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"Dengan meningkatnya permintaan layanan pemetaan dalam ruangan, metode fingerprinting telah menjadi fokus utama penelitian dan pengembangan untuk menyediakan pemetaan posisi yang akurat dan detail. Fingerprinting memungkinkan identifikasi lokasi perangkat dengan presisi tinggi, yang bermanfaat dalam navigasi gedung, pengelolaan sumber daya dalam ruangan, dan keamanan jaringan. Dengan fokus pada WiFi Fingerprinting untuk pemetaan posisi access point (AP) ilegal, yang mengidentifikasi dan memetakan lokasi perangkat berdasarkan karakteristik sinyal WiFi, model ini dapat mempelajari noise dari sinyal WiFi, merekonstruksi nilai sinyal yang bersih. Berdasarkan hasil eksperimen yang dilakukan pada penelitian ini, didapatkan hasil perhitungan eror sebesar 453.27 cm (MAE) dan 517.16 cm (RMSE) untuk hasil prediksi posisi relatif AP ilegal.

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With the increasing demand for indoor mapping services, fingerprinting methods have become a primary focus of research and development to provide accurate and detailed position mapping. Fingerprinting enables the identification of device locations with high precision, which is beneficial for building navigation, indoor resource management, and network security. Focusing on WiFi Fingerprinting for mapping the positions of illegal access points (APs), which identifies and maps device locations based on WiFi signal characteristics, this model can learn the noise from WiFi signals and reconstruct clean signal values. Based on the experimental results conducted in this study, the error calculations yielded a Mean Absolute Error (MAE) of 453.27 cm and a Root Mean Square Error (RMSE) of 517.16 cm for the predicted positions of illegal APs."

"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."

"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."