Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

Dance one culture consists of motion. This paper seeks to recognize Golek Menak Dance movement to be easily studied from Indonesia, where the dance of the dancers (actor) is performed by using the motion capture Kinect sensor which then produces motion data format with Biovision Hierarchy (BVH), where data is a tensor which has position x, y, z. This research use test data Jogetan and Sabetan movement carried out featuring by Chain Code 15 (CC-15), which is a combination of 15 directions with forward motion (1), backward (-1) and fixed (0) to obtain vector quantization which is then carried by the gesture recognition using Hidden Markov Model (HMM). The novelty in this paper use Chain Code 15 (CC-15) to conduct the introduction featuring Dance with HMM classification, which produced an accuracy of 90% of ten (10) test data movement.

Animation is a collection of frames that express a motion activity. The animation consists of actors, characters and performance components that present a story — one of the technologies in making an animation with motion capture. The results of catching motion in the form of motion record data are then synchronised to animated characters. However, the problem is that there are difficulties in synchronising animated characters with the results of catching motion so that the motion can be more subtle. On capture, this motion uses Kinect as a motion capture sensor for actors. The results of this capture then become motion data. The synchronisation of motion data from the motion capture results with Kinect then adjusts the joint body character points, frames, time with three-dimensional (3D) animated characters according to the flow of character actors. This synchronisation is adjusted to the recording of character movements starting from the motion of humans, animals and objects that invite motion activity with motion capture methods both marked and without markers. In making 3D, there are models of giving a body or bone frame (rigging) after giving the body frames is done by giving visual effects, lighting, rendering, and compositing. Rigging is the installation of bones to characters used to place and manipulate animated controls on characters that will be animated to produce the desired gesture. Animation synchronisation is to create animated characters manually; the file is used for. The synchronisation is Biovision Hierarchy (BVH) with gesture giving movement without having to save the keyboard in every animation movement manually.

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Toko kelontong adalah usaha yang tergolong masuk di Usaha Mikro Kecil dan Menengah(UMKM) yang merupakan toko tradisional  yang menjual bahan-bahan dibutuhkan masyarakat. Usaha Mikro Kecil dan Menengah(UMKM) adalah pilar penting untuk pembangunan ekonomi negara. Dalam pelaksanaan toko kelontong ini memiliki permasalahan dalam memanajemen keuangan yang menjadikan kendala dalam usahanya. Penelitian ini membantu masalah tersebut menggunakan metode apriori untuk mendukung pengelolaan uang usaha toko kelontong. Dengan menerapkan sistem metode apriori di mobile android dengan menggunakan bantuan software Android Studio, Postman, dan Database dalam membangun sistem ini. Aplikasi ini menjadikan dalam memanajemen keuangan lebih baik dengan dukungan metode Apriori untuk memilih transaksi yang paling banyak untuk menangani permasalahan keuangan sebagai rekomendasi pelunasan dalam keuangan usaha toko kelontong.