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All Journal Techno.Com: Jurnal Teknologi Informasi jurnal syntax admiration
Asriyanik Asriyanik
Universitas Muhammadiyah Sukabumi, indonesia
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Implementasi Metode Waterfall dalam Pengembangan Aplikasi Toko Online Bebasis Web di Cv Aishastore.Id Rais Akbar Wibowo; Didik indrayana; Asriyanik Asriyanik
Jurnal Syntax Admiration Vol. 6 No. 1 (2025): Jurnal Syntax Admiration
Publisher : Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jsa.v6i1.2058

Abstract

This study aims to design and develop a web-based online store application for CV AishaStore.id, a small and medium enterprise specializing in selling herbal products. The application is designed using the Software Development Life Cycle (SDLC) with the Waterfall model, which includes phases such as requirements analysis, system design, application development, testing, and maintenance. Utilizing programming languages such as PHP, JavaScript, and CSS, along with MySQL as the database, the application offers key features including stock management, product catalog, transaction system, and user account management. Testing was conducted using the Black Box Testing method to ensure all features function according to user needs. The results of the study indicate that the application successfully improves operational efficiency by automating stock management, simplifying transaction recording, and providing a responsive and user-friendly shopping experience. Additionally, the application expands market reach and supports better decision-making through a structured system. This study is expected to serve as an effective solution for SMEs in addressing digital challenges, with further development potential such as integration with mobile platforms, advanced analytics features, and enhanced data security to broaden functionality and user convenience.
Pendekatan Random Forest untuk Klasifikasi Tingkat Burnout Mahasiswa Berdasarkan Pola Aktivitas Harian Tanaya Salsabila Arliansyah; Asep Budiman Kusdinar; Asriyanik Asriyanik
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16989

Abstract

Burnout akademik merupakan kondisi kelelahan fisik, emosional, dan mental akibat tingginya tuntutan kuliah yang dapat menurunkan motivasi serta kualitas hidup mahasiswa. Oleh karena itu, diperlukan pendekatan yang mampu memberikan indikasi awal tingkat burnout secara lebih cepat dan objektif. Penelitian ini bertujuan membangun sistem klasifikasi tingkat burnout mahasiswa berdasarkan pola aktivitas harian menggunakan algoritma Random Forest dengan kerangka CRISP-DM. Data penelitian diperoleh melalui kuesioner daring dari 1.026 mahasiswa Universitas Muhammadiyah Sukabumi. Variabel prediktor meliputi jam tidur, waktu istirahat, intensitas screen time, keteraturan pola tidur, dan beban akademik, dengan target klasifikasi tingkat burnout rendah, sedang, dan tinggi. Tahap prapengolahan data meliputi transformasi data, pelabelan, penanganan class imbalance menggunakan SMOTE, serta hyperparameter tuning menggunakan GridSearchCV. Model divalidasi menggunakan Stratified K-Fold Cross-Validation dan dievaluasi dengan pembagian data 80:20. Hasil penelitian menunjukkan bahwa model Random Forest memperoleh akurasi 88,83%, dengan nilai weighted average precision, recall, dan F1-score masing-masing 0,89. Model berhasil diimplementasikan ke dalam aplikasi berbasis web menggunakan Hugging Face untuk memberikan indikasi awal tingkat burnout mahasiswa. Hasil tersebut menunjukkan bahwa algoritma Random Forest memiliki performa yang baik dalam mengklasifikasikan tingkat burnout.   Kata Kunci - Burnout Akademik, Klasifikasi, Machine Learning, Pola Aktivitas Harian, Random Forest  
Co-Authors Asep Budiman Kusdinar Didik indrayana Rais Akbar Wibowo Tanaya Salsabila Arliansyah