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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Journal of Applied Electrical Engineering Journal of Electrical Power Control and Automation (JEPCA) Jurnal Engineering Electron: Jurnal Ilmiah Teknik Elektro Jurnal Ecocentrism EJOIN : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Ilmu Komputer Akademika
Andre Rabiula
Program Studi Teknik Elektro, Fakultas Sains Dan Teknologi, Universitas Jambi, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : Akademika

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IMPEMENTASI ALGORITMA YOLO UNTUK PENGENALAN OBJEK SAMPAH: Classification, Deep Learning, Image Processing, YOLO Rabiula, Andre; Haryatama Putri, Frenti; Nehru, Nehru
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1677

Abstract

Human activities cannot be separated from production and consumption activities which have an impact on the generation of waste, such as the use of plastic. Therefore, waste detection and sorting should be carried out at the initial stage of waste management to maximize the amount of waste that can be recycled. This research aims to apply image processing and deep learning algorithms in plastic waste classification, as well as testing the performance of the classification system. The research method used refers to the research stages, namely literature study, data collection, pre-processing, system design, implementation, testing, evaluation and data analysis. The research results show that plastic waste classification system obtained accuracy, precision, recall and F1 scores, namely 98.7%, 1, 0.98 and 0.99.
INTEGRASI PROJECT-BASED LEARNING DALAM PEMBELAJARAN INTERAKSI MANUSIA DAN KOMPUTER DI PROGRAM STUDI SISTEM INFORMASI: Human-Computer Interaction, learning outcomes, Project-Based Learning, student creativity, UI/UX. Abidin, Zainil; Hutabarat, Benedika Ferdian; Rabiula, Andre
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1688

Abstract

This study aims to analyze the effect of implementing the Project-Based Learning (PjBL) model on student learning outcomes and task creativity in the Human-Computer Interaction course. The research method used is quantitative with a quasi-experimental design. The research subjects consisted of two classes: a control class using conventional methods and an experimental class applying PjBL. Data were collected through final exam tests and a rubric-based assessment of UI/UX project creativity. The analysis results showed that the PjBL class achieved significantly higher scores in both final exams and task creativity (p < 0.05). It is concluded that the PjBL model is effective in enhancing students' understanding and UI/UX design skills in the Information Systems program.
ANALISIS PERBANDINGAN MODEL GRU DAN LSTM UNTUK PREDIKSI HARGA SAHAM BANK RAKYAT INDONESIA: Deep Learning, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), Stock Price Prediction Perdana, Yogi; Raisa Hanum, Nindy; Rabiula, Andre; Anzari, Yandi
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1692

Abstract

This research implements and compares two deep learning architectures, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for predicting the stock price of Bank Rakyat Indonesia (BRI) using historical data from February 2023 to October 2024. Through systematic hyperparameter tuning and comprehensive evaluation, the study finds that GRU consistently outperforms LSTM across all regression metrics, with a 10.7% improvement in R² and an 18.5% reduction in MAPE. The optimal GRU configuration (100 units, 100 epochs, batch size 32, learning rate 0.001) achieves an MSE of 6517.5 and MAPE of 1.3764%. Visual analysis confirms GRU's superior ability to capture stock price fluctuations and adapt more quickly to trend changes. The simpler architecture of GRU with fewer parameters proves more effective for handling the high-noise characteristics and varying volatility of stock price data. While both models face challenges in predicting extreme market events, GRU demonstrates better resilience and faster recovery after such occurrences. This research contributes to the understanding of recurrent neural network applications in financial time series forecasting and provides practical insights for developing more accurate stock price prediction systems.
Co-Authors Abdul Manab Abdul Manab Abdul Manab Abidin, Zainil Afriyandi Afriyandi Aiza Yudha Pratama ALDY PRATAMA Benedika Ferdian Hutabarat Damayanti, Liza dasrinal tesal Dasrinal Tessal Dasrinal Tessal Dasrinal Tessal Dewi Fortuna Dewi Fortuna Elsa Lolita Anggraini Fuady, Samratul Gusri, Lailal Haerul Pathoni Haerul Pathoni, Haerul Haryatama Putri, Frenti Illa Aryeni Iyan Torang H Liza Damayanti M Bahrul Muttaqin Matalata, Hendi Nanda Dwi Septiawan Nazwan, Ahmad Rifki Nehru Nehru Nindy Raisa Hanum Perdana, Yogi Putri, Prameswari Amalia Ramadhani, Salisa 'Asyarina Samratul Fuady Samratul Fuady Sri Rahayu Aida Putri Togi Siholmarito Simarmata Yandi Anzari Yosi Riduas Hais