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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) REKAYASA 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 Physics Public Health Social Sciences Other

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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.
ANALISIS EFISIENSI APBN ERA PRABOWO: KAJIAN EKONOMI DAN ANALISIS SENTIMEN PUBLIK Pramesti, Retta Farah; Firdaus, Asno Azzawagama; Yulita, Khairanis; Thoyyibah, Mazraatin
Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Vol 8 No 2 (2025): Artikel Riset Juli 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi Al-Washliyah Sibolga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36778/jesya.v8i2.2054

Abstract

Studi ini mengevaluasi efisiensi anggaran dalam Anggaran Pendapatan dan Belanja Negara (APBN) 2025, khususnya penghematan Rp306,69 triliun yang dialokasikan untuk mendanai program Makanan Bergizi Gratis (MBG) di bawah pemerintahan Presiden Prabowo Subianto. Pendekatan metode campuran digunakan, menggabungkan analisis kuantitatif (korelasi Pearson dan K-Means Clustering pada data anggaran dan efisiensi dari 78 kementerian/lembaga) dengan analisis sentimen kualitatif-komputasional dari media sosial X (Twitter) menggunakan algoritma Naïve Bayes. Hasil menunjukkan korelasi yang sangat kuat (r = 0,957) antara ukuran anggaran dan efisiensi, tetapi hanya 7 kementerian yang masuk dalam klaster efisiensi tinggi. Analisis sentimen mengungkapkan persepsi publik yang dominan positif terhadap kebijakan MBG, meskipun bias model hadir karena ketidakseimbangan data. Studi ini merekomendasikan lima strategi utama: memperkuat penganggaran berbasis kinerja, memantau program MBG, mengoptimalkan teknologi manajemen anggaran, mendorong partisipasi publik, dan mendiversifikasi pembiayaan inovatif. Secara keseluruhan, temuan tersebut menyoroti pentingnya tata kelola fiskal yang tidak hanya efisien tetapi juga adaptif dan inklusif untuk mendukung pembangunan berkelanjutan.
PELATIHAN DESAIN GRAFIS SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN DALAM PEMASARAN KONTEN SEBAGAI PELUANG MENDAPATKAN PASSIVE INCOME BAGI KARANG TARUNA CIPTA RASA DAYA DI DESA KARANG SIDEMEN Syuhada, Fahmi; Saputra, Joni; Adipta, Marazaenal; Anggarista, Randa; Kumoro, Danang Tejo; Afriansyah, M.; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Firdaus, Asno Azzawagama; Pratama, Ramadhana Agung; Yamin, Muhamad
Jurnal Abdi Insani Vol 12 No 5 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i5.2235

Abstract

The Community Partnership Empowerment activity aimed to enhance the skills and knowledge of the youth in Karang Sidemen Village, Central Lombok, in the field of digital creative economy, specifically through digital content marketing that can generate passive income. The PKM program is supported by the Directorate of Research, Technology, and Community Service through the BIMA 2024 program. The activities included a socialization session on the concept of the creative economy and technical training on using Adobe Illustrator, where participants were encouraged to market their creations on platforms like Shutterstock. The outcomes of this program showed an improvement in participants' graphic design skills, as evidenced by their ability to create logos, set up Shutterstock accounts, and independently upload their work. Additionally, this activity involved students under the Merdeka Belajar-Kampus Merdeka (MBKM) scheme, providing them with experiential learning outside the campus. In conclusion, this program successfully made a positive impact on digital literacy and the creative economy in the community and is expected to contribute to the village's economic sustainability through the empowerment of local potential in a sustainable manner.
Model Deteksi Jumlah Kendaraan Bermotor Menggunakan Algoritma You Only Look Once (Yolo) V4 Di Parkiran Universitas Qamarul Huda Badaruddin Bagu Ega Silpia Aulia; Syuhada, Fahmi Syuhada; Asno Azzawagama Firdaus
SainsTech Innovation Journal Vol. 7 No. 2 (2024): SIJ VOLUME 7 NOMOR 2 TAHUN 2024
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v7i2.2024.756

