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TIERS Information Technology Journal
Published by Universitas Pendidikan Nasional
ISSN : 27234533     EISSN : 27234541     DOI : 10.38043
Core Subject : Science,
Computer Science & IT
TIERS Information Technology Journal memuat artikel Hasil Penelitian dan Studi Kepustakaan dari cabang Teknologi Informasi dengan bidang Sistem Informasi, Artificial Intelligence, Internet of Things, Big Data, e-commerce, Financial Technology, Business Digital
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 6 Documents
 1 
Search results for , issue "Vol. 6 No. 2 (2025)" : 6 Documents clear
Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce Zuriati, Zuriati; Widyawati, Dewi Kania; Arifin, Oki; Saputra, Kurniawan; Sriyanto, Sriyanto; Ahmad, Asmala
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7143

Abstract

Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder generalization. This study introduces a hybrid machine learning approach integrating SMOTE, Optuna, and SVM to enhance the accuracy of nutrient deficiency classification using digital leaf image analysis. The dataset, obtained from Kaggle, includes four categories: Nitrogen Deficiency (-N), Phosphorus Deficiency (-P), Potassium Deficiency (-K), and Fully Nutritional (FN). Image features were extracted using MobileNetV2 pretrained on ImageNet and classified with a Support Vector Machine. Three scenarios were tested: (1) SVM before SMOTE, (2) SVM after SMOTE, and (3) Optuna-SVM after SMOTE, evaluated using accuracy, precision, recall, and f1-score. The hybrid model achieved the best performance with accuracy 0.929, precision 0.946, recall 0.835, and f1-score 0.869, outperforming the other scenarios. This hybrid framework effectively addressed class imbalance and improved classification margin stability through adaptive hyperparameter tuning using the Tree Structured Parzen Estimator within Optuna. The novelty of this study lies in combining MobileNetV2 based feature extraction with SMOTE and Optuna-SVM for small agricultural datasets. The proposed approach offers an efficient, accurate, and practical solution for automated nutrient deficiency diagnosis and contributes to the development of AI-driven smart agriculture systems.
YOLOv8-Based Quality Detection of Bali MSMEs Staple Food Dewi, Ni Putu Dita Ariani Sukma; Aryasa, Jiyestha Aji Dharma; Hendrayana, I Gede; Prayoga, I Made Ade; Putri, Sulin Monica
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7144

Abstract

Ensuring the quality of staple foods such as rice, cooking oil, milk, and meat is crucial for consumer safety and health. In Indonesian Micro, Small and Medium Enterprises (MSMEs), quality assessment often depends on subjective and time-consuming visual inspection. This study develops an automatic quality detection system using YOLOv8, applied to food MSMEs in Bali, to detect 14 quality categories across the four commodities based on image data. The methodology includes dataset collection from MSMEs, image annotation, preprocessing, training YOLOv8s and YOLOv8m models, and evaluating performance using mAP50, accuracy, precision, recall, and F1-score. Results show that YOLOv8m achieved a mAP50 of 96.5%, indicating high detection accuracy. The system, implemented as a web-based application, has strong potential to improve efficiency, ensure consistent product quality, and support Sustainable Development Goals (SDGs) 2, 3, 8, and 9.
Efficient Rice Leaf Disease Classification Using Enhanced CAE-CNN Architecture Suhada, Destia; Suta Wijaya, I Gede Pasek; Widiartha, Ida Bagus Ketut; Jo, Minho
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7159

Abstract

This study introduces an enhanced Convolutional Autoencoder–Convolutional Neural Network (CAE–CNN) model designed for efficient and accurate classification of rice leaf diseases. This study aims to develop an architecture that achieves high accuracy while maintaining computational efficiency, serving as an integrative and applicative technical innovation for rice disease detection. The proposed architecture integrates a Squeeze and Excitation Block (SE-Block), Global Max Pooling (GMP), and Separable Convolution to improve feature extraction while reducing the number of parameters and inference time. A total of 7,430 labeled images from five rice disease classes were used for model training and evaluation. The model was optimized using Optuna-based hyperparameter tuning and validated through an ablation and comparative analysis to assess the impact of each component. Experimental results show that the proposed model achieves 99.39% accuracy with only 85,859 parameters, a compact size of 0.28 MB, and inference time at 0.06657 ms/image with 15,213 FPS. These findings demonstrate that the proposed CAE–CNN effectively combines high accuracy and low computational cost, making it highly suitable for real-time and edge-based rice disease classification systems.
Operating Room Scheduling Optimization Under Surgeon and Nurse Constraints Using Genetic Algorithm Swilugar, Ayu; Herliansyah, Muhammad Kusumawan
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7164

