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All Journal Jurnal Rekayasa Sistem Industri Jurnal Teknik
Adnyani, Ni Luh Saddhwi Saraswati
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Sentimen Data Ulasan Pengguna MyPertamina di Twitter dengan Metode Text Mining Hutabarat, Andita Widya Valencia; Adnyani, Ni Luh Saddhwi Saraswati; Suryadi, Kadarsah
Jurnal Rekayasa Sistem Industri Vol. 13 No. 1 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i1.6958.145-154

Abstract

To ensure that the distribution process of subsidized fuel is more well-targeted, PT Pertamina has developed an application called MyPertamina. The increasing number of MyPertamina users has led to an increasing number of reviews related to the use of MyPertamina. Reviews of MyPertamina fill various social media channels, including Twitter. However, the analysis of user perceptions through social media has not been optimal. Therefore, a better user sentiment mapping is needed. This study was conducted to answer this need by building a text mining model and designing a prototype that can extract and analyze sentiments from tweets related to MyPertamina. This research adopts the CRISP-DM methodology, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data obtained for model development reached 6,920 tweet data. Each data was classified into one of three sentiment categories, namely positive, negative, and neutral. After data preparation, 2,057 data were used for model development. The models tested in this study consist of Support Vector Machine (SVM), Multinomial Naïve Bayes, Gaussian Naïve Bayes, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms. The model that produced the best evaluation score and was selected for prototype development is the SVM model with an accuracy score of 83.74%, weighted precision of 83.96%, weighted recall of 83.74%, and weighted F1-score of 83.72%. The prototype is used for extracting and predicting sentiment for new datasets, which can then be visualized in the form of graphs and word clouds according to the user's needs.
Pembangunan Model Pendeteksi Risiko Preeklamsia pada Ibu Hamil dengan Menggunakan Metode Data Mining Ilham, Muhammad; Adnyani, Ni Luh Saddhwi Saraswati; Suryadi, Kadarsah
Jurnal Teknik: Media Pengembangan Ilmu dan Aplikasi Teknik Vol 23 No 1 (2024): Jurnal Teknik - Media Pengembangan Ilmu dan Aplikasi Teknik
Publisher : Fakultas Teknik - Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55893/jt.vol23no1.543

Abstract

Preeclampsia is a pregnancy complication indicated by an increase in blood pressure that occurs after 20 weeks of gestation and the presence of protein in the urine. If not treated quickly, preeclampsia can lead to maternal and fetal death. Therefore, a method that can help health workers to provide early detection of preeclampsia is needed. One method that can be used is data mining. This study was conducted with the aim of developing a model based on data mining methods that can be used as a tool to identify patients with preeclampsia and also to identify associated risk factors. This study was conducted using six data mining classification algorithms on 109 obstetric clinic patient data at the Jakarta Pondok Kopi Islamic Hospital (RSIJPK). The input features used as preeclampsia detection attributes were obtained based on the results of a literature study and consultations with obstetricians. Based on the results of the model evaluation, logistic regression has the best performance in detecting preeclampsia with accuracy value of 98% and precision level of 100%. In addition, this study also designed an application prototype that can be used by health workers to quickly detect the risk of preeclampsia in pregnant women.
Co-Authors Hutabarat, Andita Widya Valencia Kadarsah Suryadi Muhammad Ilham