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All Journal Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Electronic Integrated Computer Algorithm Journal
Noorachmad Muttaqin, Alif
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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PENERAPAN LAW OF UX DALAM ANALISIS DESAIN ANTARMUKA APLIKASI SHOPEE Noorachmad Muttaqin, Alif; Dwi Hary Sandy, Muhammad; Lubis, Muharman
Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Vol 7 No 2 (2025): Jurnal Sistem Informasi dan Teknologi Informasi
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/jursistekni.v7i2.473

Abstract

Desain antarmuka pengguna (UI) adalah pendorong utama kualitas pengalaman pengguna (UX) produk digital. Tujuan dari penelitian ini adalah untuk mengeksplorasi keberadaan dan implementasi prinsip-prinsip Law of UX di antarmuka Shopee, yang merupakan salah satu aplikasi seluler e-commerce yang kaya fitur. Penelitian ini menggunakan pendekatan deskriptif kualitatif dengan tinjauan literatur dan analisis visual antarmuka aplikasi Android Shopee. Dua belas prinsip Law of UX dibahas, yaitu Aesthetic-Usability Effect, Fitts's Law, Hick's Law, Jakob's Law, Tesler's Law, Law of Prägnanz, Miller's Law, Peak-End Rule, Von Restorff Effect, Zeigarnik Effect, Postel's Law, dan Doherty Threshold. Hasil dari analisis ini adalah bahwa semua prinsip kecuali beberapa prinsip diikuti dengan sempurna dalam desain UI Shopee. Setiap pedoman berkontribusi pada navigasi yang lebih baik, mengurangi beban kognitif, kenyamanan visual, dan meningkatkan pengalaman emosional. Melalui penggunaan pedoman ini, Shopee tidak hanya memberikan fungsionalitas tetapi juga pengalaman pengguna yang menyenangkan dan seragam. Penelitian ini berkontribusi pada pengetahuan yang ada tentang bagaimana prinsip-prinsip psikologi kognitif dapat diterapkan dalam merancang antarmuka e-commerce. Hasil penelitian ini juga memberikan daftar periksa yang berguna bagi desainer UI/UX saat merancang antarmuka digital yang berfokus pada pengguna. Penelitian di masa depan akan diuji dengan menerapkan metode kuantitatif atau bahkan pengujian pengguna nyata untuk menentukan dampak langsung dari praktik terbaik UX pada perilaku pengguna.
Agile-Based Application Architecture Design for Billet Management in Industrial Manufacturing Ramadhani, Rafian; Hizbullah, Fauzi; Auliya Rahman, Ilham; Ahyar Harizillah, M.; Noorachmad Muttaqin, Alif; Saidi Lubis, Fahdi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.96

Abstract

This study presents the planning and iterative development of an enterprise application architecture for the Billet Stacker Rail system in an aluminum manufacturing environment. The system is designed to enhance the management of billet logistics, including receiving, inspection, stacking, and transfer processes. Using the Agile methodology, particularly the Scrum framework, the development team collaborated closely with operational stakeholders to capture requirements and validate functionality through a series of Sprints. The process included modeling workflows, designing class and entity diagrams, and creating interactive user interface mockups. The system architecture was developed incrementally to support modularity, traceability, and real-time data recording. Each component from billet tracking to user management was prototyped and refined based on continuous feedback. The Agile approach facilitated rapid adjustments to changing requirements, reduced development risk, and supported a user-centered design process. The result is a robust and scalable application blueprint that aligns with the industrial environment’s needs for efficiency, reliability, and transparency in billet management operations.
Heart Attack Risk Prediction Using Machine Learning: A Comparative Study of Decision Tree and K-Nearest Neighbors Hizbullah, Fauzi; Noorachmad Muttaqin, Alif; Andiharsa Sih Setiarto, Rahardian; Aulia Hakim, Rizki; Abdulmana, Sahidan
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.98

Abstract

Heart disease, particularly heart attacks, is a leading cause of death worldwide, highlighting the importance of early detection and risk prediction. This study develops and evaluates machine learning models to predict heart attack risk using seven health-related attributes: age, marital status, gender, body weight category, cholesterol level, participation in stress management training, and stress level. The dataset, processed with the Orange Data Mining platform, was divided into training (66%) and testing (34%) sets. Two supervised algorithms, Decision Tree and K-Nearest Neighbors (K-NN), were implemented without extensive hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, and F1 score. The Decision Tree achieved the best results with 84.78% accuracy, 88.52% precision, 79.41% recall, and 83.72% F1 score, indicating its effectiveness in identifying at-risk individuals. Key predictors included age, stress level, and cholesterol, aligning with established medical findings. While the results are promising, limitations include a small dataset and limited algorithm scope. Future research should expand the dataset, include additional clinical features, and explore advanced algorithms to improve accuracy and reduce false negatives, enhancing applicability in preventive healthcare.
Co-Authors Abdulmana, Sahidan Ahyar Harizillah, M. Andiharsa Sih Setiarto, Rahardian Aulia Hakim, Rizki Auliya Rahman, Ilham Dwi Hary Sandy, Muhammad Hizbullah, Fauzi Muharman Lubis Ramadhani, Rafian Saidi Lubis, Fahdi