Performance and Effectiveness Evaluation of the National Digital Samsat Using the PIECES Framework

Rahma Fitria, Amanda Syakhila, Desvina Yulisda, Azham Hussain, Arafat Febriandirza

Abstract

This study evaluates the SIGNAL (National Digital Samsat) application using the PIECES framework, which includes six evaluation aspects: Performance, Information, Economics, Control, Efficiency, and Services. A questionnaire was distributed to 300 active users, and technical testing was conducted using Apptim to measure performance metrics. Results showed that most users were satisfied, with an average satisfaction score above 3.9 out of 5. Apptim test results also indicated stable technical performance, with average response times of 2.4 seconds, CPU usage at 18%, and memory usage at 170MB. However, minor issues related to document delivery delays, customer service responsiveness, and memory usage were identified. The study concludes that SIGNAL performs well overall and provides recommendations for targeted improvements to enhance efficiency and service quality.

References

Puspitasari, I., et al. (2022). Edukasi Pembayaran Pajak Melalui Implementasi E-Samsat. Jurnal Optimasi Sistem Industri, 5(2), 219–229.

Hertiarani, W. (2020). Implementasi Kebijakan E-Samsat di Jawa Barat. Jurnal Ilmu Administrasi, 13(3), 419–440.

Nurzaman, N., Suarna, N., & Prihartono, W. (2024). Analisis Sentimen Ulasan Aplikasi Threads Menggunakan Naïve Bayes. JATI, 8(1), 967–974. https://doi.org/10.36040/jati.v8i1.8708

Rafitanuri, S., Arsyida, N., & Gunawan, R. (2022). Analisis Tingkat Kepuasan Masyarakat Terhadap Aplikasi Signal. Jurnal Hukum, Politik dan Ilmu Sosial, 1(3), 92–103. https://doi.org/10.55606/jhpis.v1i3.537

Karlinda, A. E., Azizi, P., & Sopali, M. F. (2021). Pengaruh Pengalaman Kerja Terhadap Pengembangan Karir. JISAMAR, 5(2), 523–531. https://doi.org/10.52362/jisamar.v5i2

Rahman, I. F., Hasanah, A. N., & Heryana, N. (2024). Analisis Sentimen Aplikasi Signal Menggunakan Naïve Bayes. Jurnal Informasi dan Teknik Elektro Terapan, 12(2), 963–969. https://doi.org/10.23960/jitet.v12i2.4073

Kacung, S., Bagyana, C. P. P., & Cahyono, D. (2024). Analisis Sentimen Menggunakan SVM. Jurnal Mnemonik, 7(1), 118–122. https://doi.org/10.36040/mnemonic.v7i1.9557

Kakihary, N. L. (2021). PIECES Framework for IoT User Satisfaction. Jurnal ISI, 3(2), 243–252. https://doi.org/10.33557/journalisi.v3i2.119

Astuti, N. P., & Bakri, R. (2021). Pelatihan SmartPLS 3 di Masa Pandemi. CARADDE, 4(1), 614.

Hanifa, R. D., Mustafid, & Hakim, A. R. (2021). Statistik pada Departemen Statistika UNDIP. Jurnal Gaussian, 10(2), 279–292.

Esmemed, G., Testiana, G., & Nopriani, F. (2021). Analisis Kepuasan Pengguna Transportasi Online. Jurnal CISA, 2(2), 78–93. https://doi.org/10.51519/journalcisa.v2i2.62

Prayogi, R., Ramanda, K., Budihartanti, C., & Rusman, A. (2021). Evaluasi Aplikasi M-BCA dengan PIECES. Jurnal Infortech, 3(1), 7–12. https://doi.org/10.31294/infortech.v3i1.10122

Gavilanes, D. (2023). Apptim Review. Abstracta Blog. https://abstracta.us/blog/mobile-testing/apptim-review/

Palamarchuk, S. (2023). How to Test Mobile App Performance with Apptim. DEV Community. https://dev.to/sofiapalamarchuk/how-to-test-mobile-app-performance-with-apptim-3436

QA Madness. (2023). How to Use Apptim. https://www.qamadness.com/how-to-use-apptim-for-mobile-performance-testing/

DOI: https://doi.org/10.37058/innovatics.v7i2.15672

Refbacks

  • There are currently no refbacks.