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All Journal Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Puji Pramudya Marta
STMIK IKMI Cirebon
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Peningkatan Kinerja SVM pada Klasifikasi Sentimen Ulasan iPusnas Berbasis Imbalance Handling Akmal Mustafa; Rudi Kurniawan; Bani Nurhakim; Puji Pramudya Marta; Khaerul Anam
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

This study investigates sentiment classification of user reviews on the iPusnas digital library application to provide an objective overview of service quality and user experience. Numerous complaints related to login failures, application crashes, and issues in accessing digital books indicate the need for a computational approach capable of processing large volumes of user feedback. The proposed method integrates Natural Language Processing (NLP) techniques with the Support Vector Machine (SVM) algorithm. The workflow consists of collecting 2,000 reviews, applying text cleaning and normalization, tokenization, stopword removal, stemming, rating-based sentiment annotation, and feature extraction using TF-IDF. The dataset was divided using a train–test split for model training and evaluation. Experimental results show that the SVM model achieves 90.1% accuracy, demonstrating strong performance in detecting negative sentiments and moderate performance for positive sentiments due to class imbalance. These  findings highlight the effectiveness of NLP and SVM for extracting user perceptions and indicate the potential of this model as a decision-support tool for improving iPusnas application services. Overall, the study contributes to the advancement of digital service innovation in Indonesia.
Optimalisasi Convolutional Neural Network Kontra VGG16 Klasifikasi Citra Daun Sawi Rio Febriyan; Ade irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp30-34

Abstract

Manual detection of pests on mustard greens (caisim) is a major constraint in reducing harvest productivity, as manual methods are inefficient, time-consuming, and require specialized expertise. Furthermore, deep learning models often suffer from overfitting when applied to limited agricultural datasets. This study aimed to develop and compare the effectiveness of a Convolutional Neural Network (CNN) from scratch model versus the VGG16 transfer learning architecture for automatic classification of healthy and pest-affected mustard leaf images. A dataset of 1,000 images was used for training and testing across four experimental scenarios (A to D), with Percobaan C being the optimized CNN from scratch model (using data augmentation) and Percobaan D using VGG16. The results showed that the VGG16 transfer learning model achieved the highest test accuracy of 95.0% (F1-score: 0.95), while the optimized CNN from scratch model achieved 92.0% (F1-score: 0.92). Therefore, transfer learning with VGG16 is the most effective and optimal approach, demonstrating superior performance and efficiency by achieving high accuracy without complex data augmentation.
Co-Authors Ade Irma Purnamasari Akmal Mustafa Bani Nurhakim Denni Pratama Khaerul Anam Rio Febriyan Rudi Kurniawan Yudhistira Arie Wijaya