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All Journal International Journal of Artificial Intelligence Research Universal Journal of Science and Technology
Pratiwi, Adinda Putri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Enhancing Electricity Consumption Prediction with Deep Learning through Advanced Data Splitting Techniques Pratiwi, Adinda Putri; Ginardi, Raden Venantius Hari; Saikhu, Ahmad
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1204

Abstract

Energy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data.  The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets.
Peran Praktik Eco-Green dan Community-Based Marketing dalam Meningkatkan Nilai Ekonomi Ikan di Eco Baraya, Ciasin Yusup, Arilla Darmazie; Nina Sri Indrawati, Nina Sri Indrawati; Pratiwi, Adinda Putri; Bishri, Afiq Qodri; Pebriansyah, Andika Dwi; Kamal, Dicki; Taufik, Muhammad; Putri, Nadia Safadilla
UJoST- Universal Journal of Science and Technology Vol. 5 No. 1 (2026): March 2026
Publisher : Faculty of Science and Technology University of Pramita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11111/ujost.v5i1.202

Abstract

Penelitian ini bertujuan untuk menganalisis penerapan strategi pemasaran berkelanjutan (sustainable marketing) pada program Eco Baraya di Desa Ciasin, Bogor, sebagai salah satu model pengelolaan irigasi ikan berbasis ekologi. Eco Baraya mengelola saluran irigasi yang diisi ikan konsumsi dan ikan hias secara komunal, dengan memanfaatkan sisa makanan warga sebagai pakan alami dalam jumlah tidak konstan. Sistem ini tidak hanya menciptakan efisiensi biaya, tetapi juga menghadirkan praktik ramah lingkungan yang mendukung konsep eco-green. Selain itu, Eco Baraya berfungsi sebagai kawasan edukatif bagi pengunjung dan menjadi wilayah percontohan bagi daerah lain. Penelitian ini menggunakan pendekatan deskriptif dengan fokus pada proses pemasaran komunitas, keberlanjutan lingkungan, dan nilai ekonomi yang tercipta. Hasil penelitian menunjukkan bahwa keberhasilan Eco Baraya didorong oleh keterlibatan warga, minimnya limbah organik melalui pemanfaatan pakan alami, serta aktivitas edukasi yang meningkatkan citra ekologis dan memperluas pasar. Praktik pemasaran berkelanjutan yang diterapkan tidak hanya memperkuat posisi Eco Baraya sebagai komunitas ekologis, tetapi juga berpotensi meningkatkan daya saing produk ikan melalui diferensiasi berbasis nilai lingkungan.
Co-Authors Ahmad Saikhu Bishri, Afiq Qodri Ginardi, R.V. Hari Kamal, Dicki MUHAMMAD TAUFIK Nina Sri Indrawati, Nina Sri Indrawati Pebriansyah, Andika Dwi Putri, Nadia Safadilla Yusup, Arilla Darmazie