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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 14, No 3 (2020): September 2020" : 5 Documents clear
Content-based recommender system architecture for similar e-commerce products Ari Nurcahya; Supriyanto Supriyanto
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18511

Abstract

Recommendation systems are quite famous and are increasingly being used on e-commerce platforms for a variety of purposes. The recommendation system technique used also varies greatly depending on the scope and Item of recommendation. Content-based filtering, for example, is used to recommend related product items based on user preferences. However, how the recommendation system architecture should be built starts by creating a data model for bringing up related product items. This paper offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing product item documents. Product item documents are processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then a query can be applied to find similarities to items that the user has seen. In the end, the recommendation system architecture that was built produced excellent Precision using Recall and Precision testing. Tests are carried out for data using the weighting of product names and product labels. The result obtained 0.84 for the average value of Recall and 0.78 for the average value of Precision.
Application to predict the new student’s score using time series algorithm Sinar Nadhif Ilyasa; Husni Thamrin
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a17639

Abstract

With the rapid development of information technology in this era, data accuracy is essential in our daily lives to solve existing problems. The existence of information is beneficial in helping the decision-making process. Therefore, any existing information can be further processed and analyzed to be used as new knowledge so that it is useful to determine the right decision. The purpose of this research is to determine whether an application using the time series algorithm such as Auto Regression, ARMA (Auto Regression Moving Average), and Triple Exponential Smoothing model. They can forecast prediction scores that may help to solve the student's admission problem. In this case of the project, the researcher found that the Universitas Muhammadiyah Surakarta's admission system is not evaluated correctly in accepting students and controlling incoming students' quality due to the lack of insights. This time series application is one solution to help manage incoming students' quality and quantity, especially in the Universitas Muhammadiyah Surakarta. This application is developed using a web framework called Django, a full-stack Python web framework that encourages rapid growth and clean, pragmatic design. The Auto Regression model is chosen as a prediction model in One Day Service (ODS) Universitas Muhammadiyah Surakarta. It has a better performance than ARMA and Triple Exponential Smoothing and a higher chance to avoid overfitting than the other two models that are more complex for the ODS data.
Association pattern of students thesis examination using fp-growth algorithms Ika Arfiani; Herman Yuliansyah; Tia Purwantias
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a17691

Abstract

The thesis examination is the final project for students to graduate from their majors. This thesis researches scientific work between a student and a supervisor in finding solutions to a problem. In the thesis examination, students must present their research results to be criticized by the examiner. This article aims to analyze the association pattern of student thesis examinations at a private university. Although the thesis's implementation has been carried out following procedures, to determine the composition of the board of examiners needs to be analyzed by examining the pattern of relationships between research topics, supervisors, and examiners. This study uses 448 data and uses FP-Growth Algorithms to find the rules. The research methodology starts from preparing the Dataset, cleansing data, selecting data, loading data into applications, transforming data, itemset frequencies, forming patterns, and analyzing rules. This study found 145 patterns of association rules with a minimum support value = 4 and a minimum trust value = 50%. The association rule pattern of 77.78% is under scientific group data. The benefits of the association pattern produced in this study can determine the composition of the examiners on the student thesis examination according to the research topic and scientific field of the examiners.
Mobile e-detection of Banyuwangi’s citrus fruit maturity using k-nearest neighbor Chairul Anam; Solehatin Solehatin
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18183

Abstract

Banyuwangi is the largest oranges-producing city in East Java, and the orange produced is Siamese citrus fruit. Siamese is Banyuwangi local citrus fruit often found at the harvest time and has a sweet taste. To determine the citrus fruit level, people can detect it from the color and texture. In this modern era, people can use an application to determine the citrus fruits' maturity level. From the elements of color and texture, this research will add the citrus fruit's contours, namely the pore size of the citrus fruit and the distance between the curve of the tip of the orange. Taking pictures of citrus fruits will be following the application stages that will be used as the image of inputting the data. The detection is then conducted using the K-NN method based on several criteria based on the input image after the feature extraction process. The feature extraction stages are segmentation, normalization, thresholding, and thinning, which will be produced in several criteria: the maximum RGB value, the minimum RGB value, pore size, and the distance between the tip's curve of the orange. The research results that have been carried out are based on the research stages to get a similarity percentage following the inputted data. The E-Detection application can provide information to citrus farmers, especially beginner citrus farmers, to know the level of fruit maturity oranges to be harvested.
Well-Known brands recognition by automated classifiers using local and global features Hafsa Niaz; Usman Raza
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18418

Abstract

From color and type to patterns and illustrations, brands sense to be recognizable and convey their values and personality. Here patterns and color are key elements, as they can play a vital role in brand recognition. The images used for brand classification were handpicked and collectively named as HKDataset. We have explored various feature extractors used for classification and used automated classifiers named Linear SVM to achieve higher accuracy while tuning the model parameters to achieve optimal performance. It has been observed that Support Vector Machines performs better when using GIST descriptors combined with Bag of SIFT features. We hope to apply deep learning and other sophisticated classifiers to much-expanded categories of brands in the future.

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