Convolutional Neural Network with Feature Extraction to Improve the Classification Accuracy of Multi-Class Facial Skin Disorders Rismayani, Amil Ahmad Ilham, Andani Achmad, Muhammad Rifqy Yudhiestra Rachman International Journal of Online and Biomedical Engineering, 2025 This study aims to improve the accuracy of multi-class facial skin disorder classification using a convolutional neural network (CNN) enhanced with feature extraction. The CNN method for classifying multi-class facial skin disorders uses color feature extraction using color moment (CM) and Laplacian of Gaussian (LoG) for direct shape with image data. Multi-class facial skin disorders include oily, hyperpigmentation, acne, redness, blackhead, and normal. A public dataset is used with 7151 images with a balanced number of data classes. Researchers divided the data set into 80% for training and 20% for testing. Experiments are carried out through training and testing with 100 epochs, resulting in an accuracy of 85% for CNN, 66% for the CM-CNN, 80% for LoG-CNN, and 91% for CM-LoG-CNN. The highest classification accuracy is achieved with the CM-LoG-CNN combination.
Facial Skin Disorder Prediction Based on Non-Visual Information Using ANN Model Rismayani, Amil Ahmad Ilham, Andani Achmad, Muhammad Rifqy Yudhiestra Rachman Proceedings International Conference on Informatics and Computational Sciences, 2024 Facial skin disorders are common health problems affecting a person’s quality of life. While diagnosing facial skin disorders usually requires a direct visual examination by a dermatologist, non-visual information can sometimes help the diagnosis process. Currently, facial skin disorders such as acne and hyperpigmentation are the dominant disorders that are most often the problem of every patient who visits the clinic for treatment, according to experts. This study aims to apply an ANN (Artificial Neural Networks) model that can predict facial skin disorders using non-visual data. Age, gender, skin type, family history, symptoms, and risk factors are non-visual data used to train the ANN model. A method used to accurately predict various facial skin disorders using non-visual information integrated into an ANN model. Feature selection, model building, training, validation, and model performance evaluation enable the identification of facial skin disorders based on non-visual information. The results show that ANN technology and non-visual information can offer overall diagnosis efficiency to predict facial skin disorders. Furthermore, the accuracy obtained based on non-visual data is 100%.
Using Preference Selection Index for Design Decision Support System Selection of Farmers Group Recipients of Assistance Rismayani, Lande Sudianto, Abdul Malik I. Buna, Irma Surya Kumala Idris, Martina Pineng, Herlinda 7th International Conference on Inventive Computation Technologies Icict 2024, 2024 The South Sulawesi Province Food Security, Food Crops, and Horticulture Service in Indonesia distributed agricultural aid to several districts in South Sulawesi Province. Distributing aid is still being carried out by collecting agricultural data. After looking at the results of the data collection, the South Sulawesi Province Food Security, Food Crops and Horticulture Service saw that the land area and production yields were the least and needed to be assisted with agricultural equipment and superior seeds so that production results would be more significant. This study uses the Preference Selection Index method, which is used as a decision support system (DSS) because it can be used for multi-criteria and minimal and straightforward calculations based on statistical concepts without the need for weight attributes. As for the results of the research, the system that has been built can help determine selection decisions using the criteria of land age point, land area point, commodity point, production result point and previous aid point, which are processed by choosing multi-criteria and priority order so that all data is combined into one with the assessment weight that has been obtained through the assessment.
Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance Triwiyanto Triwiyanto, Wahyu Caesarendra, Mauridhi Hery Purnomo, Maciej Sułowicz, I Dewa Gede Hari Wisana, Dyah Titisari, Lamidi Lamidi, Rismayani Rismayani Micromachines, 2022 High accuracy and a real-time system are priorities in the development of a prosthetic hand. This study aimed to develop and evaluate a real-time embedded time-domain feature extraction and machine learning on a system on chip (SoC) Raspberry platform using a multi-thread algorithm to operate a prosthetic hand device. The contribution of this study is that the implementation of the multi-thread in the pattern recognition improves the accuracy and decreases the computation time in the SoC. In this study, ten healthy volunteers were involved. The EMG signal was collected by using two dry electrodes placed on the wrist flexor and wrist extensor muscles. To reduce the complexity, four time-domain features were applied to extract the EMG signal. Furthermore, these features were used as the input of the machine learning. The machine learning evaluated in this study were k-nearest neighbor (k-NN), Naive Bayes (NB), decision tree (DT), and support vector machine (SVM). In the SoC implementation, the data acquisition, feature extraction, machine learning, and motor control process were implemented using a multi-thread algorithm. After the evaluation, the result showed that the pairing of the MAV feature and machine learning DT resulted in higher accuracy among other combinations (98.41%) with a computation time of ~1 ms. The implementation of the multi-thread algorithm in the pattern recognition system resulted in significant impact on the time processing.
Using Artificial Neural Network for System Education Eye Disease Recognition Web-Based Rismayani, Martina Pineng, Herlinda Journal of Biomimetics Biomaterials and Biomedical Engineering, 2022 According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.
