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All Journal JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) Jurnal Teknik Informatika (JUTIF)
Muhammad Ihsan Fawzi
Universitas Jenderal Soedirman
Computer Science & IT Decision Sciences, Operations Research & Management

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Dampak Adopsi TI Terhadap Kinerja Perusahaan Multinasional Muhammad Ihsan Fawzi; Apol Pribadi Subriadi
JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) Vol 8, No 2 (2022): JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) MEI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jkbm.v8i2.7320

Abstract

This study aims to classify and provide an overview of the factors that influence the adoption of IT in multinational companies and the impact of IT adoption on the performance of multinational companies. The data for this study were obtained from literature searches published from 2009 to 2021. The searches were conducted on scientific journals published internationally in Elsevier or Science Direct, Emerald Insight, Willey, Taylor and Francis, and the Academy of Taiwan Information Systems Research. The search results related to themes and variables, obtained about 150 articles and selected 40 articles relevant to IT adoption in multinational companies and the impact of IT adoption on the performance of multinational companies. The results showed that technological factors, organizational factors, and environmental factors influenced IT adoption, although technological factors and organizational factors influenced IT adoption more than environmental factors. In addition, IT adoption has a positive impact on the performance of multinational companies, especially economic performance and operational performance. Even so, several studies say that IT adoption does not have a positive impact on the performance of multinational companies. The results of this study also show that to find out the factors that influence IT adoption in multinational companies is to use the TOE framework. IT adoption also has a positive impact on the performance of multinational companies, especially in terms of economic performance and operational performance.
UNRAVELING OF MEN'S FRAGRANCE PREFERENCES ON ONLINE MARKETPLACES: A MACHINE LEARNING STUDY USING DBSCAN CLUSTERING AND LINEAR REGRESSION Alkaf, Zakiyyan Zain; Fawzi, Muhammad Ihsan; Sastyawan, Murti Wisnu Ragil; Putera, Radita Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4187

Abstract

The perfume industry is undergoing significant growth, driving the need to understand consumer preferences, particularly in men’s fragrances, to optimize business strategies. This study aims to analyze and uncover men’s fragrance preferences, using machine learning techniques. A dataset of approximately 1,000 men's perfume records from Kaggle was utilized, where systematic methodologies were employed. Data preprocessing involved handling missing values, removing duplicates, standardizing categorical entries, and performing feature engineering by extracting geographic information from item locations. Exploratory Data Analysis (EDA) was conducted to uncover data distribution. Clustering analysis using DBSCAN revealed consumer segments. Additionally, regression analysis was used to predict sales based on price and location, employing a linear regression model evaluated with metrics like Mean Squared Error (MSE). The findings indicate that price exhibits a complex relationship with sales; while affordable products drive higher sales volumes, premium-priced items cater to a niche yet impactful market segment. Geographic location plays a pivotal role in sales patterns. Clustering analysis reveals two distinct consumer segments: one driven by price sensitivity and another oriented towards premium preferences, influenced by regional factors. Regression analysis demonstrated a negative correlation between price and sales volume, with a coefficient of -1.81, while availability positively influenced sales with a coefficient of 8.36. Despite a moderate model fit (R² = 0.17), the analysis highlights key market dynamics. These insights emphasize the importance of leveraging data-driven strategies to develop targeted marketing campaigns, optimize inventory management and refine market segmentation.
Forecasting Bitcoin Price Prediction with Long Short-Term Memory Networks: Implementation and Applications Using Streamlit Fawzi, Muhammad Ihsan; Ganesha, Taufik; Anugrah, Priandika Ratmadani; Zhahran, Maulana; Abimanyu, Faris Akbar; Bimantoro, Haryo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5168

Abstract

The rapid growth of cryptocurrency markets, particularly Bitcoin, has highlighted the need for accurate price prediction models to support informed decision-making. While existing studies primarily evaluate machine learning models for price forecasting, few have implemented these models in real-world applications. This paper addresses this gap by developing a Bitcoin price prediction system using Long Short-Term Memory (LSTM) networks, integrated into a user-friendly web-based application powered by Streamlit. The model forecasts Bitcoin prices at 5-minute, 1-hour, and 1-day intervals, demonstrating strong predictive performance. For the 5-minute interval, the model achieved a Mean Squared Error (MSE) of 53,479.86, Mean Absolute Error (MAE) of 150.58, Root Mean Squared Error (RMSE) of 231.26, and Mean Absolute Percentage Error (MAPE) of 0.144%. At the 1-hour interval, errors increased moderately with an MSE of 423,198.24, MAE of 499.93, RMSE of 650.54, and MAPE of 0.505%. For the 1-day interval, the model faced greater variability, reflected in an MSE of 3,089,699.07, MAE of 1,058.88, RMSE of 1,757.75, and MAPE of 2.027%. These results indicate that while predictive precision decreases over longer horizons, the model maintains strong performance across all timeframes. By embedding LSTM predictions into an interactive, real-time forecasting platform, this study demonstrates the practical integration of deep learning into complex financial systems. Beyond cryptocurrency, the approach highlights the potential of intelligent computational models to enhance decision-making processes in data-intensive domains, reinforcing the role of informatics in bridging advanced algorithms with usable technological solutions.
Classification of Worker Productivity and Resource Allocation Optimization with Machine Learning: Garment Industry Yusuf, A’isya Nur Aulia; Alkaf, Zakiyyan Zain; Nurdiniyah, Elsa Sari Hayunah; Wisudawati, Tri; Fawzi, Muhammad Ihsan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5263

