Article Per Year (5 Year)
p-Index From 2020 - 2025
3.687
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics IJoICT (International Journal on Information and Communication Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Generic
Dian Palupi Rini
Sriwijaya University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Perbandingan Algoritma Random Forest Classifier, Support Vector Machine dan Logistic Regression Clasifier Pada Masalah High Dimension (Studi Kasus: Klasifikasi Fake News) Willy, Willy; Rini, Dian Palupi; Samsuryadi, Samsuryadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3177

Abstract

Fake news is false information that looks like it is true. News can also be said as a political weapon whose truth cannot be accounted for which is spread intentionally to achieve a certain goal. Classification of news texts requires calculating a method for each word in the document. Each word processed per document means that the number of data dimensions is equal to the number of words. The more the number of words in a document, the more the number of dimensions in each data (high dimension). The large number of dimensions (high dimension), causes the model-making process (training) to be long and the shortcomings are also clearly visible in seeing the similarity of documents (document similarity). The dataset used in this study amounted to 20000 and 17 attributes. The method used in this study uses a Random Forest Classifier (RFC), Support Vector Machine (SVM) and Logistic Regression (LR) with high dimensions and the results of this study are to obtain a comparison of the accuracy values for each method used
Klasifikasi Data Penderita Skizofrenia Menggunakan CNN-LSTM dan Cnn-Gru pada Data Sinyal EEG 2D Firmansyah; Rini, Dian Palupi; Sukemi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1072

Abstract

Schizophrenia (SZ) is a brain disease with a chronic condition that affects the ability to think. Common symptoms that are often seen in SZ patients are hallucinations, delusions, abnormal behavior, speech disorders, and mood disorders. SZ patients can be diagnosed using electroencephalographic (EEG) signals. This study conducted a comparative analysis of the best method in EEG classification using the Deep Learning (DL) method. The author uses the 2D Convolutional Neural Network (2D-CNN) method with different layers. The first 2D-CNN uses a layer of Long Short Term memory(LSTM) and Gate Recurrent Unit(GRU). The dataset used consists of two types of EEG signals obtained from 39 healthy individuals and 45 schizophrenic patients during a resting state. Test results for the accuracy of the F1-score from 5 times testing the CNN method using the LSTM layer has the best accuracy value of 94.12% and 5 times testing the CNN method using the GRU layer has the best accuracy value of 94.12%.
APPLICATION OF THE ARIMA MODEL FOR PREDICTION OF MONTHLY DIVORCE RATE IN THE RELIGIOUS COURTS IN SOUTH SUMATRA Fitriati, Sri; Rini, Dian Palupi; -, Firdaus
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4649

Abstract

This research discusses predictions of divorce rates in Religious Courts in South Sumatra. This is important to do because the divorce rate has a changing trend. Sometimes high and sometimes low but always at a high trend number, so soCourt officials or social scientists in developing effective strategies for overcoming marriage problems, allocating resources, or supporting families who need counseling can be prepared in advance, especially at the Religious Courts in South Sumatra to reduce the divorce rate because the purpose of marriage is not to divorce. This research discusses the divorce rate in terms of predicting/forecasting because much research has been done on the divorce rate by examining the causes of divorce. This research uses the ARIMA (Autoregressive Integrated Moving Average) model to predict. The ARIMA model is a method that has been widely used in forecasting research to get good results. The research results are that the ARIMA model used is (1,0,2) and (2,0,2), with an error rate of only 0.48% using the MAPE method. Keywords: predictions, numbers, divorce, arima, mape.
Comparison Of The Results Of The Jaccard Similarity And KNearest Neighbor Algorithms Using The Case Based Reasoning (CBR) Method On An Expert System For Diagnosing Pediatric Diseases Hidayatullah, Altundri Wahyu; Rini, Dian Palupi; Arsalan, Osvari; Miraswan, Kanda Januar
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.55

Abstract

Health ranks highest in supporting the continuity of every human activity, especially children. The availability of a doctor is still relatively lacking, especially in remote areas. This makes people have difficulty in diagnosing certain diseases so that medical treatment becomes too late and can even be fatal for the patient. So it is necessary to create a system that has the ability to be able to diagnose diseases in children like an expert. The method used in this study is Case Based Reasoning (CBR) with the Jaccard Similarity Algorithm and K-Nearest Neighbor. Jaccard Similarity is one way to calculate the similarity of two objects (items) which are binary. Similarity calculations are used to generate values whether or not there is a similarity between new cases and existing cases in the case base. While the K-Nearest Neighbor (KNN) Algorithm belongs to the instance-based learning group. The KNN algorithm allows the program to find old cases that are most similar to the current case. Based on the test results using 50 sample data, the expert system can provide diagnostic results in accordance with expert diagnoses. The accuracy results for the K-Nearest Neighbor Algorithm are 72% while the accuracy results for the Jaccard Similarity Algorithm are 70%.
Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining Annisa, Siti; Rini, Dian Palupi; Abdiansah, Abdiansah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.802

Abstract

A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%.
Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16 Idawati, Idawati; Rini, Dian Palupi; Primanita, Anggina; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7553

