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All Journal Techno.Com: Jurnal Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika bit-Tech TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Konstelasi: Konvergensi Teknologi dan Sistem Informasi Journal of Informatics and Computer Science (JINACS) Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi Smatika Jurnal : STIKI Informatika Jurnal Journal of Applied Informatics Research
SALAMUN ROHMAN NUDIN
Universitas Negeri Surabaya
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Efektivitas Media Pembelajaran Berbasis Scratch dalam Meningkatkan Pemahaman Huruf Hijaiyah pada Siswa SD Hang Tuah 7 Surabaya Hafizhuddin Zul Fahmi; Salamun Rohman Nudin; Andi Iwan Nur Hidayat; Asmunin Asmunin; Fisma Meividianugraha Subani; Alvin Febrianto
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.1082

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman huruf hijaiyah pada siswa kelas 5 SD Hang Tuah 7 Surabaya melalui inovasi media pembelajaran interaktif berbasis Scratch. Latar belakang penelitian didasari oleh data yang menunjukkan masih rendahnya kemampuan membaca Al-Qur’an di Indonesia. Metode pelaksanaan terdiri atas tiga tahap: persiapan, pelaksanaan pembelajaran menggunakan permainan edukatif Scratch, dan evaluasi melalui pre-test dan post-test. Hasil evaluasi menunjukkan peningkatan pemahaman siswa yang signifikan, dengan rata-rata nilai post-test mencapai 4,71 dari nilai maksimal 5. Analisis statistik menggunakan metode N-Gain menghasilkan nilai 0,90 yang termasuk dalam kategori tinggi, dengan demikian dapat disimpulkan bahwa media pembelajaran berbasis Scratch terbukti efektif tidak hanya dalam meningkatkan pemahaman huruf hijaiyah, tetapi juga dalam menumbuhkan motivasi dan keterlibatan aktif siswa selama proses pembelajaran. 
Implementasi Model MobileNetV2 dalam Pengembangan Sistem Prediksi Kematangan Pisang Berbasis Mobile Mufidh, Ainul; Nudin, Salamun Rohman
TIN: Terapan Informatika Nusantara Vol 6 No 11 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i11.9559

Abstract

Bananas are a highly nutritious tropical fruit widely consumed by the public. However, the manual process of determining ripeness levels often leads to inconsistencies. Pak Sanali’s Banana Plantation in Krembung Subdistrict, Sidoarjo Regency, faces challenges in accurately determining fruit ripeness, which can potentially cause sorting errors that impact the quality and market value of the harvest. This study aims to design an image-based banana ripeness prediction system using a Deep Learning approach. The model used is a Convolutional Neural Network (CNN) with a MobileNetV2 architecture that employs transfer learning. The process begins with banana image inputs that undergo preprocessing stages of augmentation and normalization, followed by feature extraction through convolutional layers to capture visual characteristics such as the color and texture of the banana peel. These features are then processed in the classification head layer, consisting of Global Average Pooling and a fully connected layer, to generate predictions for four ripeness classes: unripe, ripe, overripe, and rotten. Test results show that the model using the Adam optimizer delivers the best performance, with an accuracy of 99.47% and a test loss of 0.39%. The model was developed using Python and TensorFlow on Google Colaboratory and implemented in a Kotlin-based application. Evaluation using a confusion matrix demonstrates excellent classification performance based on the metrics of accuracy, precision, recall, and F1-score.
Car Storage Warehouse Information System Using the LIFO Method andy wildan romadhoni; Rohman Nudin, Salamun
Journal of Applied Informatics Research Vol. 2 No. 1 (2026): July (In Progress)
Publisher : Universitas Negeri Surabaya

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

Abstract

Warehouse management in car rental businesses requires an efficient system to manage vehicle storage and retrieval processes. However, many existing systems still rely on manual recording or focus primarily on transaction management without integrating inventory prioritization methods, resulting in inefficient vehicle rotation and increased operational errors. This study aims to develop a web-based car storage warehouse information system that implements the Last In First Out (LIFO) method to improve storage efficiency and decision-making processes. The system is developed using the Research and Development (R&D) approach and the Waterfall model, with implementation based on PHP and MySQL. The LIFO mechanism is applied by prioritizing vehicles based on the most recent entry timestamp. The evaluation results show that the system reduces vehicle selection time from 3-5 minutes in manual processes to 5-10 seconds, representing an efficiency improvement of approximately 95%, while achieving 100% consistency in vehicle selection. This study contributes by integrating LIFO-based inventory control into a practical web-based warehouse system, providing a more structured, efficient, and accurate solution for vehicle storage management.
Implementasi Model MaxVit Untuk Deteksi Penyakit Daun Tanaman Bawang Merah Berbasis Mobile Amanda Khoiromaul Soviyanti; Salamun Rohman Nudin
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): Juni 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v6i1.14764

