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All Journal Telematika Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Muhammad Bayu Kurniawan
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Education

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Hyperparameter Optimization of TF-IDF and SVM via Grid Search for Sentiment Analysis of Traveloka Customer Reviews Muhammad Bayu Kurniawan; Hanafi; Riki Hikmianto; Isnawati Muslihah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 2 (2025): October 2025
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Customer reviews on digital platforms are crucial for improving services and making business decisions. This study focuses on automated sentiment analysis for Traveloka, a leading Indonesian online travel application. We propose a systematic hyperparameter optimization of a combined TF-IDF and Support Vector Machine (SVM) pipeline. A dataset of 20,200 user reviews was collected from the Google Play Store. After preprocessing and a two-stage labeling process, the data was split using stratified sampling (70% training, 30% testing). We conducted a comprehensive Grid Search with stratified 5-fold cross-validation to jointly optimize TF-IDF n-gram ranges (unigram, bigram, trigram) and SVM hyperparameters across four kernel types (Linear, RBF, Polynomial, Sigmoid). The results show that the Polynomial kernel with trigram features (C=5, gamma=1, degree=5, coef0=10) performs best. It achieves a test accuracy of 87.10% and a macro F1-score of 86.9%. Error analysis revealed the model's high reliability in detecting negative feedback (precision: 90.4%) but also its difficulty with contrastive sentences and informal language. The minimal performance differences among top configurations suggest the task is robust to specific parameter choices. However, the model's bag-of-ngrams approach shows limitations in processing contrastive sentences and informal language. For future work, employing contextual embeddings (e.g., IndoBERT) and exploring alternative algorithms like Random Forest or Neural Networks could address these challenges. This research presents a thoroughly optimized traditional ML methodology that establishes a strong baseline for automated sentiment analysis of Indonesian user feedback.
Guava Disease Detection and Classification: A Systematic Literature Review Muhammad Bayu Kurniawan; Ema Utami
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2901

Abstract

Guavas (Psidium guajava) are nutrient-rich fruits that provide significant health benefits. However, guava cultivation faces persistent threats from various diseases affecting both leaves and fruits, leading to substantial yield and quality losses. The early and accurate detection of these diseases is crucial but remains challenging due to economic constraints and limited infrastructure. While plant pathologists employ various diagnostic methods, these approaches are often time-consuming, costly, and sometimes inconsistent. Recent advancements in deep learning (DL) and machine learning (ML) have introduced innovative techniques for guava disease identification. This study conducts a Systematic Literature Review (SLR) to evaluate the existing research on guava leaf and fruit disease detection, focusing on dataset sources, identified disease categories, preprocessing and augmentation techniques, applied algorithms, and reported evaluation metrics. A comprehensive search was conducted across multiple databases, covering publications from 2017 to 2023, leading to the identification of 47 relevant studies. After applying exclusion criteria, 16 studies were selected for in-depth analysis. The findings highlight the most commonly used datasets, the predominant classification techniques, and the effectiveness of various deep learning models based on multiple performance metrics, providing insights into current research trends, existing limitations, and potential directions for future studies. This review serves as a valuable reference for researchers aiming to enhance the accuracy and efficiency of guava leaf and fruit disease diagnosis through data-driven approaches.
Co-Authors Ema Utami Hanafi Hanafi Muslihah, Isnawati Riki Hikmianto