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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) International Journal of Advances in Intelligent Informatics Journal on Education
Sani Muhamad Isa
Bina Nusantara University
Computer Science & IT Education Electrical & Electronics Engineering

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Digital Technology: the Effect of Connected World to Computer Ethic and Family Benfano Soewito; Sani Muhamad Isa
CommIT (Communication and Information Technology) Journal Vol. 9 No. 1 (2015): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v9i1.1654

Abstract

The development of digital technology such as smartphones, tablets and other gadgets grows very rapidly in the last decade so does the development of mobile applications for those mobile systems or smartphones. Unfortunately, those applications often do not specify the age range for their users. This is actually a problem in the world of digital technology and software development. It is not yet known whether the applications is good be used for children or not. Nowadays, parents are faced with the dilemma of allowing their children to use these modern gadgets, which often lead to serious addiction or keeping them in the dark and risk raising ignorant kids. This research shows 80% of respondents agree or strongly agree that the gadget will affect to the development of children social skill. Therefore, in this research, the framework for ethical assessment is introduced and it can be applied to digital technology included gadget and its application in order to mitigate the negative effect of digital technology and gadgets.
Implementation of Data Mining for Churn Prediction in Music Streaming Company Using 2020 Dataset Christian Hardjono; Sani Muhamad Isa
Journal on Education Vol 5 No 1 (2022): Journal on Education: Volume 5 Nomor 1 Tahun 2022
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v5i1.740

Abstract

Customer is an important asset in a company as it is the lifeline of a company. For a company to get a new customer, it will cost a lot of money for campaigns. On the other hand, maintaining old customer tend to be cheaper than acquiring a new one. Because of that, it is important to be able to prevent the loss of customers from the products we have. Therefore, customer churn prediction is important in retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural Network, and Logistic Regression to compare the performance generated using data from a company that develops a song streaming application. The company suffers from the churn rate of the customer. Uninstall rate of the customers reaching 90% compared to the customer’s installs. The data will come from Google Analytics, a service from Google that will track the customer’s activity in the music streaming application. After finding out the method that will give the highest accuracy on the churn prediction, the attribute of data that most influence on the churn prediction will be determined.
Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah; Zahra Nabila Izdihar; Sani Muhamad Isa
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.884

Abstract

Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgments. In addition, the process of diagnosis requires a longer time. One technique can be applied by applying advanced deep learning, namely, Transfer Learning with Deep Convolutional Neural Network (Deep CNN) and modified Multilevel Meta Ensemble Learning using Softmax. The purpose of this research was to improve the accuracy of the pneumonia classification model. This study proposes a classification model with a meta-ensemble approach using five classification algorithms: Xception, Resnet 15V2, InceptionV3, VGG16, and VGG19. The ensemble stage used two different concepts, where the first level ensemble combined the output of the Xception, ResNet15V2, and InceptionV3 algorithms. Then the output from the first ensemble level is reused for the following learning process, combined with the output from other algorithms, namely VGG16 and VGG19. This process is called ensemble level two. The classification algorithm used at this stage is the same as the previous stage, using KNN as a classification model. Based on experiments, the model proposed in this study has better accuracy than the others, with a test accuracy value of 98.272%. The benefit of this research could help doctors as a recommendation tool to make more accurate and timely diagnoses, thus speeding up the treatment process and reducing the risk of complications.
Predictive business intelligence dashboard for food and beverage business Nathaniel Wikamulia; Sani Muhamad Isa
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5162

Abstract

This research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights.
Co-Authors Benfano Soewito Christian Hardjono Ghinaa Zain Nabiilah Nathaniel Wikamulia Simeon Yuda Prasetyo Zahra Nabila Izdihar