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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 7 No. 1 (2024): December 2024" : 5 Documents clear
ANALYSIS OF PUBLIC SENTIMENT TOWARDS 2024 PRESIDENTIAL CANDIDACY USING NAÏVE BAYES ALGORITHM Rianggi; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1869.319 KB) | DOI: 10.34288/jri.v7i1.356

Abstract

This study analyzes public sentiment towards presidential nominations using text mining techniques and machine learning. The dataset consists of 670 tweets collected from social media. The analysis process includes a data pre-processing phase, encompassing text cleaning, case folding, tokenization, stopword removal, and stemming using the Sastrawi library for the Indonesian language. Sentiment labeling was was performed using NLTK's SentimentIntensityAnalyzer, categorizing tweets into positive, negative, or neutral sentiments. The analysis results reveal the sentiment distribution among the analyzed tweets. Data modeling was performed using the Naive Bayes algorithm, which achieved an accuracy of 97.78% on the Iris dataset as an implementation example. The confusion matrix and classification report demonstrate the model's excellent performance in distinguishing sentiment classes. This research provides insights into public opinion regarding presidential nominations and demonstrates the effectiveness of text mining techniques and machine learning in sentiment analysis. The method can be applied to understand public opinion trends in other political and social contexts
Digitalization Of Survey And Mapping Service Processes Through The Development Of A Web-Based System Ernawati, Siti; Hermawan, Deni
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.352

Abstract

PT. Cakrawala Pilar Nusantara is a private company engaged in survey and mapping consultancy services. Several challenges have been identified in its business processes, one of which is that service delivery for collaboration is still conducted manually. This study adopts a Design Science Research (DSR) approach, focusing on the development of an artifact in the form of a web-based service system for PT. Cakrawala Pilar Nusantara, in accordance with the objectives of the research. The DSR methodology consists of the following stages: Problem Identification and Research Motivation, Definition of Solution Objectives, Design and Development of the Artifact, Demonstration, Evaluation, and Communication. Data collection was carried out through observation and interviews with relevant parties. System design visualization was conducted using UML, represented by use case diagrams and activity diagrams. The programming language used is PHP, implementing the CodeIgniter framework. System testing was performed using the black-box testing method.The result of this research is a web-based information system that facilitates data entry, quotation submissions, reporting, and improves service processes by transforming manual record-keeping into a computerized system. The presence of this information system provides greater convenience for the company in managing its operational activities.
SENTIMENT ANALYSIS OF TWITTER DATA ON KIP-KULIAH USING TEXTBLOB AND GRADIENT BOOSTING Desi Masdin; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.353

Abstract

The Indonesian government aims to position the country among developed nations by 2045, with a primary focus on improving education quality from elementary to higher education levels. One of the key initiatives is the KIP-Kuliah (Indonesia Smart College Card) program, which supports high-achieving students from underprivileged economic backgrounds in accordance with UU No. 12/2012 on Higher Education. This study applies sentiment analysis using TextBlob and the Gradient Boosting algorithm to build a predictive model that identifies public support for the program through Twitter data. The results reveal a significant dominance of negative sentiment, with the model achieving an accuracy of 97%. These findings underscore the importance of sentiment analysis as a feedback tool for policymakers during the implementation of education-related programs. Furthermore, the results suggest that continuous monitoring of public opinion via social media can contribute to more adaptive and responsive policy development. This research highlights the need for future studies to expand the scope of analysis using more advanced natural language processing techniques for deeper understanding and broader coverage of public sentiment.
Explainable AI-Driven TabNet Model Enhanced with Bayesian Optimization for Lung Cancer Prediction and Interpretation Maulana, Ilham
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.354

Abstract

This study aims to develop an accurate and explainable lung cancer risk prediction model using a TabNet approach optimized with Bayesian Optimization and applying Explainable AI (XAI) methods through LIME (Local Interpretable Model-Agnostic Explanations). TabNet was selected for its efficiency in processing tabular data and its ability to produce high-accuracy predictions. In the initial stage, the TabNet model was tested using a dataset that was preprocessed through standardization and split into training and testing sets. The performance evaluation of the model without optimization showed an accuracy of 95.83%, precision of 95.87%, recall of 95.76%, and F1-Score of 95.81%. Subsequently, Bayesian Optimization was applied using the Optuna library to find the best hyperparameter combination for the TabNet model. The optimization results demonstrated a significant improvement, achieving an accuracy of 98.33%, precision of 98.48%, recall of 98.21%, and F1-Score of 98.32%. After optimizing the TabNet model, LIME was implemented to provide interpretability for the generated predictions. LIME was used to identify the most influential features contributing to the predictions, enhancing the model's transparency in the lung cancer risk prediction process. Through the combination of TabNet, Bayesian Optimization, and Explainable AI, this study successfully developed a lung cancer prediction model that is not only accurate but also highly interpretable. This model can assist medical professionals in identifying key risk factors and providing transparent explanations for each prediction made.
TWITTER SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL DISPUTE DECISION USING NAÏVE BAYES AND SVM Aulia Rahman, Ihsan; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.355

Abstract

Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision

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