to the body of knowledge regarding the use of Agile approaches in software development for the field of education.
Pengembangan Aplikasi ToDoList Berbasis Mobile: Pendekatan Spiral Model untuk Mendukung Manajemen Waktu Mahasiswa: Mobile-based ToDoList Application Development: A Spiral Model Approach to Support Student Time Management Indra Alfit; Fauzan G; Salomo Benny Junian; Fauziah Rahmasari; Nurrahmah Agusnaya; Marwan Ramdhany Edy
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 3 Issue 1 Maret 2025
Publisher : CV. Sakura Digital Nusantara

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Abstract

The development of modern technology has driven the use of the internet in various fields, including education. Students often still use manual methods to record their activities, which are inefficient and prone to loss. Therefore, a to-do list application is needed to help students digitally record their activities. This study aims to develop an effective and efficient mobile-based ToDoList application. The application is expected to help students plan activities, set priorities, and ensure that important tasks are not missed. The development method used is the spiral model, an iterative and incremental approach that involves the stages of planning, analysis, design, implementation, testing, and evaluation in each cycle. This study emphasizes a deep understanding of user needs and the application of good interface design principles. In addition to technical development, the research also focuses on how the application can support time management and the achievement of user goals. Application testing is conducted to ensure that features function according to user expectations. Through this research, valuable insights are expected to be gained regarding the development of an effective ToDoList application that can improve users' quality of life through better time management and more efficient task completion.
Transformasi Pembelajaran Vokasi Pertanian melalui Integrasi IoT dan Computer Vision pada Smart Greenhouse di SMKN 4 Barru: Penelitian Marwan Ramdhany Edy; Muh. Ihsan Zulfikar; Nurfauziah; Putri Nirmala; Nurrahmah Agusnaya
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.3765

Abstract

The digital agricultural transformation demands strengthening technological literacy and skills in vocational schools, particularly in the utilization of the Internet of Things (IoT) and Computer Vision to support precision farming practices. This community service program aims to improve the competency of teachers and students of SMKN 4 Barru in integrating IoT-based Smart Greenhouse technology and Computer Vision into agribusiness learning. The activity uses a Participatory Action Research (PAR) approach through the stages of preparation, training, technology implementation, mentoring-evaluation, and sustainability, involving teachers, education staff, and students. Evaluation was conducted through pre-post tests, observation, and participatory reflection. The results showed a significant increase in participants' conceptual understanding and practical skills, including the use of temperature, humidity, and soil pH sensors for IoT-based hydroponic systems, as well as the use of Computer Vision to visually detect plant conditions. Participants also reported increased confidence in operating modern agricultural technology devices and awareness of the importance of digital-based innovation in vocational learning. Key challenges identified include the integration of sensor data into monitoring systems and the development of Computer Vision algorithms, requiring further mentoring and infrastructure strengthening. Overall, this program contributes to increasing digital literacy, readiness for the implementation of precision agriculture, and the development of a creative agro-education ecosystem relevant to the demands of Industry 4.0 in Barru Regency.
Analisis Sentiment Gambar pada Media Sosial dengan Pendekatan Deep Learning Muhammad Ridha Darwis; Adam Ramadhan; Diva Nurul Azila; Siti Fatimah Azzahrah Namar; Rafiqah Amelia Kasim; Marwan Ramdhany Edy
Jurnal MediaTIK Volume 7 Issue 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/mediatik.v7i2.2767