Abstract

This study presents an integrated predictive–prescriptive framework for improving workforce management in the garment industry by combining machine learning classification with linear programming optimization. Using a publicly available dataset of 1,197 production records, productivity levels were categorized into low, medium, and high classes. Data preprocessing included handling missing values, one-hot encoding of categorical variables, and class balancing using SMOTE. Eleven classification algorithms were evaluated, with LightGBM achieving the highest performance (accuracy 78.3%, weighted F1-score 78.3%, Cohen’s Kappa 63.4%) after hyperparameter tuning via Bayesian Optimization. The optimized model’s predictions were then incorporated into a linear programming model, implemented with PuLP, to maximize the allocation of high-productivity workers across production departments under capacity constraints. The results yielded an allocation plan assigning 117 high-productivity workers, significantly enhancing potential operational efficiency. The novelty of this work lies in integrating an optimized ensemble learning model with mathematical programming for end-to-end productivity classification and resource allocation, a combination rarely explored in labor-intensive manufacturing contexts. This framework offers a scalable decision-support tool for data-driven workforce planning and could be adapted to other manufacturing domains with similar operational structures. 
Integration of K-Means Clustering and Elbow Method for Mapping Baccaurea spp. Distribution to Support Agroindustrial Development in West Sulawesi Irawan, Heri; Ihsan Fawzi, Muhammad; Hidayat, Syarif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4671

Abstract

Baccaurea spp. is a type of wild plant with potential value for sustainable agroindustrial development. This study aims to map and segment regions in West Sulawesi based on the habitat suitability for Baccaurea spp. using K-Means Clustering integrated with the Elbow Method. Field data were collected from 25 villages in Mamasa and Mamuju districts, involving five parameters: land area, production estimate, altitude, humidity, and average temperature. Based on the results of the exploration, 3 species of Baccaurea spp. have been found, namely Baccaurea Lanceolata, Baccaurea Costulata, and Baccaurea Racemosa. The analysis yielded three clusters, with Cluster 1 being identified as the top priority for agroindustrial development due to its high productivity and optimal land conditions. The findings provide a data-driven foundation for policymakers and industries to support the sustainable cultivation of Baccaurea spp. in Indonesia. This research contributes to informatics-based decision-making in agroindustry development and regional planning.
Designing AI - IoE Precision Farming to Create Sustainable Eco-Friendly Hydroponic Greenhouses Sastyawan, Murti Wisnu Ragil; Fawzi, Muhammad Ihsan; Putera, Radita Dwi; Alkaf, Zakiyyan Zain; Syhamsudin, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5260

Abstract

Conventional greenhouses, while boosting crop yields, face critical sustainability challenges due to high energy consumption and resource inefficiency, particularly in developing nations where manual management prevails. This research addresses these limitations by designing a comprehensive AI-IoE system architecture to create a smart, resource-efficient, and sustainable operational model for eco-friendly greenhouses. The development methodology involved a systematic process of requirements analysis, integrated hardware and software design, prototype assembly, and functional testing. The system utilizes an ESP32 microcontroller as its central control unit, integrating a suite of six sensors comprising light intensity, temperature, humidity, pH, Total Dissolved Solids (TDS), and CO₂ to monitor critical environmental parameters in real-time. This integration utilizes the extensive dataset for AI based predictive analysis, enabling the intelligent forecasting of environmental trends and proactive resource management. The research resulted in a complete system blueprint, including a detailed electronic circuit design, a production-ready Printed Circuit Board (PCB) layout, defined operational control logic, and an intuitive web-based dashboard for remote monitoring and management. This integrated AI-IoE architecture provides a tangible solution that surpasses previous fragmented approaches by offering holistic environmental control. The findings present a significant contribution to precision farming, establishing a scalable and efficient framework to enhance greenhouse productivity and ecological sustainability.
Co-Authors Abimanyu, Faris Akbar Alkaf, Zakiyyan Zain Anugrah, Priandika Ratmadani Apol Pribadi Bimantoro, Haryo Ganesha, Taufik Irawan, Heri Nurdiniyah, Elsa Sari Hayunah Sastyawan, Murti Wisnu Ragil Syarif Hidayat Syhamsudin, Muhammad Wisudawati, Tri Yusuf, A’isya Nur Aulia Zhahran, Maulana