http://ijadis.org/index.php/IJADIS/issue/feed International Journal of Advances in Data and Information Systems 2021-01-02T02:41:50+08:00 Editor of International Journal of Advances in Data and Information Systems info@ijadis.org Open Journal Systems <p style="text-align: justify;"><img style="float: left; width: 200px; margin-top: 8px; margin-right: 10px; border: 2px solid #184B80;" src="http://ijadis.org/public/site/images/ijadis2020/ijadis-cover-xsmall.jpg" /><strong>International Journal of Advances in Data and Information Systems (IJADIS)</strong> (e-ISSN: <strong><a title="ISSN" href="http://u.lipi.go.id/1582691866" target="_blank" rel="noopener">2721-3056</a></strong>) is a peer-reviewed journal in the field of data science and information system that is published twice a year; scheduled in April and October.</p> <p style="text-align: justify;">The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Data Science and Information System. The Journal is published by the Indonesian Scientific Journal (Jurnal Ilmiah Indonesia). Accepted paper will be available online (free access), and there will be no publication fee. The author will get their own personal copy of the paperwork.</p> <p>The IJADIS welcomes all topics that are relevant to data science and information system. The listed topics of interest are as follows: <strong><a href="http://ijadis.org/index.php/IJADIS/focus-and-scope" target="_blank" rel="noopener">Click for more detail</a></strong></p> <table> <tbody> <tr> <td width="135">Journal Title</td> <td><strong>: International Journal of Advances in Data and Information Systems</strong></td> </tr> <tr> <td>E-ISSN</td> <td><strong>: <a title="ISSN" href="http://u.lipi.go.id/1582691866" target="_blank" rel="noopener">2721-3056</a></strong></td> </tr> <tr> <td>DOI</td> <td><strong>: 10.25008</strong> by Crossref</td> </tr> <tr> <td>Chief Editor</td> <td><strong>: Achmad Pratama Rifai, Ph.D. (Scopus ID: <a href="https://www.scopus.com/authid/detail.uri?authorId=56717397100" target="_blank" rel="noopener">56717397100</a>)</strong></td> </tr> <tr> <td>Managing Editor</td> <td><strong>: Edi Sutoyo (Scopus ID: <a href="https://www.scopus.com/authid/detail.uri?authorId=56377194700">56377194700</a>)</strong></td> </tr> <tr> <td>Frequency</td> <td><strong>: 2 issues per year (April, October)</strong></td> </tr> <tr> <td>Publisher</td> <td><strong>: <a href="http://idscience.id/">Indonesian Scientific Journal</a></strong></td> </tr> <tr> <td>Indexation</td> <td><strong>: Google Scholar | Sinta | Garuda</strong></td> </tr> <tr> <td>Email</td> <td><strong>: info@ijadis.org</strong></td> </tr> </tbody> </table> http://ijadis.org/index.php/IJADIS/article/view/k-nearest-neighbor-with-k-fold-cross-validation-and-analytic-hie K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification 2020-12-27T06:13:01+08:00 Zoelkarnain Rinanda Tembusai zoelkarnaintembusai@students.usu.ac.id Herman Mawengkang hmawengkang@usu.ac.id Muhammad Zarlis m.zarlis@usu.ac.id <p>This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.</p> 2021-01-11T00:00:00+08:00 Copyright (c) 2021 Zoelkarnain Rinanda Tembusai, Herman Mawengkang, Muhammad Zarlis http://ijadis.org/index.php/IJADIS/article/view/multi-attribute-decision-making-using-hybrid-approach-based-on-b Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion 2020-12-04T21:49:59+08:00 Adriyendi Adriyendi elektronikpos@gmail.com Yeni Melia <p>Multi-Attribute Decision Making (MADM) is used to select the best alternative from multi-alternatives based on multi-attribute (fashion material) and multi-criteria (sustainable fashion). Multi-alternatives are cotton, linen, silk, wool, acrylic, nylon, polyester, rayon, spandex, and mixed. Multi-attributes are material, texture, color, characteristic, comfort, and wearability. Multi-criteria are material fiber, smooth texture, faded color, elastic clothing, useful long, chilly and comfortable. Hybrid approaches and optimal solutions are needed to determine the best choice in decision making for both producers and consumers. The hybrid approach in MADM used is Simple Multi-Attribute Rating (SMART), Multi-Factor Evaluation Process (MFEP), Multi-Object Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Weighted Product (WP). SMART and MFEP are based on the Non-Benefit Cost Model while MOORA, SAW, and WP are based on a Benefit-Cost Model. The experimental results show that the SMART model with the best alternative is the rayon with the highest value (2.8333). The selection of the MFEP Model with the best alternative is rayon with the highest value (2.8330). The choice of MOORA model with the best alternative is rayon with the highest value (0.2595). The selection of the SAW Model with the best alternative is rayon with the highest value (0.8932). The selection of the WP Model with the best alternative is rayon with the highest value (0.1285). MADM using SMART, MFEP, MOORA, SAW, and WP for sustainable fashion yields the best alternative for consumption and production for the middle-class population in Indonesia.