ini bertujuan untuk mengetahui implementasi pengelolaan keuangan dana desa di Kabupaten Ogan Ilir Sumatera Selatan dengan menggunakan data primer dari para pengambil keputusan di 26 desa. Hasil penelitian mengungkap aspek pengelolaan keuangan secara umum telah sesuai dengan apa yang diatur dalam Permendagri No. 113/2014 dan mematuhi prinsip da sar pengelolaan keuangan. Pelaporan dan pertanggungjawaban masih men jadi masalah bagi beberapa desa. Belum semua desa yang diteliti memiliki sumber daya manusia yang menguasai aspek pelaporan dan pertanggungjawaban. Berkenaan dengan komposisi belanja desa, semua desa tidak memenuhi aturan 70:30. Hal ini mengakibatkan ketimpangan dalam pelaksanaan pembangunan di pedesaan.

The purpose of this research is to know the implementation of fund management of village fund in Ogan Ilir Regency, South Sumatera. The population in this study were 224 villages in Ogan Ilir Regency that received the allocation of Village Funds in 2016. The purposive sampling technique was applied to obtain samples and produce 26 villages. The analysis will be carried out with a quantitative and qualitative description process. Based on the data obtained, the researcher tries to describe or describe systematically, accurately, and factually about matters relating to the field as fact, nature, and relationship between phenomena. Quantitative techniques will also use frequency analysis that aims to provide an overview of the general condition. The results reveal that the financial management aspects are generally in accordance with those set out in Permendagri 113/2014 and have complied with the basic principles of financial management. Reporting and accountability remain a problem for some villages. Not all of the villages studied have aspects of human resource reporting and accountability. Taking into account the composition of village budgeting, the village does not meet the rules that require a ratio of 70:30, this results in inequality in the implementation of rural development. The conclusion of this research is that the implementation of village fund management in Ogan Ilur Regency has been running well according to Permendagri 113/2014.

This study investigates the relationship between air pollution, economicgrowth, and life expectancy in Indonesia. The observation periodduring 1985-2019 used time-series data obtained from the WorldBank. Quantitative approach by applying two main models, namelythe autoregressive distributed lag (ARDL) model by considering theeffect of time-lapse and Granger causality with vector error correctionmethod. Research findings prove that air pollution has a negativeand significant effect on life expectancy in the long run. Economicgrowth has a positive and significant effect on life expectancy. In theshort run, the current life expectancy is positively and significantlyinfluenced by the life expectancy of the previous period. Air pollutionhas a negative and significant effect on life expectancy, and economicgrowth has a negative and significant effect on life expectancy. Anotherfinding in the Granger causality model is a two-way relationshipbetween air pollution and life expectancy. Other evidence exists of atwo-way relationship between economic growth and air pollution. Inaddition, evidence of a unidirectional relationship of economic growthwith life expectancy in the short run. The cointegration equationshows evidence of a long-run relationship between air pollution,economic growth, and life expectancy.

Dunia bola basket telah berkembang dengan pesat seiring dengan berjalannya waktu. Hal ini ditandai dengan munculnya berbagai macam dan jenis kompetisi dan pertandingan baik dunia maupun dalam negeri. Sehingga makin banyak dilahirkannya pemain berbakat dengan berbagai karakteristik permainan yang berbeda. Tuntutan bagi seorang pelatih/pemandu bakat, untuk dapat melihat secara jeli dalam memenuhi kebutuhan tim untuk membentuk tim yang solid. Dengan dibuatnya aplikasi ini, maka akan membantu proses analisis dan pengambilan keputusan bagi pelatih maupun pemandu bakat Aplikasi ini menggunakan algoritma Self Organizing Maps (SOM) untuk melakukan analisis cluster. Data real pemain NBA digunakan untuk keperluan proses training dan data real pemain Indonesia /pemain Universitas Kristen Petra untuk proses testing. Data pemain NBA dipersiapkan dengan melalui proses cleaning dan di transformasi ke bentuk yang dapat diolah oleh algoritma SOM. Kemudian data diolah menggunakan algoritma SOM untuk menghasilkan cluster-cluster data. Hasil cluster-cluster ini ditampilkan dalam bentuk yang mudah untuk dilihat dan digunakan sebagai analisis.Hasil tersebut dapat disimpan pula dalam bentuk file teks. Dengan menggunakan output dari aplikasi ini, yang berupa cluster pemain basket, pengambil keputusan dapat melihat statistik tiap cluster. Dengan menggunakan statistik tiap cluster, pelatih atau pemandu bakat dapat memprediksi statistik dan posisi di lapangan seorang pemain basket yang ditest, yang berada pada sebuah cluster tertentu. Informasi ini dapat membantu pelatih atau pemandu bakat dalam pengambilan keputusan.

Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy.