CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics

Integrasi Platform Desain Digital dalam Pembelajaran Berbasis Teknologi untuk Persiapan Karier

2026-04-14T11:24:45+07:00

This study aims to analyze the effectiveness of integrating the digital design platform Canva into a technology-based learning system to support career preparation through curriculum vitae (CV) writing training. The background of this study is based on the importance of user performance in producing professional English digital CVs as part of the job recruitment process, where many users still face difficulties in terms of document structure, appropriate English usage, and systematic visual presentation. This research employed a quantitative approach using a pre-experimental design with a one-group pretest–posttest model involving 30 participants. The research procedure consisted of three stages: a pre-test to assess initial user performance, technology-based training through the use of the Canva platform, and a post-test to evaluate improvements in the quality of the generated output. Data were collected using a CV assessment rubric covering document structure, English language use, clarity of information, and visual design quality, as well as a questionnaire to measure user perceptions of the use of digital platforms. The results indicate a significant improvement across all assessment aspects, with the highest increase observed in visual design quality. In addition, users demonstrated highly positive perceptions of Canva, as it enhanced creativity, improved efficiency in document preparation, and enriched user interaction with the system. These findings suggest that the integration of digital design platforms contributes significantly to improving user output quality and the effectiveness of technology-based processes. Therefore, the use of digital platforms within learning systems can serve as an innovative approach to support the development of professional skills and career readiness in the digital era.

Pemilihan Tempat Menginap di Kota Tomohon Menggunakan Metode Entropy-TOPSIS Berbasis Data Online Travel Agent

2026-05-04T23:01:21+07:00

Perkembangan platform online travel agent telah mengubah cara wisatawan mencari, membandingkan, dan memilih tempat menginap. Informasi digital seperti harga, rating, jumlah ulasan, fasilitas yang terlihat, dan urutan popularitas membantu wisatawan menilai berbagai alternatif, tetapi banyaknya informasi juga dapat menyulitkan proses pengambilan keputusan. Penelitian ini bertujuan membangun model Sistem Pendukung Keputusan pemilihan tempat menginap di Kota Tomohon menggunakan metode Entropy-TOPSIS berbasis data online travel agent. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan data sekunder yang dikumpulkan pada 1 Mei 2026. Sebanyak 12 alternatif penginapan dianalisis berdasarkan lima kriteria, yaitu harga, rating, jumlah ulasan, fasilitas terlihat, dan urutan popularitas. Metode Entropy digunakan untuk menentukan bobot kriteria secara objektif berdasarkan variasi data, sedangkan TOPSIS digunakan untuk menentukan peringkat alternatif berdasarkan kedekatan terhadap solusi ideal positif dan jarak terhadap solusi ideal negatif. Hasil penelitian menunjukkan bahwa jumlah ulasan memiliki bobot tertinggi sebesar 0,3602, diikuti fasilitas terlihat sebesar 0,3362, urutan popularitas sebesar 0,1670, harga sebesar 0,1332, dan rating sebesar 0,0034. Hasil TOPSIS menunjukkan bahwa Jhoanie Hotel memperoleh nilai preferensi tertinggi sebesar 0,7502, diikuti Grand Master Villa Tomohon sebesar 0,4799 dan Hotel Villa Emitta sebesar 0,4078. Temuan ini menunjukkan bahwa pemilihan tempat menginap perlu mempertimbangkan beberapa kriteria secara bersamaan, bukan hanya harga atau rating, sehingga rekomendasi yang dihasilkan lebih objektif.

Two-Stage Entropy-Topsis Model For Hotel And Ecotourism Destination Selection in Bitung, North Sulawesi, Indonesia

