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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Jurnal Abdi Insani Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems International Journal of Social Service and Research DECODE: Jurnal Pendidikan Teknologi Informasi SainsTech Innovation Journal Secure And Knowledge-Intelligent Research in Cybersecurity And Multimedia (SAKIRA) IJMST Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Scientific Journal of Engineering Research SainsTech Innovation Journal
Asno Azzawagama Firdaus
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Journal : Journal of Robotics and Control (JRC)

 1 
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Unveiling the Predictive Power of Machine Learning and Deep Learning: A Comparative Study on Disease Diagnosis, Detection, and Mortality Risk in Healthcare Santoso, Daniel; Firdaus, Asno Azzawagama; Yunus, Muhajir; Pangri, Muzakkir
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26223

Abstract

This study compares the roles of machine learning (ML) and deep learning (DL) in healthcare, focusing on their applications, challenges, and prospects. It addresses the increasing relevance of AI in public health systems and contributes a structured analysis of how ML and DL process different healthcare data types. A systematic literature review was conducted using sources from Google Scholar, Elsevier, Springer, IEEE, and MDPI, applying inclusion criteria based on relevance, publication quality, and recency (2018–2024). Article selection and synthesis using meta-analysis followed the PRISMA framework. The review identified four key application areas: (1) disease outbreak prediction, (2) disease forecasting, (3) disease diagnosis and detection, and (4) disease hotspot monitoring and mapping. ML techniques such as Random Forest and ensemble methods show high performance in handling structured data like patient records, whereas DL architectures like convolutional neural network (CNN) and long-short term memory (LSTM) are superior for unstructured data, including medical imaging and bio signals. Challenges common to both approaches include data quality issues, dataset bias, privacy concerns, and integration into existing healthcare infrastructures. Looking forward, promising directions include explainable AI (XAI), transfer learning, federated learning, and real-time data use from wearable and internet of things (IoT) devices. The study concludes that while ML and DL can significantly improve diagnosis, response to health threats, and resource allocation, maximizing their impact requires continuous cross-sector collaboration, transparency, and ethical governance.
Co-Authors Abao , Anrom G. Abdul Fadlil Abdul Fadlil Adipta, Marazaenal Adriyanto, Feri Afriansyah, M. Aldi Bastiatul Fawait Fawait Alfian Ma’arif Anggarista, Randa Anton Yudhana Anton Yudhana Anwar, Miftahul Ariska Fitriyana Ningrum Arqam, Mhd Lailan Aryandani, Aisyah Atmojo, Ahmad Muslih B. Talirongan, Florence Jean Costa, Apolonia Diana Sherly da Daniel Santoso Dianda Rifaldi Ega Silpia Aulia Elmalky , Ahmed Mahmoud Zaki Fahmi Syuhada Faresta, Rangga Alif Furizal Furizal Furizal Furizal Furizal Furizal, Furizal Gabriel Diemesor Gunawan, Rahmad Habibi , Ahmad Rizky Nusantara Hasanah, Rakyatol Imam Riadi Imam Riadi Insyroh, Nazaruddin Joko Slamet Saputro Joni Saputra Kariyamin, Kariyamin Khairanis Yulita Kumoro, Danang Tejo Maghfiroh, Hari Mahsun Mahsun Maulana , Adrian Maulana, Adrian Muhajir Yunus Muhamad Yamin Muhammad Ilham Muhammad Mujahid Dakwah Muhammad Yusuf Muis, Alwas Munzir, Misbahul Murinto Nakib, Arman Mohammad Palahuddin, Palahuddin Pramesti, Retta Farah Pratama, Ramadhana Agung Putra, Ahmad Fatoni Dwi Rangga Alif Faresta Rangga Faresta Sani SR, Sahrul Sherly da Costa, Apolonia Diana Sitti Rahmah Soekarta, Rendra SULISTIANI Sulton, Chaerus Suwarno, Iswanto Syahr, Khaeru Alfi Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Talirongan , Hidear Thoyyibah, Mazraatin Visman, Javan Wahyu Rahmaniar Wijaya, Setiawan Ardi Yusuf Hendra Pratama