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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Computer System and Informatics (JoSYC) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Siregar, Sandy Putra
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Implementasi Algoritma K-Medoids untuk Pengelompokkan Sebaran Mahasiswa Baru Irawan, Eka; Siregar, Sandy Putra; Damanik, Irfan Sudahri; Saragih, Ilham Syaputra
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 5, No 2 (2020): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v5i2.213

Abstract

The existence of new students at a tertiary institution is a routine activity every year in a tertiary institution and can also see the sustainability of the tertiary institution. The variety of regional origin of new students makes the party from the university want to see the distribution of new students based on the origin of the school and its place of residence. STIKOM Tunas Bangsa is one of the tertiary institutions in Pematangsiantar. It aims to promote the university. K-Medoids is able to group data on the distribution of new students in STIKOM Tunas Bangsa Pematangsiantar. The clusters produced in this study are of three clusters. The validity used in this study is the validity of Silhoutte Coefficient. The validity value generated in the K-Medoids algorithm produces a validity value of -116.47 by assuming that if the non-medoids value produced S
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.
Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction Ht. Barat, Ade Ismiaty Ramadhona; Siregar, Sandy Putra; Poningsih, Poningsih; Windarto, Agus Perdana; Solikhun, Solikhun; Sembiring, Rahmat Widia
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.7116

Abstract

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.
Co-Authors Agus Perdana Windarto Akbari, Imam Eka Irawan Ht. Barat, Ade Ismiaty Ramadhona Irfan Sudahri Damanik Poningsih Poningsih, Poningsih Saragih, Ilham Syaputra Sembiring, Rahmat Widia Solikhun Solikhun, Solikhun Wanto, Anjar