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Kota yogyakarta,
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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 419 Documents
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Information technology performance measurement and improvement recommendation in Indonesian retail company S W Perangin-angin; C H Primasari; Y P Wibisono
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a23495

Abstract

Information technology should work according to the needs and provide added value to the business. If the application of information technology does not provide added value to the business, information technology will only become a burden for the company. Therefore, it is necessary to measure performance to see to what extent the application of IT can support business processes and provide added value. This paper provides measurements and recommendations on IT governance in one of leading retail company in Indonesia. This research used descriptive quantitative research methods and IT Balanced Scorecard method that can provide an overview of IT performance in an organization based on four perspectives, such as Corporate Contribution, Customer Orientation, Operational Improvement, and Future Orientation. Based on the results of the analysis and measurement, the overall IT performance score was 62.64% where the score is in the “Moderate” category. The company contribution perspective got a score of 68.50%, the user orientation perspective was 63.00%, the operational improvement perspective was 62.06%, and the future orientation perspective was 57.44%. Several recommendations were constructed based on the consideration of the KPI value that must be improved. This can be a guide for other retail companies in formulating policies related to IT governance and enriching research in the field of IT performance measurement.
Missing Data Imputation using K-Nearest Neighbour for Software Project Effort Prediction Sri Handayaningsih; Ardiansyah Ardiansyah
Jurnal Informatika Vol 16, No 1 (2022): January 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a23494

Abstract

The accurate of software development effort prediction plays an important role to estimate how much effort should be prepared during the works of a software project so that it can be completed on time and budget. Achieving good prediction accuracy is rely on the quality of data set. Unfortunately, missing data is one of big problem regards to the software effort data set, beside imbalance, noisy and irrelevant problem. Low quality of data set would decrease the performance of prediction model. This study aims to investigating the accuracy of software effort prediction with missing data set by using KNN missing data imputation and List Wise Deletion (LWD) techniques. It was continued by applying stepwise regression with backward elimination for feature selection and implementing two effort prediction methods of Multiple Linear Regression (MLR) and Analogy. The result shows that missing data imputation using KNN and listwise deletion with multiple linear regression approach outperforms the Analogy approach significantly (p>0.05).
Image processing for maturity classification of tomato using otsu and manhattan distance methods Anindita Septiarini; Hamdani Hamdani; Muhammad Sofian Sauri; Joan Angelina Widians
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a21985

Abstract

Currently, image processing-based systems have been widely applied in various fields, one of which is agriculture. The system can be used to classify fruit maturity. Tomato is one of the agricultural products consumed by the community. Therefore, the requirement for ripe tomatoes increases. In this work, the classification method based on image processing for grading the maturity level of tomato was developed to distinguish tomato into three classes: unripe, half-ripe, and ripe. Classification is carried out based on the skin color of the tomato. The method required five main processes; initially, the detection of the region of interest (ROI) applied using the Otsu method followed by the conversion of RGB to HSV color space. Afterward, segmentation with Otsu thresholding on the S channel of the HSV color space was implemented. Subsequently, the extraction of the mean, median, max, and min features on each channel from the YIQ color space; therefore, a total number of 12 features was produced. Finally, the K-nearest neighbor (KNN) method using Manhattan distance is applied with the values of k = 1, 3, 5, 7, and 9. The dataset used consists of 90 images of tomatoes (30 raws, 30 half-ripes, and 30 ripes), where the dataset is divided into two types, including 54 images as training data and 36 images as testing data. The evaluation results were able to achieve the highest accuracy value of 0.9722.
Covid-19 diagnosis and clinical symptom expression levels in a deep learning model Minh-Dat Le Chon; Thai-Ngoc Nguyen
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a23350

Abstract

In December 2019, a new strain of virus called COVID-19 (previously designated as 2019-nCoV) caused the first detected outbreak in Wuhan City, Hubei Province, China and since then spread globally. Viruses can cause several types of damage to the respiratory tract, including Tracheitis; Bronchitis; Pneumonia. It is difficult to distinguish coronavirus pneumonia from some other microbiological causes through X-ray images. However, it can be distinguished from a normal person by chest X-ray and CT-Scan, along with clinical judgment through actual symptoms. The following article provides the process and setup of an analytical machine learning model and provides some clinical comparisons between the effectiveness of the machine learning model and the level of clinical symptomatology of a statistical sample. Medical records of some patients in Ho Chi Minh City, Vietnam.
masked face identification using the convolutional neural network method Daru Thobrani Furqon; Murinto Murinto
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25381

