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All Journal Jurnal Cendekia Ilmiah Jurnal Infortech
Rizal Ruba’i
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Engineering Social Sciences

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Implementasi Algoritma C4.5 pada Sistem Prediksi Stroke Berdasarkan Data Kesehatan Alpito Gilang Ramadhan; Hiroki Setiawan Hidayatullah; Rizal Ruba’i
J-CEKI : Jurnal Cendekia Ilmiah Vol. 4 No. 2: Februari 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v4i2.7016

Abstract

Stroke is one of the leading causes of death and disability worldwide. Early detection of the risk of stroke is crucial to reducing its impact and improving the effectiveness of treatment. This study aims to predict the probability of a person being at risk of stroke using the C4.5 Algorithm, known as a decision tree-based method. The dataset used includes variables such as age, blood pressure, blood sugar levels, medical history, smoking habits, and physical activity. After data preprocessing, including handling missing data and normalization, a predictive model was built using the C4.5 Algorithm. The results show that the model achieved an accuracy of 95.01%, a precision of 33.33%, a recall of 2.04%, and an F1-score of 3.81%. Additionally, the Negative Predictive Value (NPV) of 95.10% and specificity of 99.79% demonstrate the model's ability to detect risks effectively in negative cases. The model also generates decision rules that are easily interpretable by medical professionals, making it a potential tool to support medical decision-making in the early identification of stroke risks.
Aplikasi Pemesanan Menu Menggunakan Quick Response Code dan Payment Gateway di Kafe Kopi Insight Ade Kusna Eka Syahputra; Rizal Ruba’i; Alpito Gilang Ramadhan; Ade Suryadi
Jurnal Infortech Vol. 7 No. 2 (2025): Desember 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v7i2.11382

Abstract

Efisiensi layanan menjadi tolak ukur vital dalam keberlangsungan bisnis food and beverage. Di Bekasi, Kafe Kopi Insight menghadapi tantangan operasional serius terkait manajemen antrean dan akurasi data pesanan yang selama ini dikelola secara manual. Sebagai langkah solutif, penelitian ini merancang sebuah platform digital berbasis laman web yang mengombinasikan teknologi pindai kode QR (Quick Response Code) dengan gerbang pembayaran otomatis (payment gateway) dari Midtrans. Mekanisme ini memberikan fleksibilitas bagi pengunjung untuk melakukan pemesanan mandiri (self-service) langsung dari meja serta menyelesaikan transaksi pembayaran secara nirkontak (cashless). Konstruksi perangkat lunak dibangun menggunakan pendekatan alur Waterfall dan kerangka kerja CodeIgniter, mencakup modul vital seperti otentikasi admin, kontrol inventaris stok, input order, hingga rekapitulasi transaksi. Validasi performa sistem dilakukan melalui uji fungsionalitas (Black Box Testing), yang menghasilkan simpulan bahwa seluruh fitur beroperasi valid sesuai rancangan. Penerapan sistem ini terbukti mampu memangkas durasi pemesanan, mengurai kepadatan di area kasir, serta menekan risiko kesalahan input manusia, yang berkontribusi signifikan pada peningkatan produktivitas operasional kafe.
Co-Authors Ade Kusna Eka Syahputra Ade Suryadi Alpito Gilang Ramadhan Hiroki Setiawan Hidayatullah