Penerapan Metode TOPSIS untuk Memilih Laptop Terbaik Sesuai Kebutuhan Konsumen
Bibliographic record
Abstract
Pemilihan laptop yang sesuai dengan kebutuhan konsumen memerlukan pendekatan yang terstruktur karena banyaknya alternatif dan variasi spesifikasi. Penelitian ini menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk membantu pengambilan keputusan yang objektif. Kriteria yang digunakan meliputi RAM, berat, IPS, CPU Brand, dan SSD. Data penelitian diambil dari dataset publik di Kaggle yang relevan untuk evaluasi alternatif. Tahapan metode meliputi normalisasi matriks keputusan, penghitungan matriks solusi ideal positif dan negatif, serta perhitungan nilai preferensi untuk menentukan peringkat setiap alternatif. Hasil penelitian menunjukkan bahwa laptop Toshiba (V8) memiliki nilai preferensi tertinggi (1,0000), diikuti oleh Asus (V3) dan Lenovo (V6) pada posisi kedua. Penelitian ini membuktikan bahwa metode TOPSIS efektif untuk mendukung pengambilan keputusan yang sistematis dan dapat diterapkan pada berbagai kasus serupa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".