riset-teknologi-informasi/BLUEPRINT.md

BLUEPRINT KONSOLIDASI — SELURUH BAB (M1–M16)

Dokumen ini merangkum blueprint setiap bab secara ringkas. Gunakan sebagai peta navigasi saat menulis. Detail lengkap ada di docs/disscus04.md.

---

BAGIAN I — FOUNDATION (Thinking Phase)

---

BAB 1 — Etika Penelitian, Validitas, dan Paradigma (M1)

CPMK: CPMK01 | CPL: CPL03 | Sub-CPMK: 1.1

Signature Model: Research Trust Model

Reality → Data → Processing → Analysis → Inference → Knowledge
(setiap tahap membawa risiko distorsi; etika mengendalikan distorsi)

Konsep Inti:

• Etika = penjaga validitas ilmiah (bukan sekadar moral)
• Validitas: internal, external, construct
• Research vs Engineering Validation
• Kriteria kebenaran ilmiah
• Paradigma: positivism, interpretivism, pragmatism
• Posisi MK: positivist + design science

Case Study:

1. Basic: Manipulasi dataset ML — akurasi tinggi tapi data palsu
2. Advanced: AI bias — model terlihat bagus tapi bias tersembunyi

Cognitive Traps:

1. "Angka tinggi = benar"
2. "Data netral"
3. "Jika jalan, maka benar"
4. "Kegagalan tidak perlu dilaporkan"

Final Statement:

"Penelitian bukan tentang mendapatkan hasil, tetapi tentang memastikan hasil tersebut dapat dipercaya."

Output Praktis: Esai analisis kasus etika + posisi paradigma

---

BAB 2 — Problem Formulation & System Context (M2)

CPMK: CPMK01 | CPL: CPL03 | Sub-CPMK: 1.2

Signature Model: Problem Formation Model + Problem Quality Model

Reality → Observed Issue (Symptom) → Diagnosed Problem → Researchable Problem → Measurable Variable

Clarity → Measurability → Relevance → Testability → Impact

Konsep Inti:

• Topic vs Problem vs Research Problem (hierarki)
• Symptom vs Problem (akar masalah)
• System thinking: Input→Process→Output→Outcome + Constraints + Stakeholders
• Problem → Variable → Metric (transformasi)
• 5 Kriteria: Specific, Measurable, Relevant, Testable, Real-world

Case Study:

1. Basic: Rekomendasi film — akurasi tinggi tapi user tidak puas
2. Advanced: Fraud detection — 98% akurasi tapi fraud lolos (imbalance)

Cognitive Traps:

1. "Saya ingin menggunakan metode X"
2. "Semakin kompleks semakin bagus"
3. "Problem tidak perlu diukur"
4. "Semua problem bisa diteliti"

Final Statement:

"Penelitian tidak dimulai dari solusi, tetapi dari masalah yang dipahami secara mendalam dan dapat diuji secara ilmiah."

Output Praktis: Problem statement (spesifik, measurable, konteks sistem)

---

BAB 3 — Literature Review, Research Gap & Baseline (M3)

CPMK: CPMK01 | CPL: CPL03 | Sub-CPMK: 1.3

Signature Model: Research Positioning Model

Existing Studies → Method Comparison → Limitation Identification → Research Gap → Research Position → Contribution

Konsep Inti:

• Literature review = positioning, bukan ringkasan
• 4 jenis gap: Performance, Method, Data, Context
• Baseline: relevan, representatif, state-of-the-art
• Gap → RQ → Hypothesis → Experiment (bridge)
• Strategi pencarian: IEEE, ACM, Scopus, boolean query

Case Study:

1. Basic: Image classification — banyak paper, gap tidak jelas
2. Advanced: Deteksi penyakit — baseline lemah, kontribusi diragukan

Cognitive Traps:

1. "Semakin banyak referensi, semakin bagus"
2. "Belum ada = gap"
3. "Tidak perlu baseline"

Final Statement:

"Literature review bukan tentang apa yang sudah diketahui, tetapi tentang apa yang belum diselesaikan dan bagaimana Anda mengisinya."

