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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 International Journal of Mechanical, Industrial and Control Systems Engineering Green Engineering: Journal of Engineering and Applied Science Global Science: Journal of Information Technology and Computer Science
setiawan, very dwi
Universitas Pignatelli Triputra
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture 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.
Trust Centric Machine Learning Framework for Secure Decision Making in Decentralized Digital Service Ecosystems Deny Prasetyo; Siska Narulita; Ahmad Jurnaidi Wahidin; Rosalina Yani Widiastuti; Suyahman Suyahman; Very Dwi Setiawan; Agus Wantoro
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.197

Abstract

This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.
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.
Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity Yulaikha Maratullatifah; Dwi Utari Iswavigra; Very Dwi Setiawan; Mursalim Mursalim; Budi Wibowo
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i1.400

Abstract

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.
Hybrid Reinforcement Learning and Robust Adaptive Control Strategy for Autonomous Manufacturing Systems under Uncertain and Dynamic Production Environments Irlon Irlon; Teguh Muryanto; Sayyid Jamal Al Din; Dwi Utari Iswavigra; Yulaikha Maratullatifah; Very Dwi Setiawan
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.403

Abstract

This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning techniques like RL, deep Q-networks (DQN), and deep deterministic policy gradient (DDPG), enhance decision-making capabilities in dynamic production environments. The study develops a hybrid RL robust control framework and tests it in both simulation and real-world scenarios. Performance metrics, including production efficiency, system stability, and adaptability, are assessed under various disturbance conditions, such as machine failures and fluctuating demands. The hybrid model significantly outperforms traditional PID control in terms of efficiency and stability, demonstrating faster convergence and better adaptability in dynamic environments. Statistical analysis confirms the superiority of the hybrid system over standalone RL models and traditional PID control. This model’s scalability and adaptability make it a promising solution for Industry 4.0 applications, addressing key challenges in real-world manufacturing systems by ensuring computational efficiency and the ability to manage large-scale data. The findings contribute to the development of more robust and efficient control strategies for autonomous manufacturing systems in uncertain environments.
Carbon Neutral Industrial Process Optimization through Hybrid Machine Learning and Real Time Energy Efficiency Monitoring Framework Suyahman Suyahman; Ardy Wicaksono; Dwi Utari Iswavigra; Yogiek Indra Kurniawan; Very Dwi Setiawan; Dedi Setiadi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.285

Abstract

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.
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 Agus Wantoro Ahmad Jurnaidi Wahidin Anggiratih, Endang Ardy Wicaksono Bagas Dwi Yulianto BUDI WIBOWO Dedi Setiadi Deny Prasetyo Dwi Utai Iswavigra Dwi Utari Iswavigra Dwi Utari Iswavigra Irlon Irlon Iswavigra, Dwi Utari Kurniawan, Yogiek Indra Mar'atullatifah, Yulaikha Mar’atullatifah, Yulaikha Mursalim Mursalim Mursalim Mursalim Mursalim Mursalim Ommr, Brieva Prasetyo, Deny Rahmasari, Yunita Sayyid Jamal Al Din Siska Narulita Suyahman Suyahman Teguh Muryanto ulfa, mutia Utari Iswavigra, Dwi Wicaksono, Ardy Widiastuti, Rosalina Yani Yulaikha Maratullatifah