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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 171 Documents
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Application of the Multi-Objective Optimization By Ratio Analysis (MOORA) Method in the Chili Seed Selection System (Case Study of Asuli Village, East Luwu Towuti District) Faradilah Ilham, Huryya Zalza
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.79

Abstract

Currently chili plants are plants that are needed by the community, these plants can be processed into cooking spices, the food industry and medicines. In East Luwu Regency chili plants are also the main commodity in agriculture, many farmers make chili plants as a business opportunity, but the production of chili plants in East Luwu Regency has decreased, this decrease in production is due to the lack of yields of chili farmers. So that the supply of chili is reduced and can not meet market demand. The lack of yields was caused by farmers using different types of chili seeds because farmers still had difficulty in determining the right type of chili seeds to develop which resulted in chili cultivation failing to harvest. For this reason, a system is needed that can assist farmers in determining chili seeds that are suitable for cultivation in Asuli Village, East Towuti Luwu District. The criteria used in determining the location of the business are harvest time, number of stalks, weight of chilies, altitude and longevity. This study aims to produce a decision support system in recommending the best chili seeds so that they can help farmers increase red chili production among farmers. The method used is the MOORA method, this method was chosen because this method is very simple, stable, and robust, even this method is able to determine goals from conflicting criteria, where criteria can be of beneficial or unfavorable value (cost). In addition, MOORA also has the ability to easily separate subjective elements from an evaluation process into weighted decision criteria that have several decision-making attributes. The results showed that Salo Dua chili seeds were suitable for cultivation in Asuli Village, East Luwu Towuti District
Comparative Analysis of Machine Learning Algorithm Variations in Classifying Body Shaming Topics on Social Media X Nurul Fitri H, Sarah FIla; Fattah , Farniwati; Azis, Huzain
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.82

Abstract

Machine learning is an approach in computer science where systems or models can learn from data and experience to improve performance or perform specific tasks. There are several popular machine learning algorithms, such as naïve bayes, decision tree, K-NN, and SVM. This study aims to compare the performance of accuracy, precision, recall, and F-1 score in sentiment analysis of body shaming topics on Social Media X (formerly known as Twitter) by applying decision tree, K-NN, and SVM methods and identifying the most effective algorithm in classifying the data. Based on the classification performance testing results, it can be concluded that the classification method using the trigram feature model provides the best performance compared to other methods. The trigram model is able to achieve high recall, particularly in recognizing positive classes, without significantly compromising accuracy
A Learning Approach for The Identification of Network Intrusions Based on Ensemble XGBoost Classifier OYELAKIN, Akinyemi Moruff
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.88

Abstract

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.
Comparison of Machine Learning Land Use-Land Cover Supervised Classifiers Performance on Satellite Imagery Sentinel 2 using Lazy Predict Library Muhamad Iqbal Januadi Putra; Vincent Alexander
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.102

Abstract

The utilisation of various supervised classifier algorithms in classifying land use and land cover (LULC) from satellite imagery has been widely used worldwide, yet the implementation using lazy predict library remained unexplored. This study aims to create the LULC supervised classifier model for Sentinel 2 satellite images using lazy predict library and assess its capability for creating multiple machine learning models. The result of this study shows that lazy predict library can generate 26 machine learning models in efficient few lines of code and less time-consuming. Most LULC models generated by lazy predicts has performance metrics above 90% with time computation between 0 and 1 seconds. While lazy predict library has benefits to generate various machine learning models at once, it has drawbacks in terms of its feasibility for the machine learning production, its obstacle running in local environment, and its requirements for the RAM computation.
Comparison of Classification Algorithm Performance for Diabetes Prediction Using Orange Data Mining Hafiz Aryan Siregar; Muhammad Zacky Raditya; Aditya Nugraha Yesa; Inggih Permana
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.103

Abstract

Diabetes is a disease that contributes to a relatively high mortality rate. The human death rate due to diabetes is a widespread issue globally. The primary goal of this research is to predict individuals suffering from diabetes using a publicly available dataset from the UCI Repository with the Diabetes Disease dataset. To obtain the best classification algorithm, a comparison is made among three algorithms: KNN, Naive Bayes, and Random Forest, commonly used for predicting diabetes. The comparison results indicate that the Random Forest algorithm is the appropriate and accurate algorithm for predicting individuals with diabetes, with an accuracy rate of 97%.
Shortest Route Navigation Indoors Using Digital Maps Asis, Muhammad Arfah; Mude, Muh Aliyazid; Astiani, Ririn; Kurnia Prihandani, St Nadya
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.110

