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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Scientific Journal of Informatics
setiawan, very dwi
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Sentiment Analysis to Evaluate Public Service Perception among Surakarta City Residents Using the BiLSTM Model setiawan, very dwi; Dwi Utai Iswavigra
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15498

Abstract

The growing use of social media as a platform for public communication has opened new opportunities for understanding public opinion regarding government policies, including public services. One of the cities actively discussed on social media is Surakarta, where citizens openly express both appreciation and criticism of local government performance. This study aims to analyze public sentiment toward public services in Surakarta by employing a deep learning-based sentiment analysis approach, specifically using the Bidirectional Long Short-Term Memory (BiLSTM) model. Data were collected from Twitter/X using a web crawling technique with the keywords “pemerintah solo” (Solo government), “kota Surakarta” (Surakarta city), and “kota solo” (Solo city), resulting in 2,168 tweets. The analysis process involved several stages, including preprocessing, sentiment labeling using a lexicon-based method, feature representation with Word2Vec, and classification using five models: SVM, Random Forest, CNN, LSTM, and BiLSTM. The evaluation results show that BiLSTM achieved the best performance with an accuracy of 90.21%, precision of 91.05%, recall of 89.84%, and F1-score of 90.43%. The conclusion of this study is that BiLSTM can effectively classify public sentiment toward public services, especially in the context of informal social media texts. The implication of this research indicates that sentiment analysis can serve as a decision-support tool for designing more responsive and data-driven public policies and provide strategic insights for local governments in improving the quality of public services.
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates Setiawan, Very Dwi; Iswavigra, Dwi Utari; Anggiratih, Endang
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26320

Abstract

Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
LITERASI DIGITAL: MEMBANGUN KARAKTER ANAK DI ERA DIGITAL DI BA AISYIYAH DUWET KECAMATAN BAKI KABUPATEN SUKOHARJO Dwi Setiawan, Very; Utari Iswavigra, Dwi; Anggiratih, Endang; Mar’atullatifah, Yulaikha; Mursalim, Mursalim; Rahmasari, Yunita
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 8 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i8.%p

Abstract

Era digital menempatkan anak-anak dalam lingkungan teknologi yang intensif, sehingga literasi digital menjadi fondasi penting untuk pembentukan karakter yang bijak dan bertanggung jawab. Penelitian Pengabdian kepada Masyarakat (PkM) di BA Aisyiyah Duwet, Sukoharjo, bertujuan mengatasi kesenjangan pemahaman literasi digital di kalangan guru dan orang tua serta dampaknya pada karakter anak. Metode deskriptif kualitatif dengan observasi, wawancara, dokumentasi, dan pelatihan tatap muka melibatkan 40 peserta. Program mencakup sosialisasi, pelatihan intensif, pendampingan, dan evaluasi pre-test dan post-test. Hasil menunjukkan peningkatan signifikan pada semua aspek literasi digital peserta, seperti pemahaman konsep literasi digital naik dari 45% ke 85%, kemampuan media sosial produktif dari 50% ke 80%, dan keterampilan desain konten dari 30% ke 75%. Peningkatan ini mencerminkan keberhasilan program dalam membangun etika digital dan penggunaan teknologi yang sehat. PkM ini berhasil mengubah pola pikir peserta menjadi lebih positif terhadap teknologi sebagai alat pendidikan karakter.
PENERAPAN PERANGKAT LUNAK PYTHON UNTUK MENINGKATKAN KOMPETENSI ANALISIS DATA DALAM KEGIATAN RISET MAHASISWA Dwi Setiawan, Very; Utari Iswavigra, Dwi; Ulfa, Mutia; Anggiratih, Endang; Dwi Yulianto, Bagas; Praningki, Tutus; Suyahman, Suyahman; Wicaksono, Ardy; Mar'atullatifah, Yulaikha; Prasetyo, Deny; Mursalim, Mursalim
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 9, No 2 (2026): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v9i2.%p

