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All Journal Jurnal Teknik Informatika (JUTIF)
Alkaf, Zakiyyan Zain
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Computer Science & IT

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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.
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. 
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 Muhammad Ihsan Fawzi Nurdiniyah, Elsa Sari Hayunah Sastyawan, Murti Wisnu Ragil Syhamsudin, Muhammad Wisudawati, Tri Yusuf, A’isya Nur Aulia