Output Praktis: Tabel literatur + gap statement + baseline selection

---

BAB 4 — Research Question, Contribution & Hypothesis (M4)

CPMK: CPMK01 | CPL: CPL03 | Sub-CPMK: 1.4

Signature Model: RQ Formation Model

Problem → Research Gap → Research Question → Hypothesis → Experiment Design

Konsep Inti:

• RQ = instrumen pengarah eksperimen
• 3 jenis RQ: Comparison, Improvement, Exploratory
• Contribution: improvement, comparison, novel approach
• Hypothesis: H0 (null) + H1 (alternative) — harus testable
• RQ → Variable → Metric → Data → Analysis

Case Study:

1. Basic: RQ terlalu umum → tidak bisa diuji
2. Advanced: RQ tanpa baseline → tidak ada pembanding

Cognitive Traps:

1. "RQ = judul dalam bentuk tanya"
2. "RQ tidak perlu metric"
3. "RQ bisa dijawab tanpa eksperimen"

Final Statement:

"Research Question bukan sekadar pertanyaan, tetapi blueprint dari eksperimen yang akan dilakukan."

Output Praktis: RQ (clear & testable) + contribution statement + hypothesis (H0/H1)

---

BAGIAN II — MEASUREMENT & DESIGN (Designing Phase)

---

BAB 5 — Metric, Measurement & Data (M5)

CPMK: CPMK02 | CPL: CPL06 | Sub-CPMK: 2.1

Signature Model: Measurement Alignment Model

Problem → Concept → Variable → Metric → Data → Result

Konsep Inti:

• Concept → Metric (operationalization)
• Jenis data: nominal, ordinal, interval, ratio
• Metric selection: sesuai problem, representatif, sensitif
• Multi-metric evaluation
• Data quality: completeness, consistency, validity, representativeness

Case Study:

1. Basic: Accuracy tinggi, dataset imbalance → metric menipu
2. Advanced: User satisfaction vs system metric → metric teknis ≠ user experience

Final Statement:

"Penelitian yang baik bukan hanya mengukur, tetapi memastikan bahwa apa yang diukur benar-benar merepresentasikan realitas."

Output Praktis: Definisi variabel + metrik + tipe data + justifikasi

---

BAB 6 — System Design sebagai Experimental Artifact (M6)

CPMK: CPMK02 | CPL: CPL06 | Sub-CPMK: 2.2

Signature Model: System as Experiment Model

Research Question → Variable → System Component → Experimental Setup → Output (measured)

Konsep Inti:

• Sistem bukan tujuan → alat uji hipotesis
• Mapping RQ → system component
• 4 prinsip: Traceability, Modularity, Controllability, Measurability
• Control & isolation variabel

Case Study:

1. Basic: Model ML tidak bisa diuji (monolith, tidak modular)
2. Advanced: Multiple feature change, no clear impact

Final Statement:

"Dalam penelitian, sistem bukan dibangun untuk digunakan, tetapi untuk membuktikan sesuatu secara ilmiah."

Output Praktis: Diagram arsitektur + mapping ke variabel eksperimen

---

BAB 7 — Experimental Design & Validity (M7)

CPMK: CPMK02 | CPL: CPL06 | Sub-CPMK: 2.3

Signature Model: Experimental Validity Model

RQ → Hypothesis → Variable Design → Controlled Experiment → Data → Analysis → Conclusion (Validity Level)

Konsep Inti:

• Eksperimen = menguji hubungan sebab-akibat (causality)
• Korelasi ≠ kausalitas
• 4 validitas: internal, external, construct, conclusion
• Jenis eksperimen: comparison, ablation study, parameter study
• Controlled experiment: ubah 1, kontrol sisanya

Case Study:

1. Basic: Eksperimen tanpa kontrol → semua variabel berubah
2. Advanced: Baseline tidak fair → perbandingan bias

Final Statement:

"Eksperimen bukan sekadar menjalankan sistem, tetapi membangun bukti yang dapat dipercaya."

Output Praktis: Dokumen desain eksperimen lengkap (variabel, skenario, validity, baseline)

---

BAGIAN III — EXECUTION (Executing Phase)

---

BAB 8 — Proposal & Checkpoint / UTS

Catatan: Bab ini bersifat integratif — merangkum Bab 1–7 ke dalam proposal. Konten utama: template proposal + rubrik penilaian + tips defense.