Abstract

Kemajuan teknologi yang pesat telah mendorong berbagai inovasi dalam sistem berbasis Internet of Things (IoT), termasuk pada konsep smart city. Salah satu tantangan di era ini adalah manajemen parkir, terutama dalam mendeteksi keberadaan kendaraan bermotor. Keterbatasan ruang parkir di lingkungan pendidikan, seperti Universitas Qomarul Huda Badaruddin Bagu, sering kali menjadi penyebab kemacetan. Sistem parkir konvensional yang diawasi oleh petugas sering kali tidak efisien dan tidak menyediakan informasi real-time mengenai ketersediaan tempat parkir. Penelitian ini bertujuan untuk mengembangkan model deteksi kendaraan bermotor di area parkir Universitas Qomarul Huda Badaruddin Bagu menggunakan metode YOLO (You Only Look Once) V4. Data yang digunakan berupa gambar parkiran yang diambil dari kamera CCTV di area parkir kampus. Model YOLO diimplementasikan untuk mendeteksi kendaraan, khususnya motor, dan hasil deteksinya dibandingkan dengan perhitungan manual untuk mengevaluasi akurasinya. Program yang dihasilkan diharapkan mampu memberikan solusi yang lebih efektif dalam memantau kapasitas parkir dan memudahkan pengelolaan fasilitas parkir di kampus.
The Role of Sentiment Analysis in Election Predictions Compared to Electability Surveys Firdaus, Asno Azzawagama; Faresta, Rangga Alif; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.1-8.2025

Abstract

Indonesia has just held the voting process for the Presidential Election. This has become a discussion of various media to social media, especially Twitter. However, when making predictions based on social media it will be so difficult if there is no specific technique or method for handling it. The prediction method we found in Indonesia often uses electability surveys in elections, but this research will compare it with sentiment analysis that utilizes social media in data collection. Another novelty is the data used during candidate campaign debates using the Support Vector Machine (SVM) method in class classification. The results obtained show that there are still differences between electability and sentiment, but this is due to several factors such as the amount of data, data objects, data collection time span, and methods. Overall, the SVM method has an accuracy of more than 0.75 on all three candidate datasets, proving that this method can be applied to similar cases.
Classification of Stunting in Toddlers using Naive Bayes Method and Decision Tree Maulana, Adrian; Ilham, Muhammad; Lonang, Syahrani; Insyroh, Nazaruddin; Sherly da Costa, Apolonia Diana; B. Talirongan, Florence Jean; Furizal, Furizal; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.28-33.2025

Abstract

Child stunting is a health problem that has a major impact on their physical growth and brain development. This study aims to create a model that can predict the risk of stunting using machine learning technology, in order to provide assistance quickly. Using data from 7,573 children, which included information such as age, weight, height gender and breastfeeding status, we tried two methods, Naive Bayes and Decision Tree. As a result, Naive Bayes was more accurate and the success rate reached 92%, compared to Decision tree which was only 88%. With this model, it is hoped that health workers will find it easier to find children at risk of stunting, so that preventive action can be taken earlier. This research aims to provide technology-based solutions to overcome the problem of stunting in the community.
Data Analysis of Student Monitoring Using the K-Means Clustering Method Sulistiani; Habibi , Ahmad Rizky Nusantara; Maulana , Adrian; Talirongan , Hidear; Abao , Anrom G.; Elmalky , Ahmed Mahmoud Zaki; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.50-57.2025

Abstract

This study aims to group student monitoring data by focusing on two main variables, namely anxiety level and mood score, using the K-Means Clustering method. The research data was obtained from the Kaggle platform, which contains 1000 rows of data with nine attributes, including Student ID, Date, Class Time, Attendance Status, Stress Level, Sleep Hours, Anxiety Level, Mood Score, and Risk Level. The research process involved several stages, from problem identification, data collection, data cleaning and preprocessing, to the application of the K-Means algorithm. The analysis results showed that the data could be divided into two main groups: Cluster 1 consists of students with low to moderate anxiety levels and high mood scores, while Cluster 2 includes students with high anxiety and low mood scores. These findings provide relevant information for schools or campuses to design more effective psychological support and emotional monitoring programs. Additionally, this clustering method can serve as a foundation for developing an early detection system for psychological issues among students.
Sentiment Analysis of User Reviews of TikTok App on Google Play Store Using Naïve Bayes Algorithm Hasanah, Rakyatol; Sani SR, Sahrul; Munzir, Misbahul; Firdaus, Asno Azzawagama; Sulton, Chaerus; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.58-64.2025