Abstract

Operating room scheduling is a complex problem due to the limited availability of surgeons, nurses, and operating rooms, as well as the variability in surgery durations. Inaccurate predictions or scheduling may cause conflicts such as overlapping surgeon schedules, violations of contamination level restrictions, and unavailability of nurses or rooms, ultimately reducing the quality of hospital services. This study integrates multiprocedure surgery duration prediction using machine learning with scheduling optimization based on genetic algorithms. The prediction model considers the American Society of Anesthesiologists (ASA) physical status classification, patient profiles, and sets of surgical procedures variables. Scheduling optimization employs a lexicographic approach with three main objectives: minimizing patient waiting time, nurse overtime, and operating room idle time, while ensuring surgeon presence during critical phases and nurse availability according to shifts. The results show that the Catboost algorithm achieves the best prediction performance. Incorporating the ASA variable reduces prediction errors by 33.880 minutes in MAE and 55.575 minutes in RMSE compared to model without the ASA feature. The optimization model successfully eliminates all scheduling conflicts, ensuring full compliance with medical procedure constraints. Recovery bed utilization remains efficient, with a maximum of five units used, representing less than 50% of the total capacity.
Genre-Based Sentiment and Emotion System for Audience Insight Suwarno, Suwarno; Wesly, Wesly; Syahputra, Bayu
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7183

Abstract

Movies can influence people’s moods in different ways depending on film genre. Fear is commonly induced by horror films, whereas joy is typically associated with comedy. Understanding how genre-based expectations shape audience emotions offers valuable insights for producers and digital platforms. However, previous studies have only briefly examined this relationship, with most focusing on general sentiment analysis. This study develops a genre-based sentiment and emotion model to analyze how film genres influence audience reactions. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework was applied to 46,173 IMDb reviews using Term Frequency–Inverse Document Frequency (TF-IDF) features and three machine learning models: Logistic Regression, Linear Support Vector Classification, and One-vs-Rest Logistic Regression. The results show that Fear (0.704) and Anger (0.684) are the most dominant emotions, indicating that audiences tend to be more emotionally engaged with intense genres. The model was also implemented in a Flask–React web-based system that allows users to analyze and visualize reviews in real time. These findings provide practical implications for filmmakers, producers, and streaming platforms in adjusting genre design, content recommendation, and promotional strategies to align with audience emotional responses.
Optimization CatBoost using GridSearchCV for Sentiment Analysis Customer Reviews in Digital Transportation Industry Ifriza, Yahya Nur; Sanusi, Ratna Nur Mustika; Febriyanto, Hendra; Kamaruddin, Azlina
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7201

Abstract

The rapid expansion of ride-hailing services has generated a massive volume of user feedback, making automated sentiment analysis essential for understanding customer satisfaction. This study aims to classify public sentiment towards the Uber application into positive, neutral, and negative categories using the CatBoost algorithm, a gradient boosting method prioritized for its Ordered Boosting mechanism, which effectively prevents overfitting and enhances the model's generalization capabilities. Despite the use of TF-IDF for numerical text representation, CatBoost is selected for its superior performance on heterogeneous datasets compared to other boosting frameworks like XGBoost and LightGBM. The dataset comprises customer reviews collected 12.000 from the Google Play Store between January and March 2024 using web scraping techniques upload in Kaggle. The data underwent rigorous preprocessing, including lemmatization and TF-IDF vectorization, to structure the textual features, to maximize model performance, hyperparameter optimization was conducted using GridSearchCV. The experimental results demonstrate that the optimization process successfully improved the model's generalization capabilities, raising the Accuracy from 0.907 to 0.910 and the F1-Score from 0.893 to 0.897. Most significantly, the AUC score increased from 0.949 to 0.957, indicating a superior ability to distinguish between sentiment classes. However, while the model exhibited high precision in identifying positive and negative polarities, analysis of the confusion matrix revealed limitations in correctly predicting the neutral class, suggesting challenges related to class imbalance. These findings confirm that an optimized CatBoost model is a robust tool for sentiment classification, though future work is recommended to address minority class detection.

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