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All Journal Jurnal Computer Science and Information Technology (CoSciTech) JPNM : Jurnal Pustaka Nusantara Multidisiplin
Maulana, Irfan Zidny
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Perancangan Antarmuka Aplikasi Booking Jasa “GOSERVICE” Berbasis Web Menggunakan Prototyping Farzana, Adelia Rafa; Wjianarko, Christoporus Dimas; Anggoro, Aloysius Gonzaga Verrel Evan; Maulana, Irfan Zidny; Arifian, Rizki Mukti; Irawan, Bambang
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 3 No. 3 (2025): October : Jurnal Pustaka Nusantara Multidisiplin
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v3i3.619

Abstract

Di era digital saat ini, kebutuhan akan layanan pemesanan secara online semakin meningkat, terutama untuk jasa harian seperti salon, kebersihan, dan servis kendaraan. Masyarakat kini menginginkan kemudahan dalam mengakses berbagai layanan tanpa perlu datang langsung ke lokasi penyedia jasa. Namun, masih banyak platform digital yang belum menyediakan antarmuka pengguna (user interface) yang ramah pengguna dan sesuai dengan kebutuhan mereka. Oleh karena itu, penelitian ini bertujuan untuk merancang antarmuka aplikasi booking jasa berbasis web menggunakan pendekatan metode prototyping, guna menghasilkan tampilan yang interaktif, responsif, dan mudah dipahami. Proses perancangan dilakukan secara bertahap, dimulai dari identifikasi kebutuhan pengguna, pembuatan sketsa antarmuka awal, hingga pembuatan prototipe dan pengujian oleh calon pengguna. Metode prototyping dipilih karena mendukung pengembangan sistem yang cepat, fleksibel, dan berorientasi pada kebutuhan pengguna. Selain itu, pendekatan ini memungkinkan dilakukannya evaluasi dan perbaikan secara berulang berdasarkan umpan balik langsung dari pengguna, sehingga desain akhir menjadi lebih optimal. Hasil penelitian berupa prototipe antarmuka web yang sederhana namun fungsional, memenuhi kriteria responsif di berbagai perangkat, mudah digunakan oleh pengguna awam, dan memiliki struktur navigasi yang jelas. Pengujian awal dengan metode observasi menunjukkan bahwa calon pengguna dapat memahami alur pemesanan dengan mudah dan memberikan respon positif terhadap tampilan serta kemudahan penggunaan sistem. Temuan ini mengindikasikan bahwa pendekatan prototyping dalam perancangan UI/UX dapat meningkatkan pengalaman pengguna secara keseluruhan dan mempercepat proses pengembangan aplikasi berbasis web yang efisien dan ramah pengguna.
Klasifikasi kecanduan smartphone mahasiswa universitas esa unggul menggunakan machine learning dan sas-sv Verrel, Anggoro; Maulana, Irfan Zidny; Liu, Vico Andrean; Prabowo, Ary
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9817

Abstract

The digital era has made smartphones an inseparable part of students' lives, but it also raises the risk of addiction that negatively impacts academic achievement and mental health. This research aims to develop and evaluate machine learning models capable of classifying the level of smartphone addiction among Esa Unggul University students. Data were collected from 32 student respondents through an online questionnaire adopting the validated psychometric instrument, the Smartphone Addiction Scale-Short Version (SAS-SV). Addiction levels were categorized into two classes: 'High', which refers to the gender-specific addiction risk threshold from Kwon et al. (2013), and 'Moderate', which includes scores below that threshold. Four classification algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest—were implemented to compare their performance. To address class imbalance in the data, the SMOTE oversampling technique was applied to the training data. Model evaluation was based on accuracy, precision, recall, and F1-score. The research results show that the Logistic Regression model achieved the best performance with an accuracy of 1.0000 and an average F1-score of 1.00 on the test data. Nevertheless, it should be noted that this perfect performance was obtained from a very limited test data size (8 samples), so generalization requires further validation. Feature importance analysis from the Random Forest model identified that the question related to Planned tasks/work often interrupted by smartphone use (Q0) was the most dominant predictor. These results indicate that machine learning models based on psychometric scales have initial potential as a screening and exploratory tool to identify students at risk of smartphone addiction, but require extensive development and validation on larger data before practical implementation.
Co-Authors Anggoro, Aloysius Gonzaga Verrel Evan Arifian, Rizki Mukti Farzana, Adelia Rafa Liu, Vico Andrean Prabowo, Ary Sembiring, Rinawati Verrel, Anggoro Wjianarko, Christoporus Dimas