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All Journal CommIT (Communication & Information Technology) G-Tech : Jurnal Teknologi Terapan GUYUB: Journal of Community Engagement
Rachmawati, Oktavia Citra Resmi
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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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.
The Comparison of Deep Learning Models for Indonesian Political Hoax News Detection Rachmawati, Oktavia Citra Resmi; Darmawan, Zakha Maisat Eka
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.10929

Abstract

Indonesia is the world’s fourth most populous country and has a diverse sociopolitical landscape. Political fake news exacerbates existing social divisions and causes political polarization in Indonesian society. Hence, studying it as a specific challenge can contribute to broader discussions on the impact of fake news in different contexts. The researchers propose a hoax news detection system by developing a deep learning model with various lapses against a data set preprocessed using term-frequency and token filtering to represent the most prominent words in each class. The researchers compare the layers with the potential to have high performance in predicting the falsity of Indonesian political news data by observing the models based on training history plots, model specification, and performance metrics in the classification report module. The deep learning models include One-Dimensional Convolution Neural Networks (1D CNN), Long-Term Short Memory (LSTM), and Gated Recurrent Unit (GRU). The news data are obtained from the Kaggle site, containing 41.726 rows of data. Based on the experiments with the text data that has been preprocessed in the form of vectors and the specific parameters before starting, the results show that GRU achieves the highest performance value in accuracy, recall, precision, and F1 score. Although GRU becomes the model with the smallest file size, it is the slowest model to generate predictions from text news data. It also has a higher potential to be an overfitted model due to parameters than a simple RNN.
Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Darmawan, Zakha Maisat Eka; Dianta, Ashafidz Fauzan; Fathoni, Kholid; Rachmawati, Oktavia Citra Resmi; Apriandy, Kevin Ilham
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6649

Abstract

Weather classification is crucial in various sectors, including agriculture, transportation, and disaster management. Accurate weather prediction can help mitigate risks and improve decision-making in these fields. However, classifying weather conditions remains challenging due to the complex and dynamic nature of meteorological data. This study aims to compare different machine learning classification methods to determine the most effective model for weather classification. The research employs a structured methodology consisting of seven key steps: literature study, data understanding, exploratory data analysis, data preparation, modeling, evaluation, and hyperparameter tuning. The study used Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, AdaBoost, and Extra Trees to identify the best-performing classifier. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Gradient Boosting achieved the highest performance, surpassing other models with an accuracy of 90.15%. To optimize the model further, hyperparameter tuning was conducted using GridSearchCV, and feature selection was done using SelectKBest. This process resulted in an improved accuracy of 90.22%, demonstrating the effectiveness of model optimization.
The Implementation of Agile Kanban in the Development of an IoT-Based Sugarcane Growth Monitoring System Sari, Sekar; Rachmawati, Oktavia Citra Resmi
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7845

Abstract

This research stems from the urgent demand for modernisation of sugarcane farming in Indonesia, which faces challenges such as declining productivity due to climate change, limited cultivation technology, and weather uncertainty. The main problem is the absence of a real-time environmental monitoring system that can support farmers in making timely and accurate cultivation decisions. The objective of this study is to develop an IoT-based Sugarcane Growth Monitoring System equipped with four sensors—temperature, humidity, air pressure, and light intensity—using the Agile Kanban project management method. The methodology consists of literature study, planning, implementation, and analysis, carried out iteratively with the aid of a Kanban Board to structure and monitor progress. The results demonstrate that the system successfully integrates hardware, software, and user interfaces to deliver real-time environmental data. At the same time, Agile Kanban proves effective in managing the complexity of the development process. This research contributes not only academically, by showing the applicability of Agile Kanban in agricultural IoT projects, but also practically, by providing sugarcane farmers with decision-making support tools that can enhance efficiency, reduce resource waste, and improve cultivation productivity.
Co-Authors Afifah, Izza Nur Ali Ridho Barakbah APRIANDY, KEVIN ILHAM Arna Fariza Ashafidz Fauzan Dianta, Ashafidz Fauzan Bima Sena Bayu Dewantara Dadet Pramadihanto Darmawan, Zakha Maisat Eka Fathoni, Kholid Hamida, Silfiana Nur Huda, Achmad Thorikul Idris Winarno iwan Syarif Rudi Kurniawan Sari, Sekar Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Susanti, Puspasari Tessy Badriyah, Tessy Tri Harsono