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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Poningsih
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Quantum Perceptron in Predicting the Number of Visitors to E-Commerce Websites in Indonesian Solikhun, Solikhun; Carissa Arishandy, Dinda; Batubara, Ela Roza; Poningsih
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2334

Abstract

In the current digital era, e-commerce has become the backbone of Indonesia's digital economy, which is experiencing rapid growth. However, competition in this industry is becoming increasingly fierce, indicating the importance of predicting the number of website visitors for an effective marketing strategy. Quantum Perceptron, the latest quantum computing innovation, promises a more accurate and efficient approach compared to conventional methods such as classical Perceptron. This research proposes the use of Quantum Perceptron to predict the number of visitors on large e-commerce platforms in Indonesia. The data used in the research is data on the number of e-commerce visitors obtained from the katadata.com website. Data from Shopee, Tokopedia, Lazada, Blibli, and Bukalapak were used to analyze and compare predictions with classical perceptron methods, showing the significant potential of Quantum Perceptron in supporting the development of more efficient business strategies. The research results show that the Quantum Perceptron algorithm can make predictions very well compared to the classical perceptron, proven by the Quantum Perceptron having a perfect accuracy of 100% with a total of 2 epochs while the classical perceptron has 100% accuracy with a total of 10 epochs. Quantum perceptron has better performance and shorter time, this can be seen from the smaller number of epochs.
Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning Sundari Retno Andani; Poningsih; Abdul Karim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6357

Abstract

TThis study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy.
Application of the C5.0 Algorithm to Determine the Level of Public Satisfaction with the E-KTP Recording Service at the Bandar Sub-District Office Hardani, Dini Fadila; Poningsih; Purba, Yuegilion Pranayama
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.344 KB) | DOI: 10.59934/jaiea.v1i1.49

Abstract

Community satisfaction at the Bandar Sub-district Office is one of the most important things in assessing the level of e-KTP recording services provided by the agency to the community. The purpose of this study was to determine the quality of the e-KTP recording service at the Bandar Sub-district Office in terms of the Service Procedure, Time, Behavior and Facilities aspects of the Bandar sub-district community. At the Bandar Camat Office these four aspects have not been measured with certainty, so the agency finds it difficult to determine which aspects must be improved. The method used in this study is the C5.0 Algorithm, where the data source used is a questionnaire/questionnaire technique given to the people of Bandar sub-district. The research test process uses Rapid Miner software to create a decision tree. The results of the study obtained 12 rules for classifying the level of community satisfaction with e-KTP recording services. The C5.0 algorithm can be used in cases of community satisfaction with an accuracy rate of 100%. From these results, it is expected to improve the quality of service for the e-KTP recording of the Bandar Sub-District Office to be even better.
Co-Authors Abdul Karim Batubara, Ela Roza Carissa Arishandy, Dinda Hardani, Dini Fadila Purba, Yuegilion Pranayama Solikhun Solikhun, Solikhun Sundari Retno Andani