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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 10 Documents
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Search results for , issue "Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science" : 10 Documents clear
Spatial Prediction of Stunting Incidents Prevalence Using Support Vector Regression Method Andi Widya Mufila Gaffar; Sugiarti; Dewi Widyawati; Andi Muhammad Kemai Arief Hidayat Paharuddin; Andi Vania Anastasia
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.68

Abstract

Stunting in toddlers is a major nutritional problem faced by Indonesia, with a high incidence rate occurring in several provinces across the country. This nutritional issue can occur at any age, starting from the prenatal stage, infancy, childhood, adolescence, adulthood, and even in the elderly. To reduce the prevalence of stunting in affected provinces, prevention efforts are essential, including predicting the spread of stunting incidents in each region. Therefore, this research conducted spatial prediction of the prevalence rate of stunting incidents using Machine Learning, specifically Support Vector Machine based Regression. The results of this study produced a prediction model with an RMSE (Root Mean Square Error) value of 0.008689303 and a multiple correlation coefficient of 0.65912721. Based on these findings, the predictive model utilized demonstrated satisfactory performance in predicting the prevalence rate of stunting incidents in each area
Personal Voice Assistant: from Inception to Everyday Application Ilman Shazhaev; Dmitry Mikhaylov; Abdulla Shafeeg; Arbi Tularov; Islam Shazhaev
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.69

Abstract

The creation of automatic speech recognition systems is a popular trend in the development of information technology. These technologies are developing at a very fast pace, gradually covering more and more areas: already now we can say how firmly they have settled in our lives. The term "speech technologies" means a fairly large layer of information technologies, but one of the most advanced products in this area is a voice assistant, which includes the use of all types of speech technologies: speech recognition, speech synthesis, a system for developing and analyzing voice information, and voice biometrics. A voice assistant is software that allows you to control your device using voice commands. The range of possibilities does not end with the execution of commands; a modern assistant is even able to maintain a conversation with the user. Since the voice assistant is a complex innovation that consists of many different technologies, the task of a smart assistant is to ensure that they work smoothly with each other. Now there are many voice assistants on the market that can make life easier for a person. Now you don’t have to manually enter a question into a search engine or search for a song, you just need to say what you want, and the voice assistant will find everything on its own. But every year there are more and more assistants and it becomes more and more difficult for the average user to choose, because each assistant has its own characteristics. Nevertheless, the daily use of this technology is becoming more widespread. Another area that is rapidly developing is gaming. Using more and more innovations, the next logical step is to implement a voice assistant, at least at the training stage. However, real progress is out of the question for the time being.
Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification Al Danny Rian Wibisono; Syahrul Hidayat; Humam Maulana Tsubasanofa Ramadhan; Eva Yulia Puspaningrum
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.70

Abstract

Heart disease has become a global health issue that can threaten anyone, regardless of age. Numerous research efforts have been made to develop classification methods that can aid in diagnosing heart disease. In this study, we compared two classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree, by applying Principal Component Analysis (PCA) technique to the heart disease classification. The dataset used contains relevant clinical attributes. After analyzing the dataset and performing data preprocessing, we applied PCA to reduce the dataset's dimensions. PCA models with KNN and Decision Tree were implemented and evaluated using performance metrics such as Confusion Matrix, F1 Score, and Accuracy. The analysis results showed that the PCA model with Decision Tree outperformed the PCA model with KNN in terms of accuracy. The Decision Tree model successfully classified all data correctly, while KNN had some misclassifications. This research recommends using the PCA model with Decision Tree for heart disease classification with the best performance. However, further research with larger datasets is needed for a deeper understanding
Comparison of Performance of Four Distance Metric Algorithms in K-Nearest Neighbor Method on Diabetes Patient Data Dewi Ratnasari
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.71

Abstract

Diabetes is a chronic disease that occurs when the pancreas no longer produces insulin or when the body cannot effectively use the insulin it produces. The aim of this study is to analyze and compare the classification performance on diabetes patient dataset using four distance metric algorithms in the K-Nearest Neighbor (K-NN) method. Based on previous research, the performance values obtained were not sufficiently high, not exceeding 80%. Therefore, some actions are needed with the hope of obtaining new performance values and making comparisons with previous studies. Based on the test results using the confusion matrix, the accuracy level using Euclidean distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. The Manhattan distance measurement obtained the best performance value at k=25 with 10-k fold, with an accuracy of 85.53%, precision of 85.54%, recall of 85.53%, and F-measure of 85.10%. The Minkowski distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. On the other hand, the Hamming distance measurement obtained the best performance value at k=23 with 10-k fold, with an accuracy of 75.32%, precision of 79.27%, recall of 75.32%, and F-measure of 71.45%.
Design and Implementation of Health Supplies Inventory Monitoring System Using First Expired First Out Method Sri Wulandari Samsul; Harlinda L; Sugiarti
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.72

