Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Nia Mardiah
Universitas Satya Terra Bhinneka
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ANALISIS SENTIMEN PENGGUNA INSTAGRAM TERHADAP STATEMENT MENTERI KEUANGAN TENTANG "KEBIJAKAN GAJI GURU DAN DOSEN" MENGGUNAKAN NAIVE BAYES M. Imam Santoso; Nia Mardiah; Asha Sembiring
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4385

Abstract

Abstract: The Minister of Finance's statement regarding the teacher and lecturer salary policy sparked mixed public reactions, particularly on social media. This study aims to analyze the sentiment of the Indonesian public, particularly Instagram users, regarding this statement using the Nae Bayes algorithm. Data was collected through scraping using Instant Data Scrapper in August 2025, totaling 939 data points. Preprocessing steps included cleansing, tokenizing, stopword removal, and stemming. The data were then classified into two sentiment categories: positive and negative. The results showed that the majority of Instagram user sentiment tended to be negative (859 data points, representing 91.5%), while positive sentiment accounted for 89 data points, representing 9.5%. The Nae Bayes model achieved an accuracy of 0.87 in classifying public opinion. These findings indicate that the Nae Bayes algorithm is effective in analyzing public opinion on sensitive issues on social media. Furthermore, the results of this study can serve as a reference for the government and policymakers in understanding public perception and formulating more appropriate communication strategies related to education policy. Keyword: Sentiment Analysis, Nae Bayes, Minister of Finance, Instagram, Text Analysis. Abstrak: Pernyataan Menteri Keuangan tentang kebijakan gaji guru dan dosen memicu beragam reaksi publik, khususnya di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia, terutama pengguna Instagram, terhadap pernyataan tersebut dengan menggunakan algoritma Nae Bayes. Data dikumpulkan melalui proses scraping menggunakan Instant Data Scrapper pada Agustus 2025 dengan jumlah 939 data. Kemudian dilakukan tahap praproses meliputi cleansing, tokenizing, stopword removal, dan stemming. Selanjutnya, data diklasifikasikan ke dalam dua kategori sentimen, yaitu positif dan negatif. Hasil penelitian menunjukkan bahwa mayoritas sentimen pengguna Instagram cenderung Negatif (859 data dengan persentase 91,5%), sedangkan sentimen positif berjumlah 89 data dengan persentase 9,5%. Model Nae Bayes mencapai tingkat akurasi sebesar 0,87 dalam mengklasifikasikan opini publik. Temuan ini mengindikasikan bahwa algoritma Nae Bayes efektif dalam menganalisis opini publik pada isu sensitif di media sosial. Selain itu, hasil penelitian ini dapat menjadi acuan bagi pemerintah dan pemangku kebijakan dalam memahami persepsi publik serta merumuskan strategi komunikasi yang lebih tepat terkait kebijakan pendidikan. Kata kunci: Sentimen Analisis, Nae Bayes, Menteri Keuangan, Instagram, Analisis Teks.
KLASIFIKASI SENTIMEN PUBLIK TERHADAP PROGRAM SDG 1 PENGENTASAN KEMISKINAN DI INDONESIA MENGGUNAKAN SVM DAN INDOBERT Nia Mardiah; M. Imam Santoso; Putri Athirah Thaibur; Ayu Andini Br Sitepu
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6582

Abstract

Abstract: The first goal of the Sustainable Development Goals (SDGs), namely poverty eradication, is a major priority of Indonesia’s national development agenda. Public responses to poverty alleviation initiatives provide valuable insights for assessing the effectiveness of government policies. This study examines public sentiment regarding the implementation of SDG 1 using posts collected from the X platform and compares the classification performance of Support Vector Machine (SVM) and IndoBERT models. A dataset consisting of 1,002 Indonesian-language posts was gathered through web scraping and Application Programming Interface (API) techniques. The research process included data preprocessing, sentiment labeling through a lexicon-based approach, TF-IDF feature extraction for the SVM model, and fine-tuning for the IndoBERT model. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The findings indicate that negative sentiment is more dominant than positive sentiment toward poverty alleviation programs. The SVM model achieved an accuracy of 80.60%, while IndoBERT reached 80.10%. These results suggest that SVM performed slightly better on the dataset used in this study. Overall, the findings demonstrate that artificial intelligence-based sentiment analysis can support monitoring public perceptions and evaluating poverty reduction policies in Indonesia more effectively and comprehensively for evidence-based decision making processes nationwide. Keywords: sentiment analysis; SDG 1; poverty; SVM; IndoBERT.   Abstrak: Tujuan pertama Sustainable Development Goals (SDGs), yaitu menghapus kemiskinan, menjadi salah satu agenda strategis pembangunan nasional Indonesia. Respons masyarakat terhadap berbagai program pengentasan kemiskinan dapat digunakan sebagai bahan evaluasi terhadap efektivitas kebijakan yang diterapkan pemerintah. Penelitian ini menganalisis sentimen publik mengenai implementasi SDG 1 dengan memanfaatkan unggahan pada platform X serta membandingkan kinerja model Support Vector Machine (SVM) dan IndoBERT dalam proses klasifikasi. Sebanyak 1.002 unggahan berbahasa Indonesia dikumpulkan melalui teknik scraping dan pemanfaatan API. Tahapan penelitian meliputi praproses data, pemberian label sentimen menggunakan pendekatan leksikon, pembentukan fitur TF-IDF pada SVM, serta fine-tuning model IndoBERT. Kinerja model dievaluasi menggunakan confusion matrix dengan indikator accuracy, precision, recall, dan F1-score. Hasil penelitian memperlihatkan bahwa opini negatif lebih dominan dibandingkan opini positif terhadap program pengentasan kemiskinan. Model SVM memperoleh tingkat akurasi sebesar 80,60%, sedangkan IndoBERT mencapai 80,10%. Temuan tersebut menunjukkan bahwa SVM memiliki performa yang sedikit lebih unggul pada dataset yang digunakan. Penelitian ini mengindikasikan bahwa analisis sentimen berbasis kecerdasan buatan berpotensi menjadi alat pendukung dalam memonitor persepsi masyarakat serta mengevaluasi kebijakan pengurangan kemiskinan di Indonesia. Kata kunci: analisis sentimen; SDG 1; kemiskinan; SVM; IndoBERT.
Co-Authors Asha Sembiring Ayu Andini Br Sitepu M. Imam Santoso Putri Athirah Thaibur