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All Journal JAIS (Journal of Applied Intelligent System) Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Muzaky, Rifky Khoerul
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Opinion Mining on Chat GPT based on Twitter Users Nashrulloh, Muhammad Rikza; Julianto, Indri Tri; Muzaky, Rifky Khoerul
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.8399

Abstract

The presence of Chatbots can assist humans in their everyday lives. Chat GPT is one of the commonly used Chatbots that humans rely on to support their work, serve as an assistant, or even create artistic works or writings. The purpose of this research is to investigate opinions regarding the presence of Chat GPT. This Opinion Mining method is conducted by crawling data from Twitter, which can be categorized into three opinions: Positive, Negative, or Neutral. To calculate the accuracy level of the model created, two algorithms, Naïve Bayes and K-Nearest Neighbour, are compared. The model validation process utilizes K-Fold Cross Validation by varying the value of k (k=2, k=4, k=6, k=8, and k=10) and different sampling methods, namely Linear, Shuffled, and Stratified, to obtain optimal accuracy values. The research results indicate that the K-Nearest Neighbour Algorithm achieves the highest accuracy value of 92.40%. Based on this comparison, the K-Nearest Neighbour Algorithm is deemed suitable for modeling Opinion Mining of Chat GPT. The distribution of Twitter users' opinion percentages regarding Chat GPT is as follows: Positive 9.4%, Negative 1.4%, and Neutral 89%. Neutral opinions dominate the results of the conducted Opinion Mining.Keyword : chat GPT, opinion mining, twitter
EVALUASI PEMETAAN WILAYAH DESA DAWUNGSARI DENGAN MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (SIG) Fatimah, Dini Destiani Siti; Muhtadin, Fauzan Azmi; R, Alvarizky Putra Kurniawan; Febrian, Rivan; Husaeni, Fachri Ahmad Al; Saputra, Muhamad Dzaki; Maridjan, Maula Muhammad; Fitriyani, Dila; Mustofa, Muhamad Zaenal; Diniyaturobiah, Hanipah; Muzaky, Rifky Khoerul; Resita, Rasty; Mujahid, Wildan; Mulyana, Abdurrahman; Rafiqi, Putri Aufa; Maulana, Rifki Ilham; Noviansyah, Ikhwan; Adawiyah, Alya; Khaerurijal, Fajar; Baasith, Azry Abdul; Ardhillah, Zian Zaky
Jurnal PkM MIFTEK Vol 6 No 1 (2025): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.6-1.1972

Abstract

Amidst the rapid development of urbanization and increasing population, the challenges in regional planning and management are increasingly complex, especially in rural areas such as Dawungsari Village. This study aims to map the distribution of small businesses and public facilities, such as places of worship, sports facilities, educational facilities, and other village infrastructure. Data were collected through direct observation and local sources, then visualized in the form of a regional map. This mapping provides a comprehensive representation of the distribution of public facilities and services in the village, and provides a basis for supporting more effective development planning. The evaluation shows that the mapping method used is able to produce relevant information for regional management, although there are constraints related to data accuracy and limited local resources.
Comparison of Score-Based and Content-Based Automatic Sentiment Labeling Using a K-Nearest Neighbor Classifier Muzaky, Rifky Khoerul; Diniyaturobiah, Hanipah
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.120

Abstract

This study investigates the performance gap between two automatic sentiment labeling strategies one relying on star ratings and the other derived from textual content in classifying application reviews using the K-Nearest Neighbor (KNN) algorithm. Each review is converted into TF-IDF vectors, and the influence of both labeling approaches on the resulting classifier is examined. Performance is evaluated using accuracy, precision, recall, and F1-score to ensure a comprehensive assessment, with the content-based method achieving an accuracy of 0.81, indicating a more reliable outcome than the score-based variant. The score-driven approach shows weaker consistency, largely due to mismatches between numerical ratings and the sentiment conveyed in written text. Despite these findings, the study is limited by its focus on a single application domain and its reliance on a single classical baseline classifier, which may be sensitive to class imbalance. Future work is encouraged to incorporate more diverse datasets, adopt modern text representation techniques such as word embeddings or transformer-based encodings, and explore classification algorithms that better accommodate uneven class distributions.
Analysis of Earthquake Notification Complaint Topics in Info BMKG Reviews Using BERTopic Diniyaturobiah, Hanipah; Muzaky, Rifky Khoerul
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.123

Abstract

Reliable earthquake notification services in public information applications play a critical role in supporting public awareness and preparedness in seismically active regions. This study examines user complaints about earthquake notification features in the Info BMKG mobile application by analyzing publicly available Google Play Store user reviews. A total of 1,500 reviews were collected and examined, with complaint reviews operationally defined as those with star ratings of 3 or lower. Prior to analysis, the dataset underwent text preprocessing and a balancing procedure to ensure adequate representation of complaint-related content. Topic modeling was conducted using BERTopic, a transformer-based approach that enables context-aware clustering of short, informal text, followed by descriptive temporal analysis to examine variations in complaint occurrence over time. The analytical workflow included text normalization, embedding generation, topic extraction, and temporal mapping of complaint patterns. The results reveal several recurring complaint themes, including delayed or missing notifications, clarity of information, application performance issues, and user responses to system updates. Temporal variations indicate periods of increased complaint activity that align with heightened application usage, reflecting shifts in user engagement rather than direct evidence of system failure. Topic validity was assessed through qualitative inspection of representative reviews to ensure semantic consistency and interpretability. Overall, this study provides a structured, descriptive overview of user concerns regarding earthquake notification services and demonstrates the applicability of topic-level and temporal analysis as an evaluative approach for mobile disaster information applications, without making causal performance claims.
Co-Authors Adawiyah, Alya Ardhillah, Zian Zaky Baasith, Azry Abdul Dini Destiani Siti Fatimah Diniyaturobiah, Hanipah Febrian, Rivan Fitriyani, Dila Husaeni, Fachri Ahmad Al Indri Tri Julianto Khaerurijal, Fajar Maridjan, Maula Muhammad Maulana, Rifki Ilham Muhammad Rikza Nashrulloh Muhtadin, Fauzan Azmi Mujahid, Wildan Mulyana, Abdurrahman Mustofa, Muhamad Zaenal Noviansyah, Ikhwan R, Alvarizky Putra Kurniawan Rafiqi, Putri Aufa Resita, Rasty Saputra, Muhamad Dzaki