Article Per Year (5 Year)
p-Index From 2020 - 2025
1.197
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics
Trisna, I Nyoman Prayana
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Sentiment Analysis of Unemployment in Indonesia During and Post COVID-19 on X (Twitter) Using Naïve Bayes and Support Vector Machine Setiawati, Putu Ayulia; Suarjaya, I Made Agus Dwi; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.713

Abstract

The COVID-19 pandemic has impacted health, economy, and society. Social distancing measures and quarantine policies have restricted economic activities, leading to downturns in COVID-19-affected regions and a subsequent rise in unemployment rates, particularly in urban areas. Concurrently, there has been a remarkable surge in the utilization of the X (Twitter) platform, with Indonesia ranking 6th globally in X (Twitter) users. This study aims to understand the diverse perspectives of society on unemployment and the factors influencing society's views on unemployment through sentiment analysis of X (Twitter) data. By analyzing 576,764 tweets from April 2020 to October 2023, tweets are categorized into positive, neutral, and negative classes. Classification model was built to classify tweet data by implementing TF-IDF for word weighting, and a pair of machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Model evaluation yielded the highest accuracy of 81.5% using Naïve Bayes. The classification outcomes highlight prevalent negative perceptions of unemployment among Indonesians, totaling 50.03%. This research contributes to the literature by providing a large-scale analysis of social media data to uncover public sentiment trends and offering insights for policymakers to address unemployment and improve welfare.
Analysis of Digital Marketing Strategy Based on SOSTAC Method Patni, Ni Putu Sri Ratih Dia; Susila, Anak Agung Ngurah Hary; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.795

Abstract

PT Kana Bali Nature is a retail company of beauty and body care products in Denpasar, Bali, which has problems with decreased brand awareness and cost overruns due to suboptimal digital marketing implementation. This research aims to analyze and implement a thorough and comprehensive digital marketing strategy using the SOSTAC method which is flexible and effective enough to be used. The SOSTAC method includes situation analysis, setting 5S objectives and SMART Goals, STP Strategy, 7P Marketing Mix Tactic, Action, and Control with KPI metrics. The results of the 3-month research show that the digital marketing strategy carried out at PT Kana Bali Nature is quite effective. The majority of KPI indicators in each aspect managed to achieve targets such as an increase in revenue of up to 55.6% even though several other indicators were still declining. Suggestions and recommendations are given to maximize the company's future strategy. Keywords: Digital Marketing, SOSTAC method, marketing strategy, retail company
Indonesian Health Question Multi-Class Classification Based on Deep Learning Vihikan, Wayan Oger; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.838

Abstract

The health online forum is commonly used by Indonesian to ask questions related to diseases. A well-known example, Alodokter, has hundreds of thousands of health questions which are assigned to certain topics. Building a model to classify questions into a topic is important for better organization and faster response by relevant health professionals. This research experimented on 20 deep learning methods from RNN, CNN, and IndoBERT with different configurations to see the performance of each model when classifying questions into six different most common diseases that cause death in Indonesia. The results show the majority of the model can outperform the SVM as baseline. Bidirectional RNN such BiLSTM and BiGRU combined with CNN show a good metric score even though a certain version of the IndoBERT model generally outperforms all the other models.
Abstractive Text Summarization to Generate Indonesian News Highlight Using Transformers Model Putri, I Gusti Agung Intan Utami; Trisna, I Nyoman Prayana; Rusjayanthi, Ni Kadek Dwi
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1082

Abstract

The increasing volume of information has led to the phenomenon of information overload, a condition where individuals struggle to filter and comprehend information efficiently within a limited time. To address this issue, automatic text summarization serves as an essential approach. This research aims to assess effectiveness of two transformer-based models, IndoT5 and mBART, by comparing their ability to generate abstractive summaries (highlight) of Indonesian news articles. The abstractive approach allows models to generate new sentences with more natural language structures compared to extractive methods. Fine-tuning for both models was conducted using a dataset comprising 10,410 news articles from Tempo.co, each containing full news content and a corresponding highlight used as a reference. ROUGE and BERT-Score metrics were employed in the evaluation process to assess structural and semantic correspondence between the references and the generated summaries. Results show that IndoT5 outperformed in terms of ROUGE-1 (0.43087), ROUGE-2 (0.29143), ROUGE-L (0.39224), BERT-Score Recall (0.89130), and F1 (0.87708), indicating its capability to generate complete and relevant news highlight. Meanwhile, mBART achieved a higher BERT-Score Precision (0.86717) but tended to generate less informative outputs. The findings of this research are expected to aid in enhancing the coherence and efficiency of abstractive summarization systems.
Web-Based Makeup Recommendation System Using Hybrid Filtering Utami, Putu Mia Setya; Trisna, I Nyoman Prayana; Vihikan, Wayan Oger
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9339

