@mastersthesis{digilib72172,
           month = {June},
           title = {KOMPARASI ALGORITMA RANDOM FOREST DAN NAIVE BAYES PADA INDOOR POSITIONING SYSTEM DENGAN METODE FINGERPRINTING (STUDI KASUS : GEDUNG KULIAH TERPADU UIN SUNAN KALIJAGA YOGYAKARTA)},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22206051010 Bella Tasya Kumala Dewi},
            year = {2025},
            note = {Dr. Bambang Sugiantoro, S.Si., M.T., IPU., ASEAN Eng},
        keywords = {Indoor Positioning System, Fingerprinting, Wi-Fi, Random
Forest, Naive Bayes, K-Means Clustering},
             url = {https://digilib.uin-suka.ac.id/id/eprint/72172/},
        abstract = {The Global Positioning System (GPS) exhibits limited accuracy in indoor
environments due to weak satellite signal penetration. Therefore, an alternative
solution is needed in the form of an Indoor Positioning System (IPS). This study
aims to design and analyze an IPS using a Wi-Fi signal-based fingerprinting method
by comparing the performance of the Random Forest and Na{\"i}ve Bayes algorithms
and evaluating the impact of K-Means Clustering as a data preprocessing technique.
Data were collected from the Integrated Lecture Building of UIN Sunan Kalijaga
Yogyakarta using the ?Cek Sinyal? application, which recorded parameters such as
Received Signal Strength Indicator (RSSI), frequency, and environmental
conditions. The dataset was subsequently processed through preprocessing, model
training, and testing using evaluation metrics such as precision, recall, and f1-score.
The results indicate that the Na{\"i}ve Bayes algorithm achieved a very high f1-score
(approaching 1.000) after the application of K-Means Clustering. However, the
performance is likely affected by overfitting due to sensitivity to the clustered data
distribution. Conversely, the Random Forest algorithm demonstrated more stable
and consistent performance, achieving a maximum f1-score of 0.94, with greater
resilience to signal fluctuations and noise. It is concluded that integrating
fingerprinting methods with machine learning algorithms can produce an accurate
and efficient IPS. The Random Forest algorithm is recommended for long-term
implementation due to its superior generalization capability and reliability in
dynamic real-world indoor environments.}
}