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All Journal Journal of Information Systems Management and Digital Business JTH: Journal of Technology and Health
Nurhaeka Tou
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Medicine & Pharmacology Nursing

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ANALISIS TINGKAT KEPUASAN PENGGUNA TERHADAP APLIKASI GRAB : MENGGUNAKAN SYSTEM USABILITY SCALE (SUS) indri, Indriany Ayunda; Rifda Zakeya Humaera; Wildan Nasuhan Yusuf; Iski Zaliman; Nurhaeka Tou; Putri Mentari Endraswari
Journal of Information Systems Management and Digital Business Vol. 3 No. 2 (2026): Januari
Publisher : Yayasan Nuraini Ibrahim Mandiri

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Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pengguna terhadap aplikasi Grab berdasarkan aspek kegunaan (usability) dari perspektif pengguna di Kota Pangkalpinang. Metode penelitian yang digunakan adalah metode kuantitatif deskriptif dengan pendekatan System Usability Scale (SUS), di mana data dikumpulkan melalui penyebaran kuesioner kepada 35 responden yang merupakan pengguna aktif aplikasi Grab, dengan instrumen berupa 18 pernyataan menggunakan skala Likert 1–5. Hasil penelitian menunjukkan bahwa nilai rata-rata System Usability Scale (SUS) yang diperoleh sebesar 67,2, yang berada pada kategori OK/Fair atau acceptable, sehingga mengindikasikan bahwa aplikasi Grab sudah cukup mudah digunakan dan dapat diterima oleh pengguna, meskipun masih terdapat beberapa aspek usability yang perlu ditingkatkan. Simpulan, bahwa aplikasi Grab telah memberikan pengalaman penggunaan yang cukup baik dan memuaskan bagi sebagian besar pengguna, namun pengembang masih perlu melakukan perbaikan pada aspek kejelasan navigasi, konsistensi tampilan, dan efisiensi sistem agar tingkat kepuasan pengguna dapat meningkat ke kategori yang lebih baik.  
COMPARATIVE ANALYSIS OF LOGISTIC REGRESSION AND RANDOM FOREST FOR PREDICTING HEALTH RISKS AMONG THE ELDERLY WITH COMORBIDITIES IN BANGKA Putri Mentari Endraswari; Nurhaeka Tou; Syilva Qonita Yuriko
JTH: Journal of Technology and Health Vol. 3 No. 3 (2026): January: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v3i3.788

Abstract

The comorbidity of hypertension and diabetes in the elderly is a significant health problem because it increases the risk of complications and the burden on medical services. In resource-constrained healthcare facilities, machine learning approaches can facilitate more efficient risk-based screening. This study aimed to evaluate the ability of machine learning models to predict diabetes, hypertension, and their comorbidities in elderly patients in Bangka, and to identify key clinical factors contributing to these risks. This study used medical record data from 279 elderly patients in the emergency department. Two models were compared: logistic regression and Random Forest, with predictor variables including age, sex, BMI, waist circumference, and blood pressure. The results showed that the Random Forest model had moderate discrimination ability for diabetes (AUC 0.67) and hypertension (AUC 0.64), but poor discrimination ability for comorbidities (AUC 0.56–0.57). Adiposity (BMI and waist circumference) and blood pressure were the main determinants of risk. Overall, this model has the potential to serve as an initial screening tool, though it still requires recalibration, the addition of clinical features, and external validation before operational implementation.
Co-Authors indri, Indriany Ayunda Iski Zaliman Putri Mentari Endraswari Rifda Zakeya Humaera Syilva Qonita Yuriko Wildan Nasuhan Yusuf