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INDONESIA
Indonesian Journal of Applied Technology and Innovation Science
Published by Institut Riset dan Publikasi Indonesia
ISSN : 30327466     EISSN : 30327474     DOI : doi.org/10.57152
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
IJATIS: Indonesian Journal of Applied Technology and Innovation Science is a scientific journal published by the Institute of Research and Publication Indonesian (IRPI). The main focus of the IJATIS Journal is Engineering, Applied Technology, Informatics Engineering, and Computer Science. IJATIS is published 2 (two) times a year (February and August). IJATIS is written in English, consisting of 8 to 12 A4 pages, using Mendeley or Zotero reference management and similarity/ plagiarism below 20%. Manuscripts for IJATIS are submitted via the Open Journal Systems (OJS) in Microsoft Word (.doc or .docx) format. The IJATIS review process uses a Closed System (Double-Blind Reviews) with 2 reviewers per article. Articles are published in open access and are open to the public.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 28 Documents
 1   2   3 
Application of Artificial Neural Network, K-Nearest Neighbor and Naive Bayes Algorithms for Classification of Obesity Risk Cardiovascular Disease Aulia Wulandari; Anggi Mulya; Tri Dermawan; Ryando Rama Haiban; Aghnia Tatamara; Habibah Dian Khalifah
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1095

Abstract

The rate of obesity sufferers continues to increase every year. This happens due to improper lifestyle and diet, as well as various physical conditions. This research aims to analyze the level of obesity using data mining techniques with classification algorithms. This research was conducted on people from countries on the American continent between the ages of 14 and 61 years. Data is collected and information is processed using a web platform that includes surveys where anonymous users answer each question to obtain 17 attributes and 2111 records. This research uses 3 algorithms, namely the Artificial Neural Network algorithm, K-Nearest Neighbor and Naive Bayes. People who are obese are also at higher risk of experiencing health problems, such as asthma, stroke, heart disease, diabetes and cancer. The results after comparing the three algorithms, it is better to use the k-nearest neighbor algorithm compared to Artificial Neural Network and Naive Bayes because the accuracy is 95.74%. Therefore, the K-Nearest Neighbor algorithm is very suitable to use when classifying data.
Comparison of Support Vector Machine, Random Forest, and C4.5 Algorithms for Customer Loss Prediction Bima Maulana; Dany Febrian; Irgie Rachmat Fachrezi; Muhammad Ferdi Zeen
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 1 (2025): IJATIS February 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i1.1102

Abstract

Loss of customers has been discussed and many studies have been conducted, starting from using the Bayesian network algorithm, Decision tree, random vorest, Support vector machine, and neyral network Algorithms Support Vector Machine (SVM), Random Forest, and Decision Tree or C4.5 are algorithms used for prediction and have several advantages Random forest has the advantage of being able to combine many predictions from decision trees that have a tendency to reduce overfitting. This research uses the C4.5 algorithm, SVM and random forest. Research shows that the Random Forest method has the highest accuracy of 87.02% compared to the Support Vector Machine and Decision Tree methods. In contrast, Decision Tree gets low accuracy results with a value of 78.52%. Experimental results show that the Random forest method for customer loss prediction achieves an average classification accuracy of 4% - 9% higher than the Support Vector Machine and Decision Tree methods.
Performance Comparison K-Nearest Neighbor, Naive Bayes, and Decision Tree Algorithms for Netflix Rating Classification Zulkarnain Zulkarnain; Risma Mutia; Jane Astrid Ariani; Zidny Alfian Barik; Habil Azmi
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1104

