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INDONESIA
JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 24 Documents
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Search results for , issue "Vol. 12 No. 3 (2025): Juni 2025" : 24 Documents clear
Analisis Klusterisasi Stunting Pada Balita Menggunakan Algoritma K-Medoids Untuk Mengidentifikasi Faktor Dominan Saragih, Leonardo; Pasaribu, Nanda Sabrina; Harefa, Novi Karlianti; Tajrin, Tajrin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8713

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use.
Boosting Methods for Multi-label Data Cyberbullying Farasalsabila, Fidya; Aritonang, Mhd Adi Setiawan; Jabnabillah, Faradiba; Moniva, Anip; Lestari, Verra Budhi; Handayani, Rizky
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8721

Abstract

Easy accessibility to the internet and social media allows individuals to communicate anonymously, providing opportunities for abusive and harmful behavior. The psychological impact of cyberbullying can be very detrimental, triggering stress, depression, and even causing more serious consequences such as suicide. This paper describes cyberbullying sentiment analysis with a focus on the use of four different boosting methods, namely Gradient Booster, Gradient Booster, XGBoost, AdaBoost, dan LightGBM on a multi-label public dataset covering 6 categories. The aim of this research is to compare and analyze the relative performance of these boosting methods in overcoming the challenges of multi-label sentiment analysis in the context of cyberbullying. Results reveal that XGBoost and LightGBM have a tendency to more effectively overcome the challenges of detecting cyberbullying in more complex categories, making a positive contribution to the development of superior detection systems in the context of multi-label sentiment analysis. This research contributes to the field by providing a comparative analysis of state-of-the-art boosting algorithms, highlighting their strengths in multi-label classification tasks, and offering practical insights for developing more accurate and reliable cyberbullying detection systems. The findings from this study are expected to serve as a reference for future development of machine learning-based tools that can help mitigate the psychological harm caused by online abuse, particularly in detecting subtle and complex forms of cyberbullying behavior.
Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8727

Abstract

This study addresses the challenge of accurately predicting rainfall in regions with complex climate dynamics, such as Malang Regency, East Java. It evaluates the performance of a Long Short-Term Memory (LSTM) model enhanced with the Bahdanau Attention Mechanism, comparing it with a Standard LSTM model in forecasting daily rainfall based on historical weather parameters including average temperature, relative humidity, sunshine duration, and wind speed. Using daily data from BMKG covering 2000 to 2023, both models underwent a structured machine learning process including data preprocessing, feature selection, model training, and evaluation. The Attention-Based LSTM consistently outperformed the Standard LSTM, particularly in handling rainfall anomalies, achieving an MSE of 0.00800 and RMSE of 0.08948, compared to 0.00817 and 0.09039 respectively for the Standard LSTM. These results demonstrate that integrating Bahdanau Attention improves the model’s focus on relevant temporal features, enhancing prediction accuracy and robustness. The architecture consisting of two LSTM cells combined with the attention mechanism effectively captures complex sequential patterns that the standard model tends to overlook. This research highlights the potential of attention mechanisms in time series weather prediction, contributing to more reliable early warning systems, adaptive agricultural strategies, and disaster risk reduction frameworks. Future work could explore hybrid models or incorporate additional weather features to further improve performance and generalization.
Penerapan Word2Vec dan SVM dengan Hyperparameter Tuning untuk Deteksi Phishing Wicaksana, Hilman Singgih; Huda, Khairul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8729

Abstract

The advancement of information technology in today’s digital age takes place very rapidly from one time to another. This phenomenon is accompanied by increasing cybersecurity threats like phishing. Phishing links are often designed with uniform resource locator (URL) structures that appear convincing and are difficult to distinguish from genuine links. This research proposes a word-to-vector (Word2Vec) and Support Vector Machine (SVM) approach with hyperparameter tuning where Word2Vec is a word embedding technique used to create a word vector representation of a particular URL, SVM is used as a machine learning (ML) approach used in this research, and hyperparameter tuning is used as a technique to find the best combination of parameters to produce an optimal SVM model in detecting phishing. The purpose of this research is to compare the performance between SVM and XGBoost models that have been optimized and deploy ML models into a prediction system using the Streamlit framework to detect phishing based on input made by users in the form of certain URLs. The findings of this study indicated that the SVM model performed very well compared to the XGBoost model, with precision, recall, f1-score, and accuracy values of about 99.84% for SVM. On the other hand, the XGBoost model recorded precision, recall, f1-score, and accuracy values of about 99.70% each. Thus, the SVM model is the optimal model to detect phishing precisely and accurately.

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