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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Dwi Prastyo, Dedy
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Computer Science & IT Engineering

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Perbandingan Complexity Invariant Distance (CID) dan Dynamic Time Warping (DTW) dalam Analisis Klaster Deret Waktu pada Nilai Tukar Petani di Indonesia Fathiyaturrahmi, Laila; Andriano; Almiatus Soleha, Harista; Dwi Prastyo, Dedy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Analisis klaster yang merupakan bagian dari data mining yang membagi data kedalam beberapa kelompok berdasarkan kedekatan karakteristik tertentu. Konsep utama dalam klaster adalah memaksimalkan kedekatan data di dalam klaster dan meminimalkan kesamaan data antar klaster. Analisis klaster juga bisa digunakan pada berbagai jenis data termasuk data deret waktu.  Pengukuran kesamaan menjadi hal yang utama pada analisis klaster. Metode yang bisa digunakan dalam pengukuran jarak yaitu Complexity Invariant Distance (CID) dan Dynamic Time Warping (DTW). Analisis pengukuran jarak CID dan DTW dapat digunakan pada pengelompokkan data deret waktu salah satunya pada data Nilai Tukar Petani (NTP). NTP dapat menggambarkan daya beli petani karena diperoleh dari perbandingan indeks harga yang diterima petani dibandingkan dengan yang harus dibayarnya, atau dapat dinyatakan sebagai kemampuan petani dalam memnuhi kebutuhan sehari-hari dari hasil pertanian. Sehingga dilakukan analisis untuk membandingkan metode pengukuran jarak CID dan DTW pada klastering data deret waktu pada nilai tukar petani pada 34 Provinsi di Indonesia. Hasil analisis yang diakukan menunjukkan klaster terbaik adalah pengklasteran dengan banyak klaster dua (k=2) menggunakan ukuran jarak CID terlihat dari nilai silhouette 0.8776 yang lebih tinggi dibandingkan klaster lain. Dimana klaster satu terdiri dari 25 Provinsi dan klaster dua terdiri dari 9 Provinsi.   Abstract Cluster analysis is a part of data mining which divides data into several groups based on the proximity of certain characteristics. The main concept in clusters is to maximize data similarity within clusters and minimize data similarity between clusters. Cluster analysis can also be used on various types of data, including time series data. Measuring similarity is the main thing in cluster analysis. The methods that can be used to measure distance are Complexity Invariant Distance (CID) and Dynamic Time Warping (DTW). CID and DTW distance measurement analysis can be used to group time series data, one of which is Farmer’s Terms of Trade (NTP) data. The farmer's terms of trade is a ratio between the price index received by farmers and the price index paid by farmers. In general, it can be interpreted as the farmer's ability to meet their daily needs through agricultural products. So an analysis was carried out to compare the CID and DTW distance measurement methods in clustering time series data on farmer’s terms of trade according to 34 provinces in Indonesia. The results of this analysis show that the best cluster is clustering with two clusters (k=2) using the CID distance measure because it has the highest silhouette coefficient value, namely 0.8776. Where cluster one consists of 25 provinces and cluster two consists of 9 provinces.
AI-Driven Transformation in the Textile Industry: A Bibliometric Analysis and Scoping Review Pitarsi Dharma, Fajar; Laksono Singgih, Moses; Dwi Prastyo, Dedy
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.516

Abstract

Artificial Intelligence (AI) is rapidly reshaping the global textile industry, driving efficiency, precision, and sustainability across its value chain. Yet despite growing enthusiasm, the integration of AI remains fragmented, with limited statistical understanding of where, how, and why these technologies take root. This study addresses that gap by combining bibliometric network analysis and systematic scoping review to map and statistically interpret two decades (2003–2023) of research on AI applications in textiles. Using association strength normalization, VOS modularity clustering, and thematic centrality density mapping, we identified eight manufacturing clusters ranging from fabric defect detection and supply chain optimization to textile waste management and sustainability that structure the field. The novelty of this work lies in repositioning bibliometric analysis as a statistical instrument, not merely a descriptive tool. Keyword co-occurrence networks and citation trajectories are translated into evidence-based research agendas, connecting cluster signals to methodological pathways such as regression modeling, support vector machines, neural networks, and hybrid ML-statistical frameworks. This statistical logic is used to surface gaps. Particularly in empirical validation, predictive modeling, and cross-cluster integration and to chart future directions for data-driven textile innovation. By grounding future agendas in measurable statistical patterns rather than narrative interpretation alone, this study offers a rigorous analytical framework that links research structure to methodological opportunity. The resulting roadmap invites scholars and practitioners to bridge AI, textile engineering, and applied statistics, shifting the field from fragmented experimentation toward coherent, evidence-based innovation.
Co-Authors Almiatus Soleha, Harista Andriano Fathiyaturrahmi, Laila Laksono Singgih, Moses Pitarsi Dharma, Fajar