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All Journal International Journal of Artificial Intelligence Research Journal of Applied Informatics Research
Adinda Putri Pratiwi
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Computer Science & IT Electrical & Electronics Engineering 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.
Optimization of Personalized Fashion Recommendations for H&M: A Collaborative Filtering Algorithm Approach with Temporal Time Interval Analysis Moch Deny Pratama; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44593

Abstract

This study presents a personalized fashion recommendation system for the H&M dataset, utilizing a cosine similarity-based collaborative filtering algorithm. This study investigates the effect of temporal segmentation on recommendation performance by conducting three experiments using datasets divided into two-week, one-month, and two-year time intervals. The experimental results show that the two-year interval achieves the best performance, producing a Mean Average Precision (MAP) of 0.02254 with a computational time of 2741.7 seconds. In contrast, the two-week interval achieves a MAP of 0.00915 in 1609.2 seconds, while the one-month interval produces a MAP of 0.00554 with a computational time of 3118.9 seconds. The main contribution of this study lies in the optimization of data structure transformation through dictionary-based modeling, which significantly improves training efficiency. These findings underscore the crucial role of temporal granularity in improving the accuracy and computational efficiency of collaborative filtering-based personalized fashion recommendation systems.
Enhancing Clickbait Headline Identification Performance Without Preprocessing Through Feature Reduction and Sentiment Analysis Moch Deny Pratama; Anisa Nur Azizah; Misbachul Falach Asy'ari; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi; Dina Zatusiva Haq
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44659

Abstract

This study addresses the challenge of identifying clickbait headlines without relying on conventional text preprocessing, which can be resource-intensive and may degrade contextual integrity. To enhance detection performance, we examine three feature extraction methods: TF-IDF, Word2Vec, and Headline2Vec, an embedding technique designed for short texts like headlines. These features are optimized using feature selection algorithms, including Pearson Correlation Coefficient (PCC), Neighborhood Component Analysis (NCA), and Relief, to reduce dimensionality and enhance relevant signal retention. Sentiment polarity is also integrated as a complementary feature. A comparative evaluation is conducted using several machine learning classifiers, namely Support Vector Classifier (SVC), Random Forest, LightGBM, and XGBoost, across all combinations of feature extraction and selection methods. Results show that the optimal configuration Headline2Vec with Relief and SVC achieves the highest accuracy at 94.40%, outperforming other approaches. This demonstrates the effectiveness of combining semantic vectorization and feature selection for clickbait detection in the absence of traditional preprocessing. The findings support the development of streamlined and scalable classification models capable of maintaining high accuracy while reducing preprocessing overhead, making the proposed method particularly suitable for real-time and large-scale content moderation and news verification systems.
Co-Authors Ahmad Saikhu Anisa Nur Azizah Binti Kholifah Dimas Novian Aditia Syahputra Dina Zatusiva Haq Ginardi, R.V. Hari M Adamu Islam Mashuri Misbachul Falach Asy'ari Moch Deny Pratama Rifqi Abdillah