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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Annual Research Seminar Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik Jiko (Jurnal Informatika dan komputer) Jurnal Generic

Dian Palupi Rini

Universitas Sriwijaya

Author-ID : 2406578

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 18 Documents Claim Missing Document

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Deep ensemble learning for skin lesions classification with convolutional neural network Renny Amalia Pratiwi; Siti Nurmaini; Dian Palupi Rini; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp563-570

One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.

Segmentation of Squamous Columnar Junction on VIA Images using U-Net Architecture Akhiar Wista Arum; Siti Nurmaini; Dian Palupi Rini; Patiyus Agustiansyah; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 10 No 3 (2021)
Publisher : Universitas Sriwijaya

Cervical cancer is the second most common cancer that affects women, especially in developing countries including Indonesia. Cervical cancer is a type of cancer found in the cervix, precisely in the squamous columnar junction (SCJ). Early screening for cervical cancer can be reduce the risk of cervical cancer. One of the popular screening tool methods for the detection of cervical pre-cancer is the Visual Inspection with Acetic Acid (VIA) method. This is due to the level of effectiveness, convenience and low cost. This paper proposes a method for the detection and segmentation of the SCJ region on VIA images using U-Net. This study is the first research conducted using the CNN method to perform segmentation tasks in the SCJ region. The best performance results are shown from the Pixel Accuracy, Mean IoU, Mean Accuracy, Dice coefficient, Precision and Sensitivity values, namely 90.86%, 56.5%, 75.69%, 34.09%, 41.24%, and 56.91%. Keywords: Cervical Pre-cancer, Screening VIA, SCJ, U-Net.

The Combination of Naive Bayes and Particle Swarm Optimization Methods of Studentâ€™s Graduation Prediction Evi Purnamasari; Dian Palupi Rini; Sukemi Sukemi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

This research conducted classification testing on the study case of student graduation prediction in a university. It aims to assist the university in maintaining academic development and in finding solutions for improving timely graduation. This study combined two methods, i.e., Naive Bayes and Particle Swarm Optimization, to produce a better level of accuracy. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. That will be further enhanced using the Particle Swarm Optimization method to produce a better level of accuracy. There are 10 (ten) samples in this study randomly selected from the alumni data of UIGM students in 2011-2014. From the test results, this research resulted in an accuracy value of 90% from the Naive Bayes algorithm testing, after testing the Naive Bayes with Particle Swarm Optimization, which produced an accuracy value of 100%. The conclusion obtained from the results is the Naive Bayes method has a higher accuracy value if combined with Particle Swarm Optimization. Thus the university can more easily predict whether or not the students graduate on time for the upcoming graduation period. The results of this test prove that to predict student graduation using the Naive Bayes method with Particle Swarm Optimization is appropriate.

Text Classification Using Long Short-Term Memory With GloVe Features Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95

Perancangan Data Mining untuk Klasifikasi Prediksi Penyakit ISPA dengan Algoritma C4.5 Dwi Meylitasari Tarigan; Dian Palupi Rini; Verlly Puspita
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Terdapat beberapa kasus kematian anak dibawah umur 1 tahun meninggal akibat terkena penyakit ISPA (Infeksi Saluran Pernafasan Akut) , pada tahun 2015 mencapai 579 kasus kematian anak disebabkan oleh ISPA. Saat ini teknologi dapat memberikan informasi yang cepat dan akurat khususnya di lingkungan kesehatan baik untuk tim kesehatan, dokter, perawat bahkan untuk pasien sendiri agar lebih mudah mengontrol kesehatan mereka. Data mining berhubungan dengan pencarian data untuk menemukan pola atau pengetahuan dari data keseluruhan. Ternyata kumpulan data yang besar dapat menghasilkan sebuah data yang hasilnya dapat memberikan informasi pengetahuan yang baru. Data mining adalah sebuah langkah penting dalam proses menemukan pengetahuan. Pada penelitian ini akan dibahas tentang perancangan data mining menggunakan algoritma C4.5 untuk memprediksi penyakit ISPA akut atau tidak akut pada anak dengan memilih kandidat kriteria yang digunakanpada penelitian ini sehingga dapat memberikan kontribusi kepada tim medis di lingkungan kesehatan untuk mengatahui dan menindak lanjut pasien yang terkena penyakit ISPA.

Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana Ika Oktavianti; Ermatita Ermatita; Dian Palupi Rini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.

Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy Mona Cindo; Dian Palupi Rini; Ermatita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.

Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short-Term Memory dengan Word2Vec Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik; Iman Saladin B. Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 2 (2020): April 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

Seleksi Fitur menggunakan Algoritma Particle Swarm Optimization pada Klasifikasi Kelulusan Mahasiswa dengan Metode Naive Bayes Evi Purnamasari; Dian Palupi Rini; Sukemi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The study of the classification of student graduation at a university aims to help the university understand the academic development of students and to be able to find solutions in improving the development of student graduation in a timely manner. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. The classification accuracy can be improved by selecting the appropriate features. Particle Swarm Optimization is an evolutionary optimization method that can be used in feature selection to produce a better level of accuracy. The testing results of the alumni data using the Naive Bayes method that optimized with the Particle Swarm Optimization algorithm in selecting appropriate features, producing an accuracy value of 86%, 6% higher than the classification without feature selection using the Naive Bayes method.

Seleksi Fitur pada Klasifikasi Penyakit Gula Darah Menggunakan Particle Swarm Optimization (PSO) pada Algoritma C4.5 Dwi Meylitasari Tarigan; Dian Palupi Rini; Samsuryadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Diabetes Mellitus (DM) is a disease caused by blood sugar level increased were higher than the maximum limit. Food consumed tends to contain uncontrolled sugar which could cause the drastic increase of blood sugar level. It is necessary to efforts, to increasing the public awareness to controlling blood sugar and the risks of increasing blood sugar level so as to determine of preventive and early detection measures One of used of data mining technique is information technology in the health sector which used a lot as a decision maker to predicting and diagnosing a several disease. This research aims to optimizing the features on classification of the data mining with the C4.5 algorithm using Particle Swarm Optimization (PSO) to detect the blood sugar level in patient. The dataset used is the effect of physical activity to the Blood Sugar Level at H. Abdul Manan Simatupang Kisaran Regional Public Hospital. The amount of dataset used is 42 record with 10 attributes. The result of this research obtained that the Particle Swarm Optimization (PSO) may increasing the accuracy performance of C4.5 from 86% to 95%. Whereas the evaluation result of the AUC Value increasing from 0,917 to 0,950. From those 10 attributes which are then selection with using PSO into 7 attributes used to determine the prediction of sugar level. Therefore the Algorithm C4.5 using the Particle Swarm Optimization (PSO) may provide the best solution to the accuracy of detection blood sugar levels.

Co-Authors Akhiar Wista Arum andre hardoni Darmawahyuni, Annisa Defrian Afandi Deris Stiawan Desty Rodiah Dwi Meylitasari Tarigan Dwi Meylitasari Tarigan Endang Lestari Ruskan Ermatita Ermatita Ermatita Evi Purnamasari Ika Oktavianti Iman Saladin B. Azhar Mona Cindo Muhammad Naufal Rachmatullah Muhammad Naufal Rachmatullah Muhammad Nejatullah Sidqi Patiyus Agustiansyah Qurrota ‘Aini Muthmainnah Rahma Satila Passa Renny Amalia Pratiwi Reza Firsandaya Malik Reza Firsandaya Malik Samsuryadi Samsuryadi Samsuryadi Siti Nurmaini Sukemi Sukemi Tri Kurnia Sari Verlly Puspita Winda Kurnia Sari

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