Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Mandiri IT
Saadah, Zulfatun Nikmatus
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Library & Information Science Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of MobileNetv2 and MobileNetv3 architectures in rice leaf disease classification using transfer learning Mifthauddin, Adlim; Lutfi, Moch.; Saadah, Zulfatun Nikmatus
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.459

Abstract

Rice is of the main food commodities in Indonesia that is susceptible to various leaf diseases, one of which is Bacterial Blight, Brown Spot, and Leaf Smut. Manual identification by farmers is often less accurate and time-consuming, thus requiring a technology-based detection system. The objective of this research is to categorize rice leafdiseases through the use of deep learning with a transfer learning approach based on MobileNetV2 and MobileNetV3 architectures. The dataset, comprising 4,684 rice leaf images, was divided into training and validation sets using an 80:20 ratio. Preprocessing included resizing images to 224×224 pixels, normalization, and augmentation to increase data variation. Training was carried out across 30 epochs with a mini-batch size set to 32. while applying an EarlyStopping mechanism to reduce the likelihood of overfitting. The result of the experiment indicate that MobileNetV2 reached an 96% accuracy, while MobileNetV3 outputperformed is with an accuracy of 99%. Therefore, MobileNetV3 is more effective for rice leaf disease classification.
Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection Arsanto, Arief Tri; Faizin, Arif; lutfi, Moch; Saadah, Zulfatun Nikmatus
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (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.v13i6.4719

Abstract

The use of credit cards in the modern era is increasing. Therefore, it is necessary to prevent it with the use of technology such as address verification systems (AVS), card verification methods (CVM), and personal identification Numbers (PIN). Dataset analysis needs to be carried out to analyze the history of transactions that have been carried out. In the fraud detection dataset, it can be seen that there are attributes that cause data imbalance. Class imbalance in a dataset is a significant problem in machine learning that can affect overall model performance. The number of majority samples is more significant in one class than the number of minority classes. This research used an oversampling approach using a combination of smote and tomek-link. The focus of this research is card fraud classification. Detection of imbalanced datasets or imbalanced classes is carried out using the Naive Bayes method as a classification algorithm. In addition, a combination of resampling techniques is also applied to overcome imbalanced classes in this dataset through the SMOTETomek approach. SMOTETomek is a method that reduces the number of samples by considering two adjacent data from the minority and majority classes. Meanwhile, from the problems above, the results of the performance of Naïve Bayes, which experienced issues with data imbalance in this study, a resampling method was proposed in the hope of improving the performance of the Naïve Bayes algorithm and in the results of the AUC ROC curve, the SMOTETomek method could improve the performance of the Naïve Bayes algorithm. The higher the ROC score. -AUC, the better the model performance in terms of its ability to differentiate between two classes, but the accuracy results do not experience a significant change.
Co-Authors Arsanto, Arief Tri Faizin, Arif Lutfi, Moch Lutfi, Moch. Mifthauddin, Adlim