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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Syaputra, Rezki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering

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SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION Negara, Edi Surya; Syaputra, Rezki; Erlansyah, Deni; Andryani, Ria; Saksono, Prihambodo Hendro; Aditya, Ferdi; Agam, Padel Mohammad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.35254

Abstract

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.
COMPARING DEEP LEARNING AND MACHINE LEARNING FOR DETECTING FAKE NEWS ON SOCIAL MEDIA Andryani, Ria; Julian, Dedek; Syaputra, Rezki; Syazili, Ahmad; Rusli, Ahmad; Ramadan, Rahmat; Negara, Edi Surya
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46370

Abstract

One of the critical issues resulting from the increasing penetration of social media is the spread of fake news. This can damage public information and influence mass opinion, leading to conflict. To overcome this problem, machine learning and deep learning-based approaches have been continuously developed to detect fake news on various social media platforms automatically. This article aims to compare the effectiveness of these two approaches in detecting fake news. The methods used include the implementation of traditional machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest, as well as deep learning-based approaches, including Long Short-Term Memory and Self-Organizing Maps. Datasets containing real and fake news from various social media sources are used to train and evaluate these models. Model performance is measured based on accuracy, precision, recall, and F1-score. This study aims to determine which approach is more effective and identify challenges in implementing these algorithms in a dynamic social media environment. The results obtained show that the Random Forest algorithm achieves an accuracy level of 100%, surpassing other algorithms, including Long Short-Term Memory with an F-1 Score of 97%, Self-Organizing Map with an F-1 Score of 96%, and Support Vector Machine with an F-1 Score of 92%.
Rancang Bangun Sistem Informasi Pelayanan Publik pada Kecamatan Lais dengan Metode Agile Silviana, Ririn; Syaputra, Rezki; Agam, Padel Mohammad
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.6616

Abstract

Government institutions are increasingly compelled to optimize public services through integrated digital systems in response to rapid advancements in information technology. The Lais District Office continues to face operational challenges, including manual service submissions, delays in verification processes, and limited access to service status information. This study aims to design and develop a web-based public service information system to advance efficiency, transparency, and accessibility. The Agile method with an iterative approach was employed, encompassing planning, design, development, testing, and evaluation stages. Data were collected through observation, interviews, and literature review to identify system requirements. The system provides features including online service submission, multi-level verification, real-time status tracking, and digital document management. Functional testing confirmed that all primary features operate correctly without critical errors. The evaluation involved 30 internal staff members, and reliability testing demonstrated adequate consistency (Cronbach's Alpha > 0.7). Statistical analysis further reveals that system quality exerts a significant effect on user satisfaction (p < 0.05), with a coefficient of determination (R²) of 0.521, indicating that the developed system successfully establishes a structured digital workflow and strengthens transparency in service status monitoring.
Co-Authors Aditya, Ferdi Agam, Padel Mohammad Ahmad Syazili Andryani, Ria Deni Erlansyah Edi Surya Negara Julian, Dedek Ramadan, Rahmat Rusli, Ahmad Saksono, Prihambodo Hendro Silviana, Ririn