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All Journal Teknologika Bulletin of Intelligent Machines and Algorithms
Tarsinah Sumarni
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Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mechanical Engineering Other

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PENERAPAN KANSEI ENGINEERING PADA PERANCANGAN USER INTERFACE WEBSITE DIGITECT UNIVERSITY Fahmi, Fahmi; Tarsinah Sumarni
Jurnal Teknologika Vol 13 No 2 (2023): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v13i2.316

Abstract

Seiring dengan semakin pesatnya perkembangan teknologi diantaranya perkembangan teknologi website saat ini maka semakin mudah akses terhadap informasi yang dibutuhkan. Website merupakan salah satu faktor penting bagi setiap organisasi khususnya lembaga pendidikan karena dapat memberikan kemudahan dan dapat menyajikan banyak informasi yang dibutuhkan oleh masyarakat. Tampilan website menjadi salah satu faktor penting oleh karena itu dibutuhkan perancangan user interface yang menarik dan efektif agar pesan dapat tersampaikan dengan baik. Dalam proses desain sebuah produk yang penggunanya adalah manusia, maka tidak terlepas dari ilmu Human Computer Interaction (HCI). Thomas T. Hewett dan rekan-rekannya dalam organisasi Special Interest Group on Computer-Human Interaction (SIGCHI) mendefinisikan HCI sebagai disiplin ilmu yang berkaitan dengan desain, evaluasi dan implementasi sistem komputasi interaktif untuk penggunaan oleh manusia dan untuk studi seputar fenomena yang ada di sekitarnya (Hewett, 2009). Penelitian ini telah mengimplementasikan Kansei Engineering untuk menghasilkan rekomendasi konsep desain tampilan website Digitect University. Daftar kansei word yang digunakan sebanyak 15 kata dan spesimen website yang dipilih sebanyak 15 spesimen. Penelitian ini melibatkan 70 partisipan yang terdiri dari6 0orang mahasiswa dan 10 orang dosen. Hasil kuesioner dari partisipan kemudian diolah dengan menggunakan analisis statistik multivariat yakni Cronbach’s Alpha, Coefficient Correlation Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA) dan analisis Partial Least Square (PLS). Penelitian ini menghasilkan produk desain dengan konsep desain untuk seluruh partisipan menghasilkan konsep emosi “MEWAH”. Kata kunci: Kansei Engineering, User Interface, Perasaan, Perguruan Tinggi, Website
An LSTM-Based Approach for Short-Term Solar Power Forecasting with Diurnal and Intra-Day Variability Darsiti, Darsiti; Tarsinah Sumarni; Fahmi Abdullah; Budiman
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 2 (2026): BIMA January 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i2.7

Abstract

The increasing penetration of solar photovoltaic (PV) systems into modern power grids demands accurate, reliable short-term power forecasting to ensure operational stability and efficient energy management. However, solar power generation exhibits strong nonlinearity, non-stationarity, and pronounced temporal dependencies, driven by diurnal cycles and rapid environmental variations, which pose significant challenges for conventional forecasting approaches. This study aims to develop an efficient Long Short-Term Memory (LSTM)-based framework for short-term DC power prediction that effectively captures the temporal dynamics of solar power generation while maintaining low computational complexity. The proposed approach utilizes historical power and operational data collected from two utility-scale solar PV plants in India. A comprehensive time-series preprocessing pipeline is applied, including temporal feature extraction, categorical transformation, and Min–Max normalization. Multiple LSTM architectures with varying numbers of hidden units are systematically evaluated to identify an optimal balance between model complexity and predictive performance. Model training is conducted using the Adam optimizer with exponential learning rate decay and early stopping to prevent overfitting. Experimental results demonstrate that the proposed LSTM model with a 25–50 unit configuration achieves the best performance, yielding a test Mean Squared Error of 51.92 and a prediction error of only 0.36%. Visual and quantitative analyses confirm that the model accurately reconstructs diurnal patterns and intra-day fluctuations, with strong generalization capability on unseen data. The findings indicate that a carefully configured LSTM can deliver high forecasting accuracy without relying on complex hybrid architectures or additional weather data, making it suitable for practical solar energy management applications.
Co-Authors Budiman Darsiti, Darsiti Fahmi Abdullah Fahmi Fahmi