Abstract

Netflix is a streaming service platform that is growing along with the increasing number of internet users. This research aims to classify movie and TV show rating datasets on Netflix by comparing the KNN, Naive Bayes and Decision Tree algorithms to determine the accuracy comparison of the three algorithms. From the results of the analysis, it is found that the three algorithms produce a comparison of the accuracy of movie and tv show rating classification data on Netflix with different values. Based on the confusion matrix, namely Accuracy, Precision, and Recall, it is found that the Naive Bayes algorithm has the highest accuracy of 72%, the Decision Tree algorithm is 70% and the KNN algorithm has the lowest accuracy of 61%. From these results it can be stated that the Naive Bayes algorithm can classify movie and tv show rating data on Netflix better than compared to the other two algorithms.
Implementation of K-Means, K-Medoid and DBSCAN Algorithms In Obesity Data Clustering Elsa Setiawati; Ustara Dwi Fernanda; Suci Agesti; Muhammad Iqbal; Muhammad Okten Adetama Herjho
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1109

Abstract

Obesity is an excessive accumulation of body fat and can be harmful to health. This study aims to understand the patterns and relationships between obesity data that have been obtained, so a data clustering step will be carried out using the K-Means, K-Medoid and DBSCAN algorithms. This study utilizes the Davies Bouldin Index (DBI) to determine the best cluster value comparison and validated. So the results of the best cluster value in processing obesity data are using the K-Means K2 algorithm with a value of 0.604. The K-Medoid algorithm obtained the best cluster k2, with a DBI value of around 0.614. and the DBSCAN algorithm clustering trial K3, with a value of 1.040. Thus in this study the comparison results of the application of 3 clustering algorithms, the results obtained are the K-Means algorithm shows the value of the resulting cluster is the best of other algorithms in clustering obesity data with a value of 0.604.
Comparison of Analytical Hierarchy Process and Analytical Network Process Methods In Determining The Best Tourist Attractions In West Sumatra Province Kana Kurnia; M. Bryant Rama; Triyawan Bagus Subakja; Syukri Sastrawan; Yogi Septiandri; Muhammad Igo Ilham
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1113

Abstract

A tourist attraction is a place visited for its natural beauty, culture, history, or recreation, such as beaches, mountains, national parks, historical buildings, museums, and amusement parks. Each has its unique charm, with some becoming iconic symbols and major tourist destinations. Tourist attractions are vital to the tourism industry, boosting economic growth and preserving cultural and natural heritage.West Sumatra is one of the provinces in Indonesia that is rich in natural and cultural beauty. In an effort to develop the tourism industry in this area, the selection of the right tourist sites is very important. There are 5 references in determining the best tourist attractions in West Sumatra Province, which include Natural Beauty, Cultural Diversity, Infrastructure, Price, and Cleanliness. Meanwhile, there are 5 alternatives that are used as references in determining the best tourist attractions in West Sumatra Province, namely Padang Beach, Carocok Beach, Sianok Gorge, Harau Valley, and Anai Valley. From the experiments that have been carried out, the rank changes that occur with the AHP and ANP methods are shown in Table 13. It can be seen that the AHP method has a higher percentage of sensitivity with a total percentage of 0.25% compared to the percentage of 0.14% in the ANP method. This proves that the AHP method is better than the ANP method for determining tourist attractions in West Sumatra Province.
Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification Alfi Syahri; Umi Fariha; Rival Afandi; Intan Nurliyana
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1116

Abstract

Diabetes mellitus is a chronic disease that affects the way the body regulates sugar (glucose). High blood sugar levels can lead to health complications including heart problems, eye disorders, nerve damage, kidney and blood vessel disorders. It is important for early detection of diabetes by utilizing data mining technology. Data mining has various classification models that can be used to detect diabetes, including logistic regression, random forest and adaboost. The comparison of the three algorithms aims to find out which algorithm is most appropriate in the classification of diabetes. From the results obtained, the random forest algorithm has the best performance in the classification of diabetes mellitus compared to other algorithms.
Performance Comparison of Classification Algorithms for Chronic Kidney Disease Prediction Farin Junita Fauzan; Celine Mutiara Putri; Prita Laura
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1120