Abstract

Digital maps have revolutionized our ability to navigate to desired locations by providing the shortest routes, primarily in open spaces. However, this functionality is limited to outdoor environments. This study aims to extend this capability by enabling the determination of the shortest routes within indoor spaces. The research employs the Haversine method for distance measurement and integrates the Dijkstra algorithm for route determination. The findings demonstrate the feasibility of implementing the Haversine method and the Dijkstra algorithm for route determination within enclosed spaces. The research results show that searching for the shortest route indoors be done and the route can be displayed on a digital map. It was observed that the routing machine between route nodes did not perform optimally, prompting its replacement with a polyline.
Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases Mastrika Giri, Gst. Ayu Vida; Musdar, Izmy Alwiah; Angriani, Husni; Taruk, Medi
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.111

Abstract

This study explores the application of machine learning techniques in the agricultural domain, focusing on the classification of two common diseases in mango leaves: Powdery Mildew and Sooty Mould. Utilizing the MangoLeafBD dataset, the research employs a Gradient Boosting Classifier, enhanced with mean shift image segmentation and Hu moments for feature extraction. The performance of the model was rigorously evaluated through 5-fold cross-validation, yielding insights into its accuracy, precision, recall, and F1-score. The results demonstrate moderate success, with the highest accuracy and precision observed in the initial fold, indicating the model's potential for reliable disease identification. The study addresses the challenge of distinguishing between diseases with similar symptomatic appearances, offering a novel, data-driven approach for disease management in mango cultivation. This research contributes to the growing field of precision agriculture, highlighting the potential of machine learning in enhancing disease diagnosis and treatment strategies, thus supporting sustainable agricultural practices.
A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel Suhendra, Christian Dwi; Najwaini, Effan; Maria, Eny; Faizal, Edi
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.112

Abstract

This study explores the classification of Daisy and Dandelion flowers using a Gaussian Naive Bayes classifier, enhanced by Sobel segmentation and Hu moment feature extraction. The research adopted a quantitative approach, utilizing a balanced dataset of Daisy and Dandelion images. The Sobel operator was employed for image segmentation, accentuating the floral features crucial for classification. Hu moments, known for their invariance to image transformations, were extracted as features. The Gaussian Naive Bayes algorithm was then applied, with its performance evaluated through a 5-fold cross-validation process. The results exhibited moderate accuracy, with the highest recorded at 60%, and precision peaking at 62.60%. These findings indicate a reasonable level of effectiveness in distinguishing between the two species, though variations in performance metrics suggested room for improvement. The study contributes to the field of botanical image classification by demonstrating the potential of integrating image processing techniques with machine learning for flower classification. However, it also highlights the limitations of the Gaussian Naive Bayes approach in handling complex image data. Future research directions include exploring more advanced machine learning algorithms and expanding the feature set to enhance classification accuracy. The practical implications of this research extend to ecological monitoring and agricultural studies, where efficient and accurate plant classification is vital
Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset Zaky, Umar; Naswin, Ahmad; Sumiyatun, Sumiyatun; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.113

Abstract

Ensuring water potability is paramount for public health and safety. This research aimed to assess the efficacy of the Decision Tree classification algorithm in predicting water potability using the Water Quality and Potability dataset. Employing a 5-fold cross-validation technique, the model showcased a moderate performance with an average accuracy of approximately 54.33%. While the Decision Tree provides a baseline and interpretable mechanism for classification, the results emphasize the need for further exploration using more intricate models or ensemble methods. This study contributes to the broader effort of leveraging machine learning techniques for water quality assessment and provides insights into the potential and limitations of such models in predicting water safety
Rice Leaf Disease Classification with Machine Learning: An Approach Using Nu-SVM Setiawan, Rudi; Zein, Hamada; Azdy, Rezania Agramanist; Sulistyowati, Sulistyowati
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.114

Abstract

This study explores the application of machine learning for classifying rice leaf diseases, employing the Nu-Support Vector Machine (Nu-SVM) algorithm, analyzed through a 5-fold cross-validation approach. The research focuses on distinguishing between healthy leaves and those affected by BrownSpot and LeafBlast diseases. The dataset, comprising segmented rice leaf images processed using Sobel edge detection and Hu Moments feature extraction, is utilized to train and test the model. Results indicate a moderate level of accuracy (52.12% to 53.81%) across the cross-validation folds, with precision and recall metrics demonstrating variability and highlighting the challenges in precise disease classification. Despite this, the model maintains a consistent ability to identify true positives. The study contributes to the field of precision agriculture by showcasing the potential and limitations of using machine learning for plant disease diagnosis. It underscores the need for advanced image processing techniques and diverse feature extraction methods to enhance model performance. The findings are pivotal for developing more effective, automated diagnostic tools in agriculture, thereby aiding in better disease management and potentially improving crop yields. This research serves as a foundational step towards integrating machine learning in agricultural disease detection, emphasizing its importance in sustainable farming practices.

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