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All Journal JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Soft Computing Exploration Journal of Student Research Exploration
Ningsih, Maylinna Rahayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Classification Email Spam using Naive Bayes Algorithm and Chi-Squared Feature Selection Ningsih, Maylinna Rahayu; Unjung, Jumanto; Farih, Habib al; Muslim, Much Aziz
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.9695

Abstract

Spam email is a problem that disturbs and harms the recipient. Machine learning is widely used in overcoming email spam because of its ability to classify emails into spam or non-spam. In this research, the Naïve Bayes algorithm is initiated with the Chi-Squared selection feature to classify spam emails. So that the implementation is able to increase accuracy for better performance in classification. The feature selection method is used to direct the model's attention to features that are related to the target variable. In this study, the chi squared feature uses a value of K = 2500, with an accuracy of 98.83% which shows an increase in model performance compared to previous research. So that the Naïve Bayes model with the Chi-Squared selection feature is proven to provide better performance. 
Sentiment based-emotion classification using bidirectional long short term-memory (Bi-LSTM) Utami, Putri; Ningsih, Maylinna Rahayu; Pertiwi, Dwika Ananda Agustina; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.461

Abstract

Social media is now an important platform for sharing information, expressing opinions, and daily feelings or emotions. The expression of emotions such as anger, sadness, fear, happiness, disappointment, and so on social networks can be further analyzed either for business purposes or just analyzing the habits of a community or someone's posts.  However, analyzing manually will be a time-consuming process, and the use of conventional methods can affect the results of less accurate accuracy. This research aims to improve the accuracy of recognizing emotions in text by using the Bidirectional Long Short Term Memory (Bi-LSTM) method, which is a subset of RNNs that tend to be more stable during training and show better performance on various NLP and other processing tasks. The method used includes several stages, namely preprocessing, tokenization, sequence padding, and modeling. The results of this study show that the Bi-LSTM model is capable of predicting emotions in text with an accuracy of 94.45% because it excels in handling the temporal context and can avoid vanishing gradients.
Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification Ningsih, Maylinna Rahayu; Unjung, Jumanto; Pertiwi, Dwika Ananda Agustina; Prasetiyo, Budi; Muslim, Much Aziz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1888

Abstract

Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.
Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding Ningsih, Maylinna Rahayu; Wibowo, Kevyn Aalifian Hernanda; Dullah, Ahmad Ubai; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.193

Abstract

The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.
Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model Qohar, Bagus Al; Tanga , Yulizchia Malica Pinkan; Utami, Putri; Ningsih, Maylinna Rahayu; Muslim, Much Aziz
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.16-32

Abstract

The Olympics are a world-class sporting event held every four years, serving as a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. Significant work has been conducted to develop prediction models, with a primary focus on enhancing the accuracy of predicting Olympic outcomes. However, low-performance regression algorithms are the main problem with prediction. By integrating custom seasonality with the Facebook Prophet prediction model, this study aims to enhance the accuracy of Olympic predictions. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results indicate that this prediction model provides a reliable estimate of the total medals earned. On the Olympic Games (1994-2024) dataset, the model exhibits a very low error, as indicated by its MAE, MSE, and RMSE, and achieves an R² score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy.
Analyzing reading preferences based on gender and education with decision tree method Sari, Jelita Permata; Ningsih, Maylinna Rahayu
Journal of Student Research Exploration Vol. 3 No. 1 (2025): January 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i1.341

Abstract

This study aims to analyze the suitability of book genre selection with gender and education level. A classification method using a decision tree algorithm with four different criterion parameters is used to examine reading preferences based on various demographic factors, namely Gain Index, Information Gain, Gini Index, and accuracy. Data was obtained from a dummy dataset involving 120 records with three main attributes. The results show variations in accuracy depending on the criteria selected, with the highest accuracy rate achieved being 78.57%.
Co-Authors Budi Prasetiyo, Budi Dullah, Ahmad Ubai Farih, Habib al Jumanto Jumanto, Jumanto Jumanto Unjung Much Aziz Muslim Pertiwi, Dwika Ananda Agustina Putri Utami, Putri Qohar, Bagus Al Sari, Jelita Permata Tanga , Yulizchia Malica Pinkan Wibowo, Kevyn Aalifian Hernanda