International Journal of Advances in Data and Information Systems
EISSN : 2721-3056
Current Publisher: Jurnal Komuniksi ISKI (10.25008)
Total articles ≅ 23
Latest articles in this journal
Published: 27 March 2021
International Journal of Advances in Data and Information Systems, Volume 2, pp 62-72; doi:10.25008/ijadis.v2i1.1217
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.
Published: 27 March 2021
International Journal of Advances in Data and Information Systems, Volume 2, pp 53-61; doi:10.25008/ijadis.v2i1.1214
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).
Published: 2 January 2021
International Journal of Advances in Data and Information Systems, Volume 2; doi:10.25008/ijadis.v2i1.1197
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.
Published: 2 January 2021
International Journal of Advances in Data and Information Systems, Volume 2; doi:10.25008/ijadis.v2i1.1198
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.
Published: 2 January 2021
International Journal of Advances in Data and Information Systems, Volume 2; doi:10.25008/ijadis.v2i1.1204
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.
Published: 2 January 2021
International Journal of Advances in Data and Information Systems, Volume 2, pp 45-52; doi:10.25008/ijadis.v2i1.1206
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.
Published: 2 January 2021
International Journal of Advances in Data and Information Systems, Volume 2; doi:10.25008/ijadis.v2i1.1200
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.
Published: 3 October 2020
International Journal of Advances in Data and Information Systems, Volume 1, pp 9-16; doi:10.25008/ijadis.v1i1.7
Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.
Published: 23 May 2020
International Journal of Advances in Data and Information Systems, Volume 1, pp 69-79; doi:10.25008/ijadis.v1i2.188
The main purpose of this research work is to investigate the challenges preventing students of educational administration and planning from using ICT for learning in Nigeria higher institutions: a case study of university of Abuja, Nigeria. The sample for this study was all the students in university of Abuja. 50 students from each level of the department of educational administration and planning totaling 200 were randomly selected from the department using simple random sampling technique. One hypothesis and three research questions were postulated as a guide to this study and a seven sub-items questionnaire divided into two sections was used to get the required information. A simple percentage and chi-square were used to test the hypotheses at 0.95% level of significance. It was found out that there are challenges preventing students of educational administration and planning from using ICT for learning. The challenges preventing students of educational administration and planning from using ICT for learning includes; unstable power supply, lack of personal laptop or computer system, unstable ICT Network services, lack of computer literacy by the students, High cost of ICT services, poor infrastructural facilities of ICT in higher institutions and poor computer literacy of the lecturers. Base on the findings, the researchers recommends that the government should increase the funding of education in Nigeria to enable schools administrators provide necessary ICT facilities in their various schools.
Published: 15 May 2020
International Journal of Advances in Data and Information Systems, Volume 1, pp 103-115; doi:10.25008/ijadis.v1i2.183
This study postulates that propose global proxy index is a significant conduit to evaluate the shocks in volatile stock markets i.e. PSX and SSE, alike. The two separate models i.e. Log-GARCH (1, 1) and ARMA-GARCH (1, 1) have been used along with the value at risk (V-a-R) @ 5% criteria for choosing best-fitted model. The study results showed Log-GARCH (1, 1) model proves to the best. This study results are not driven by political-level risks and thus independent study can be conducted to evaluate the detrimental consequences on investment opportunities under volatile environments.