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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Building of Informatics, Technology and Science Jurnal Borneo Saintek Just TI (Jurnal Sains Terapan Teknologi Informasi) TEPIAN Buletin Poltanesa Journal of Social Responsibility Projects by Higher Education Forum Informatika Wicida Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ilmu Sosial dan Humaniora Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora
Yunita Yunita
STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Neuroscience Nursing Social Sciences Transportation

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Employee Acceptance Decision Support System Using The Smart Method (Case Study on Association of The Indonesian Electrical and Mechanical Contractors Samarinda City) Aldy Septian Derry; Bartholomius Harpad; Yunita Yunita
TEPIAN Vol. 2 No. 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.357

Abstract

Decision Support System (DSS) is a system that can assist someone in making accurate and targeted decisions. Many problems can be aasolved by using SPK, one of which is the acceptance of employees at the Gaklimdo Samarinda Association using the SMART (Simple Multi Attribute Rating Technique) method. The purpose of this research is to produce a Decision Support System for Employee Admission Using the SMART Method (Case Study of the Gaklimdo Association of Samarinda City) with the hope that the selection of employees will be carried out objectively. By using the PHP programming language and the database used is MySql. In this study, the data collection techniques used were literature study, observation and interviews. The result of this research is the creation of a decision support system to make employee decisions that are accepted, and not accepted as employees. Users can input prospective employee data, criteria data and sub-criteria data. Then the system will look for a solution using the SMART (Simple Multi Attribute Rating Technique) method. After the decision is obtained, the system will display the decision.
Optimalisasi Instagram Guna Meningkatkan Daya Saing UMKM Soto Ayam Cak Pon melalui Pendampingan Digital Marketing Muhamad Amir Ariandi; Yunita Yunita; Amelia Yusnita; Muhammad Rinaldi; Nardi Nardi
ABDISOSHUM: Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdisoshum.v4i4.7005

Abstract

This community service program aims to enhance the competitiveness of the MSME Soto Ayam Cak Pon by optimizing the use of Instagram as a digital marketing tool. The mentoring activities were carried out through a hands-on practice approach to ensure that the business owner could directly understand and apply various Instagram business features. Data collection techniques included observation, interviews, and documentation to identify the partner’s initial needs and to assess improvements in digital literacy after the mentoring process. The results indicate that the MSME owner successfully created a business email, developed and optimized an Instagram account, arranged an informative business profile, linked WhatsApp contact and business location, and produced reels content to support visual promotion. The activation of the professional dashboard also enabled the owner to monitor account performance and understand audience engagement patterns. These improvements demonstrate that structured social media utilization can expand promotional reach and strengthen the business’s market presence. This program highlights the essential role of digital literacy in improving the competitiveness of culinary MSMEs in the digital era and serves as a foundation for further development of sustainable digital marketing strategies.
Comparative Performance Analysis of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) Algorithms in Gold Price Prediction Siti Lailiyah; Yunita Yunita; Hanifah Ekawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8605

Abstract

Gold is one of the most important investment commodities in the global financial system, widely recognized for its role as a safe-haven asset and its ability to preserve value during periods of inflation, economic instability, and geopolitical uncertainty. Despite its relative stability compared to other financial instruments, gold prices exhibit significant volatility driven by various macroeconomic factors, including exchange rate movements, inflation dynamics, global monetary policy decisions, and market sentiment. As a result, accurate gold price prediction remains a critical challenge for investors, financial analysts, and policymakers. This study aims to conduct a comparative performance analysis of two machine learning algorithms, namely Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), in predicting gold prices represented by the XAU/USD currency pair. The research utilizes daily historical gold price data from 2004 to 2025 obtained from the Kaggle platform. The dataset includes key financial attributes such as Open, High, Low, Close prices, and trading Volume. Data preprocessing steps involve data cleaning, chronological sorting, handling missing values through linear interpolation, feature selection, and normalization using the Min-Max scaling technique. The dataset is then divided sequentially into training and testing sets with an 80:20 ratio to preserve temporal dependencies. The LSTM model is designed to capture long-term temporal patterns using the closing price as a time series input, while the SVR model leverages multiple input features to model non-linear relationships through kernel-based regression. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The experimental results demonstrate that the LSTM model outperforms the SVR model across all evaluation metrics. The LSTM achieved an RMSE of 0.0082, an MAE of 0.0060, and an R² value of 0.9969, indicating a very high level of predictive accuracy and strong generalization capability. In contrast, the SVR model recorded an RMSE of 0.0289, an MAE of 0.0143, and an R² of 0.9611, reflecting lower precision, particularly during periods of high price volatility. These findings confirm that LSTM is more effective in capturing complex temporal dependencies and non-linear dynamics inherent in gold price time series data. Consequently, LSTM is recommended as a superior approach for long-term gold price forecasting, while SVR may serve as a complementary or baseline predictive model in financial time series analysis.
Co-Authors Abd. Rasyid Syamsuri Ahmad Sabirin Aisyah Fajriantini Aldy Septian Derry Alvin Hernanda Ekaputra Amelia Yusnita Andi Yusika Anisa Purnama Dewi Rosita Dwi Andrian Nurbawono Ekawati, Hanifah Hanifah Ekawati Harpad, Bartolomius Hendri Setiawan Heny Pratiwi Ita Arfyanti Masdar Masdar Muhamad Amir Ariandi Muhammad Fahmi Muhammad Fauzan Rizky Maulana Muhammad Nur Madani Muhammad Rinaldi Nabila Marsya Zikryanti Raharjo Nardi Nur Luthfiani Anfasyah Nurhuda, Asep Nursobah, Nursobah Pitrasacha Adytia Presa Taruna Oliver Rachmad Fauzi Renni Mayasari Rizky Zakariyya Rasyad Salmon Salmon Sapran Hanafi Siti Lailiyah Siti Lailiyah Siti Qomariah Sony Darmawan Syamsuddin Mallala