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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
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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 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.
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.
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.
Determining Zakat Recipients Using Simple Multi Attribute Rating Technique with Analytic Hierarchy Process Eigen Preference Muhammad Ridho Anugrah; Rafi Rasyid Parmana
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.v2i1.1771

Abstract

Paying zakat for Muslims is an obligation to alleviate the burden of recipients. However, difficulties arise in determining the right individuals for zakat distribution because each type of mustahiq or zakat recipient can seem similar to one and another therefore become hard distinguish. This research aims to enhance accuracy using a Decision Support System (DSS) with criteria like Number of Dependents, Income, Occupation, Home Ownership, Marital Status, House Walls, House Floors, and House Roof. The Analytic Hierarchy Process (AHP) method simplifies unstructured problems into a hierarchy, and the Simple Multi-Attribute Rating Technique (SMART) offers flexibility in analysis. Decision outcomes are rankings with the highest scores, ordering those most deserving of zakat. Weighting results highlight Number of Dependents with the highest weight at 0.335 for determining zakat recipients. Based on ranking, alternative A1 secures the top position with a score of 0.077.
Analyzing Customer Sentiment Towards Marketplace Reviews Using Classification Algorithms Nabiilah Nabiilah; Siti Rohimah; Septi Kenia Pita Loka
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.v2i1.1774

Abstract

Numerous online marketplaces like Shopee and Lazada have been developed in Indonesia due to the rapid growth of e-commerce. The Shopee and Lazada apps link buyers and sellers in transactions to purchase and sell products and services. About 100 million users have downloaded both applications as of this writing. Since releasing these programs, the community has voiced various thoughts and complaints. Based on this, user sentiment regarding the Shopee and Lazada applications on the Google Play Store is determined using sentiment analysis using the K-Nearest Neighbor (KNN), Nave Bayes, and Support Vector Machine (SVM) algorithms. Data selection, pre-processing, transformation, data mining, and assessment are the five stages of the Knowledge Discovery in Databases (KDD) approach. For each E-commerce application, 2000 reviews were used as the data. With an accuracy of 85.71% for Gaussian-NB modeling for the Lazada dataset and an accuracy of 85.67% for Bernoulli-NB modeling for the Shopee dataset, the Naive Bayes algorithm has the highest accuracy in experiments on each dataset.
Comparative Analysis of the Combination of AHP-SAW and AHP-WP in Making Decisions on Hiring New Employees Rizki Andreas; Margareta Amalia MP; Sri Maharani Sinaga; Teguh Brahmana; Dian Kusmawati
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.v2i1.1777

Abstract

This paper's web-based employee recruitment the goal is to help Human Resource Development (HRD) managers automatically calculate criterion weights and alternative weights, refinement of potential employees  and a faster selection process. Recommendation system applications use Combination of Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The AHP method determines the importance of each professional criterion is at the moment. SAW, on the other hand, determines the position or priority of a potential employee, calculated from alternative options. In the AHP method, criteria influence the outcome of a decision. The resulting calculations are examined using the specified priority weights to see which criteria are most important. The weight value for the CI criterion was 0.0603, and the CR value was 0.0538. However, a sensitivity analysis of criterion priorities is required to examine the extent to which small effects on criterion weights change the ranking of alternatives. Based on the ranking results using AHP-WP, Fajar ranked first with a preference value of 0.1037289. You can also see how important the selection criteria are to the ranking results.
PM 2.5 Prediction Using the Long Short-Term Memory Algorithm Syaid El Hasyim; Nurazizah Nurazizah; Muhammad Yudha Pratama; Umairah Rizkya Gurning; Batrisia Khairunnisa
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 2 (2025): IJATIS August 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i2.1779

Abstract

Air pollution poses a serious threat to human health and the environment, with far-reaching impacts on various aspects of life. Among its most harmful components is particulate matter less than 2.5 micrometers in diameter (PM2.5), which contributes significantly to degraded air quality. Accurate prediction of PM2.5 concentrations is crucial for public health protection and policy-making. This study employs the Long Short-Term Memory (LSTM) algorithm, a deep learning method well-suited for modeling large, complex, and time-dependent datasets, to forecast PM2.5 levels in Delhi, India. The dataset comprises daily records from January 1, 2015, to July 1, 2020. The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 25.22%, indicating moderate predictive accuracy. These results demonstrate that the LSTM algorithm can serve as an effective tool for forecasting PM2.5 concentrations, providing valuable insights for air quality management and environmental planning.

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