PEMETAAN DAN ANALISIS RANTAI NILAI (VALUE CHAIN) PRODUK BATIK PADA SENTRA INDUSTRI BATIK DI BAYAT, KLATEN
Bibliographic record
Abstract
Batik menjadi salah satu warisan budaya dunia dari Indonesia yang wajib dilindungi dan dilestarikan. Dalam menghadapi tingkat persaingan perdagangan global, industri dituntut memiliki daya saing yang tinggi dan produk yang berkualitas. Penelitian ini bertujuan untuk menganalisa dan memetakan rantai nilai dari produk batik tulis pada sentra industri batik di Bayat, Klaten, Jawa Tengah. Analisis rantai nilai juga berfungsi untuk mengidentifikasi tahap-tahap rantai nilai di mana industri dapat meningkatkan nilai tambah (value added) bagi pelanggan dan mengefisiensikan biaya yang dikeluarkan. Industri mampu menjadi lebih kompetitif melalui efisiensi biaya atau peningkatan nilai tambah (value added) yang di peroleh melalui aktivitas rantai nilainya. Metode yang digunakan dalam penelitian ini yaitu metode analisis dan pemetaan rantai nilai Porter (1985) dan digabungkan dengan metode menurut Pearce & Robinson (2009). Pengumpulan data dilakukan dengan mengadakan observasi langsung, wawancara, kuesioner, dokumentasi dan studi pustaka. Pengambilan sampel menggunakan metode snowball sampling. Batik is one of the world's cultural heritages from Indonesia that must be protected and preserved. To face the level of global trade competition, the industry is required to have high competitiveness and quality products. This study aims to analyze and map the value chain of batik tulis products at the batik industry center in Bayat, Klaten, Central Java. Value chain analysis also uses to identify the stages of the value chain where the industry can increase value added for customers and cost efficiency. The industry becomes more competitive through cost efficiency or increased value added (value added) through value chain activities. The method used in this research is the value chain analysis and mapping method according to Porter's (1985) and combined with the method according to Pearce & Robinson (2009). Data were collected by conducting direct observations, interviews, questionnaires, documentation, and literature studies. Sampling used the snowball sampling method.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".