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All Journal Jurnal Ergonomi dan K3 INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY
Yudhistira, Titah
Fakultas Teknologi Industri Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Energy Environmental Science Industrial & Manufacturing Engineering Public Health

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Pengaruh Gender dan Usia Pada Memori terhadap Iklan Banner Titah Yudhistira; Tota Simatupang
Jurnal Ergonomi dan K3 Vol 6, No 1 (2021): MARET 2021
Publisher : Perhimpunan Ergonomi Indonesia

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Abstract

Bidang kajian ergonomi kognitif, khususnya terkait dengan interaksi-manusia komputer, menjadi semakin penting dan relevan dewasa ini. Dengan berkembangnya pertukaran informasi dan perdagangan elektronik melalui internet, semakin banyak informasi terkait produk dan jasa yang disampaikan melalui iklan banner di berbagai website. Salah satu ukuran performansi dari iklan banner adalah konsumen dapat mengingat informasi, khususnya merek, yang diiklankan. Untuk itu perlu diketahui bagaimana perbedaan karakteristik konsumen memengaruhi memori akan banner tersebut. Dalam penelitian ini karakteristik konsumen yang diteliti difokuskan pada gender dan usia. Untuk menjawab efek dari gender dan usia terhadap kemampuan melakukan recall dan recognition terhadap banner dan merek di dalam banner tersebut, digunakan data hasil survei di tujuh negara yang melibatkan 21 kampanye iklan banner dan 8736 responden. Hasil pengolahan data menggunakan multilevel structural equation model menunjukkan bahwa pria memiliki kemampuan recall dan recognition pada banner dan merek lebih baik dibandingkan perempuan. Selain itu, sejalan dengan meningkatnya usia terdapat penurunan yang signifikan pada memori. Di dalam makalah ini juga didiskusikan alternatif penjelasan atas temuan ini serta implikasinya bagi rancangan banner yang lebih efektif dalam meningkatkan memori konsumen.
Leveraging Time Series Analytics for Sustainable Urban and Environmental Development: A Global SDG Trajectory Framework Gama Harta Nugraha Nur Rahayu; Kadarsah Suryadi; Titah Yudhistira; Ferani Eva Zulvia; Rohollah Ghasemi; Muhammad Rizki
INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY VOLUME 9, NUMBER 1, APRIL 2026
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/urbanenvirotech.v9i1.24113

Abstract

Achieving the Sustainable Development Goals (SDGs), including the environmental goals (7, 12, 13, 14, and 15), requires analytical methods that capture long-term national trajectories. Existing studies have not widely used approaches that integrate similarity measurement, clustering, and forecasting. Aims: This study proposes a hybrid framework of similarity measurement, clustering, and forecasting for global SDG trajectories. By comparing cluster structures from historical SDG data with those generated using historical and forecasted trajectories, the study identifies how countries’ development patterns may shift over time. Methodology and results: The framework integrates time-series clustering and predictive modeling. Clustering utilizes both historical data from 2000 to 2025 and a combination of historical and forecasted values, employing DTW variants to measure similarities across 167 countries. K-Means, Agglomerative, and Spectral clustering algorithms are evaluated to identify the most coherent grouping. ARIMA, LSTM, GRU, and Prophet forecasting algorithms are assessed to determine the most accurate SDG score projections for 2026 to 2030. Results show that Soft DTW with K-Means produces the most coherent clusters, and ARIMA yields the lowest forecasting errors. The clustering reveals three groups representing different development pathways: strong SDG index but uneven environmental performance; strong environmental scores despite low SDG index performance; and high SDG performance with moderate environmental outcomes. These patterns highlight diverse sustainability trajectories and the multidimensional nature of global development progress. Conclusion, significance, and impact study: The study validates elastic similarity measures integrated with clustering and forecasting and provides a data-driven decision support framework to improve policy coherence and strengthen international cooperation.
Co-Authors Ferani Eva Zulvia Gama Harta Nugraha Nur Rahayu Kadarsah Suryadi Muhammad Rizki Rohollah Ghasemi Tota Simatupang