2026-05-04T23:05:43+07:00

This study develops a Two-Stage Entropy-TOPSIS model for hotel and ecotourism destination selection in Bitung, North Sulawesi, Indonesia. The model reflects a realistic tourist decision process, where tourists first select a hotel from the airport and then choose ecotourism destinations from the selected hotel as the starting point. Data were collected from Google Maps, including rating, number of reviews, distance, travel time, hotel facility score, and ecotourism suitability score. In the first stage, eight hotel alternatives were evaluated based on Google Maps rating, number of reviews, distance from Sam Ratulangi Airport, travel time from the airport, and facility score. The Entropy method was used to determine objective criteria weights, while TOPSIS was applied to rank alternatives. The results showed that favehotel Bitung ranked first with a preference value of 0.9999 and was selected as the origin point for the second stage. In the second stage, ecotourism destinations were evaluated using the selected hotel as the starting point. The main calculation included destinations with complete road-based accessibility data. The results showed that Kebun Binatang Tandurusa ranked first with a preference value of 0.9989, followed by Batuangus Beach, Pantai Tanjung Merah, and Pantai Lilang. These findings indicate that the proposed model can provide structured, sequential, and data-driven recommendations for tourism decision-making based on Google Maps data

Aplikasi Web Prediksi Cuaca Berbasis API BMKG dengan Pemilahan Wilayah Pesisir dan Non-Pesisir: Studi Kasus Kota Manado

2026-05-04T23:10:16+07:00

Cuaca merupakan faktor penting yang memengaruhi aktivitas masyarakat, terutama bagi nelayan tradisional dan pemancing di wilayah pesisir Kota Manado. Ketersediaan informasi prakiraan cuaca yang akurat, mudah dipahami, dan relevan hingga tingkat kelurahan diperlukan untuk mendukung keselamatan, efisiensi, serta perencanaan aktivitas harian. Penelitian ini bertujuan mengembangkan aplikasi web prediksi cuaca berbasis API BMKG dengan fitur pemilahan wilayah pesisir dan non-pesisir pada studi kasus Kota Manado. Metode penelitian menggunakan pendekatan rekayasa perangkat lunak berbasis integrasi data terbuka, yang meliputi identifikasi kebutuhan pengguna, perancangan arsitektur sistem, pengelompokan wilayah berdasarkan kode ADM4, implementasi aplikasi web, serta pengujian fungsional dan usability sederhana. Aplikasi dikembangkan menggunakan Flask berbasis Python sebagai server, serta HTML, CSS, dan JavaScript sebagai antarmuka pengguna. Data prakiraan cuaca diperoleh dari API publik BMKG dan ditampilkan dalam bentuk visual berdasarkan interval waktu tiga jam. Hasil pengujian menunjukkan bahwa aplikasi mampu mengambil data secara real-time, menampilkan informasi cuaca secara konsisten sesuai data JSON, serta memberikan waktu respons yang cepat. Uji coba pada pengguna menunjukkan bahwa antarmuka aplikasi mudah dipahami, termasuk oleh nelayan tradisional. Penelitian ini memberikan kontribusi dalam pemanfaatan open data BMKG untuk penyajian prakiraan cuaca lokal yang lebih kontekstual, khususnya melalui pemilahan wilayah pesisir dan non-pesisir. Pengembangan selanjutnya dapat diarahkan pada integrasi data pasang surut, edukasi cuaca maritim, serta rekomendasi waktu melaut

Multi-Criteria Approach in Selecting Optimal Retail Store Locations Using Integration of LODECI and ERVD Methods

2026-05-05T18:30:14+07:00

Selecting an optimal retail store location is a complex multi-criteria decision-making problem involving conflicting factors such as cost, accessibility, demographics, competition, and market potential. This study proposes an integrated approach combining the LODECI (Logarithmic Decomposition of Criteria Importance) method and the ERVD (Election based on Relative Value Distances) method to improve the objectivity, accuracy, and stability of decision results. LODECI is applied to determine criterion weights based on data distribution characteristics using logarithmic decomposition, reducing subjectivity in the weighting process. Subsequently, ERVD is utilized to evaluate and rank alternatives based on their relative distances to ideal and non-ideal solutions, enabling a more comprehensive assessment of each location. The research results show that the proposed integration effectively produces consistent and discriminative rankings, with Location F having a value of 0.9759 identified as the best alternative, followed by Location E with a value of 0.8461 and Location C with a value of 0.7882. Overall, the integration of LODECI and ERVD provides a robust decision-making framework that enhances reliability in selecting optimal retail store locations in complex and heterogeneous environments.