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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Aiti: Jurnal Teknologi Informasi
Bernadus Very Christoko
Universitas Semarang
Computer Science & IT Engineering

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Pengenalan Karakter Optis untuk Pencatatan Meter Air dengan Long Short Term Memory Recurrent Neural Network Victor Utomo; Agusta Praba Ristadi Pinem; Bernadus Very Christoko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (320.153 KB) | DOI: 10.29207/resti.v5i1.2807

Abstract

Clean water service providers in Indonesia are still recording water meters as water usage data with manual recording by record collector. Alternative solutions for recording water meters from previous research use the Internet of Things (IoT) or image recognition that is processed on a server. The solutions rely on the Internet which is unsuitable with Indonesia’s condition. This study proposes a water meter reading system that can work on mobile devices without using the Internet. The system works by utilizing optical character recognition (OCR) using the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method. LSTM-RNN is a classification method in artificial neural network which has feedback. The results show that the water meter reading system could work without using an Internet connection. The average time it takes to perform the reading process is 2285ms even on Android device with low specification. The overall reading accuracy is 86%. Single value reading accuracy, when the digit meter displays only 1 number, is 97%, while the accuracy of double value reading, when the digit meter displays 2 numbers, is 18%.
Penentuan Pola Asosiatif Data Tracer Study Universitas Semarang dengan Algoritma Hash Based Bernadus Very Christioko; Khoirudin Khoirudin; Atmoko Nugroho
AITI Vol 20 No 2 (2023)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v20i2.150-166

Abstract

Data hasil tracer study Universitas Semarang saat ini belum diolah ke dalam bentuk pola asosiatif yang dapat dimanfaatkan kampus guna memperoleh feedback evaluasikeberhasilan proses belajar mengajar yang telah dilaksanakan. Penelitian ini menggunakan metode data mining dengan algoritma Hash based untuk menemukan pola asosiatif dari data tracer study. Proses pembentukan pola asosiatif menggunakan tahapan yang ada pada Knowledge Discovery in Database yang meliputi proses cleaning data, integrasi, transformasi data, data mining, evaluasi pola dan representasi pengetahuan. Hasil penelitian dengan support minimal sebesar 50% dan confidence 75%, diperoleh pola datatracer study sebanyak 3 pola asosiatif. Pola yang dihasilkan ini dimaksudkan dapat untuk berkontribusi pada pengembangan kurikulum dan sarana penunjang dalam proses pembelajaran.
Co-Authors Agusta Praba Ristadi Pinem Atmoko Nugroho Khoirudin Khoirudin Victor Gayuh Utomo