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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Journal of Information Systems and Informatics International Journal Of Computer, Network Security and Information System (IJCONSIST)
Prayoga, Aji
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Other

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Classifying Legendary Pokémon with SF-Random Forest Algorithm Prayoga, Aji; Via, Yisti Vita; Diyasa, I Gede Susrama Mas
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.859

Abstract

Here’s an improved version of the abstract with better articulation: Accurate classification of legendary Pokémon is essential due to their distinct characteristics compared to regular Pokémon, impacting various domains such as research, gaming, and strategy development. This study employs the SF-Random Forest algorithm, an advanced variant of Random Forest, designed to effectively handle data heterogeneity and complexity. The dataset comprises 800 Pokémon samples, including attributes like type, base stats (HP, Attack, Defense, etc.), and other relevant features. To address the inherent imbalance between legendary and non-legendary Pokémon, the data preprocessing phase includes outlier removal, handling of missing values, normalization through Min-Max Scaling, and class balancing using the SMOTE (Synthetic Minority Over-sampling Technique) method. The preprocessed data is then used to train the SF-Random Forest model, with performance evaluated using metrics such as accuracy, precision, recall, and F1-score. The results reveal that SF-Random Forest achieves perfect scores across all metrics, demonstrating 100% accuracy, precision, recall, and F1-score. This highlights the algorithm's superior ability to identify key features and manage data imbalance compared to traditional classification methods. The study underscores the efficiency and robustness of SF-Random Forest as a classification tool, paving the way for the development of more advanced classification systems applicable to various fields requiring complex pattern recognition.
PERILAKU KONSUMSI RUMAH TANGGA PETANI KARET DITINJAU DARI TINGKAT PENDAPATAN (Studi Kasus Masyarakat Petani Karet di Dusun Berona, Kabupaten Sekadau) (SB) Prayoga, Aji; Basri, Muhammad; Syamsuri, Syamsuri
Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Vol 14, No 1 (2025): Januari 2025
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jppk.v14i1.66811

Abstract

This research aims to analyze the consumption behavior of rubber farmer households in Berona Hamlet, Sekadau Regency, taking into account their income level. The research method used is a qualitative approach with a case study approach. Data collection techniques are carried out through observation, interviews, and documentation. Data analysis is carried out using the Miles and Huberman model, and data validity is checked through triangulation techniques. This research is conducted because the fluctuation of rubber prices in recent years has affected the ability of rubber farmers to meet their household needs. The research findings show that the consumption behavior of rubber farmer communities in Berona Hamlet varies depending on their income level. Each household has different income levels, which are influenced by the amount of rubber they sell and the size of the land where they farm rubber. Income from rubber farming ranges fromRp. 325,000 to 975,000 per month, which is a low income level, thus affecting their consumption patterns.
Enhancing Guest Security in Smart Hospitality: Face Recognition-Based Hotel Room Verification Using Haar Cascade Algorithm Putra, Adzanil Rachmadhi; Prayoga, Aji; Gumiwang, Zacky Yaser Malik; Karim, Mohammad Daniel Sulthonul; Wicaksono, Muhammad Galang Satrio; Faishol, Olive Khoirul Lukluil Maknun Al; Prisyanti, Affifiana
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.163

Abstract

This study aims to design and implement a hotel room verification system based on facial recognition using the Haar Cascade algorithm. The research was motivated by the growing need to enhance both security and service efficiency in the modern hospitality industry. The study was conducted through several stages, including facial image data collection using a webcam, preprocessing (RGB to grayscale conversion, image resizing, and cropping), model training, and real-time face recognition testing. The Haar Cascade algorithm was employed to detect facial features by utilizing Haar-like features combined with the Adaboost method to accelerate classification. The experimental results showed a recognition accuracy of 55% under varying lighting conditions and viewing angles. These findings indicate that the Haar Cascade algorithm performs adequately in detecting faces under ideal conditions, although further optimization is required to handle lighting variations and facial stability. This research contributes to the application of artificial intelligence technology in hotel security systems, with potential future improvement through the integration of deep learning methods to enhance accuracy and reliability in face verification. Keywords: face recognition, Haar Cascade, hotel room verification, facial detection, digital security.
Co-Authors Adzanil Rachmadhi Putra Affifiana Prisyanti Diyasa, I Gede Susrama Mas Faishol, Olive Khoirul Lukluil Maknun Al Gumiwang, Zacky Yaser Malik Karim, Mohammad Daniel Sulthonul Muhammad Basri Syamsuri Syamsuri Wicaksono, Muhammad Galang Satrio Yisti Vita Via