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All Journal International Journal of Electrical and Computer Engineering International Journal of Applied Sciences and Smart Technologies GUYUB: Journal of Community Engagement
Afifah, Izza Nur
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Water Quality Control System Based on Web Application for Monitoring Shrimp Cultivation in Sidoarjo, East Java Fariza, Arna; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Barakbah, Aliridho; Pramadihanto, Dadet; Winarno, Idris; Badriyah, Tessy; Harsono, Tri; Syarif, Iwan; Sesulihatien, Wahjoe Tjatur; Susanti, Puspasari; Huda, Achmad Thorikul; Rachmawati, Oktavia Citra Resmi; Afifah, Izza Nur; Kurniawan, Rudi; Hamida, Silfiana Nur
GUYUB: Journal of Community Engagement Vol 4, No 3 (2023)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v4i3.7245

Abstract

Shrimp farming plays a crucial role to the Indonesian economy, but it is facing challenges from shifting weather patterns and global warming. This research focuses on the development and implementation of a web-based water quality monitoring system for shrimp farming to address these concerns. The research, conducted in collaboration with shrimp farmers in Sidoarjo, East Java, introduces PENS Aquaculture program, which is designed to efficiently monitor pH, salinity, and temperature. The system employs Internet ofThings (IoT) technology, which allows farmers to register several ponds, analyze water parameters, and receive real-time data through tables and graphs. The research takes a mixed-methods approach, integrating quantitative data from IoT devices with qualitative insights gathered through surveys and interviews with shrimp farmers. The study aims to evaluate the influence of IoT technology on shrimp pond quality and its contribution to the production. The findings show that PENS Aquaculture application is helpful in increasing shrimp farming efficiency, providing significant insights for the fisheries and cultural sectors.
Indonesian speech emotion recognition: feature extraction and neural network approaches Afifah, Izza Nur; Santoso, Tri Budi; Dutono, Titon
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3769-3778

Abstract

This study explored the challenges of emotion recognition in Indonesian speech using deep learning techniques, addressing the complex nuances of emotional expression in spoken language that posed significant difficulties for automatic recognition systems. The research focused on the application of feature extraction methods and the implementation of convolutional neural networks (CNN) and a hybrid convolutional neural networks-long short-term memory (CNN-LSTM) model to identify emotional states from speech data. By analyzing key features of speech signals, including mel frequency cepstral coefficient (MFCC), zero crossing rate (ZCR), root mean square energy (RMSE), pitch, and spectral centroid, the study evaluated the models’ ability to capture both spatial and temporal patterns in the data. Testing was conducted using an Indonesian dataset comprising 200 samples. The CNN model, utilizing four features (MFCC, ZCR, RMSE, and pitch), and the CNN-LSTM model, which used three features (MFCC, ZCR, and RMSE), both achieved an emotion classification accuracy of approximately 88%. The result showed that the CNN-LSTM model achieved comparable performance with a simpler feature set compared to the CNN model. This highlighted the significance of choosing the appropriate techniques in feature extraction and classification to enhance the accuracy of identifying emotions from speech data while also managing computational complexity.
Implementation of Scrum in the manufacture of non-invasive blood sugar detection devices using PPG signals Kanza, Rafly Arief; Febrianti, Erita Cicilia; Afifah, Izza Nur; Maulana, Rifqi Affan; Fariza, Arna; Rante, Hestiasari
International Journal of Applied Sciences and Smart Technologies Volume 06, Issue 1, June 2024
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v6i1.7719

Abstract

This study presents the effective integration of Scrum methodology in the production process of non-invasive blood sugar testing devices using Photoplethysmography (PPG) signals. During three months, a team consisting of a Product Owner, Scrum Master, and Developer Team successfully utilized Scrum's agile structure to manage the challenges of PPG signal processing, hardware integration, and software development. The repeated sprint cycles enabled swift adjustment to new obstacles and stakeholder input, guaranteeing both effectiveness and agility in the development process. The dynamic approach facilitated both the punctual delivery of complex medical equipment and the cultivation of a culture focused on ongoing enhancement, establishing a model for the future use of agile approaches in healthcare technology. The successful implementation highlights the effectiveness of Scrum in managing the complexities of medical device development. It provides a model for improving non-invasive blood sugar detection devices and establishes agile methodologies as a key driver of innovation in healthcare technology.
Co-Authors Ali Ridho Barakbah Arna Fariza Bima Sena Bayu Dewantara Dadet Pramadihanto Febrianti, Erita Cicilia Hamida, Silfiana Nur Huda, Achmad Thorikul Idris Winarno iwan Syarif Kanza, Rafly Arief Maulana, Rifqi Affan Rachmawati, Oktavia Citra Resmi Rante, Hestiasari Rudi Kurniawan Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Susanti, Puspasari Tessy Badriyah, Tessy Titon Dutono Tri Budi Santoso Tri Harsono