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Data Science with less than a year in Data Analysis & Machine Learning.
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Assessing your cultural and operational fit
Data Science Graduate (Cum Laude) from Telkom University with a strong foundation in data analysis, reporting, and statistical modeling. Proficient in SQL, Microsoft Excel (including automation and validation metrics), and data visualization via Power BI. Experienced in data cleaning, validation, and managing datasets through structured academic research and a public sector internship. Focused on delivering clear, data-driven insights to support business decision-making in a remote environment.
Telkom University, Purwokerto Campus, Central Java
Bachelor of Data Science · Data Science
August 1, 2022 – June 30, 2026
Al-Abidin Bilingual Boarding High School, Surakarta, Central Java
Information and Communication Technology (ICT) Class Program · Information and Communication Technology
June 1, 2019 – May 31, 2022
Dinas Komunikasi, Informatika, Statistik Dan Persandian (DKISP)
Statistical Analysis Intern
July 1, 2025 – August 1, 2025
Tarakan, North Kalimantan, Indonesia
A Multimodal Deep Learning System for Soil Plant Analysis Development (SPAD) Prediction on Spinach Leaves
January 1, 2027 – January 1, 2027
Addressed limitations of expensive sensor data in agricultural chlorophyll monitoring by co-developing the "SPINet" multimodal system. Designed a data-fusion pipeline using FastSAM for leaf segmentation, integrating visual features with GLCM texture and Hu Moments metadata. Successfully achieved an outstanding 98% test accuracy and a low RMSE of 5.46.
Hyperparameter Optimization of Multi-Target Support Vector Regression with Sigmoid Particle Swarm Optimization-based Acceleration Coefficients for Electricity Consumption Prediction
January 1, 2026 – January 1, 2026
Resolved high forecasting errors caused by suboptimal parameter tuning in standard Multi-Target Support Vector Regression (MTSVR). Coded and integrated the Sigmoid Particle Swarm Optimization (PSO-SBAC) algorithm to accelerate model convergence and automate hyperparameter selection. Optimized the forecasting architecture to significantly minimize prediction errors, achieving a low test RMSE of 4878.1.
TelUP Human Fall Dataset: A Motion Forecasting Study of Human Falls
January 1, 2025 – January 1, 2025
Addressed the shortage of specialized, open-source video datasets for accident detection and human fall classification. Managed a high-fidelity data extraction pipeline using YOLOv11 to isolate and extract 17-keypoint human poses across annotated frames. Established the "TelUP Human Fall Dataset" and achieved a top classification accuracy of 92.89% using sequential LSTM models.
Gambling Comments Detection on YouTube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models
January 1, 2025 – January 1, 2025
Targeted toxic content moderation challenges on highly imbalanced social media data by scraping and manually labeling 11673 YouTube comments. Built and benchmarked LightGBM, LSTM, and GRU architectures to evaluate sequential patterns and handle severe data skewness. Identified the gradient-boosting approach as optimal, delivering the highest performance with a holdout F1-score of 0.8737.
Optimalisasi Pengelolaan Data Tumbuh Kembang Anak PAUD dengan Aplikasi Excel: Studi Kasus KB Kenanga Desa Pesantren
January 1, 2024 – January 1, 2024
Targeted manual processing errors and workflow inefficiencies in regional child development reporting for KB Kenanga in Pesantren Village. Secured an internal university grant to digitize records by building a customized Microsoft Excel automation and validation tool with automated health index calculations. Streamlined local administrative tasks, improving reporting accuracy and ease of record-keeping for rural educators.
Penerapan Metode Stacking Ensemble Untuk Klasifikasi Status Pinjaman Nasabah Bank
January 1, 2024 – January 1, 2024
Aimed to reduce credit default risks and improve loan approval accuracy for banking institutions. Integrated SMOTE to balance the dataset and engineered a Stacking Ensemble machine learning pipeline using a Random Forest meta-learner. Validated the integrated architecture to optimize predictive performance, securing a final classification accuracy of 90.7%.
Hypothesis Testing: Experimental Design
Codeacademy
January 1, 2026 – Present
Data Analytics Virtual Experience
Quantium [Forage]
January 1, 2026 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' exam, indicating a very strong command of the subject matter and related skills. This high score reflects deep technical proficiency.
Strengths
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of interests across various domains, from agriculture and energy to social media and banking, indicating adaptability and a willingness to tackle diverse challenges. The focus on impactful projects (e.g., reducing credit default risks, improving reporting accuracy) suggests a desire to contribute meaningfully. The co-authorship on several papers indicates collaborative potential. However, the low psychometric test score could be a concern regarding cultural fit, particularly in areas like team collaboration and work attitude, which are crucial for a healthy team environment. More information is needed to fully assess cultural alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to work on complex, multi-faceted problems. The internship experience, though brief, suggests an understanding of data entry, validation, and reporting in a public sector context. The academic nature of most projects implies strong research and analytical skills. However, the psychometric test score is low (227/500), which might indicate potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration. Further assessment would be needed to understand the implications of this score.
Limitations