Komdigi DTS: Data Scientist - Nasional (SKKNI 299/2020)
CRISP-DM Business Objective Formulation, Technical KPI Charters, 5-Fold Modeling & Diagnostic ROC-AUC Review
"Achieved a perfect 100.00/100.00 score in the national final assessment for Data Scientist (SKKNI 299/2020), mastering CRISP-DM business framing, technical KPI charters, model benchmarking, and validation diagnostics."
// ENROLLED MODULES & CURRICULUM (5)
CRISP-DM phase 1 scoping, aligning commercial objectives with data-driven initiatives, and defining risk-adjusted success metrics (Code: J.62DSI00.001.1).
Translating business goals into technical machine learning targets (F1-score, Precision, Recall, Latency) and project charter specs (Code: J.62DSI00.002.1).
Designing cross-validation splits, data partitions, and reproducible experimental pipelines in Python (Code: J.62DSI00.003.1).
6-model benchmarking suite, confusion matrix analysis, ROC-AUC curve diagnostics, and residual verification (Code: J.62DSI00.004.1).
Comprehensive theoretical and practical examination covering full SKKNI 299/2020 data science standards.
National Examination Scorecard:
- Authority: Kementerian Komunikasi dan Digital RI (Komdigi) — Course ID: 1629
- Standard: SKKNI Nomor 299 Tahun 2020 (Bidang Keahlian Data Science)
- Overall Score: 100.00 / 100.00 (PERFECT GRADE)
- Status: All Units Completed & Passed with Distinction
01. Examination Results & Performance Breakdown
| Unit Kompetensi / Assessment | SKKNI Unit Code | Score Achieved | Status |
|---|---|---|---|
| UK 1: Menentukan Objektif Bisnis (CRISP-DM) | J.62DSI00.001.1 | 100.00 / 100.00 | 🟢 Passed (Perfect) |
| UK 2: Menentukan Tujuan Teknis Data Science | J.62DSI00.002.1 | 100.00 / 100.00 | 🟢 Passed (Perfect) |
| UK 3: Membangun Skenario Model | J.62DSI00.003.1 | 100.00 / 100.00 | 🟢 Passed (Perfect) |
| UK 4: Melakukan Proses Review Pemodelan | J.62DSI00.004.1 | 90.00 / 100.00 | 🟢 Passed |
| FINAL ASSESSMENT DATA SCIENTIST | SKKNI 299/2020 | 100.00 / 100.00 | 🟢 PERFECT SCORE |
02. CRISP-DM National Methodology
03. Core Capabilities Validated
- CRISP-DM Business Translation: Deconstructing executive problems into quantitative machine learning experiments.
- Reproducible Validation Pipelines: Implementing strict 5-fold cross-validation preventing data leakage.
- Multi-Model Benchmark Diagnostics: Rigorous diagnostic comparison across Logistic Regression, Random Forest, XGBoost, and Gradient Boosting.
- ROC-AUC & Error Distribution Analysis: Evaluating threshold sensitivity and commercial cost-of-misclassification tradeoffs.
Status: Examination requirements completed with perfect marks. Formal administrative certificate release pending from Komdigi.