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For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams' compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly. [ABSTRACT FROM AUTHOR]

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

Breakdown script is a breakdown of the scenario into parts that describe each detail of the scene for shooting. The scenario is broken down into more detailed parts using the parsing algorithm. The film script used is a script in Bahasa Indonesia. The process starts from the film script file/scenario in FBX format uploaded to the website then is solved using a parsing algorithm into film elements such as cast members, extras, props, costumes, makeup, vehicles, stunts, special effects, music and sound. The results of this breakdown into sheets according to film elements. The purpose of this research is to produce breakdown sheets from film scripts according to film elements. The parsing algorithm test results showed the correct results of 12 scenes out of 19 scenes. [ABSTRACT FROM AUTHOR]