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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

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Aplikasi QR-code untuk sistem daftar hadir: Solusi digitalisasi administrasi di SMA dan SMK Rodiah, Desty; Yusliani, Novi; Abdiansah; Utami, Alvi Syahrini; Miraswan, Kanda Januar; Marieska, Mastura Diana; Yunita; Rini, Dian Palupi
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 8 No 2 (2025)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v8i2.22696

Abstract

Kebijakan Merdeka Belajar dari Kemendikbud RI mendorong guru untuk menerapkan pendekatan pengajaran yang fleksibel dan adaptif melalui integrasi teknologi dalam kegiatan pembelajaran. Dalam konteks ini, program pengabdian kepada masyarakat memberikan pelatihan untuk mengembangkan sebuah aplikasi daftar hadir berbasis QR-Code menggunakan Python untuk guru SMA dan SMK. Aplikasi ini dirancang untuk mencatat kehadiran siswa secara cepat, tepat, dan efisien, serta mendukung kemudahan administrasi dan memberikan pengalaman langsung dalam penggunaan teknologi pemrograman. Kegiatan pengabdian ini menerapkan metode Participatory Action Research (PAR), yang meliputi lima tahap: To Know (menggali kebutuhan mitra melalui survei), To Understand (mengevaluasi pelatihan sebelumnya), To Plan (menyusun materi dan instrumen evaluasi), To Act (melaksanakan pelatihan melalui presentasi dan praktikum), dan To Change (melakukan evaluasi). Evaluasi dilakukan dengan pendekatan N-Gain dan skala Likert. Hasil N-Gain sebesar 20,90% menunjukkan efektivitas pelatihan yang kurang meskipun terdapat peningkatan nilai rata-rata sebesar 7,37 poin. Hal ini dipengaruhi oleh latar belakang peserta yang sudah berpengalaman, sehingga materi dan soal perlu dikembangkan lebih lanjut. Di sisi lain, hasil Likert menunjukkan persepsi peserta yang sangat positif. Kendala koneksi internet sempat memengaruhi praktikum, namun narasumber dan mahasiswa aktif membantu peserta yang mengalami hambatan tersebut.
Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model Rini, Dian Palupi; Afandi, Defrian; Rodiah, Desty
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

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Abstract

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.
Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network Sari, Dwi Mei Rita; Nurmaini, Siti; Rini, Dian Palupi; Sapitri, Ade Iriani
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 1 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298.
Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method Sari, Tri Kurnia; Rini, Dian Palupi; Samsuryadi
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 2 (2023)
Publisher : Universitas Sriwijaya

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Abstract

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1- score values on the epilepsy signal EEG data than the LeNet-5 architecture.
Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification Sidqi, Muhammad Nejatullah; Rini, Dian Palupi; Samsuryadi
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 1 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Liver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body's metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder and has abnormalities that are very difficult to detect because the liver works as usual even though some liver functions have been damaged. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight value of neural networks with the Particle Swarm Optimization algorithm. The results of optimizing the PSO weight value get the best accuracy of 92.97% of the Hepatitis dataset, 79.21%, Hepatitis 91.89%, and Hepatocellular 92.97% which is greater than just using a Deep Neural Network.
Residual pixel-wise semantic segmentation for assessing enlarged fetal heart: a preliminary study Roseno, Muhammad Taufik; Nurmaini, Siti; Rini, Dian Palupi; Saputra, Tommy; Mirani, Putri; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Syaputra, Hadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9244

