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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. 2 (2024): IJATIS August 2024" : 7 Documents clear
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.
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
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.
Using Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) Methods in Criteria and Alternative Perspectives for Ranking Qhairani Frilla F. Safiesza; Laras Mayangda Sari; M. Yogi; Alvin Andiran Sunarya; Muhammad Naufal Farras; Muhammad Fikri Evizal
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.v1i2.1137

Abstract

The application of Analytic Hierarchy Process (AHP) and Fuzzy - Analytic Hierarchy Process (F-AHP) is used as the main method in conducting the ranking process in the case of choosing a major in the Department of Information System UIN Suska Riau. The perspectives of both methods are applied in two levels of hierarchy, namely on criteria and alternatives. In this experiment, AHP was used to rank the criteria, while F-AHP was used for the alternatives. The results of the experiment show that both have a CR value smaller than 0.1. The rankings obtained in order on the criteria side are RP, PD, MMK, Kindergarten and MAP. On the alternative side, the CRM course is followed by SCM, SIC, DS, ITQ, and ITG. This assessment is based on the calculation of the pairwise comparison matrix of some of the best objects from 10 experiments conducted. The conclusion that can be given is that both methods can be implemented in the case study being worked on and all have a good consistency ratio.
Implementation of Analytic Hierarchy Process Method In the Decision Support System for Selecting Department in University Syahida Nurhidayarnis; Anisa Putri; Raja Zaid Ibnu Zarier Ismail; Meisya Delila Br Ginting; Walovi Lestari Nurrafa
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.v1i2.1138

Abstract

Every year, high school graduates show a strong desire to pursue higher education. However, many of them experience limitations in choosing a Department or study program, creating the "Wrong Department" phenomenon and facing a serious dilemma. The selection of a Department in the Faculty of Science and Technology is considered a crucial stage in the academic journey. Errors in decision-making not only affect students' career development, but also have implications for their contribution to the progress of society and the world. In this case, a systematic and measurable approach is needed to provide assistance to prospective students in making the right decision in choosing a Department. Decision Support System (DSS) becomes a crucial instrument in overcoming the complexity of the decision-making process. One method that is often applied in DSS is the Analytic Hierarchy Process (AHP). AHP helps to explicitize key factors through the formation of a hierarchy of relevant criteria, but in situations of uncertainty, Fuzzy Logic is integrated. Fuzzy Logic allows handling data uncertainty by modeling it as a membership variable in a set. The results of this study show that the most influential criteria in choosing a Department are interest and talent.
Comparison of Machine Learning Algorithms in Diabetes Risk Classification Zairy Cindy Dwinnie; Zaira Cindya Dwynne; Mohammed Jahidul Islam; Noviarni Noviarni
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.v1i2.1141

Abstract

Diabetes is a disease in which blood sugar levels are excessive without insulin control so that body functions do not function normally. Diabetes is also a disease that many people suffer from and is one of the main causes of death throughout the world. For this reason, we need to know the factors that are indicators of someone suffering from diabetes. This research compares the Decision Tree, Logistic Regression, and K-Nearest Neighbors algorithms with accuracy and Confusion Matrix parameters to determine diabetes sufferers in 520 data with the main indicator attributes supporting diabetes. From the test results of the three algorithms, the Decision Tree and K-Nearest Neighbors models have the highest accuracy of 86%. The Logistic Regression Algorithm has a fairly good accuracy of 83%.
Performance Evaluation of Machine Learning Algorithms in Predicting Global Warming: A Comparative Study of Random Forest, K-Nearest Neighbors and Support Vector Machine Anisa Putri; Refri Martiansah; Qhairani Frilla F. Safiesza; Muhammad Fahri Abduh
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.v1i2.1194

Abstract

Global Warming is a global warming phenomenon that has a significant impact on human health and the environment. This research aims to apply Machine Learning algorithms, namely the Random Forest algorithm, K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) in predicting global warming. First, global warming data downloaded from Kaggle via dataset is used as research material. Then, a global warming prediction model is built using this algorithm and then evaluated using criteria such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean squared error (RMSE), R2, and Confusion Matrix. Finally, based on the evaluation results, research confirms that the K-NN algorithm shows the best performance, with the highest R2 value and low prediction error compared to other algorithms, such as Random Forest which shows the lowest performance. In terms of classification, K-NN achieved the highest accuracy (96.55%) and excellent performance in the confusion matrix and classification report. Overall, the findings of this study emphasize the dominance of K-NN in this context, thereby providing a strong basis for selecting models for predicting global warming phenomena.

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