Abstract

Bawang merah (Allium cepa var. aggregatum) merupakan komoditas hortikultura penting di Indonesia. Namun, produktivitasnya sering menurun akibat serangan penyakit daun yang sulit dikenali secara visual. Penelitian ini bertujuan mendeteksi penyakit daun bawang merah menggunakan model Multi-Axis Vision Transformer (MaxViT) dengan teknik transfer learning melalui klasifikasi citra daun. Dataset yang digunakan adalah Onion Dataset yang terdiri dari empat kelas, yaitu Healthy, Purple Blotch, Leaf Blight, dan Iris Yellow Spot Virus. Proses pelatihan model dilakukan menggunakan Python, TensorFlow, dan Google Colab dengan membandingkan optimizer Adam, AdamW, dan SGD. Evaluasi dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa optimizer Adam menghasilkan performa terbaik dengan akurasi pengujian sebesar 98%. Model terbaik kemudian diimplementasikan ke dalam aplikasi mobile berbasis Flutter untuk mendukung deteksi penyakit daun bawang merah secara cepat dan mudah diakses.
DETECTION OF SUGARCANE LEAF DISEASES USING MOBILENETV3LARGE-BASED TRANSFER LEARNING FOR MOBILE APPLICATIONS Frida Nur Cahyani; Salamun Rohman Nudin
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.542

Abstract

Sugarcane is one of Indonesia's key plantation commodities with a critical role in fulfilling national sugar demand and supporting bioethanol production. However, sugarcane productivity remains low due to leaf diseases that reduce crop quality and yields, while slow or inaccurate identification accelerates their spread. This study proposes and develops a mobile-based sugarcane leaf disease detection system using transfer learning with the MobileNetV3Large architecture to classify 11 disease classes. Two dataset scenarios were applied: Scenario 1 using the SLD Dataset with 6,748 images and Scenario 2 combining the SLD and Sugarcane Smut datasets totaling 14,804 images. Each scenario was trained under three optimizer configurations: Adam, RMSprop, and SGD, to identify the best-performing combination. Results show that Adam achieved the highest validation accuracy in both scenarios, reaching 94.24% in Scenario 1 and 97.43% in Scenario 2, with corresponding test accuracies of 94.91% and 97.31% respectively. The final model was deployed as a Flutter-based mobile application capable of performing real-time disease detection through image upload or camera capture, providing an accessible tool for farmers to identify sugarcane leaf diseases efficiently.
lmplementasi YOLOv11 untuk Penghitungan Kerumunan Real-Time dalam Arsitektur Microservices pada Video CCTV Naim, M.Sultonun; Nudin, Salamun Rohman
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2378

Abstract

The provision of accurate and easily accessible public information regarding the condition of urban parks remains a challenge in the management of public open spaces. People still do not have a system that allows them to monitor park crowd levels directly, making real-time observation of park conditions difficult. Previous studies on crowd counting have mainly focused on improving object detection performance, while the implementation of crowd monitoring systems as public information services remains limited. Therefore, this research integrates the YOLOv11 algorithm with a microservices architecture to provide real-time crowd information through a public park monitoring website. This research develops a park visitor counting information system based on computer vision using the YOLOv11 algorithm to detect and count crowds from CCTV video streams. The system is designed using a microservices architecture with the FastAPI framework to support real-time detection and data integration into the Surabaya park monitoring website. The research process involves several stages, including dataset preparation, data labeling using Roboflow, YOLOv11 model training, selection of the most optimal optimizer, and implementation of the system on the detection backend. The results show that the YOLOv11m model with the SGD optimizer achieved the best performance, obtaining an mAP@50 score of 92.76%, a recall value of 89.75%, and an F1-score of 90.07%. In addition, the system successfully performed real-time crowd detection and counting under various crowd density levels, lighting conditions, and CCTV camera angles.
Co-Authors Abdul Khamid Abdul Khamid Aditia Syahputra, Dimas Novian Alamsyah, Shandy Ilham Alen Bachtiar Heriyanto Alvin Febrianto Amanda Khoiromaul Soviyanti Andi Iwan Nur Hidayat Andi Iwan Nurhidayat andy wildan romadhoni Anggraeni, Erika Kanya Arifin, Achmad Nurs Syururi Asmunin Asmunin Asyadana, Aldi Naufal Augusta Jannatul Firdaus, Reza Azzah, Aini Celia Angelina Nogo Koban Chuluq, Khusnul Dodik Arwin Dermawan Elok Nur Fauziyah Enggarbela Ogi Intan Pratiwi Enggarbela Ogi Intan Pratiwi Faris Abdi El Hakim, Faris Abdi Fisma Meividianugraha Subani Fitria Hidayanti Fitria Hidayanti Frida Nur Cahyani Ghoffar, Alghifar Abdul Ghoffar, Alghifar Abdul Hafizhuddin Zul Fahmi Handika Dio Pradana Laili Indah Liana Mufidh, Ainul Naim, M.Sultonun Natanael Chrisanta Rikandi Panji Sulanggalih, Mochammad Pratama, Moch Deny Prawira, Indra Waskita Prismana, I Gusti Lanang Putra Eka Qholisa, Siti Nur Rahmaditya Putri Lailatul 'Ismi Rama Syaiful Arif Rifky Fajar Oktanugraha Shinta Yuan Ayu Pratiwi SUGIARTO - Sugiarto - Yisti Vita Via Yisti Vita Via