Abstract

Media sosial telah menjadi platform penting untuk berbagi konten dan interaksi, dengan jutaan gambar diposting setiap hari yang mencerminkan berbagai pengalaman dan emosi pengguna. Namun, analisis sentimen tradisional lebih sering dilakukan pada teks dibandingkan gambar. Penelitian ini bertujuan untuk mengembangkan model deep learning, khususnya Convolutional Neural Network (CNN), untuk melakukan analisis sentimen pada gambar. Model ini dirancang untuk mengekstraksi fitur visual dan mengklasifikasikan gambar berdasarkan sentimen (positif, negatif, atau netral). Metode yang digunakan meliputi pengumpulan 50 gambar dari platform media sosial seperti Instagram, Twitter, dan Facebook, yang diberi label sentimen secara manual oleh anotator terlatih. Data kemudian diproses dengan membaginya menjadi data latih (40 gambar), validasi (5 gambar), dan uji (5 gambar). Pengembangan model menggunakan arsitektur CNN seperti VGG16 dengan teknik transfer learning, diikuti dengan evaluasi performa menggunakan data uji. Hasil penelitian menunjukkan bahwa model deep learning yang dikembangkan mampu memprediksi sentimen gambar dengan akurasi 80%, menggunakan metrik seperti precision, recall, dan F1-score. Confusion matrix memberikan gambaran rinci mengenai prediksi yang benar dan salah untuk setiap kategori sentimen. Kesimpulannya, pendekatan deep learning, khususnya CNN, menunjukkan potensi besar dalam analisis sentimen gambar di media sosial. Meskipun ada beberapa kesalahan prediksi, model ini mampu memberikan wawasan berharga tentang reaksi pengguna terhadap konten. Untuk penelitian selanjutnya, disarankan untuk memperbaiki kesalahan prediksi dan meningkatkan akurasi model dengan metode pelabelan data dan pemilihan fitur yang lebih efektif. Teknologi ini juga dapat digunakan untuk mendukung pemantauan konten media sosial guna menciptakan lingkungan online yang lebih aman dan sehat bagi pengguna.
Klasifikasi Citra Dengan Pendekatan Transfer Learning Pada Gambar Fauna Terbang Andi Nurul Inaya; Azzah Ulima Rahma; Miftakhul Jannah; Luthfiyah Ramadhani K. Arafah; Lulu Latifa Ishak; Marwan Ramdhany Edy
Jurnal MediaTIK Volume 7 Issue 1, Januari (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/mediatik.v7i1.2785

Abstract

Indonesia, dengan kekayaan alamnya yang luar biasa, menjadi rumah bagi beragam fauna, termasuk burung. Namun, melindungi dan mengkatalogisasi keragaman ini memerlukan metode yang efisien dan akurat. Dalam konteks ini, pendekatan transfer learning menonjol sebagai alat yang dapat meningkatkan klasifikasi citra fauna terbang. Penelitian ini menggunakan Google Colab sebagai lingkungan pengkodean, memanfaatkan kemudahan penyimpanan dan akses data melalui Google Drive. Kami memproses dataset ImageNet dengan metode transfer learning menggunakan bahasa pemrograman Python. Hasil dari penelitian ini diharapkan dapat memberikan kontribusi dalam berbagai aplikasi, termasuk pengenalan objek, deteksi wajah, dan segmentasi objek. Secara khusus, dalam pengembangan perangkat lunak, klasifikasi citra seperti ini dapat diterapkan dalam sistem pengenalan hewan berbasis gambar, keamanan berbasis kamera, atau aplikasi pencarian berdasarkan gambar.
Co-Authors Abd Majid Adam Ramadhan Ainun Mardiah Amanda Putri Andi Dio Nurul Awalia Andi Jaedil Bugdady Andi Nurul Inaya Aqsa Mahmud Aswar Azzah Ulima Rahma Diva Nurul Azila Fadhil Dhanendra Fadila Husnul Khatimah Fauzan G Fauziah Fauziah Rahmasari Femmy Angreany Hidayat Nurwahid Ilham Daffa Maulana Indra Alfit Kurnia Prima Putra Lulu Latifa Ishak Luthfiyah Ramadhani K. Arafah M, Wahyu Hidayat M. Aflah Ogi Daffa Miftakhul Jannah Muh Sahlan Syah Muh. Ihsan Zulfikar Muhammad Argya Yunansyah Muhammad Ilham Suherman Muhammad Januar Anshari Muhammad Ridha Darwis Novita Nurhidayah Nur Aeni Rahman Nur Ainung Nur Fakhrunnisaa Nurfaisa Riono Nurfauziah Nurhidayat Nurrahmah Agusnaya Putri Nirmala Rafiqah Amelia Kasim Ria Sakinah Waji Risha Febrianti Salomo Benny Junian Sanatang Siti Fatimah Azzahrah Namar Sul Fitriana Syahrul Syntha Dewi, Shabrina Yudha Nurfaiz