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Car Storage Warehouse Information System Using the LIFO Method andy wildan romadhoni; Salamun Rohman Nudin
Journal of Applied Informatics Research Vol. 2 No. 1 (2026): July
Publisher : Universitas Negeri Surabaya

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

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.

TRANSFER LEARNING WITH EFFFICIENTNET-B0 FOR CAT BREED CLASSIFICATION: A COMPARATIVE EVALUATION OF OPTIMIZERS Aini Azzah; Salamun Rohman Nudin
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Cats are widely kept as companion animals and exhibit substantial breed level variation in appearance and behavior that influences their care. This study develops a lightweight, image based classifier for identifying twelve common cat breeds using transfer learning on the EfficientNet-B0 backbone. Experiments contrasted four optimization algorithms (SGD, AdaGrad, RMSProp, and Adam) to identify the training strategy that balances convergence speed and generalization. Model effectiveness was measured with confusion matrix analysis and common classification indicators (accuracy, precision, recall, and F1-score). The best performing setup, EfficientNet-B0 fine tuned with the Adam optimizer attained 92% training accuracy, 89% validation accuracy, and 88% on the held out test partition. Subsequently, we integrated the trained model into a Flask web application, backed by an SQLite database, and conducted black-box testing to assess its functional reliability. All system functions met specifications and runtime predictions corresponded closely to ground truth labels. This platform provides a rapid and accurate tool for cat owners and enthusiasts to identify breeds in real-world scenarios, highlighting the usefulness of transfer learning in a streamlined web based implementation.

ResNet50-Based Mobile Application for Big Five Personality Detection Using Handwriting Adelia Salsabila Arifin; Salamun Rohman Nudin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12902

Handwriting reflects a person's unique traits and has long been studied in the field of graphology to uncover personality characteristics. However, traditional graphological analysis is subjective, time-consuming, and prone to inter-rater differences. This study aims to develop a PenaKepribadian mobile application using ResNet50 transfer learning to automatically identify Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). The HiEnWrite dataset contains 327 English handwritten images annotated by a certified graphologists, was utilized with an 80:20 train-test split. Three optimizers such as SGD, RMSprop, and Adam were comparatively evaluated. Adam achieved the best performance with a training PCC of 0.5872 with 91.90% accuracy and a testing PCC of 0.4719 with 89.95% accuracy, outperforming both SGD and RMSprop. A testing PCC of 0.4719 indicates moderate correlation, suggesting promising yet improvable results. Robustness testing across varying lighting conditions, paper backgrounds, and writing media showed consistent performance, with mean prediction deviations ranging from 0.070 to 0.126. All Black Box Testing scenarios returned valid results. These findings confirm that ResNet50 transfer learning effectively extracts handwriting features for personality prediction, though further improvements remain necessary before high-stakes deployment. This research contributes to personality computing and opens avenues for efficient, automated, and accessible personality assessment systems.

Implementasi Transfer Learning Model InceptionV3 Untuk Deteksi Penyakit Daun Jagung Berbasis Mobile Dion Danianto; Salamun Rohman Nudin
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3842

Maize represents a highly crucial agricultural staple within the Indonesian nation, where productivity is often affected by leaf diseases. Diseases like blight, common rust, and gray leaf spot prove hard to recognize by hand since the task demands time and risks personnel mistakes. Current research constructs a maize leaf disease classification model applying transfer learning based on InceptionV3 and evaluates the capabilities of three optimizing algorithms, specifically Adam, Stochastic Gradient Descent (SGD), and RMSProp. The dataset consists of 8,040 images collected from Kaggle and Mendeley, separated into four categories: blight, common rust, gray leaf spot, and healthy. Model training was executed using three dataset scenarios to assess the generalization ability of this suggested method. The empirical findings indicate that the Adam optimizer implemented on the merged dataset attained the highest effectiveness, reaching an exactness of 97.26%, as well as the highest precision, recall, and F1-score compared to SGD and RMSProp. The best-performing model was then converted to TensorFlow Lite and launched within a Flutter-driven Android software to help individuals with the initial spotting of maize leaf diseases.Keywords: Maize; Leaf Disease; Convolutional Neural Network (CNN); InceptionV3 AbstrakJagung adalah salah satu komoditas pangan penting di Indonesia yang kerap mengalami kendala produksi akibat serangan penyakit pada daunnya. Penyakit seperti hawar, karat, dan bercak daun abu-abu sukar dikenali secara manual karena memerlukan waktu yang lama dan berisiko mengalami kesalahan. Studi ini mengembangkan model klasifikasi penyakit daun jagung menggunakan transfer learning berbasis InceptionV3 dengan membandingkan kinerja tiga algoritma optimasi, yaitu Adam, SGD, dan RMSProp. Dataset yang digunakan terdiri dari 8.040 citra dari Kaggle dan Mendeley yang terbagi ke dalam empat kelas, yaitu blight, common rust, gray leaf spot, dan healthy. Pelatihan dilakukan pada tiga skenario dataset untuk mengevaluasi kemampuan generalisasi model. Temuan memperlihatkan bahwa algoritma Adam pada data kombinasi memberikan performa tertinggi dengan akurasi 97,26% serta nilai precision, recall, dan F1-score tertinggi dibandingkan SGD dan RMSProp. Model terbaik dikonversi ke TensorFlow Lite dan diimplementasikan ke dalam aplikasi Android berbasis Flutter sebagai alat bantu mengenali gejala awal penyakit tanaman jagung.Kata kunci: Jagung; Penyakit Daun; CNN; InceptionV3

