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ANALISIS SUMBER PENDAPATAN PENDIDIKAN DI SEKOLAH ISLAM VICTORIA, AUSTRALIA (2015–2023) RAHARJO, NADA DIKURNIA; PUTRA, HELDY RAMADHAN; MUNADI, M.; SUHARDI, MUHAMAD
ACADEMIA: Jurnal Inovasi Riset Akademik Vol. 4 No. 4 (2024)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/academia.v4i4.3776

Abstract

This research analyzes income sources at Islamic Schools of Victoria from 2015 to 2023. The focus of the study includes income diversification, the contribution of each source to total income, and cross-year trends. This research method uses a library research approach, data analysis techniques with data reduction, data presentation and drawing conclusions. Data is processed from the school's annual financial report. The research results show that government grants dominate the source of income, with a contribution that continues to increase from 79.29% in 2015 to 84.73% in 2023. In contrast, the contribution to school fees shows a relative decline and total income shows an increasing trend despite fluctuations. Total revenue increased from $27 million in 2015 to $44.7 million in 2023, for an average annual increase of 5.7%. The small decline in 2020 can be attributed to the COVID-19 pandemic affecting tuition payments and donations. The main income therefore comes from government grants-operating, which contributed more than 80% of total income since 2020. Other sources, such as tuition fees, donations and investment income, show significant fluctuations. This study highlights the importance of strategic financial management for the sustainability of educational institutions. ABSTRAKPenelitian ini menganalisis sumber pendapatan di Sekolah Islam Victoria (Islamic Schools of Victoria) dari tahun 2015 hingga 2023. Fokus kajian mencakup diversifikasi pendapatan, kontribusi setiap sumber terhadap total pendapatan, dan tren lintas tahun. Metode Penelitian ini menggunakan pendekatan penelitian kepustakaan, teknik analisis data dengan reduksi data, penyajian data dan penarikan kesimpulan Data diolah dari laporan keuangan tahunan sekolah. Hasil penelitian menunjukkan Hibah pemerintah mendominasi sumber pendapatan, dengan kontribusi yang terus meningkat dari 79,29% pada 2015 menjadi 84,73% pada 2023. Sebaliknya, kontribusi biaya sekolah menunjukkan penurunan relatif dan Pendapatan total menunjukkan tren peningkatan meskipun terdapat fluktuasi. Total pendapatan meningkat dari $27 juta pada 2015 menjadi $44,7 juta pada 2023, dengan rata-rata kenaikan tahunan sebesar 5,7%. Penurunan kecil pada 2020 dapat dikaitkan dengan pandemi COVID-19 yang memengaruhi pembayaran biaya sekolah dan donasi. Oleh karena itu pendapatan utama berasal dari hibah pemerintah (government grants-operating), yang berkontribusi lebih dari 80% dari total pendapatan sejak 2020. Sumber lain, seperti biaya sekolah, donasi, dan pendapatan investasi, menunjukkan fluktuasi yang signifikan. Studi ini menyoroti pentingnya pengelolaan keuangan strategis untuk keberlanjutan institusi pendidikan.
Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, M.; Diki, M.
Jurnal Polimesin Vol 22, No 5 (2024): October
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i5.5308

Abstract

The adoption of image processing-based technologies in the textile sector is rising. This technology is commonly utilized to replace traditional sensor systems that are limited to a single function while also improving product quality control functions. Defects during the manufacturing process are a common problem in the textile business, particularly with fabric products. This study created a fabric quality control system that detects fabric problems using machine learning-based picture classification techniques. A D320p web camera detects rare and slap flaws, which are classified using open-source Google teaching machine software and processed on a Raspberry Pi 3B device. The laboratory-scale measurement was carried out on a prototype cloth rolling machine using the confusion matrix method. The test results reveal an average inference speed of 143.5 milliseconds, a frame rate of 6.45 fps, and a 98.56% accuracy rate. These results demonstrate that the proposed system is effective and efficient for detecting fabric defects, offering a promising solution for enhancing quality control in the textile industry. Future research could focus on scaling the system for industrial use and enhancing real-time performance.