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All Journal Jurnal Dimensi JOURNAL OF ICT APLICATIONS AND SYSTEM AKPEM : Jurnal Akuntansi Keuangan Dan Akuntansi Pemerintahan RJOCS (Riau Journal of Computer Science) Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Yulfita Aini
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Other

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MODEL KESELAMATAN DAN KESEHATAN KERJA DALAM UPAYA MENINGKATKAN PRODUKTIVITAS KERJA KARYAWAN PERUSAHAAN KELAPA SAWIT DI KABUPATEN ROKAN HULU Seprini Seprini; Yulfita Aini; Heffi Christya Rahayu; Sri Langgeng Ratnasari
JURNAL DIMENSI Vol 12, No 1 (2023): JURNAL DIMENSI (MARET 2023)
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/dms.v12i1.4777

Abstract

Tujuan penelitian ini adalah untuk mengetahui Model Keselamatan Dan Kesehatan Kerja Dalam Upaya Meningkatkan Produktivitas Kerja Karyawan Perusahaan Kelapa Sawit di Kabupaten Rokan Hulu.
Implementasi Business Intelligence Untuk Analisa Dan Visualisasi Data Honda Menggunakan Platform Data Studio Lullail, Jamalul; Agung Setiawan; Fimawahib, Luth; Yulfita Aini
RJOCS (Riau Journal of Computer Science) Vol. 10 No. 1 (2024): RJOCS (Riau Journal of Computer Science)
Publisher : Fakultas Ilmu Komputer, Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjocs.v10i1.2579

Abstract

Sektor otomotif menghasilkan banyak data dari berbagai sumber yang dapat membantu bisnis dengan memberikan informasi yang bermanfaat untuk membuat keputusan terbaik. Namun, karena kompleksitasnya, data ini sulit dievaluasi dan diinterpretasikan secara manual. Tujuan proyek ini adalah untuk menggunakan alat Business Intelligence (BI) Google Data Studio untuk menganalisis dan memvisualisasikan data rating produk Honda. Pendekatan ini memiliki signifikansi penting dalam konteks bisnis mobil dan rencana pemasaran Honda. Honda merupakan salah satu produsen terkemuka solusi transportasi fleksibel, cepat, dan tangkas bagi pelanggannya. Namun, pemahaman preferensi konsumen sangat penting dalam membuat keputusan strategis terkait kesuksesan kampanye pemasaran serta kemungkinan pengembangan produk baru.Untuk mengevaluasi indikator kinerja penjualan termasuk pendapatan total dan laba kotor, dibutuhkan koneksi antara semua data penjualan menjadi database seperti BigQuery sebelum membuat laporan interaktif atau dashboard dengan tabel atau grafik sebagai representasi visual. Proyek ini memiliki implikasi penting bagi para profesional yang bekerja di industri otomotif yang tertarik melacak tren pasar melalui alat BI sambil mengoptimalkan proses pengambilan keputusan mereka berdasarkan wawasan interpretatif dari analisis rating pada produk-produk Honda
PKM USAHA IDA MODISTY DI DESA RAMBAH HILIR KABUPATEN ROKAN HULU Yulfita Aini
Tepak Sirih : Jurnal Pengabdian Masyarakat Madani Vol. 1 No. 01 (2022): Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Publisher : LPPM Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/jpmm.v1i01.475

Abstract

Tujuan Pengabdian untuk meningkatan Adanya peningkatan pengetahuan mitra melalui pelatihan terkati dengan pembuatan label baju dan pelatihan terkait dengan perubahan strategi pemasaran, Semakin luas daerah pemasaran dan Semakin berkembangnya metode pemasaran produk dengan menfaatkan media sosial.
DETERMINAN FAKTOR YANG MEMPENGARUHI INCOME SMOOTHING PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2019-2021: INDONESIA Zulkarnain; Nurhayati; Heffi Christya Rahayu; Yulfita Aini
AKPEM : Jurnal Akuntansi Keuangan dan Pemerintahan Vol. 5 No. 1 (2023): Akpem : Jurnal Akuntansi Keuangan dan Pemerintahan (Juni 2023)
Publisher : Program Studi Akuntansi Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/akpem.v5i1.2086

Abstract

The purpose of this study was to determine the effect of profitability, financial leverage, liquidity, company size, and managerial ownership on income smoothing in manufacture companies listed on the Indonesian stock exchange in 2019-2021. This study uses secondary data obtained from company financial reports for manufacture companies listed on the Indonesian stock exchange for 2019-2021.The method used is a quantitative method. The population in this study amounted to 192 companies and obtained a sample of 29 companies with a total of 87 data. The determinant factors that influence the practice of income smoothing in this study are profitability (X1), financial leverage (X2), liquidity (X3), firm size (X4) and managerial ownership (X5). The analytical method used is logistic regression analysis using the SPSS 25 application.The results of this study indicate that the financial leverage variable and company size variable has an effect on income smoothing, while profitability, liquidity and managerial ownership have no effect on income smoothing, and simultaneous profitability, financial leverage, liquidity, company size, and managerial ownershiphave an effect on income smoothing.
Automatic Food Label Detection in Images Using Convolutional Neural Network with Food-101 Dataset Natasya, Ccely; Aisyah, Nur; Prasiwiningrum, Elyandri; Yulfita Aini
Journal of ICT Applications System Vol 4 No 1 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i1.432

Abstract

automatic detection of food labels from digital images has emerged as a crucial application in dietary analysis, nutrition monitoring, and smart culinary systems. This study presents the implementation of a Convolutional Neural Network (CNN) model for food label recognition using the Food-101 dataset, which consists of over 101,000 images from 101 distinct food categories. The proposed system follows a systematic pipeline that includes image resizing, normalization, and data augmentation to enhance model robustness and performance. The CNN architecture is designed with multiple convolutional and pooling layers, followed by dense and softmax output layers for final classification. The training was conducted using the Adam optimizer with a learning rate of 0.0001, batch size of 32, and dropout regularization to prevent overfitting. Experimental results demonstrate a classification accuracy of 24.45% after one training epoch, highlighting both the capability and limitations of the baseline CNN model. Despite moderate accuracy, the model successfully identifies visually distinguishable food items and sets a foundation for future improvements through transfer learning and fine-tuning. This research confirms the potential of CNN-based models for food label detection and provides insights for the development of more accurate food recognition systems in health, dietary, and culinary applications
Co-Authors AGUNG SETIAWAN Lullail, Jamalul Luth Fimawahib Natasya, Ccely Nur Aisyah Nurhayati Prasiwiningrum, Elyandri Seprini Seprini Sri Langgeng Ratnasari Zulkarnain