Abstract

Perkembangan teknologi informasi menuntut mahasiswa memiliki kompetensi analisis data yang memadai untuk mendukung kegiatan riset akademik. Namun, kenyataannya masih banyak mahasiswa yang mengalami keterbatasan dalam pemanfaatan perangkat lunak analisis data berbasis komputasi dan cenderung bergantung pada aplikasi spreadsheet sederhana. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi analisis data mahasiswa melalui penerapan perangkat lunak Python dalam kegiatan riset. Pelatihan dilaksanakan di Universitas Islam Batik Surakarta melalui kolaborasi antara Program Studi Teknik Industri Universitas Batik Surakarta dan Program Studi Teknik Industri Universitas Nahdlatul Ulama Jepara. Metode yang digunakan adalah pelatihan berbasis praktik langsung (hands-on training) yang meliputi pengenalan dasar pemrograman Python, pengolahan dan preprocessing data, serta visualisasi data penelitian menggunakan pustaka Pandas, NumPy, Matplotlib, dan Seaborn. Evaluasi kegiatan dilakukan melalui pre-test dan post-test untuk mengukur peningkatan kompetensi peserta. Hasil evaluasi menunjukkan peningkatan yang signifikan pada seluruh aspek kompetensi, termasuk pemahaman konsep dasar Python, kemampuan pengolahan dan pembersihan data, keterampilan visualisasi data, serta pemanfaatan Python dalam penyusunan laporan penelitian. Peningkatan nilai post-test yang lebih tinggi dibandingkan pre-test mengindikasikan bahwa pendekatan pelatihan yang diterapkan efektif dalam meningkatkan literasi komputasional dan kualitas analisis data mahasiswa. Kegiatan ini berkontribusi positif terhadap peningkatan mutu riset mahasiswa serta mendorong pemanfaatan perangkat lunak open-source dalam lingkungan akademik. Pelatihan ini juga berpotensi menjadi model Pengabdian kepada Masyarakat yang berkelanjutan dalam pengembangan kompetensi analisis data di perguruan tinggi.
Sentiment Analysis Using Bidirectional Encoder Representations from Transformers for Indonesian Stock Price Prediction with Long Short-Term Memory and Gated Recurrent Unit Models Iswavigra, Dwi Utari; Setiawan, Very Dwi; Ulfa, Mutia; Ommr, Brieva
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5383

Abstract

The advancement of artificial intelligence based market analytics has driven the need for stock price prediction models capable of representing market behavior both technically and psychologically. This study aims to improve stock price forecasting in the Indonesian capital market by integrating sentiment analysis with deep learning time-series models. It evaluates whether public sentiment can contribute to enhancing prediction accuracy when combined with historical stock data. Textual sentiments were extracted using IndoBERT and converted into positive, negative, and neutral scores, which were then merged with historical stock prices. These data were modeled using LSTM, GRU, and a hybrid LSTM–GRU architecture. Model evaluation was conducted using MSE, MAE, RMSE, and MAPE metrics across six Indonesian stocks ANTM, BBCA, BBRI, SCMA, TLKM, and UNVR. The hybrid LSTM–GRU model produced the lowest prediction errors for BBCA and BBRI, with MSE scores of 0.151 and 1022.062, respectively. GRU delivered the best performance for highly volatile stocks, such as SCMA MAPE 1.65% and UNVR MAPE 0.51%, while LSTM demonstrated the most stable performance for TLKM with an MSE of 606.93 and RMSE of 24.63. Across all cases, sentiment scores improved model responsiveness, particularly during price spikes ANTM mid-2025 and price declines BBRI early year. The integration of sentiment significantly enhances prediction relevance by combining psychological market indicators with technical price trends. This framework provides more reliable decision-making support for investors, strengthens algorithmic trading strategies in Indonesia, and contributes to intelligent financial analytics that reflect local market behavior.
Co-Authors Anggiratih, Endang Bagas Dwi Yulianto Dwi Utai Iswavigra Iswavigra, Dwi Utari Mar'atullatifah, Yulaikha Mar’atullatifah, Yulaikha Mursalim Mursalim Mursalim Mursalim Ommr, Brieva Prasetyo, Deny Rahmasari, Yunita Suyahman Suyahman ulfa, mutia Utari Iswavigra, Dwi Wicaksono, Ardy