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All Journal JURNAL SISTEM INFORMASI BISNIS Sistemasi: Jurnal Sistem Informasi
Masparudin, Masparudin
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Development of Apple Fruit Classification System using Convolutional Neural Network (CNN) MobileNet Architecture on Android Platform Masparudin, Masparudin; Fitri, Iskandar; Sumijan, Sumijan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3533

Abstract

In the current digital era, image classification of fruits, particularly apples, has become crucial for various applications, ranging from agriculture to retail. This research focuses on the utilization of Convolutional Neural Network (CNN) with the MobileNet architecture to classify apple fruit images. Using the Python programming language, three models were successfully trained: Model 1 for apple fruit types, Model 2 for apple fruit diseases, and Model 3 for apple fruit ripeness levels. All three models underwent training and validation, with the final results at epoch 10: Model 1 for apple types achieved an accuracy of 100% and a loss of 0.0046, Model 2 for apple diseases achieved an accuracy of 100% and a loss of 0.0075, while Model 3 for apple ripeness levels achieved an accuracy of 99.76% and a loss of 0.0439. Subsequently, these models were tested on an Android device, and there were two testing scenarios. In the first scenario, each model was tested with 15 images individually. The results showed 100% accuracy for Models 1 and 2, while Model 3 achieved a lower accuracy of 86.67%. In the second scenario, all three models were tested simultaneously using 30 test images, resulting in an accuracy of 55.55%. Several factors, such as limitations in the apple image dataset, particularly in the ripeness dataset, object backgrounds, image capture distances, color and texture similarities, as well as lighting quality, influenced the classification outcomes. To enhance future performance, improved data preprocessing and a combination of detection and classification techniques are needed. This research provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.
Enhancing Air Traffic Forecasting Accuracy at Hang Nadim Airport Using ARIMA-Neural Network Masparudin, Masparudin; Abdullah, Abdullah; Saragih, Raymond Erz; Pernando, Yonky; Syafrinal, Ilwan
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6265

Abstract

Passenger traffic fluctuations at Hang Nadim International Airport exhibit extreme volatility influenced by the unique characteristics of the Free Trade Zone (FTZ). Single statistical methods often fail to capture non-linear patterns in this high-variability data. Therefore, this study proposes a Hybrid ARIMA-Neural Network model to enhance forecasting accuracy. The primary variable used is the total monthly passenger volume (arrivals and departures). The research stages began with data preprocessing (80:20 train-test ratio), linear component modeling using ARIMA, residual extraction, and non-linear component modeling using Multi-Layer Perceptron (MLP) to correct residual errors on a one-step-ahead basis. Evaluation results show that the standalone ARIMA model is slow to anticipate extreme surges, resulting in a Mean Absolute Percentage Error (MAPE) of 23.75%. The hybrid model integration proved successful in compensating for these weaknesses, reducing the MAPE value to 12.51%. This achievement represents a 47.33% error reduction from the baseline. In terms of novelty, this hybrid approach provides a highly reliable computational solution for airport management with dual characteristics (tourism and industry) in mitigating uncertainty in capacity planning.
Early Detection of Patient Surge Anomalies in Hospitals: A Comparative Analysis of Gradient Boosting, Random Forest, and SVM Masparudin, Masparudin; Marfuah, Marfuah; Abdullah, Abdullah
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

Unpredictable fluctuations in patient visits often lead to resource unpreparedness and decreased service quality in hospitals. This study aims to develop an early warning system for patient surges across 110 healthcare service units. Unlike conventional approaches utilizing static thresholds, this study proposes a Statistical Anomaly Detection method based on Z-Score for dynamic labeling and applies Synthetic Minority Over-sampling Technique (SMOTE) to address extreme data imbalance. Three classification algorithms—Gradient Boosting Classifier (GBC), Random Forest (RF), and Support Vector Machine (SVM)—were compared using time-series lag features and volatility trends. Experimental results demonstrate that Gradient Boosting outperformed other methods, achieving the highest F1-Score of 37.35% and a Recall of 48.98%, proving its robustness in detecting anomalies within imbalanced data. This study concludes that integrating statistical anomaly-based labeling with ensemble boosting algorithms effectively mitigates noise in heterogeneous hospital visit data, thereby serving as a reliable basis for proactive managerial decision-making.
Co-Authors Abdullah Abdullah Ilwan Syafrinal Iskandar Fitri, Iskandar Marfuah Marfuah Pernando, Yonky Saragih, Raymond Erz Sumijan Sumijan