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All Journal Sistemasi: Jurnal Sistem Informasi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Ningrum, Meriana Prihati
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Applying A Supervised Model for Diabetes Type 2 Risk Level Classification Dhani, Ahmad; Lestari, Danur; Ningrum, Meriana Prihati; Fakhrizal, M. Andhika; Gandini, Ganis Lintang
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1105

Abstract

Diabetes can lead to heart attacks, kidney failure, blindness, and increased risk of death. This research was conducted with the aim of classifying a diabetes risk dataset. In this context, performance comparison was carried out on three supervised learning algorithms: K-Nearest Neighbor, Naive Bayes, and Random Forest, against a dataset containing information on specific indicators related to diabetes risk. Additionally, this study also aimed to evaluate the accuracy comparison of the results produced by these three algorithms. The results of this research show that Random Forest performs very well in detecting diabetes, prediabetes, and non-diabetes, with high precision, recall, and F1-score levels. Meanwhile, although the results are still below Random Forest, both Naive Bayes and K-NN still demonstrate significant performance, especially regarding prediabetes cases. In conclusion, from the comparison results, the Random Forest algorithm shows the highest accuracy level at 99%, followed by K-Nearest Neighbor with an accuracy of 85%, while Naive Bayes has the lowest accuracy rate of 74%. This research indicates that the Random Forest algorithm excels in classifying data compared to the other two algorithms.
Sentiment Analysis of Twitter Reviews on Google Play Store Using a Combination of Convolutional Neural Network and Long Short-Term Memory Algorithms Ningrum, Meriana Prihati; Mutia, Risma; Azmi, Habil; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1625

Abstract

In this era of rapidly evolving technology, the use of social media has become widespread and has become a major platform for sharinhabibahdian.khalifah@ogr.deu.edu.trg people's opinions and views. Google Play Store, as one of the main platforms for digital content, provides access to various applications including Twitter, which allows users to provide reviews and ratings. This research aims to conduct sentiment analysis of Twitter reviews on the Google Play Store using two algorithms namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The data used is 4999 reviews after the scraping process. From the experimental results, an accuracy value of 84.67%, recall of 81%, and precision of 84% were obtained on CNN, and an accuracy of 82.19% recall of 69%, and precision of 87% on LSTM. From these results, it can be seen that the highabibahdian.khalifah@ogr.deu.edu.trhest accuracy value is obtained in the CNN algorithm. Although the difference in accuracy is small, the CNN algorithm provides better results in classifying sentiment analysis data on Twitter reviews on the Google Play Store.
User Experience Evaluation of the Maxim Application using the HEART Metrics Method Ningrum, Meriana Prihati; Megawati, Megawati; Saputra, Eki; Fronita, Mona; Anofrizen, Anofrizen
SISTEMASI Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5286

Abstract

The rapid growth of online transportation applications has led to increasing user expectations for efficient, intuitive, and reliable user experiences (UX). Maxim, as one of the emerging platforms in Indonesia, offers competitive pricing and wide service coverage, yet still faces challenges such as GPS inaccuracy, fare inconsistencies, and limitations with cashless payments. This study aims to evaluate the User Experience (UX) of the Maxim application in Pekanbaru using the HEART Metrics framework, which consists of five variables: Happiness, Engagement, Adoption, Retention, and Task Success. Data were collected through a Likert-scale questionnaire distributed to 100 respondents, with the sample size determined using the Lemeshow formula. The data were tested for validity and reliability, and analyzed using SPSS and Microsoft Excel. This study provides insight into user expectations and preferences as a foundation for service improvement. The results show that Happiness (73.8%), Adoption (72.4%), and Task Success (70.2%) are at a high level of usability, while Engagement (65.8%) and Retention (67.7%) did not meet the 70% usability benchmark. Overall, the Maxim application offers a good user experience, but improvements are needed in user engagement and retention through the addition of innovative features, GPS optimization, and enhanced service quality.
Co-Authors Anofrizen Anofrizen Azmi, Habil Dhani, Ahmad Eki Saputra Fakhrizal, M. Andhika Gandini, Ganis Lintang Khalifah, Habibah Dian Lestari, Danur Megawati - Mona Fronita, Mona Mutia, Risma