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All Journal Journal of Information Systems and Technology Research Jurnal Sains dan Teknologi
Ajeng Dwi Asti
Universitas Andalas
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics Transportation

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Digitalization of Rural Water Management: Android-Based Billing for Community Systems using the ADDIE model Nurfiah Nurfiah; Afdhal Dinilhak; Luthfil Khairi; Budy Satria; Anggi Hadi Wijaya; Ajeng Dwi Asti; Arifan Rahman
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1135

Abstract

The integration of information technology in everyday life has changed the way people work, learn, socialize, make transactions, and make decisions. The use of Android-based smartphones is a real example of the use of technology. Android, which is open-source, has encouraged the development of applications widely according to need. Access to water is a fundamental human right. PAMSIMAS is a flagship program of the regional and central governments that seeks to meet water needs through the provision of clean water services in line with the Sustainable Development Goals (SDGs). In Durian Seribu Village, PAMSIMAS is a service to meet the water needs of the community and become a solution for rural communities to get clean water at low cost, but its management is still manual, such as recording water usage and billing, so it is inefficient, time-consuming, and prone to errors. From these problems, this study proposes the development and implementation of an Android application designed to simplify the recording and billing process for the PAMSIMAS program in Durian Seribu Village. This application aims to simplify management, increase data transparency, and simplify reporting. The results of tests that have been carried out using the black box method show that this application can facilitate officers in recording and billing payments for PAMSIMAS water usage. Officers only need to enter the total water usage, and the application will automatically calculate and print a receipt as proof of payment. Officers also do not need to calculate manually when reporting the total payment to the administrator. For administrators, this application makes it easier to monitor and evaluate the performance of recording officers. After the application was used for recording and billing, PAMSIMAS's revenue increased by around 30% from the revenue before using the application.
Klasifikasi Emosi Suara Berbasis Citra Spektogram Menggunakan Jaringan Saraf Konvolusional Muhammad Dawi Syauqi; Arifan Rahman; Ajeng Dwi Asti; Nurfiah Nurfiah
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Speech Emotion Recognition (SER) is a crucial area in human-computer interaction, where selecting effective feature representations remains a key challenge. This study proposes an SER system using Log-Mel Spectrogram images and a 2D Convolutional Neural Network (CNN). The Toronto Emotional Speech Set (TESS) dataset, comprising 2,800 audio samples across 7 balanced emotion classes (400 samples each), was employed. Each audio file was converted into a 128×128-pixel Log-Mel Spectrogram image. The CNN architecture consists of three convolutional blocks (32, 64, and 128 filters) with Batch Normalization and Max Pooling, followed by a Fully Connected layer (128 neurons), Dropout (rate 0.5), and a 7-class Softmax output layer. The model was trained using the Adam optimizer, with early stopping triggered at epoch 22. Experimental results demonstrate that the model achieves 100% classification accuracy on 560 test samples, yielding perfect precision, recall, and F1-score (1.00) for all classes. This research confirms that visual spectrogram representations combined with CNNs can automatically and effectively extract emotional features without manual feature engineering.
Co-Authors Anggi Hadi Wijaya Afdhal Dinilhak Arifan Rahman Budy Satria Luthfil Khairi Muhammad Dawi Syauqi Nurfiah