Abstract

Chronic Kidney Disease (CKD) is an abnormal kidney function or failure of the kidneys to filter the bloodstream and remove metabolic waste that progresses over months or years. Chronic kidney disease is asymptomatic in its early stages. It has no age limit, and if you already suffer from chronic kidney disease, the likelihood of a sudden decline in kidney function increases. The medical record data of chronic kidney disease patients can be utilized to make predictions and can be processed using machine learning to classify the risk of death. This research will use Ensemble Learning, which combines Decision Tree, XGBoost, and Extra Trees algorithms. In the pre-processing stage, value filling is carried out using the random sampling method. It was concluded that the highest accuracy value in Extra Trees was 96%. In comparison, the Decision Tree was 94%, and the XGBoost method obtained 95% accuracy so that Pathologists can use it in developing a program to predict chronic kidney disease
Implementation of Supervised Learning Algorithm on Spotify Music Genre Classification Muhammad Fiqri; Farhan Bin Siddik Lizen; Muhammad Ikrom
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 1 (2025): IJATIS February 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i1.1123

Abstract

Spotify is a music streaming application that has been around since 2008. In the application, users can compile a playlist of songs they want to listen to. Users can determine the name of the singer, type of music, music genre and tempo of the music they want to listen to play as needed. The genre received by each user from his device will produce different recommendations, this is due to the classification process based on music listening behavior, such as songs that are often, rarely, or even never listened to or played at all by users. Therefore, the process of classifying music genres on spotify with the help of machine learning using supervised learning algorithms with algorithms namely Naïve Bayes, K-Nearest Neighbors (K-NN), Random Forest and Decision Tree with the aim of comparing the accuracy of each algorithm so as to get the best model for calcification. The results of this study obtained Random Forest has the highest accuracy value of 79.40%, followed by Decision Tree at 79.30%.  In the next position Naïve Bayes with an accuracy value of 77.28%, the algorithm with the lowest accuracy is K-NN with an accuracy value of 60.74%. Meanwhile, evaluation with the t-test algorithm with the best performance is obtained from the Random Forest algorithm with a value of 0.794. It can be concluded that the best algorithm in music genre classification on Spotify is using Random Forest.
Sentiment Analysis of Public Opinion on Films Taylor Swift Eras Tour on the Twitter Platform Using the Machine Learning Nova Idriani R; Annisa Dahlia; Pratiwi Ningum
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1130

Abstract

Sentiment analysis is the understanding of opinions, feelings, or attitudes conveyed in texts, such as tweets, reviews, or other forms. The film “Taylor Swift: The Eras Tour” is trending among teenager, narrating Taylor Swift’s journey in the “Eras Tour” concert across various countries, encapsulated in a music-filled film. This has prompted research on sentiment analysis of netizens’ tweets about this film, considering the possibility of negative reviews. Three algorithms, Naïve Bayes, Decision Tree, and Random Forest. were used with an 80:20 data ratio and the SMOTE oversampling method, which is a unique in this research to ensure data sizes for the three sentiments: positive, negative, and neutral. The final result of this research is a word cloud for each sentiment towards the film, with the Decision Tree algorithm achieving the highest accuracy at 91%. The hope for future research is to implement and focus on the emotional aspect in conducting sentiment analysis.
Comparison of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means and X-Means Algorithms on Shopping Trends Data Vina Wulandari; Yulia Syarif; Zhevin Alfian; Muhammad Adil Althof; Maylina Mufidah
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1135

Abstract

This study extensively compares the efficacy of three clustering algorithms of DBSCAN, K-Means, and X-Means in analyzing shopping trend data, utilizing the Davies-Bouldin Index (DBI) for group validity assessment. The dataset, sourced from Kaggle.com, encompasses various customer attributes. Results indicate that the DBSCAN algorithm demonstrates superior cluster validity, outperforming K-Means and X-Means. Specifically, with an Eps value of 0.3 and MinPts value of 3, DBSCAN achieves an optimal DBI value of 0.1973. K-Means follows with a DBI value of 2.2958, and X-Means attains its best value (2.5663) with k=3. This research underscores the pivotal role of clustering algorithms in understanding shopping trends and customer preferences, offering valuable insights into their comparative performance.

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