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All Journal Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA
Salamun Rohman Nudin
Universitas Negeri Surabaya, Surabaya
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Implementasi Model MaxViT untuk Deteksi Penyakit Daun Cabai Berbasis Mobile Nur Puspita Amalia; Salamun Rohman Nudin
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10025

Abstract

Chili is an important horticultural commodity in Indonesia, but its productivity often decreases due to leaf diseases such as Cercospora leaf spot, Powdery Mildew, Mites and Thrips, and Nutritional deficiency. Manual disease identification requires more time and may produce less accurate diagnoses. This study aims to develop a mobile-based chili leaf disease detection system using the Multi-Axis Vision Transformer (MaxViT) model. The dataset consisted of 10,987 chili leaf images divided into five classes and split into training, validation, and testing data with a ratio of 70:15:15. Model training was carried out using four optimizer scenarios, namely a standard baseline model, SGD, Adam, and AdamW. The result showed that the Adam optimizer archieved the best performance with 99,45% accuracy, 99,44% precision, 99,45% recall, and 99,45% F1-Score. The best model was converted into TensorFlow Lite format with a file size of 32.0 MB and deployed in a mobile application. The application can detect diseases through camera capture or gallery images and provide prediction results along with disease descriptions and treatment recommendations. Testing results indicate that the system performs well under various usage conditions. This system is expected to help users indentify chili leaf diseases quickly, practically, and accurately.
Implementasi Model MobileNetV2 dalam Pengembangan Sistem Prediksi Kematangan Pisang Berbasis Mobile Ainul Mufidh; Salamun Rohman Nudin
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.
Superioritas Optimizer AdaGrad pada Arsitektur Spasio-Temporal PolyFace-LSTM untuk Prediksi Kepribadian Berbasis Video Muhammad Zidan Alif Oktavian; Salamun Rohman Nudin
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10945

Abstract

Conventional personality assessment generally relies on psychometric questionnaires, such as the Big Five Inventory (BFI). However, this method is susceptible to self-report bias and requires a relatively long evaluation time. On the other hand, the development of automated video-based personality analysis systems faces high computational challenges in simultaneously integrating facial spatial features and the temporal dynamics of micro-expressions. This study aims to propose a solution in the form of a hybrid spatio-temporal neural network architecture utilizing transfer learning techniques to predict personality more objectively. The proposed system integrates the Multi-Task Cascaded Convolutional Networks (MTCNN) algorithm for automatic face detection, the PolyFace model as a spatial feature extractor, and the Long Short-Term Memory (LSTM) algorithm to model temporal relationships between frames. Experiments were conducted by comparing three optimization algorithms, namely Adam, AdaGrad, and SGD, using the ChaLearn LAP 2017 dataset. The results show that the AdaGrad optimizer achieved the best generalization and prediction performance during the validation phase, with an average accuracy of 89.13%, outperforming SGD (88.34%) and Adam (88.33%). Theoretically and mathematically, the superiority of AdaGrad in this architecture is attributed to its ability to dynamically adjust the learning rate based on the accumulation of historical gradients. This mechanism makes AdaGrad considerably more stable in extracting complex spatio-temporal micro-expression features without becoming trapped in local minima. The highest performance achieved by AdaGrad was observed in the Agreeableness dimension, reaching an accuracy of 90.14%. The main contribution of this study is the development of an efficient (low-latency), precise spatio-temporal personality prediction model that avoids local minima, making it suitable for implementation in real-world automated psychological detection systems.
Co-Authors Ainul Mufidh Muhammad Zidan Alif Oktavian Nur Puspita Amalia