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Edi Sutoyo
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journalijadis@gmail.com
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Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
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International Journal of Advances in Data and Information Systems
Published by Indonesian Scientific Journal
ISSN : -     EISSN : 27213056     DOI : https://doi.org/10.25008/ijadis
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Advances in Data and Information Systems (IJADIS) (e-ISSN: 2721-3056) 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. 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. 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. IJADIS welcomes all topics that are relevant to data science, and information system. The listed topics of interest are as follows: Data clustering and classifications Statistical model in data science Artificial intelligence and machine learning in data science Data visualization Data mining Data intelligence Business intelligence and data warehousing Cloud computing for Big Data Data processing and analytics in IoT Tools and applications in data science Vision and future directions of data science Computational Linguistics Text Classification Language resources Information retrieval Information extraction Information security Machine translation Sentiment analysis Semantics Summarization Speech processing Mathematical linguistics NLP applications Information Science Cryptography and steganography Digital Forensic Social media and social network Crowdsourcing Computational intelligence Collective intelligence Graph theory and computation Network science Modeling and simulation Parallel and distributed computing High-performance computing Information architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 7 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems" : 7 Documents clear
Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods I Putu Agus Eka Pratama
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1198

Abstract

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.
Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion Adriyendi Adriyendi; Yeni Melia
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1200

Abstract

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.
K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification Zoelkarnain Rinanda Tembusai; Herman Mawengkang; Muhammad Zarlis
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1204

Abstract

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.
Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path Muhammad Rhifky Wayahdi; Subhan Hafiz Nanda Ginting; Dinur Syahputra
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1206

Abstract

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.
Features Selection for Entity Resolution in Prostitution on Twitter Reisa Permatasari; Nur Aini Rakhmawati
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1214

Abstract

Entity resolution is the process of determining whether two references to real-world objects refer to the same or different purposes. This study applies entity resolution on Twitter prostitution dataset based on features with the Regularized Logistic Regression training and determination of Active Learning on Dedupe and based on graphs using Neo4j and Node2Vec. This study found that maximum similarity is 1 when the number of features (personal, location and bio specifications) is complete. The minimum similarity is 0.025662627 when the amount of harmful training data. The most influencing similarity feature is the cellphone number with the lowest starting range from 0.997678459 to 0.999993523.  The parameter - length of walk per source has the effect of achieving the best similarity accuracy reaching 71.4% (prediction 14 and yield 10).
Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine Wasim Bourequat; Hassan Mourad
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1216

Abstract

Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.
Implementing Data Privacy of Cloud Data on a Remote Server using Symmetric Cryptographic Algorithms David Livingston; Ezra Kirubakaran; Eben Priya David
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1217

Abstract

Cloud Computing is an excellent technology for Micro Medium and Small Enterprises, which operate under budget shortage for setting up their own Information Technology infrastructure that requires capital investment on resources such as computers, storage and networking devices. Now-a-days, major Cloud Providers like Google and Amazon provide cloud services to its customers for managing their email, contact list, calendar, documents, and their own websites. MSME can take advantage of the cloud-based solutions offered by various Cloud Service Providers for equipping their own employees in doing their day to day activities more effectively and on the cloud. Though cloud computing promotes less expensive and collaborative work environment among a group of employees, it involves risks in keeping the resources such as computing and data secured. Different mechanisms are available for securing the data on the cloud among which encryption of data using cryptographic algorithm is the widely used one. Among various encryption symmetric algorithms, Advanced Encryption Standard is the more secured symmetric encryption algorithm for implementing data privacy on the cloud. In this paper, the authors have discussed some of the issues involved in adopting the cloud in an organization and proposed solutions that will benefit an organization while uploading and managing data in files and databases on the cloud.

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