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Richki Hardi
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INDONESIA
REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Published by CV Mazaya Cahaya Utama
ISSN : -     EISSN : 31104908     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
REKADATA adalah jurnal yang secara spesifik mempublikasikan hasil penelitian orisinal di bidang ilmu data (data science) dan kecerdasan buatan (artificial intelligence). Topik yang diterima mencakup (namun tidak terbatas pada): Machine Learning dan Deep Learning, Penambangan Data (Data Mining), Analitika Big Data (Big Data Analytics), Pemrosesan Bahasa Alami (Natural Language Processing), Visi Komputer (Computer Vision), Sistem Pakar dan Sistem Pendukung Keputusan, Rekayasa dan visualisasi data. REKADATA Terbit 2 (dua) kali setiap tahun di bulan Agustus dan Februari.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSING RICE PLANT DISEASES USING FORWARD CHAINING METHOD Endah Nurjanah
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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Abstract

This study presents the development of an expert system for diagnosing rice plant diseases using the forward chaining method. Rice is the staple food for most of the Indonesian population, and plant diseases significantly reduce productivity. Farmers often face difficulties in identifying diseases due to limited agricultural knowledge and lack of experts in the field. The proposed system was designed to assist farmers in diagnosing rice diseases based on symptoms entered into the system. The forward chaining inference technique was implemented to match symptoms with disease rules in a knowledge base. The system was tested using several common rice diseases such as blast, bacterial leaf blight, tungro virus, and brown spot. Results show that the system can accurately provide disease diagnosis recommendations with clear explanation facilities. The novelty of this research lies in the application of a simple but effective reasoning method combined with a user-friendly interface for farmers. This study concludes that the system can be used as an alternative decision support tool for early disease detection in rice plants.
DATA ENGINEERING AND VISUALIZATION OF STUDENT ACADEMIC PERFORMANCE USING BUSINESS INTELLIGENCE TOOLS Fahmi Fahmi
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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Abstract

The rapid growth of data in higher education institutions has created challenges in managing, analyzing, and interpreting student academic performance information. Academic data such as course grades, attendance, and assessment results are often scattered across multiple sources, making it difficult for decision-makers to gain comprehensive insights. This study presents the development of a data engineering and visualization framework designed to process and present student academic performance using business intelligence (BI) tools. The methodology involves several stages: data acquisition from academic information systems, data cleaning and transformation through a structured pipeline, and integration into a centralized database. Visualization was carried out using BI tools to generate interactive dashboards that provide multi-dimensional analysis of student achievement. The results demonstrate that the developed framework successfully consolidated student performance data into a single repository, enabling efficient analysis and visualization. Key performance indicators such as GPA trends, course completion rates, and subject-specific weaknesses were visualized in real time. These visualizations support lecturers, academic administrators, and students in identifying performance patterns, predicting potential risks, and formulating appropriate interventions. The novelty of this research lies in the combination of data engineering processes with user-friendly BI dashboards tailored for the education sector in Indonesia. In conclusion, the proposed system enhances transparency, accessibility, and decision-making in academic performance monitoring. It highlights the importance of integrating data engineering and visualization techniques in higher education, providing a foundation for more advanced analytics such as predictive modeling and personalized learning recommendations.
APPLICATION OF DATA MINING FOR PREDICTING STUDENT ACADEMIC PERFORMANCE USING CLASSIFICATION ALGORITHMS Faisal Faisal
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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Abstract

The ability to predict student academic performance has become increasingly important for higher education institutions in supporting academic success and reducing dropout rates. Academic performance is influenced by multiple factors such as attendance, previous grades, participation in coursework, and demographic information. However, these data are often underutilized in academic decision-making. Data mining techniques, particularly classification algorithms, provide an effective approach to analyzing historical student data and generating predictive models for future performance. This study applies classification-based data mining methods to predict student academic performance using a dataset containing course grades, attendance records, and cumulative GPA. Several algorithms were tested, including Decision Tree, Naïve Bayes, and k-Nearest Neighbors (k-NN). The methodology involved data preprocessing, feature selection, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that classification algorithms are effective in predicting student performance, with the Decision Tree model achieving the highest accuracy at 87%, followed by Naïve Bayes at 82% and k-NN at 80%. These findings demonstrate that data mining can support educational institutions in identifying at-risk students early, enabling timely academic interventions and personalized support. The novelty of this research lies in the comparative analysis of multiple classification algorithms applied to student academic data within the Indonesian higher education context. This study concludes that integrating data mining into academic information systems can significantly enhance decision-making processes and contribute to improved learning outcomes.
A BIG DATA ANALYTICS APPROACH FOR FORECASTING AGRICULTURAL COMMODITY PRICES Erniza Erniza
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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Abstract

Agricultural commodity prices play a crucial role in economic stability and food security, particularly in developing countries such as Indonesia. Price volatility in key commodities such as rice, chili, and shallots often affects household expenditure, trade balance, and national inflation. Conventional forecasting methods are limited in capturing the complexity and scale of agricultural market data, which is often generated from multiple heterogeneous sources including government reports, wholesale markets, and social media. Big Data Analytics provides an opportunity to address these challenges by integrating large-scale datasets and applying advanced forecasting techniques to generate more accurate predictions. This study proposes a Big Data Analytics framework for forecasting agricultural commodity prices. The framework consists of four main stages: data acquisition from public datasets and online sources, data preprocessing and transformation using distributed computing systems, analytical modeling with machine learning algorithms, and visualization of price forecasts through interactive dashboards. The research implemented Apache Spark for data processing and applied time series forecasting models, including ARIMA and Long Short-Term Memory (LSTM) neural networks, to predict short-term price fluctuations. The experimental results indicate that LSTM outperformed ARIMA in terms of accuracy, with a Mean Absolute Percentage Error (MAPE) of 6.5% compared to 9.8% for ARIMA. Visualization of the forecasts provided clear insights into potential price increases, enabling policymakers, traders, and farmers to make proactive decisions. The novelty of this research lies in the integration of a distributed Big Data processing framework with predictive modeling tailored to agricultural commodity markets in Indonesia. In conclusion, the proposed Big Data Analytics approach demonstrates significant potential to improve forecasting accuracy and support decision-making in agricultural economics. The findings highlight the importance of adopting Big Data-driven solutions for enhancing national food security and market stability.
SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA UTILIZING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES Dyah Nur Rochmah
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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Abstract

The swift expansion of social media has generated a vast quantity of unstructured textual data that mirrors public sentiment on diverse subjects. Examining this data yields significant insights for enterprises, governments, and scholars. This research seeks to create a sentiment analysis system utilizing Natural Language Processing (NLP) and machine learning techniques to categorize social media messages as positive, negative, or neutral sentiments. The proposed system comprises several essential stages: text preprocessing, feature extraction via Term Frequency–Inverse Document Frequency (TF-IDF), and classification employing machine learning methods including Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. A dataset including 10,000 social media postings was meticulously collected and extensively annotated to guarantee precision in sentiment classification. Experimental results indicated that SVM attained superior performance, achieving an accuracy of 87.4% and an F1-score of 0.86, surpassing both Naïve Bayes and Logistic Regression. The results illustrate the efficacy of natural language processing integrated with machine learning in the analysis of extensive social media datasets, providing a reliable method for sentiment classification. The study underscores the efficacy of sentiment analysis in gauging public opinion, facilitating commercial decisions, and identifying nascent social trends.

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