Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Informatics for Educators and Professional : Journal of Informatics Rengganis Jurnal Pengabdian Masyarakat Journal of Science and Technology: Alpha
Gede Yogi Pratama
Universitas Bumigora
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Perbandingan Metode Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam Memprediksi Harga Saham Telkom Gede Yogi Pratama; Onis Alamsyah; Hanif Aljauziah; Muh Sohibul Ihsania; Mohammad Mirza; Lathifah Laili Andita
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.475

Abstract

Accurate stock price prediction remains a challenging task due to the highly volatile nature of financial markets and the influence of various macroeconomic factors and market sentiment. PT Telkom Indonesia Tbk (TLKM), one of the largest publicly listed companies in Indonesia, has attracted significant attention from investors because of its substantial market capitalization and active stock trading. This study aims to compare the performance of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting TLKM stock prices using time series data. The dataset consists of historical TLKM stock data, including the Open, High, Low, Close, Adjusted Close, and Volume variables. Data preprocessing involved data cleaning, normalization using the Min-Max Scaling technique, and time series sequence generation through the sliding window approach. Both LSTM and GRU models were developed using comparable network architectures and trained with the Adam optimizer and the Mean Squared Error (MSE) loss function. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that both models effectively capture historical stock price patterns. However, the GRU model consistently outperformed the LSTM model by achieving lower prediction errors while requiring lower computational complexity and training time. These findings suggest that GRU is a more effective and computationally efficient approach for predicting TLKM stock prices based on time series data.
Peningkatan Kompetensi Digital Siswa melalui Pelatihan Pengembangan Aplikasi Web dalam Mendukung Kualitas Sumber Daya Manusia Mohammad Najib Roodhi; Rahayun Amrullah Husaini; Gede Yogi Pratama; Rifqi Hammad; I Nyoman Switrayana; Muhammad Haris Nasri; Gilang Primajati
Rengganis Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Mei 2026
Publisher : Pendidikan Matematika, FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/rengganis.v6i1.1102

Abstract

The rapid development of digital technology demands an increase in the quality of human resources (HR) that are adaptive to change, especially in the field of information technology. One of the competencies needed is the ability to develop web-based applications, which are increasingly relevant to industry needs. This community service activity aims to improve students' digital competencies and reduce the dynamic skills gap through web application development training. The partners in this activity were grade X students of SMKN 2 Mataram with a total of more than 20 participants. The training method used was a learning-by-doing approach, which included material delivery, demonstrations, direct practice, and evaluation. The results of the activity showed an increase in the average score of participants from 65 in the pre-test to 85 in the post-test, indicating a significant increase in participant understanding. In addition, participants were also able to develop simple web applications and demonstrated improved problem-solving skills and self-confidence. This activity contributes to improving digital competencies and strengthening the quality of human resources who are better prepared to face technological developments in the digital era.
Integrasi Association Rule Mining dan Cost-Plus Pricing untuk Optimasi Paket Produk dan Profitabilitas UMKM Muhammad Haris Nasri; Gede Yogi Pratama; Rifqi Hammad; I Nyoman Switrayana; Rahayun Amrullah Husaini
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 11 No. 1 (2026): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v11i1.3886

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy, yet they still face challenges in developing product packages and optimal pricing. This study aims to integrate the Apriori algorithm and the Cost-Plus Pricing method in developing product packages and determining optimal selling prices. The data used are 2,000 MSME sales transactions processed through preprocessing, data transformation, and analysis using the Apriori algorithm with a minimum support of 0.01 and a confidence of 0.4. The results show that 54 frequent itemsets and association rules were obtained with an average support value of 0.564, a confidence of 0.926, and a lift of 1.694. The best rule produces two main product packages, namely the Coffee and Dodol package and the Chicken Special Grill, Plecing Kangkung, and Sambal package. Furthermore, the package prices were determined using the Cost-Plus Pricing method and a profit increase simulation was conducted. The results show that profits increased from 48,293,400 to 53,517,360, representing an increase of 5,223,960. Thus, the integration of these two methods has proven effective in increasing profitability and can be used as a data-driven marketing strategy for MSMEs.
Co-Authors Gilang Primajati Hanif Aljauziah I Nyoman Switrayana Lathifah Laili Andita Mohammad Mirza Mohammad Najib Roodhi Muh Sohibul Ihsania Muhammad Haris Nasri Onis Alamsyah Rahayun Amrullah Husaini Rifqi Hammad