Abstract

The increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study explores the implementation of Content-Based Filtering (CBF) using TF-IDF and Cosine Similarity, Collaborative Filtering (CF) with Singular Value Decomposition (SVD), and a Hybrid Filtering approach integrating both methods through Weighted Hybrid techniques. The system's performance is evaluated across two user scenarios: new users (without prior ratings) and old users (with rating history). The evaluation method includes Precision, Normalized Discounted Cumulative Gain (NDCG), and accumulation of the best scenario based on user opinion. Results show that Hybrid Filtering outperforms CBF and CF, with notable differences between user groups. For new users, 32% prefer Scenario 1, which emphasizes CBF, achieving 80.8% Precision and 89.73% NDCG. For old users, 23% favor Scenario 2, attaining 83.4% Precision and 90.31% NDCG.
Fine-Tuned Transformer Models for Keyword Extraction in Skincare Recommendation Systems Ni Putu Adnya Puspita Dewi; Putri, Desy Purnami Singgih; Trisna, I Nyoman Prayana
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9687

Abstract

The skincare industry in Indonesia is experiencing rapid growth, with projected revenues reaching nearly 40 billion rupiah by 2024 and expected to continue to increase. The large number of products in circulation makes it difficult for consumers to find products that suit their needs. In this context, a text-based recommendation system that utilizes advances in Natural Language Processing (NLP) technology is a promising solution. This research aims to develop a skincare product recommendation system based on user needs by applying the DistilBERT model, which is specifically fine-tuned with text in the skincare recommendation domain to perform keyword extraction. The resulting keywords are then used as parameters to provide recommendations by using co-occurrence as well as using a modification of Jaccard Similarity to assess the suitability between the content and benefits of the product and user preferences. The trained extraction model achieved the best performance with a micro F1-score of 0.96 at the token level and an exact match rate of 74.25% at the entity level. The evaluation of the recommendation system showed excellent results, with an nDCG value of 0.96 and a user satisfaction rate (CSAT) of 91.9%.
Classifying Indonesian Hoax News Titles with SVM, XGBoost, and BiLSTM Trisna, I Nyoman Prayana; Putra, I Made Wiraharja Jaya; Vihikan, Wayan Oger
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.106608

Abstract

This study investigates the automated detection of hoaxes related to President Jokowi in Indonesian news by analyzing only news titles, aiming for efficient detection and reduced traffic to harmful websites. We compared the performance of traditional (SVM, XGBoost) and deep learning (BiLSTM) algorithms, with and without Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in a dataset scraped from trusted news sources (CNN Indonesia, Detik News) and a fact-checking platform (turnbackhoax.id). The results indicate that BiLSTM generally outperformed SVM and XGBoost, demonstrating the potential of deep learning for this task. However, applying SMOTE negatively impacted BiLSTM's performance, suggesting overfitting. Notably, precision consistently exceeded recall across all models, indicating high reliability in identifying hoaxes but a potential for missing a significant number of actual hoaxes. This highlights a trade-off between avoiding false positives and ensuring comprehensive detection. The findings also suggest that language-specific characteristics influence algorithm effectiveness. This research contributes to developing efficient and accurate tools for combating misinformation in the Indonesian online environment, emphasizing the importance of title-based analysis and careful consideration on data balancing.
Co-Authors I Made Agus Dwi Suarjaya Ni Putu Adnya Puspita Dewi Patni, Ni Putu Sri Ratih Dia Putra, I Made Wiraharja Jaya Putri, Desy Purnami Singgih Putri, I Gusti Agung Intan Utami Rusjayanthi, Ni Kadek Dwi Setiawati, Putu Ayulia Susila, Anak Agung Ngurah Hary Utami, Putu Mia Setya Wayan Oger Vihikan, Wayan Oger