Abstract

            In times of a pandemic like this, masks are part of the main needs in daily activities when outside the home. Because masks can help us avoid the Covid-19 virus, it often happens among people when doing activities outside the home that they forget to wear masks, therefore the level of public awareness of the importance of wearing masks is decreasing.  This study aims to create a system that can classify people who wear masks and do not wear masks as an evaluation material for the level of public awareness of the importance of wearing masks. The total data used in this study were 600 data samples which were divided into two, namely 300 data samples wearing masks and 300 data samples not wearing masks. The CNN architecture in this study is the same as the CNN architecture in general, the  difference is the depth level of the convolution layer and pooling which consists of 5  convolution layers, 5 max pooling, and finally, 2 layers dense In the training process, it gets  the highest accuracy rate of 98%, while in the validation process it gets the highest level of  accuracy at 95%. Therefore, the results of these two processes show that the application of deep earning by utilizing the convolutional neural network can classify objects that wear masks and do not wear masks properly. The results of testing the research dataset are quite maximal by using 40 new dataset testing data to test the convolutional neural network that has been created by the researchers to get an overall accuracy result of 97.5%.
RANDOM FOREST ALGORITM TO PREDICT LANDSLIDE BASED RAINFALL PARAMETERS A H Pratomo; Wilis Kaswidjanti; E T Paripurno; J D Peasetyo; O Y Siregar
Jurnal Informatika Vol 16, No 1 (2022): January 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a25420

Abstract

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Random forest algorithm for algorithm for prediction of high school science students acceptance snmptn based on students assesment report U Pujianto; A R Taufani; J A Aziz
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a25413

Abstract

National Selection for State University (SNMPTN) is one of the selectionlines for admission of new students in Indonesia to enter State Universities byinvitation. Report card grades are one component of the assessment ofadmission of new students to enter state universities on this pathway. Thedifference in standards between universities in determining the admission ofSNMPTN applicants, causing the need to predict based on several relatedfactors. This research uses data mining techniques with Random forestalgorithm. From the results of research that has been done, it was found thatthe Random Forest algorithm can be used to predict students who are acceptedat SNMPTN based on report card grades, obtained from the results of theclassification process with the student report card report survey datasetreceived by SNMPTN, This is indicated by the accuracy, precision, and recallvalues of 93%. Optimization of the random forest algorithm using theoversampling technique with the SMOTE method can improve the classifier'sperformance due to the imbalanced class problem.
Predictive Analytics on Product Sales at Heva Inc. Using K – Means Method Qurrota Nastiti Rizqita Aura Syifa; Murein Miksa Mardhia
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a24754

Abstract

Prediction is the process of estimating something that is most likely to happen in the future based on previous and current knowledge that is owned, with the goal of minimizing the error. Prediction allows people to recognize and then solve difficulties that are occurring or are expected to arise.This study began with preparation, literature review, data collection, and knowledge discovery in databases (KDD). One of the processes is data mining using the K – Means method, which is critical for obtaining the research's results and conclusions. This research also uses the RapidMiner application as a comparison of the results with the results obtained by python coding.By using 4 clusters, products were categorized into 4 labels, namely very good products, good products, bad products, and very bad products.  The research resulted in 11 products in the bad product category, 12 products in the good product category, 10 products in the very good category, and 18 products in the very good product category.  The very good product label was further clarified with visualization to show the best time to restock each recommended product.
Time series clustering of Malaysia Air quality time series data Mohd Aftar Abu Bakar; Fatin Nur Afiqah Suris; Noratiqah Mohd Ariff; Kamarulzaman Ibrahim; Tan Zhen Jie
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25421

Abstract

Air quality is often associated with the area location and activities where air quality in cities is usually more polluted than in rural areas. This study aims to study the pattern of time series data from air quality stations by performing cluster analysis of air quality station based on the particulate matter 10 micrometres or less in diameter (PM10) and particulate matter 2.5 micrometres or less in diameter (PM2.5) time series data. The clusters obtained from the cluster analysis were compared with the station area category and station location. This study which uses air quality data obtained from the Department of Environment, Malaysia from 5 July 2017 until 30 June 2019, shows five types of air quality patterns in Malaysia. The results also show that none of the clusters is dominated by any station's category. Therefore, it is less appropriate to relate the air quality patterns and the station area category. However, the results show that air quality patterns were related to the station's location, where nearby stations have similar air quality patterns.
Spatial and topology feature extraction on batik pattern recognition: a review A A Kasim; M Bakri; A Hendra; A Septriani
Jurnal Informatika Vol 16, No 1 (2022): January 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a25415

Abstract

Abstract. Batik is an Indonesian cultural heritage that has been recognized by UNESCO as an international cultural heritage on October 2, 2009. Patterns of batik produce geometric shapes unique, the number and name of the batik patterns make it difficult to recognize each motif. The objective classification of batik is split image into classes according to the pattern motif motive so easy to recognize in accordance with its feature. Batik can be classified based on the shape of the motive, namely geometric motifs, geometric motifs and motifs non specific. Spatial information is an important aspect of image processing such as computer vision and recognition structure / pattern in the context of modelling and resolution of the uncertainty caused by the ambiguity in the low-level features. Shortcomings inherent in combining two colours and spatial features are not adaptive pattern recognition process of the region across multiple images and histogram matching is not appropriate to capture the colours on the image content. This study discussed a model of spatial features and feature combinations topology with the aim to improve the validation batik image pattern recognition so that the level of the pattern recognition motif batik image could be better. Some of the features that have been used include colour features and spatial features. In addition, this paper discusses the possibility of combining the features in pattern recognition. This paper proposes a combination of features that will be able to improve the validation of image pattern recognition of batik.

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