Abstract

Breast cancer classification is a process to determine the type and characteristics of breast cancer based on the characteristics of cancer cells. In this research, a system is designed to classify breast cancer using ultrasound images which are then processed using the Convolutional Neural Network method with the VGG-16 architecture. The aim of the research is to develop a breast cancer classification system using Convolutional Neural Network (CNN) and evaluate the classification results using Convolutional Neural Network (CNN) with the VGG-16 architecture. In breast cancer classification, three classes are considered: normal, benign, and malignant. The steps in the classification process include image input, filtering, resizing, data augmentation, and data digitization. The best results were obtained in this test using the SGD optimizer hyperparameter, learning rate 0.001, epoch 20 and batch size 32 producing an accuracy value of 78.87%, a precision value of 75.69%, an AUC value of 79.85% and an f1 score value of 74.67%.
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition Rini, Dian Palupi; Sari, Tri Kurnia; Sari, Winda Kurnia; Yusliani, Novi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp547-560

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients Nanda, Dika Kurnia; Rini, Dian Palupi
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1042

Abstract

The Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately.
Image Clustering Optimization: A Comparison of Single vs Hybrid Feature Extraction Technique Arum, Akhiar Wista; Rini, Dian Palupi; Dzaky, Dewa Sheva; Syamsidi, Iman Carrazzi; Arrizal, Affandi; Zikri, Anharul; Nugraha, Wahyu
Generic Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v17i1.236

Abstract

In image processing, clustering techniques aim to group images into distinct classes. These methods utilize feature extraction, the first step toward pattern recognition, and analyze unprocessed data to derive valuable information. The impact of various feature extraction methods on the clustering outcomes is the focus of this research study. In this case, the issue to answer is: does the use of different feature extraction methods affect the effectiveness of clustering? The featured methods of extraction in this study are Histogram of Oriented Gradients, Local Binary Patterns, and HOG-LBP. These methods were used in conjunction with the Self-Organizing Map and K-means. The results show that LBP with K-Means gives exceptionally effective results with silhouette value 0.3615, a Davies-Bouldin index of 0.8128, and a Calinski–Harabasz index of 51940.5105. These results confirm the effectiveness of the extraction method used and the clustering algorithm. Like the other methods of extraction, the dependability of effectiveness centers on the capability of the feature selection algorithm’s ability to differentiate the dataset. This is one of the feature extraction challenges for image clustering in large datasets – the focus is on feature extraction and the need for precise definition is critical.
Penerapan Hierarchical Agglomerative Clustering Untuk Penentuan Faktor Penyebab Ketidaktuntasan Belajar Matematika Arnelawati; Rini, Dian Palupi; Ermatita
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.200

Abstract

Clustering merupakan sebuah topik yang menarik dan banyak dibahas oleh peneliti. Penerapan metode-metode dalam clustering telah banyak menyelesaikan permasalahan di dunia nyata. Di sisi lain, ketuntasan belajar matematika masih menjadi salah satu permasalahan besar di dunia pendidikan, khususnya pendidikan menengah. Berbagai entitas dan variabel mempengaruhi ketuntasan dalam belajar sesorang siswa. Clustering merupakan upaya untuk mengelompokkan berbagai objek sesuai dengan spesifikasi yang memiliki karakteristik kedekatan. Metode Agglomerative Hierarchical Clustering (AHC) merupakan salah satu metode clustering yang tergolong baik dan banyak diterapkan dalam menyelesaikan permasalahan. Pada penelitian ini AHC diterapkan untuk menyelesaikan klasterisasi faktor yang menyebabkan ketidaktuntasan belajar matematika. Hasil yang diperoleh dari 19 faktor penyebab, maka dapat diklaster menjadi 4 kelompok besar, yaitu (1) Kualitas Pengajaran dan Bimbingan Guru, (2) Minat dan Motivasi Siswa, (3) Pemahaman Materi, dan (4) Keterlibatan dan Lingkungan Kelas. Kesimpulan yang dapat diperoleh dari penelitian ini adalah Metode AHC dapat melakukan klasterisasi faktor penyebab ketidaktuntasan siswa dalam belajar matematika dengan baik.
Co-Authors Abdiansah Abdiansah, Abdiansah Afandi, Defrian Alvi Syahrini Utami Arnelawati Arrizal, Affandi Arum, Akhiar Wista Astuti, Dwi Lydia Zuharah Cahyadi, Gabriel Ekoputra Hartono Cindo, Mona Darmawahyuni, Annisa Desty Rodiah Dzaky, Dewa Sheva Ermatita - Firdaus - Firmansyah Fitriati, Sri Hidayatullah, Altundri Wahyu Idawati Idawati Kanda Januar Miraswan Kurnia Sari, Winda M. Nejatullah Sidqi Mastura Diana Marieska Muhammad Naufal Rachmatullah Muhammad Taufik Roseno, Muhammad Taufik Nanda, Dika Kurnia Novi Yusliani Osvari Arsalan Passa, Rahma Satila Primanita, Anggina Putri Mirani Rosemari, Pita Samsuryadi Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sari, Tri Kurnia Siti Annisa, Siti Siti Nurmaini Sukemi Syamsidi, Iman Carrazzi Syaputra, Hadi Utari, Aspirani Wahyu Nugraha Willy, Willy Winda Kurnia Sari YUNITA Zikri, Anharul