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Saturnus: Jurnal Teknologi dan Sistem Informasi
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30319935     EISSN : 30319943     DOI : 10.61132
Core Subject : Science,
Computer Science & IT
Saturnus : Jurnal Teknologi dan Sistem Informasi memuat naskah hasil-hasil penelitian di bidang Teknologi, dan Sistem Informasi
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 113 Documents
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Analisis Prediksi Penjualan Bisnis Retail Menggunakan Metode Decision Tree dan Random Forest Agung Narayana Adhi Putra; I Wayan Sudiarsa; I Kadek Adi Gunawan; Kadek Bagus Karunia Dwi Dharmayasa; I Wayan Eka Saputra
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1409

Abstract

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.
Implementasi Jaringan Syaraf Tiruan dalam Peramalan Harga Cpo Menggunakan Backpropagation Eva Andini; Lailan Sofinah Harahap; Siti Nurjanah
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1410

Abstract

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.
Peran Media Digital dalam Menyatukan Pandangan Masyarakat dan Pemerintah untuk Mewujudkan Smart City Medan Rio Irawan Munthe; Putri Nabila; Dermawan Wijaya Harahap; Ahmad Tamrin Sikumbang
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1413

Abstract

Digital media is currently the main key to public communication to convey aspirations in government policies. Medan is also one of the major cities in Indonesia that is striving to realize the concept of a smart city through digital media. The role of digital media is very important in realizing a smart city. This study aims to analyze how digital media plays a role in uniting the views of the community and the Medan city government in the effort to realize a smart city in Medan. The method used in this study is a qualitative descriptive approach with a library research approach and observation of digital communication activities, online news, and public statements from the government to the public in Medan. In realizing a smart city, optimizing digital communication is essential to strengthen the synergy between the community and the government. The results of the study indicate that digital media plays a crucial role as a means of two-way communication, information dissemination, and as a medium for public participation. However, challenges such as weak digital literacy and limited infrastructure are obstacles to the sustainability of this effort.

Page 12 of 12 | Total Record : 113

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