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All Journal CEMARA Jurnal Kebidanan Midwiferia Jurnal Midpro Jurnal Ilmiah Pendidikan Matematika Al Qalasadi International Journal of Education, Social Studies, And Management (IJESSM) INTERNATIONAL JOURNAL OF CULTURAL AND SOCIAL SCIENCE Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) Formosa Journal of Applied Sciences (FJAS) Journal of Social Studies, Arts and Humanities (JSSAH) Journal of Artificial Intelligence and Digital Business Innovative: Journal Of Social Science Research Madani: Multidisciplinary Scientific Journal Jurnal Manajemen Teknologi dan Sistem Informasi West Science Interdisciplinary Studies Kreativitas Pada Pengabdian Masyarakat (Krepa) West Science Interdisciplinary Studies Riwayat: Educational Journal of History and Humanities Karimah Tauhid Journal of Economics Education Jurnal Faidatuna
Aulia, Dinda
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Analysis of the Effect of Taxpayer Knowledge and Awareness on Taxpayer Compliance at XYZ Consulting Aulia, Dinda; Utama, Ida Bagus Made; Masari, Ni Made Galih
Formosa Journal of Applied Sciences Vol. 3 No. 8 (2024): August 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjas.v3i8.10800

Abstract

Given that taxes are the main source of state treasury funding, it is difficult to implement state initiatives such as national development without state treasury funding from the taxation sector. At XYZ Consulting, the researcher's objective is to evaluate and assess how compliance awareness in filing taxes is affected by taxation knowledge. Descriptive methods and quantitative research techniques were combined in this study. This research involves individuals or entities from XYZ Consulting who are taxpayers. To determine the research sample, 54 responses were collected using the Slovin formula calculation method. According to XYZ Consulting, the Study findings show that taxpayer compliance is significantly influenced by taxpayer awareness and knowledge and both factors significantly improve compliance at the same time.
The Peran Subsidi Pendidikan dalam Meningkatkan Akses dan Kualitas Pendidikan di Indonesia Raja, Adelia Mestwo Lumban; Nababan, Amanda Agustina; Ayudis, Diaz; Aulia, Dinda; Lubis, Lara Nauly
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3552

Abstract

Pendidikan merupakan bagian penting dalam pembangunan sosial dan ekonomi di Indonesia. Pemerintah sudah menerapkan beberapa kebijakan subsidi pendidikan agar semua orang bisa mendapatkan akses pendidikan yang adil dan kualitasnya meningkat, terutama bagi keluarga yang memiliki ekonomi lemah. Penelitian ini menggunakan analisis berdasarkan literatur untuk melihat peran subsidi pendidikan dalam meningkatkan akses dan kualitas pendidikan. Peneliti menganalisis berbagai sumber seperti buku akademik, artikel jurnal, laporan pemerintah, serta data dari situs resmi. Hasil penelitian menunjukkan bahwa subsidi pendidikan membantu anak-anak dan remaja untuk bisa mengikuti pendidikan formal. Meskipun begitu, untuk meningkatkan kualitas pendidikan, subsidi perlu didukung oleh kebijakan lain, seperti pelatihan guru, perbaikan sarana sekolah, dan pengembangan kurikulum yang inovatif. Penelitian ini juga menyatakan bahwa subsidi pendidikan harus diintegrasikan dalam strategi pembangunan pendidikan secara keseluruhan agar bisa memberi manfaat yang berkelanjutan bagi masyarakat. Dalam ranah ekonomi publik, dukungan keuangan untuk pendidikan dapat dipandang sebagai langkah strategis pemerintah guna meminimalkan kesenjangan sosial dan meningkatkan efisiensi tenaga kerja di masa depan. Statistik resmi menunjukkan bahwa ada peningkatan tingkat partisipasi sekolah antara 5-10% di kawasan yang kurang beruntung setelah penerapan dukungan tersebut, yang menunjukkan keefektifan dalam memperluas akses pendidikan. Namun, ada berbagai tantangan seperti praktik korupsi dalam pembagian dana dan ketidakmerataan fasilitas yang masih ditangani. Penelitian ini menawarkan suatu model yang menggabungkan subsidi dengan program peningkatan kapasitas bagi pengajar dan modernisasi kurikulum agar dapat memastikan dampak yang berkelanjutan. Dengan cara ini, subsidi bukan hanya sebagai bantuan finansial, melainkan menjadi pendorong untuk perubahan pendidikan yang lebih insklusif dan berkualitas, mendukung visi pembangunan berkelanjutan di tingkat nasional. 
Perbandingan Algoritma Regresi Linier Berganda dan Random Forest Regression untuk Prediksi Konsentrasi Particulate Matter 2,5 (PM2,5) Kota Jambi Aulia, Dinda; Jasmir; Sharipuddin
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 5 No 2 (2025): JMS Vol 5 No 2 September 2025
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2025.5.2.2273

Abstract

The problem of increasing air pollution that occurs globally also occurs in Indonesia, including in Jambi Province. Air pollution has dust particles in it, one of which is Particulate Matter (PM2.5). The concentration of PM2.5 in the air is influenced by the meteorological conditions of an area, as well as the events around it, whether it is a natural event or an event caused by human activities. This study predicted PM2.5 concentration in Jambi city using Multiple Linear Regression and Random Forest Regression algorithms with air temperature, air humidity, wind speed, rainfall and hot spots as independent variabels. In the process, this study compares the two algorithms and assesses the accuracy of each algorithm. The Multiple Linear Regression algorithm is able to generate a model that can describe the relationship between air temperature, air humidity, wind speed, rainfall and hot spots to PM2.5 concentration, although the error value is larger than that of the Random Forest Regression algorithm. The Random Forest Regression algorithm produces a model with an RMSE of 0.033μg/mm3 smaller than the Multiple Linear Regression algorithm. In the accuracy test with MAPE, the Random Forest Regression algorithm has a value of 74,0% where Multiple Linear Regression has a value of 73,0% so that the Random Forest Regression algorithm has a higher accuracy in predicting PM2.5 concentrations.
Co-Authors Ahmad Rifki Al Karim Rambe, Rahmansyah Fadlul Aldine, Thia Thania Alfarabi Alliyah, Rusi Rusmiati Ardana, Mira Shaila Ayudis, Diaz Azizah , Nurul Billa, Salsa Cut Khairunnisak Cut Morina Zubainur Dahrani, Eza Fira Darus, M. Aza Raja Desrivariza, Putri Djaguna, Nurafmi Eka Andriani, Eka Elvandari, Miliyantri Eni Karsiningsih Farida Sembiring, Ella Fitri Hayati Fitriana, Farida iskandar, intan cahya Isnani Isnani Jasmir Jayanti, Ratna Dwi Khairunnisa, Meifiza Laksmi Diana Lubis, Lara Nauly Marlina, Neneng Cucu Masari, Ni Made Galih Maulia meylina, Mey Lina Mariani Sipayung Monica Dewi, Akwila Muliani, Amelia Sri Nababan, Amanda Agustina Natasha, Ficha NURUL AZIZAH Pane, Arifah S Zidana Purwasih, Rati Putri, Khumairoh Annisa Putri, Rentaulina Putri, Siti Rahmayanti Raja, Adelia Mestwo Lumban Rasmitadila, Rasmitadila S, Irwan Sembiring, Ella Farida Sharipuddin Simbolon, Rista Dewi Utama, Ida Bagus Made Wardhana, Manggala Pasca Wismanto Wismanto Yulianti Yusri Hazmi