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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Information System Research
Poningsih Poningsih, Poningsih
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Comparative Study of Mobilenet and Resnet for Watermelon Leaf Disease Classification Using Deep Learning Ahmad, Abdullah; Wanto, Anjar; Windarto, Agus Perdana; Poningsih, Poningsih
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7543

Abstract

Watermelon leaf diseases, caused by various factors such as fungi, viruses, and bacteria, can have a significant impact on agricultural yields. To increase the amount and quality of watermelon produced, early diagnosis of this disease is essential. This study aims to compare the performance of two Convolutional Neural Networks (CNN) architectures included in Deep Learning, namely MobileNet and ResNet, in classifying watermelon leaf diseases using a dataset taken from Kaggle. This dataset consists of 1000 watermelon leaf images with three conditions, namely Downy Mildew (380 images), Healthy (205 images), and Mosaic Virus (415 images). ). 95% accuracy, 96% precision, 94% recall, and 95% f1-score are the results of the MobileNet model. In contrast, the ResNet model performs better, with 97% accuracy, 96% precision, 97% recall, and 97% f1-score. The study's findings show that ResNet outperforms MobileNet in the classification of watermelon leaf illnesses, despite both models' excellent and effective performance for automatic plant disease detection applications.
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Abstract

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
OPTIMIZATION OF SVM ALGORITHM FOR OBESITY CLASSIFICATION WITH SMOTE TECHNIQUE AND HYPERPARAMETER TUNING Nur, Khairun Nisa Arifin; Wanto, Anjar; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6878

Abstract

Excessive fat accumulation that impairs personal health and raises the risk of chronic diseases is the hallmark of obesity, a global health issue. Decision Tree (DT) has been widely used for obesity classification, but it tends to suffer from overfitting and poor performance on imbalanced datasets. To overcome these limitations, this study proposes an optimization of the Support Vector Machine (SVM) algorithm using Synthetic Minority Over-sampling Technique (SMOTE) and Hyperparameter Tuning. SMOTE was applied to balance the class distribution, whereas Grid Search was utilized to determine the optimal combination of hyperparameters (C, gamma, and kernel). The dataset employed in this research comprises multiple features related to individual health and lifestyle, with obesity level as the target class. The experimental results demonstrate that the optimized SVM model demonstrated strong classification performance, attaining 97% in accuracy, precision, recall, and F1-score. This high performance is significant because it enables more accurate early detection of obesity risk, which can support timely medical intervention and personalized treatment planning, ultimately contributing to better public health outcomesThese findings suggest that incorporating SMOTE and Hyperparameter Tuning substantially improves SVM performance, establishing it as a robust approach for obesity classification and early risk detection.
Fuzzy Mamdani DSS for STIKOM Student Boarding House Selection Batubara, Ela Roza; Poningsih, Poningsih; Siadari, Nina Helnida; Sidauruk, Nadia
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 2 (2026): InfoTekjar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i2.13243

Abstract

A Decision Support System (DSS) is a system designed to assist users in decision-making based on valid data, involving multiple criteria within a short timeframe. This study employs the Fuzzy Mamdani method as the data processing formula for selecting boarding houses for STIKOM Tunas Bangsa Pematangsiantar students. Selecting a boarding house is a crucial decision faced by migrant students, involving subjective criteria such as price, distance, facilities, room size, and security. Without a systematic tool, students may struggle to choose boarding houses that best suit their preferences, potentially leading to dissatisfaction. This research aims to design and implement a DSS using the Fuzzy Mamdani method to assist students in boarding house selection. The method was chosen for its ability to accommodate linguistic and ambiguous criteria, transforming them into objective and measurable decisions. Five input variables were used: price (Rp 500,000-2,000,000), distance (0-5 km), facilities (0-100), room size (4-30 m²), and security (0-100), with feasibility (0-100) as output. Fuzzy sets and membership functions were defined for each variable, and 45 IF-THEN rules were constructed. Manual calculation for Kost Bio (price Rp 250,000, distance 2.2 km, facilities 88, room size 20.16 m², security 0) produced a feasibility score of 85 (Very Feasible), close to the expert assessment of 93. The system successfully provides objective recommendations, helping students make informed boarding house choices efficiently.
Refining CNN-Based Models for Multi-Class Corn Leaf Disease Classification Wanto, Anjar; Poningsih, Poningsih; GS, Achmad Daengs; Andini, Silfia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7400

Abstract

Corn leaf disease significantly impacts agricultural productivity and national food security, particularly in regions with high dependence on maize as a staple commodity. Manual disease identification remains challenging due to the need for expert agronomists, inconsistent environmental conditions, and visual similarities among disease patterns, often resulting in delayed decision-making and inaccurate control measures. Deep learning-based image classification has emerged as an effective solution for plant disease identification; however, existing models often face limitations regarding overfitting, poor generalization, and insufficient performance when applied to multi-class agricultural image datasets. Therefore, this research aims to develop an Improved EfficientNetB0 model for the multi-class classification of maize leaf diseases comprising Healthy, Leaf Blight, Leaf Rust, and Leaf Spot categories. A dataset of 4,000 images was used and processed through resizing, normalization, and augmentation techniques. Five CNN backbones; EfficientNetB0, MobileNetV2, ResNet50, DenseNet121, and InceptionV3—were initially evaluated, and EfficientNetB0 demonstrated the highest baseline performance. The model was subsequently enhanced through fine-tuning, regularization (dropout and batch normalization), and cosine learning rate scheduling. Experimental results show that the Improved EfficientNetB0 achieved superior performance with an accuracy of 0.9671, macro precision of 0.9665, macro recall of 0.9666, and macro F1-score of 0.9661, exceeding all baseline models. These findings demonstrate that the proposed framework effectively improves maize disease classification accuracy and contributes a robust solution for smart agriculture applications. Future work may integrate real-time deployment and mobile-based decision support for field-level monitoring.
Co-Authors Abdullah Ahmad Agus Perdana Windarto Akbari, Imam Alkhairi, Putrama Amalya, Nanda Ambariyanto Ambariyanto Batubara, Ela Roza Bintang, Daud Damanik, Cantika Audy Eka Irawan GS , Achmad Daengs Hartama, Dedy Hartama, Dedy Ht. Barat, Ade Ismiaty Ramadhona Irawan Irawan Jalalludin, Jalalludin Lili Tanti Lumbantobing, Dedy Kristianto Nainggolan, Darwis William Nainggolan, Joshua Fines Nasution, Siti Andry Yani Nur, Khairun Nisa Arifin Nur, Khairun Nisa Arifin Okprana, Harly Pakpahan, Clara Marsella Putri, Nirwanda Desnila Rahmat Widia Sembiring - Safii, M Siadari, Nina Helnida Sidauruk, Nadia Silaban, Rido Syahputra Silfia Andini, Silfia Siregar, Abdul Zikri Siregar, Sandy Putra Solikhun Solikhun, Solikhun Sumarno . Sundari Retno Andani Sundari, Riski Wanto, Anjar Wibowo, Subastian Widodo Saputra