</p> 2021-01-11T00:00:00+08:00 Copyright (c) 2021 Adriyendi Adriyendi, Yeni Melia http://ijadis.org/index.php/IJADIS/article/view/self-diagnosis-of-web-based-pregnancy-and-childbirth-disorders-u Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods 2020-12-04T21:46:03+08:00 I Putu Agus Eka Pratama eka.pratama@unud.ac.id <p>The high mortality rate for pregnant women and childbirth in Bali, Indonesia, is caused by a lack of initial diagnosis of the diseases and complaints experienced by pregnant women during pregnancy, as well as a lack of health medical personnel scattered throughout Bali, to be able to provide optimal health services. It is necessary to have an online information system that helps pregnant women to be able to independently and online diagnose diseases, complaints, and symptoms experienced during pregnancy. The system must be able to be accessed anytime and anywhere, with high reliability and availability, and provide fast diagnostic results. Focus of this research is design and implementation of an Information System for Diagnosis of Pregnancy Disorders Based on Cloud Computing based on Forward Chaining Method, using Design Science Research Methodology (DSRM) and tested using the Technology Acceptance Model (TAM) method. The application is placed on the Hybrid Cloud. The results of this research, can help pregnant women in diagnosing diseases and complaints online, to reduce the mortality rate for pregnant women and giving birth.</p> 2021-01-11T00:00:00+08:00 Copyright (c) 2021 I Putu Agus Eka Pratama http://ijadis.org/index.php/IJADIS/article/view/text-detection-based-on-faster-r-cnn-algorithm-with-skip-pooling Text Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Hindi Handwritten Characters 2020-12-04T21:52:34+08:00 Abhishek Mehta abhishek.mehta7067@paruluniversity.ac.in Subhashchandra Desai Ashish Chaturvedi <p>Significant learning is at present the standard system for object disclosure. Speedier territory based convolutional neural association (Faster R-CNN) has a basic circumstance in significant learning. It has stunning area impacts in standard scenes. Regardless, under unprecedented conditions, there can even now be inadmissible acknowledgment execution, for instance, the thing having issues like hindrance, contorting, or little size. This paper proposes a novel and improved estimation reliant on the Faster R-CNN framework got together with the Faster R-CNN figuring with skip pooling and mix of consistent information. This computation can improve the revelation execution under uncommon conditions dependent on Faster R-CNN. The improvement basically has three segments: The underlying portion adds a setting information incorporate extraction model after the conv5_3 of the convolutional layer; the resulting part adds skip pooling so the past can totally secure the coherent information of the article, especially for conditions where the thing is hindered and distorted; and the third part replaces the area recommendation association (RPN) with a more capable guided anchor RPN (GA-RPN), which can keep up the survey rate while improving the revelation execution. The last can get more positive information from different segment layers of the significant neural association figuring, and is especially centered around scenes with little articles. Differentiated and Faster R-CNN, you simply look once plan, (for instance, YOLOv3), single shot pointer, (for instance, SSD512), and other article revelation computations, the estimation proposed in this paper has an ordinary improvement of 6.857% on the mean typical precision (mAP) appraisal list while keeping up a particular audit rate. This unequivocally exhibits that the proposed methodology has higher ID rate and disclosure efficiency for this circumstance.</p> 2021-01-11T00:00:00+08:00 Copyright (c) 2021 Abhishek Mehta, Subhashchandra Desai, Ashish Chaturvedi