---

BAB 9 — Implementation & Environment (M9)

CPMK: CPMK03 | CPL: CPL06 | Sub-CPMK: 3.1

Signature Model: Reproducible Implementation Model

Experiment Design → Implementation → Environment Setup → Execution Consistency → Reproducibility → Trustworthy Result

Konsep Inti:

• Implementasi ≠ coding biasa → memastikan konsistensi & reproducibility
• Environment control: hardware, software, dependency, OS
• Repeatability vs Reproducibility
• Dokumentasi wajib: setup, parameter, dataset
• Best practice: version control, config logging, environment isolation

Output Praktis: Dokumentasi setup + README eksperimen

---

BAB 10 — Experiment Execution & Data Collection (M10)

CPMK: CPMK03 | CPL: CPL06 | Sub-CPMK: 3.2

Signature Model: Experiment Execution Pipeline

Design → Execution Plan → Controlled Execution → Data Collection → Data Logging → Dataset for Analysis

Konsep Inti:

• Execution plan: skenario, jumlah run, variasi parameter
• Multiple run wajib (bukan single run)
• Data logging: ID, timestamp, parameter, result, environment
• Konsistensi eksekusi

Output Praktis: Log eksperimen + dataset mentah

---

BAB 11 — Data Validation & Integrity (M11)

CPMK: CPMK03 | CPL: CPL06 | Sub-CPMK: 3.3

Signature Model: Data Trust Model

Raw Data → Data Cleaning → Consistency Check → Validation → Trusted Data → Analysis Ready

Konsep Inti:

• 4 pilar data quality: accuracy, consistency, completeness, validity
• Validation process: format → range → consistency → logic
• Anomaly detection: outlier, missing, inconsistency
• Data vs experiment alignment

Output Praktis: Dataset tervalidasi + catatan anomali

---

BAGIAN IV — ANALYSIS & SCIENTIFIC COMMUNICATION

---

BAB 12 — Result Presentation & Visualization (M12)

CPMK: CPMK04 | CPL: CPL03 | Sub-CPMK: 4.1

Signature Model: Data → Insight Model

Validated Data → Structured Presentation → Visualization → Pattern Recognition → Insight

Konsep Inti:

• Tabel (presisi) vs grafik (insight)
• Mapping: tujuan → jenis visualisasi
• Multi-metric presentation
• Visualization bias: scale manipulation, selective data, misleading

Output Praktis: Tabel + grafik + observasi awal

---

BAB 13 — Data Preprocessing (M13)

CPMK: CPMK04 | CPL: CPL03 | Sub-CPMK: 4.2

Signature Model: Data Refinement Pipeline

Raw Data → Cleaning → Transformation → Normalization → Processed Data → Analysis Ready

Konsep Inti:

• Cleaning: missing values, duplicates, errors
• Transformation: encoding, aggregation, feature creation
• Normalization & scaling
• 4 prinsip: consistency, transparency, reproducibility, minimal distortion

Output Praktis: Dataset bersih + dokumentasi preprocessing

---

BAB 14 — Data Analysis, Interpretation & Failure Analysis (M14)

CPMK: CPMK04 | CPL: CPL03 | Sub-CPMK: 4.3

Signature Model: Data → Knowledge Model

Data → Analysis → Interpretation → Explanation → Knowledge

Konsep Inti:

• Analysis vs interpretation ("apa yang terjadi" vs "mengapa terjadi")
• Link wajib: result → RQ → hypothesis → conclusion
• Failure analysis: kegagalan = sumber insight
• Limitation: wajib diakui
• Statistical + logical reasoning

Output Praktis: Hasil analisis + interpretasi + failure analysis + limitation

---

BAB 15 — Scientific Writing (M15)

CPMK: CPMK05 | CPL: CPL02 | Sub-CPMK: 5.1

Signature Model: Scientific Argument Flow

Problem → Gap → RQ → Method → Result → Analysis → Conclusion → Contribution

Konsep Inti:

• Penulisan = menyusun argumen ilmiah (bukan dokumentasi)
• IMRAD + extension
• Logical flow: Why → What → How → Result → So What
• Konsistensi antar bagian (problem↔RQ↔method↔result↔conclusion)
• Writing quality: clarity, precision, conciseness, consistency

Output Praktis: Laporan ilmiah lengkap (IMRAD)

---

BAB 16 — Presentation & Defense (M16)

CPMK: CPMK06 | CPL: CPL02 | Sub-CPMK: 6.1

Signature Model: Scientific Defense Model

Research Work → Presentation → Questioning → Defense (Argumentation) → Evaluation → Acceptance

Konsep Inti:

• Presentasi = simulasi peer-review langsung
• Argumentation: claim + evidence + reasoning
• Anticipating questions: problem, gap, method, metric, result
• Handling questions: langsung, data-based, akui keterbatasan

Output Praktis: Slide + defense argument + jawaban berbasis data

---

Dokumen ini merupakan peta navigasi untuk seluruh proses penulisan buku. Terakhir diperbarui: 30 Maret 2026