Identifikasi Penyakit Daun Kentang Menggunakan Transfer Learning Model EfficientNetB3 Berbasis Mobile Reza, R. BG Moch. Faishal; Rohman Nudin, Salamun
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 4 (2026): Agustus 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i4.3657

Abstract Potato leaf disease is one of the main factors that can reduce crop quality and yield, thus requiring an accurate and efficient identification method. Manual identification is often inconsistent and time-consuming. This study aims to develop a deep learning-based model using the EfficientNetB3 architecture with a transfer learning approach to identify potato leaf diseases. The dataset was obtained from Kaggle, namely the Potato Disease Leaf Dataset (PLD), and processed through preprocessing stages including augmentation and normalization. The model was trained using the Adam optimizer to achieve optimal performance. The evaluation results indicate that the model achieved a test accuracy of 99.50%, with precision of 1.00, recall of 1.00, and F1-score of 1.00. The model was then deployed into a Flutter-based mobile application and demonstrated the ability to accurately identify real-world data. Keywords: Potato leaf disease identification; EfficientNetB3; Deep learning; Transfer Learning; TensorFlow Lite. Abstrak Penyakit daun kentang merupakan salah satu faktor utama yang dapat menurunkan kualitas dan hasil panen sehingga diperlukan metode identifikasi yang akurat dan efisien. Identifikasi secara manual sering tidak konsisten dan membutuhkan waktu yang relatif lama. Penelitian ini bertujuan untuk mengembangkan model berbasis deep learning menggunakan arsitektur EfficientNetB3 dengan pendekatan transfer learning untuk mengidentifikasi penyakit daun kentang. Dataset yang digunakan berasal dari Kaggle, yaitu Potato Disease Leaf Dataset (PLD), yang diproses melalui tahap preprocessing berupa augmentasi dan normalisasi. Model dilatih menggunakan optimizer Adam guna memperoleh kinerja optimal. Hasil evaluasi menunjukkan bahwa model mencapai test accuracy sebesar 99,50% serta nilai precision sebesar 1,00, recall sebesar 1,00, dan F1-score sebesar 1,00. Model kemudian diimplementasikan ke dalam aplikasi berbasis mobile menggunakan Flutter serta mampu mengidentifikasi data dunia nyata secara akurat. Kata kunci: Identifikasi penyakit daun kentang; EfficientNetB3; Deep learning; Transfer Learning; TensorFlow Lite.

Implementasi Transfer Learning Model InceptionV3 Untuk Deteksi Penyakit Daun Jagung Berbasis Mobile Dion Danianto; Salamun Rohman Nudin
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3842

Maize represents a highly crucial agricultural staple within the Indonesian nation, where productivity is often affected by leaf diseases. Diseases like blight, common rust, and gray leaf spot prove hard to recognize by hand since the task demands time and risks personnel mistakes. Current research constructs a maize leaf disease classification model applying transfer learning based on InceptionV3 and evaluates the capabilities of three optimizing algorithms, specifically Adam, Stochastic Gradient Descent (SGD), and RMSProp. The dataset consists of 8,040 images collected from Kaggle and Mendeley, separated into four categories: blight, common rust, gray leaf spot, and healthy. Model training was executed using three dataset scenarios to assess the generalization ability of this suggested method. The empirical findings indicate that the Adam optimizer implemented on the merged dataset attained the highest effectiveness, reaching an exactness of 97.26%, as well as the highest precision, recall, and F1-score compared to SGD and RMSProp. The best-performing model was then converted to TensorFlow Lite and launched within a Flutter-driven Android software to help individuals with the initial spotting of maize leaf diseases.Keywords: Maize; Leaf Disease; Convolutional Neural Network (CNN); InceptionV3 AbstrakJagung adalah salah satu komoditas pangan penting di Indonesia yang kerap mengalami kendala produksi akibat serangan penyakit pada daunnya. Penyakit seperti hawar, karat, dan bercak daun abu-abu sukar dikenali secara manual karena memerlukan waktu yang lama dan berisiko mengalami kesalahan. Studi ini mengembangkan model klasifikasi penyakit daun jagung menggunakan transfer learning berbasis InceptionV3 dengan membandingkan kinerja tiga algoritma optimasi, yaitu Adam, SGD, dan RMSProp. Dataset yang digunakan terdiri dari 8.040 citra dari Kaggle dan Mendeley yang terbagi ke dalam empat kelas, yaitu blight, common rust, gray leaf spot, dan healthy. Pelatihan dilakukan pada tiga skenario dataset untuk mengevaluasi kemampuan generalisasi model. Temuan memperlihatkan bahwa algoritma Adam pada data kombinasi memberikan performa tertinggi dengan akurasi 97,26% serta nilai precision, recall, dan F1-score tertinggi dibandingkan SGD dan RMSProp. Model terbaik dikonversi ke TensorFlow Lite dan diimplementasikan ke dalam aplikasi Android berbasis Flutter sebagai alat bantu mengenali gejala awal penyakit tanaman jagung.Kata kunci: Jagung; Penyakit Daun; CNN; InceptionV3

Analisis Kepuasan Pengguna Aplikasi JConnect Mobile Menggunakan Metode End User Computing Satisfaction (EUCS) dan Importance Performance Analysis (IPA) Siti Nur Qholisa; Salamun Rohman Nudin
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 2 (2023): Vol. 04 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i2.54974

Pengembangan Sistem Informasi Persediaan Barang Di Cv. Nusantara List Supplay Menggunakan Metode FIFO Berbasis Website Dengan Framework Laravel Aldi Naufal Asyadana; Salamun Rohman Nudin
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 1 (2024): Vol. 05 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i1.58935

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