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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 7 Documents
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Search results for , issue "Vol. 1 No. 1 (2024): IJATIS February 2024" : 7 Documents clear
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.
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 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.
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.
Comparison of TOPSIS and SMARTER Methods in Selecting Delivery Services Delvi Nur Aini; Aditya Rezky Pratama; Puji Dwi Rinanda; Assad Hidayat; Allam Jaya Prakash
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.v1i1.1139

Abstract

The rapid growth of the e-commerce world has propelled the demand for freight forwarding services, a pivotal component in maintaining the smooth flow of this business. Major companies like JNE, TIKI, Kantor Pos Indonesia, SiCepat, and J&T Express are involved. However, despite this convenience, various challenges often accompany the shipping process. Some of these include delayed deliveries, lost or damaged items, or even misdeliveries to the wrong customers. This presents a dilemma for leading e-commerce companies in selecting the most suitable delivery service partner. Hence, a decision support model is necessary in choosing a freight forwarding service. This study will outline comparison methods based on both Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Multi Attribute Rating Technique Exploiting Ranks (SMARTER). The proposed methods have strong relevance to the rapid growth of the e-commerce world. This research emphasizes the importance of selecting freight forwarding services in maintaining the smooth operation of e-commerce businesses. The results of this study have obtained ranking results from the final value of each method. The first place in the TOPSIS method with a value of 0.7033 at sensitivity 3 and the lowest in the SMARTER method with a value of 0.1303 at the first sensitivity, and at the second sensitivity, all methods have the same value of 0.2. The conclusion is that TOPSIS is the best method compared to the SMARTER method as a decision support for the selection of freight forwarding services.
Comparation of Decision Tree Algorithm, Naive Bayes, K-Nearest Neighbords on Spotify Music Genre Desvita Hendri; Diana Nadha; Faishal Khairi Basri; Muhammad Farid Wajdi; Nurul Nadhirah
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.v1i1.1219

Abstract

Comparison of Decision Tree, Naive Bayes, K-Nearest Neighbords Algorithm on Spotify Music Genre Decision Tree, Naive Bayes, K-Nearest Neighbords This research aims to compare three algorithms Decision Tree, Naive Bayes and K-Nearest Neighbors (K-NN) in classifying Spotify music genres using dataset from Kaggle. The results show that the Decision Tree algorithm produces an accuracy of 23%, Naive Bayes 17%, and K-Nearest Neighbors 19%. This research provides an overview of Spotify music listeners in choosing music genres. Based on research results, the Decision Tree algorithm has the highest accuracy in classifying Spotify music genres, with the Electric Dance Music (EDM) genre being the most popular among Spotify music fans, followed by rap, pop, r&b, Latin and rock. . Meanwhile, the Naive Bayes and K-Nearest Neighbors algorithms show lower accuracy.

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