Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika INTI Nusa Mandiri JUSTIAN - Jurnal Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak
Julianto, Muhammad Fahmi
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PERBANDINGAN PENERAPAN ALGORITMA DEEP LEARNING DALAM PREDIKSI HARGA EMAS Julianto, Muhammad Fahmi; Iqbal, Muhammad; Hidayat, Wahyutama Fitri; Malau, Yesni
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5559

Abstract

Digital investment is trending because advancements in information technology make access easy through smartphones. Various digital investment instruments attract much interest from the public. Post COVID-19 pandemic, the economic impact of the pandemic is still felt until the end of 2022, requiring people to be smart in managing their finances. Gold investment is considered profitable due to its high value and tendency to increase, unlike the fluctuating stocks. Although easily accessible, investments carry risks, so investors must have sufficient knowledge to maximize profits. This research aims to predict gold prices using several deep learning models, namely Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The dataset used was taken from the Kaggle website, which includes historical gold price data. In this research, various deep learning models were applied and evaluated to determine the best model for predicting gold prices. The results show that the CNN model with Adam optimization and Mean Squared Error (MSE) loss function provides the best performance. The CNN model achieved the lowest Mean Absolute Error (MAE) of 0.004848717761305338, the lowest MSE of 4.3451079619612133, and the lowest Root Mean Squared Error (RMSE) of 0.006591743291392053. These results indicate that the CNN model is more effective in predicting gold prices compared to the ANN, RNN, and LSTM models on the used dataset.
Implementasi Enterprise Resource Planning Berbasis Odoo Pada Toko Terbis Siti, Siti Nurdiani; Ihsan, Muhammad Ifan Rifani; Julianto, Muhammad Fahmi
Jurnal Sistem Informasi Akuntansi Vol. 6 No. 1 (2025): Periode Maret 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v6i1.8711

Abstract

Bagi usaha seperti Toko Terbis, pengelolaan operasional bisnis sehari-hari menjadi tantangan yang signifikan. Tantangan ini meliputi pengelolaan stok yang tidak terstruktur, pencatatan keuangan yang rawan kesalahan, serta pengelolaan pesanan yang sering memakan waktu. Proses manual yang selama ini diterapkan sering kali mengakibatkan ketidakakuratan data, penurunan produktivitas, dan kurang optimalnya penggunaan sumber daya. Hal ini tidak hanya berdampak pada efisiensi internal tetapi juga pada kualitas layanan yang diberikan kepada pelanggan. Penerapan sistem ERP berbasis Odoo menawarkan solusi yang relevan untuk mengatasi tantangan tersebut. Odoo adalah platform ERP bersifat open source yang dirancang untuk memberikan fleksibilitas dan kemudahan dalam mengelola berbagai aspek operasional bisnis.
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using BERT Julianto, Muhammad Fahmi; Malau, Yesni; Hidayat, Wahyutama Fitri
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.169

Abstract

News about the war between Russia and Ukraine can not be denied affecting various aspects of life worldwide. It affects the writings of every world citizen on various social media platforms, one of which is Twitter. Sentiment analysis is a process of identifying and making sentiment categories computationally. The sentiment analysis process is also intended to make computers understand the meaning of human sentences by processing algorithms. This research uses the deep learning method of the BERT (Bidirectional Encoder Representation Form Transform) model language to analyze the sentiments in the tweets written about the wars between Russia and Ukraine by Twitter social media users. The sentiment will be divided into positive, neutral, and hostile. The hyperparameters in this study used ten epochs, with a learning rate of 2e-5 and a batch size of 16. The test used in sentiment analysis was the BERTbase Multilingual-cased-model model, and the accuracy was 97%. Suggestions for further research are the need for a more balanced dataset between positive, neutral, and negative sentiments. They reward the dataset before training so that better results are expected.
Prototype Aplikasi Edukasi Anak Berbasis Mobile Hidayat, Wahyutama Fitri; Malau, Yesni; Julianto, Muhammad Fahmi
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 3 No. 1 (2022): Mei 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v3i1.1185

Abstract

Teknologi informasi dunia dimana saat ini sedang berkembang dengan pesat telah menyasar berbagai aspek di masyarakat yaitu ekonomi, kebudayaan, seni, politik, dan tak terkecuali dunia pendidikan. Tujuan dari penelitian yag dilakukan yaitu menghasilkan prototype aplikasi yang berberbasiskan mobile application dengan diberikan nama Aplikasi Edukasi Anak. Batasan yang digunakan di penelitian ini yaitu hanya akan dibahas mengenai merancangan aplikasi berdasarkan model prototype. Hasil dari penelitian ini berupa rancangan Aplikasi Edukasi Anak berbasis mobile yang dirancang menggunakan aplikasi Justinmind. Hasil rancangan yang dilakukan pengujian dengan digunakannya metode BlackBox dapat menghasilkan fitur aplikasi prototype yang dirancang dapat berjalan sesuai dengan rancangan dan diterima.
Co-Authors Mochammad Imron Awalludin Muhammad Ifan Rifani Ihsan Siti, Siti Nurdiani Wahyutama Fitri Hidayat Yesni Malau