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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization GUYUB: Journal of Community Engagement Journal of Applied Data Sciences
Rachmawati, Oktavia Citra Resmi
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics 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.
Programming Language Selection for The Development of Deep Learning Library Rachmawati, Oktavia Citra Resmi; Barakbah, Ali Ridho; Karlita, Tita
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2437

Abstract

Recently, deep learning has become very successful in various applications, leading to an increasing need for software tools to keep up with the rapid pace of innovation in deep learning research. As a result, we suggested the development of a software library related to deep learning that would be useful for researchers and practitioners in academia and industry for their research endeavors. The programming language is the core of deep learning library development, so this paper describes the selection stage to find the most suitable programming language for developing a deep learning library based on two criteria, including coverage on many projects and the ability to handle high-dimensional array processing. We addressed the comparison of programming languages with two approaches. First, we looked for the most demanding programming languages for AI Jobs by conducting a data-driven approach against the data gathered from several Job-Hunting Platforms. Then, we found the findings that imply Python, C++, and Java as the top three. After that, we compared the three most widely used programming languages by calculating interval time to three different programs that contain an array of exploitation processes. Based on the result of the experiments that were executed in the computer terminal, Java outperformed Python and C++ in two of the three experiments conducted with 5,4047 milliseconds faster than C++ and 231,1639 milliseconds faster than Python to run quick sort algorithm for arrays that contain 100.000 integer values. 
Process Design of Software Library Development for Deep Learning Module in Java Programming with Four-Phase Methodology: Preparation, Identification, Design, and Development Barakbah, Ali Ridho; Rachmawati, Oktavia Citra Resmi; Karlita, Tita
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.989

Abstract

Recent advances in deep learning have driven remarkable achievements across various domains, including computer vision, natural language processing, and medical diagnostics. However, prevailing DL libraries often expose monolithic and tightly coupled codebases, making it difficult for researchers to inject custom mathematical formulations into core training routines. To address this limitation, we introduce a modular software library that empowers users in both academia and industry to extend and modify training functions with minimal friction. This paper focuses on the preparatory stages of library development in Java Programming, presenting a four-phase methodology comprising Preparation (ideation, research questions, literature review), Identification (term extraction, goal definition, environment setup), Design (architecture modeling, class and attribute specification, task scheduling), and Development (component exploration, functionality construction). Through these sequential activities, we have produced eleven detailed design documents, including vision statements, quality-attribute scenarios, architectural decision records, and API specifications, that collectively capture the rationale and technical blueprint of our library. By sharing our step-by-step process, we aim to provide a replicable framework for future researchers undertaking the architectural design of specialized Deep Learning libraries.
Exploratory Data Analysis for Monitoring The Environment Variables of Sugarcane Growth Sari, Sekar; Rachmawati, Oktavia Citra Resmi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31360

Abstract

Sugarcane is vital to the national sugar industry and food security; however, its productivity is significantly affected by environmental factors, including temperature, light intensity, soil moisture, and pH. Fluctuations in these variables frequently lead to erratic yields and diminished sugar quality. Data obtained from IoT-based monitoring systems is often affected by noise, absent values, and outliers, complicating analysis. This research employs exploratory data analysis (EDA) on IoT-based sensor data to obtain comprehensive insights into environmental factors influencing sugarcane growth. The dataset contains 1,811 non-null entries from sensors that measure temperature, light, soil moisture, and pH. Data preparation encompassed cleansing, addressing missing values, and eliminating outliers. Univariate and multivariate analyses were conducted to evaluate variable distributions and correlations. The findings indicated that eliminating outliers improved data consistency and showed that temperature and pH had near-normal distributions, whereas light and soil moisture were skewed. A correlation study revealed moderate associations between light and pH, while regression analysis confirmed a favorable relationship between light intensity and pH. This research emphasizes enhancing the dependability and interpretability of IoT-based monitoring data through EDA, providing significant insights for precision agriculture. Future research may concentrate on predictive modeling and real-time decision-support systems to enhance farming operations.
Co-Authors Afifah, Izza Nur Ali Ridho Barakbah Arna Fariza Bima Sena Bayu Dewantara Dadet Pramadihanto Darmawan, Zakha Maisat Eka Hamida, Silfiana Nur Huda, Achmad Thorikul Idris Winarno iwan Syarif Rudi Kurniawan Sari, Sekar Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Susanti, Puspasari Tessy Badriyah, Tessy Tita Karlita Tri Harsono