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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) IIJSE
Akbari, Imam
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques Siregar, Sandy Putra; Akbari, Imam; Poningsih, Poningsih; Wanto, Anjar; Solikhun, Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6410

Abstract

Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling was employed to reduce dimensionality while retaining crucial information. This paper offers an improved CNN through hyperparameter tuning and compares it to Transfer Learning models such as InceptionV3, NASNetMobile, and VGG16, which were chosen for their efficiency and accuracy. The dataset comprises 7,178 photos classified as Healthy, Leaf Late Blight, Septoria Leaf Spot, and Yellow Leaf Curl Virus, collected from Kaggle.. The dataset is separated into three sections: training, validation, and testing, with a ratio of 70:15:15. The results of this study revealed that the proposed method achieved the highest accuracy of 98.24%. In the application of transfer learning, the inceptionV3 model achieved an accuracy of 96.94%, whereas NASNetMobile obtained 97.50%, and VGG16 showed an accuracy of 96.76%. The evaluation is based on accuracy, precision, recall, F1-score and Inference time to determine the optimum model for accuracy and computing efficiency. This project uses the proposed method and Transfer Learning Techniques to categorize illness images on tomato leaves. These findings will drive further research to improve tehe performance of the proposed method for foliar disease classification and comparable applications.
The Effect of Servant Leadership and Loyalty on Employee Performance at Khairu Ummah Syariah Service Cooperative Akbari, Imam; Hidayatulloh, Furqon Syarief; Saleh, Amiruddin
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 9 No 1: Sharia Economics
Publisher : Universitas KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v9i1.8313

Abstract

This study aims to examine the influence of servant leadership on employee performance, with employee loyalty as a mediating variable, at Koperasi Jasa Syariah Khairu Ummah. Based on data analysis and structural model testing, several key findings were identified. First, servant leadership has a positive and significant effect on employee loyalty, indicating that a leadership style prioritizing employee needs, support, and empathy enhances emotional attachment and commitment to the organization. Second, employee loyalty positively and significantly influences employee performance, suggesting that loyal employees are more motivated and likely to perform better. Third, servant leadership does not directly affect employee performance, implying the presence of a mediating variable. Finally, employee loyalty is proven to mediate the relationship between servant leadership and employee performance, meaning that effective implementation of servant leadership increases loyalty, which in turn improves performance. Overall, the findings emphasize the crucial role of servant leadership in fostering employee loyalty as a foundation for enhancing performance, especially in value-based, sharia-oriented organizations.
OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Akbari, Imam; Hartama, Dedy; Wanto, Anjar
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.v11i2.7140

Abstract

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values ​​of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Co-Authors Amiruddin Saleh Furqon Syarief Hidayatulloh Hartama, Dedy Poningsih Poningsih, Poningsih Siregar, Sandy Putra Solikhun Solikhun, Solikhun Wanto, Anjar