Abstract

In recent years, user interaction through mobile applications has grown rapidly, making user reviews an important source of feedback for improving service quality. This study explores sentiment analysis on 5,000 user reviews of the TikTok application, collected from the Google Play Store using the google-play-scraper library. The data underwent several preprocessing steps, such as case folding, text cleaning, and selecting relevant columns like review content and rating score. Sentiment labeling was based on rating values: scores of 4 and 5 were treated as positive, while scores of 1 and 2 were considered negative. From the results, it was observed that negative reviews appeared more frequently, indicating an imbalance in the dataset. Despite this, the Naïve Bayes classification algorithm still achieved a reasonably good performance in categorizing the sentiments. These findings suggest that even with simple models, valuable insights can be gained from user-generated content. Moreover, the results provide meaningful input for TikTok developers to better understand user concerns and emphasize the potential need for applying balancing techniques in future analysis. Further studies are encouraged to explore other algorithms that may improve sentiment classification accuracy on more complex datasets.    
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.
Utilizing Game Based Applications for Teaching Historical Content Syahr, Khaeru Alfi; Pratama, Yusuf Hendra; Firdaus, Asno Azzawagama
Secure And Knowledge-Intelligent Research in Cybersecurity And Multimedia (SAKIRA) Vol. 3 No. 01 (2025): The Role of Digital Technology in Enhancing Education, Security, and Business
Publisher : Universitas Islam Al-Azhar Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36679/s4kira.v3i1.27

Abstract

This research investigates the integration of game-based applications as an instructional medium for conveying historical content within educational contexts. With the rapid advancement of digital technologies, interactive learning platforms such as educational games are becoming increasingly prominent in classroom environments to foster deeper student engagement and understanding. History, which is traditionally regarded as a static and memorization-heavy subject, is being reimagined through the use of interactive digital media that incorporates visual storytelling, narrative design, and dynamic challenges, thereby offering a more experiential learning process. Employing a qualitative descriptive methodology, the study synthesizes insights from literature reviews and participatory observations of various game-based educational tools centered on Indonesian historical themes. Among the platforms examined are Wordwall, Android-based applications focusing on the Indonesian independence movement, and narrative-driven games introducing national heroes and ancient kingdoms. The findings reveal that these digital interventions not only enhance learner motivation but also contribute to improved cognitive retention and active classroom participation. The implementation of Wordwall in high school settings, for example, demonstrates measurable benefits in engagement through gamified quiz formats. Despite these advantages, notable barriers persist. Limited access to necessary technological infrastructure and the need to ensure historical accuracy in digital content remain critical issues. Consequently, interdisciplinary collaboration particularly involving historians, educators, and game developers is crucial to maintaining the pedagogical and factual integrity of these tools. In summary, the utilization of educational games represents a transformative approach to history instruction. By blending technology with pedagogy, such applications enable educators to deliver historical narratives in a manner that is not only academically rigorous but also engaging and meaningful for today’s digital-native learners.  
Co-Authors Abao , Anrom G. Abdul Fadlil Abdul Fadlil Adipta, Marazaenal Adriyanto, Feri Afriansyah, M. Ahmad Fatoni Dwi Putra Aldi Bastiatul Fawait Fawait Alfian Ma’arif Ana Tsalitsatun Ni'mah 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 Faresta, Rangga Alif 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 Muzakkir Pangri Nakib, Arman Mohammad Palahuddin, Palahuddin Pramesti, Retta Farah Pratama, Ramadhana Agung Rangga Alif Faresta Sani SR, Sahrul Sa’adatai, Yuan Sherly da Costa, Apolonia Diana Sitti Rahmah Soekarta, Rendra SULISTIANI Sulton, Chaerus Suwarno, Iswanto Syahr, Khaeru Alfi Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Tajuddin, Muhammad Talirongan , Hidear Thoyyibah, Mazraatin Visman, Javan Wahyu Rahmaniar Wijaya, Setiawan Ardi Yusuf Hendra Pratama