Abstract

The manual management of health supplies inventory by pharmacists at UPTD Puskesmas Toari, Southeast Sulawesi, poses a challenge in monitoring the stock of health supplies due to the manual recording, resulting in less effective monitoring of the inventory. This study aims to design a monitoring system for health supplies inventory using the First Expired First Out (FEFO) method. The FEFO method is suitable for managing the dispensing of health supplies that are closest to their expiration date by utilizing them first to avoid any expired supplies being consumed. The development of this health supplies inventory monitoring system will utilize the waterfall method, consisting of five stages: Requirement Analysis, Design, Implementation, Testing, and Maintenance. The system will be web-based, providing efficient management of health supplies inventory, including medications, disposable medical materials (BMHP), and medical equipment, while also providing timely and accurate information. The research findings indicate that the system can effectively monitor the health supplies inventory at the UPTD Puskesmas Toari Pharmacy Warehouse, utilizing the FEFO method to prioritize the dispensing of health supplies with the nearest expiration date. The system also includes a notification feature to inform the need for immediate dispensing of health supplies. Questionnaire results showed that respondents agreed with the implementation of the monitoring system at the UPTD Puskesmas Toari Pharmacy Warehouse
Comparison Analysis of Random Forest Classifier, Support Vector Machine, and Artificial Neural Network Performance in Multiclass Brain Tumor Classification Amaliah Faradibah; Dewi Widyawati; A Ulfah Tenripada Syahar; Sitti Rahmah Jabir; Poetri Lestari Lokapitasari Belluano
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.73

Abstract

This study aims to analyze and compare the performance of three main classification models, namely Random Forest Classifier, Support Vector Machine, and Artificial Neural Network, in classifying Multiclass brain tumors based on MRI images. The research method includes exploratory data analysis (EDA), dataset preprocessing with image segmentation using the Canny method, and feature extraction using the Humoment method. The performance of the classification models is evaluated based on accuracy, precision, recall, and F1 score. The analysis results show variations in the performance of the three classification models, with Random Forest Classifier having an accuracy of 0.7, weighted precision of 0.55, weighted recall of 0.7, and weighted F1 score of 0.59; Support Vector Machine having an accuracy of 0.71, weighted precision of 0.5, weighted recall of 0.71, and weighted F1 score of 0.59; and Artificial Neural Network having an accuracy of 0.62, weighted precision of 0.6, weighted recall of 0.62, and weighted F1 score of 0.61. Visualization using box plots also reveals outliers in the performance of the three models. These findings indicate variations and outliers in the performance of the classification models for Multiclass brain tumor classification. Further analysis is needed to understand the factors that influence performance differences and identify ways to improve the classification model performance for brain tumor diagnosis based on MRI images
Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine Dewi Widyawati; Amaliah Faradibah; Putri Lestari Lokapitasari Belluano
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.76

Abstract

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently.
Performance Comparison Analysis of Classifiers on Binary Classification Dataset Rahmat Fuadi Syam; Rahmadani
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.77

Abstract

In this study, we compared the performance of Random Forest Classifier and Decision Tree using classification evaluation methods. The results showed that Random Forest Classifier had a higher overall accuracy rate but also produced more outliers. On the other hand, Decision Tree demonstrated consistency in classification with fewer outliers. These findings provide insights into the trade-off between accuracy and consistency when selecting the appropriate classification method. Furthermore, further research is needed to understand the impact of outliers on classification performance and to take appropriate steps in addressing them.
Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods Nurul Rismayanti; Aulia Putri Utami
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.78

Abstract

This study aims to compare the performance between Random Forest Classifier and Gaussian Naïve Bayes Classifier in classification. Several evaluation metrics such as accuracy, precision, recall, and F1-score were used to analyze the performance of both models. The dataset used has specific characteristics that influence the evaluation results. The research findings indicate that Random Forest Classifier outperforms Gaussian Naïve Bayes Classifier in most of the evaluation metrics. Random Forest Classifier achieves higher accuracy and better precision, recall, and weighted F1-score. However, it should be noted that Random Forest Classifier also has more outliers compared to Gaussian Naïve Bayes Classifier when visualized using boxplots. Therefore, in selecting a classification model, a trade-off between higher performance and sensitivity to outliers needs to be considered. Further statistical testing and advanced evaluation are required to gain a deeper understanding of the impact and interpretation of the obtained results. This study provides valuable insights into understanding the comparison between these two classification models and their implications in different contexts.
M2SmallLint : software health monitoring tool Hayatou Oumarou; Nurul Rismayanti
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.90

Abstract

Developing error-free applications is a major challenge for computer scientists. Tools to remedy this problem have been developed, notably Rule Checkers and proof assistants. As a particular case of error, a bug is by nature intangible, invisible and difficult to trace. We propose to investigate the correlations between the alerts generated by rule checkers and the internal quality of the software system. In this first version of the work, we present M2SmallLint, a tool for visualizing and navigating through source code properties in order to locate potential errors. This tool enables the visualization of software health.

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