Abstract

The four-chamber view is a crucial scan plane routinely employed in both second-trimester perinatal screening and fetal echocardiographic examinations. Sonographers typically measure biometrics in this plane, such as the cardiothoracic ratio (CTR) and heart axis, to diagnose fetal heart anomalies. However, due to the echocardiographic artifacts, the assessment not only suffers from low efficiency but also inconsistent results depending on the operators’ skills. This study proposes a residual pixel-wise semantic segmentation, which segmented the fetal heart and thoracic contours in a 4-chamber view for assessing an enlarged fetal heart condition. The accuracy of intersection-over-union (IoU) and dice coefficient similarity (DCS) is used for model validation to further regulate the evaluation procedure. We use 1174 US images, comprising about 560 enlarged heart images, and about 614 normal heart images. Out of these data, 248 images are used for unseen data, and the remaining for training/validation processes. The performance of the proposed model, when tested on unseen data, achieved satisfactory results with 97.71% accuracy, 90.36% IoU, and 94.93% DCS. These metrics collectively demonstrate the satisfactory performance of the proposed model compared to existing segmentation models. The outcomes underscore that the proposed model establishes a state-of-the-art standard for enlarged fetal heart detection.
Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals Rini, Dian Palupi; Kurnia Sari, Winda
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.857

Abstract

EEG emotion is a research topic that has gained significant attention in the development of emotion classification systems. This study focuses on optimizing the hyperparameters of CNN (Convolutional Neural Network) and DNN (Deep Neural Network) for classifying EEG emotion signals. The data is divided into three train-test data ratio scenarios: 80:20, 70:30, and 60:40. After modeling and the classification process, hyperparameter tuning was conducted on both models to achieve the best results. Experimental results showed the highest accuracy of 98.36% for CNN, while DNN reached 98.18% in the 80:20 data ratio scenario. Despite the high accuracy, the differences in the loss curves between CNN and DNN reflect the complexity of the performance of both models. The train-test data ratio was also found to significantly impact the performance of both models, with the 80:20 data split yielding the best results, while the 70:30 and 60:40 splits resulted in slightly lower accuracies.
Klasifikasi Sinyal EEG Untuk Mengenali Jenis Emosi Menggunakan Recurrent Neural Network Utari, Aspirani; Rini, Dian Palupi; Sari, Winda Kurnia; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7162

Abstract

This research focuses on in-depth exploration and analysis of the application of two types of Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The two models are drilled with the same parameters, consist of three layer, use the relu activation function, and apply 1 dropout level. In order to compare the performance of the two, experiments were carried out using five groups of datasets for training and performance evaluation purposes. The evaluation includes metrics such as accuracy, recall, F1-score, and area under the curve (AUC). The dataset used is Eeg Emotion which contains 2458 unique variables. In terms of performance, LSTM succeeded in outperforming GRU in the task of classifying emotional data based on EEG signals. On the other hand, GRU shows advantages in accelerating the training process compared to LSTM. Although the accuracy of both methods is almost similar in all data divisions, in the evaluation of the ROC curve, the LSTM model demonstrates superiority with a more optimal curve compared to GRU.
Klasifikasi Sinyal EEG Untuk Mengenali Jenis Emosi Menggunakan Deep Learning Rosemari, Pita; Rini, Dian Palupi; Sari, Winda Kurnia
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7172

Abstract

This research focuses on in-depth exploration and analysis of the application of three types of deep learning, namely Convolutional Neural Networks (CNN), Bidirectional LSTM (BI-LSTM) and Deep Neural Network (DNN). The three models are trained with the same parameters, consisting of three layers, using the Relu activation function, and applying 1 dropout level. In order to compare the performance of the three, experiments were carried out using three dataset groups for training and evaluation of performance. The evaluation includes metrics such as accuracy, recall, F1-Score, and areas under the curve (AUC). The dataset used is EEG Emotion which consists of 2458 unique variables. In terms of performance, BI-LSTM succeeded in outperformed the performance of CNN and DNN in the task of classification of emotional data based on EEG signals. On the other hand, CNN and DNN show excess in the acceleration of the training process compared to BI-LSTM. Although the accuracy of the two methods is almost similar in all data distribution, but in the evaluation of the ROC curve, the BI-LSTM model demonstrates superior with a more optimal curve than CNN and DNN.
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 : Universitas 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%.
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