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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Technomedia Journal Scientific Journal of Informatics
Puji Widodo, Aris
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Saran Implementasi Sistem ERP Berdasarkan Keuntungan dan Tantangan: Literature Review: Suggestions for ERP System Implementation Based on Benefits and Challenges: Literature Review Muhammad Syaifuddin, Nur; Zaini, Ahmad; Suriansyah, Muhammad; Puji Widodo, Aris
Technomedia Journal Vol 8 No 3 Februari (2024): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v8i3.2176

Abstract

This research is a literature review that aims to investigate the implementation of Enterprise Resource Planning (ERP) systems and provide practical suggestions based on the benefits and challenges associated with ERP systems. ERP is software designed to integrate various functions and departments in a company into one integrated system. ERP implementation can provide benefits such as increased operational efficiency, better monitoring of company performance, and better decision making. However, ERP implementation also presents challenges such as high costs, change resistance from employees, and integration problems with existing systems. This research uses the Systematic Literature Review (SLR) method to identify, assess and interpret findings related to ERP implementation. The results of this research provide practical suggestions, including a focus on key benefits such as increased efficiency, reduced costs, and improved data quality. In addition, companies also need to overcome challenges such as high costs, change resistance, and integration problems. By paying attention to these suggestions, companies can maximize the benefits of implementing an ERP system and improve their operational performance and strategy. Keywords : Advantages, Challenge, Suggestion, Implementation, ERP
Penerapan Tata Kelola Teknologi Informasi pada Instansi: Systematic Literature Review Alfajri, Willy Bima; Puji Widodo, Aris; Adi, Kusworo
Jurnal Nasional Teknologi dan Sistem Informasi Vol 7 No 3 (2021): Desember 2021
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v7i3.2021.191-198

Abstract

Tata kelola TI sudah banyak diterapkan dalam beberapa instansi pemerintah maupun swasta, penerapan ini bertujuan untuk meningkatkan kualitas instansi dalam pesatnya perkembangan teknologi saat ini. Banyaknya instansi yang menerapkan tata kelola TI tanpa adanya aturan apapun yang membuat belum adanya standarisasi yang harus diikuti setiap instansi dalam penerapan tata kelola TI. Artikel ini membahas tentang framework dan domain apa yang sering digunakan dalam evaluasi tata kelola TI serta instansi apa yang sering melakukan evaluasi terhadap tata kelola TI-nya. Systematic literature review digunakan sebagai metode penelitian dengan mencari artikel terkait dengan tema yang dipilih. 142 artikel ditemukan, dengan kriteria inklusi dan eksklusi digunakan untuk memperoleh hasil penelusuran artikel yang lebih baik. Setelah menerapkan kriteria inklusi dan eksklusi tersisa 77 artikel yang dapat ditinjau lebih jauh. Dari 77 artikel yang masuk dalam kategori studi kandidat, setelah ditinjau judul, abstrak dan kesimpulan artikel hanya 30 artikel yang sesuai dengan tujuan penelitian. Hasil yang didapatkan menyebutkan bahwa Control Objectives for Information and Related Technology (COBIT) 5 adalah framework yang paling banyak diaplikasikan dalam Studi Evaluasi maupun Audit Tata Kelola TI pada instansi di Indonesia. Dan domain deliver, service, support (DSS) adalah domain yang paling banyak digunakan dalam artikel, serta instansi swasta merupakan instansi yang paling banyak melakukan evaluasi terhadap tata kelola TI-nya.
Comparative Performance Analysis of Deep Learning Models for Cryptocurrency Price Forecasting Pambudi, Ryo; Mutiara Kusumo Nugraheni, Dinar; Puji Widodo, Aris
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35653

Abstract

Purpose: A cryptocurrency's high volatility and nonlinear market dynamics make it extremely difficult to predict its price with any degree of accuracy. This study aims to evaluate and contrast the predictive capabilities of five Deep Learning architectures for the same reason: LSTM, GRU, BiLSTM, Transformer, and Performer, to identify the best model capable of predicting the price of cryptocurrencies. It is aimed at providing an empirical base for making such predictions with high reliability in such volatile financial markets. Methods: The dataset used in this study, namely the price per minute data for BTC, ETH, BNB, and XRP, was obtained from Kaggle. Data processing includes normalization using MinMaxScaler and sequence generation through the Sliding Window technique. An 80:20 data split is used to train and validate each deep learning model, and four metrics consisting of MAE, MSE, RMSE, and MAPE are used for evaluation. Standardized experimental protocols were guaranteed by Python-based frameworks.  Result: The Transformer model created the best results for the lowest MAPE value across all datasets, the smallest being BTC and ETH at 0.20%, BNB at 0.29%, and XRP at 0.36% demonstrating high accuracy and generalization. The BiLSTM was ranking second since it captured effectively the bidirectional temporal dependencies; the GRU was moderate but stable in its performance. The data showed that the accuracy of LSTM and Performer varied. Novelty: This research provides a comprehensive comparison between various models, highlighting the Transformer's self-attention mechanism as the most superior in capturing long-term temporal dependencies and nonlinear market behavior compared to other deep learning methods. These findings provide valuable insights for the development of advanced AI-based forecasting frameworks in financial analysis.
Co-Authors Ahmad Zaini Alfajri, Willy Bima Kusworo Adi Muhammad Syaifuddin, Nur Mutiara Kusumo Nugraheni, Dinar Ryo Pambudi, Ryo Suriansyah, Muhammad