Classification of Papuan Batik Motifs Using Deep Learning and Data Augmentation Suhardi Aras, Arief Setyanto, Rismayani 2022 4th International Conference on Cybernetics and Intelligent System Icoris 2022, 2022 Papuan Batik motifs began to appear in 1984 and are only known in general with one designation, namely Papuan batik, even though these various motifs can be classified according to the area of origin of manufacture, culture and flora and fauna that have meaning. local wisdom of the community. The ability to recognize every batik motif from Papua requires experience and knowledge from certain circles, so that knowledge and meaning are maintained, a tool is needed that can classify various batik motifs from Papua. This study uses four classes of datasets, namely Cendrawasih Motifs, Raja Ampat Motifs, Tifa Honai Motifs and Asmat Motifs which are non-geometric. It is proposed to classify these motifs using deep learning using Vgg16 and Resnet50 architectures with fine tuning, to add data with various combinations in order to obtain better performance. The test results showed that without data augmentation on the VGG16 architecture, an accuracy of 78.79% was obtained and the Resnet50 architecture obtained an accuracy of 81.82% with several combinations of augmentation techniques giving the same better results without data augmentation with the results on the VGG16 architecture giving 84 results., 85% and on the Resnet50 architecture it gives a yield of 87.88%.
VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming Joe Y. Mambu, Rismayani, Jay Idoan Sihotang, Vivi Peggy Rantung 2022 4th International Conference on Cybernetics and Intelligent System Icoris 2022, 2022 There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as “good” therefore its usability is acceptable.
ILMU KOMPUTER: Landasan Teoretis, Implementasi Praktis, dan Ragam Aplikasi NTS Saptadi, ENF Dewi, AB Trisnawan, SC Sumarta, A Polma, ... Sada Kurnia Pustaka , 2026 2026
Analisis Pengaruh Implementasi Teknologi Blockchain Terhadap Keamanan Transaksi Cryptocurrency Di Pasar Indonesia R Rismayani, A Syam, AP Bahri DIPAKOMSI 18 (2), 47-54 , 2026 2026
LEVERAGING MULTIMODAL DATA FOR CLASSIFICATION OF FACIAL SKIN DISORDER USING CNN-MLP MODEL AI Amil, R Rismayani, A Andani ICIC Express Letters Part B: Applications 17 (3), 279-286 , 2026 2026
Clustering Data Demografi Penduduk Daerah Pesisir Sulawesi Selatan Menggunakan Metode K-Means VAN Yunus, KJ Siow, R Rismayani, S Piu Dipanegara Komputer Teknologi Informatika 16 (2), 28-38 , 2025 2025 Citations: 1
Digital Forensik: Prinsip, Teknik, dan Implementasi dalam Era Siber Modern IF Ashari, IP Sari, R Rismayani, S Suryani, RT Iswahyudi, D Sasongko, ... Yayasan Kita Menulis , 2025 2025 Citations: 1
Learning Difficulty Levels Prediction of Elementary School Student Mathematics Using Machine Learning Model R Rismayani, NS Layuk, M Patasik, AH Endang Journal of Information Technology and Its Utilization 8 (1), 1-9 , 2025 2025
Convolutional Neural Network with Feature Extraction to Improve the Classification Accuracy of Multi-Class Facial Skin Disorders. AA Ilham, A Achmad, MR Yudhiestra Rachman International Journal of Online & Biomedical Engineering 21 (3) , 2025 2025 Citations: 1
Convolutional neural network architectural models for multiclass classification of aesthetic facial skin disorders AA Ilham, A Achmad, MRY Rachman International Journal of Advanced Technology and Engineering Exploration 12 … , 2025 2025 Citations: 3
Facial Skin Disorder Prediction Based on Non-Visual Information Using ANN Model AA Ilham, A Achmad, MRY Rachman 2024 7th International Conference on Informatics and Computational Sciences … , 2024 2024 Citations: 5
Using Preference Selection Index for Design Decision Support System Selection of Farmers Group Recipients of Assistance R Rismayani, L Sudianto, AMI Buna, ISK Idris, H Herlinda, M Pineng 2024 International Conference on Inventive Computation Technologies (ICICT … , 2024 2024
Implementasi Analytical Hierarchy Proses Pada Sistem Pendukung Keputusan Pemilihan Beras Berkualitas R Rismayani Komputika: Jurnal Sistem Komputer 13 (1), 93-101 , 2024 2024 Citations: 1
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Computer Vision R Rismayani, P Wahyuningsih, SF Ramadhani, A Jalil, PH Putra, A Riadi, ... Yayasan Kita Menulis , 2024 2024
Model-View-Controller Design System of Motorcycle Damage Detection Using Forward Chaining Method Rismayani, M Wahinuddin Tahir, M Darwis, Nurani, M Pineng Journal of Information Technology and Its Utilization 6 (2), 51-59 , 2023 2023
Model-View-Controller Design System of Motorcycle Damage Detection Using Forward Chaining Method R Rismayani, MW Tahir, M Darwis, N Nurani, M Pineng Journal of Information Technology and Its Utilization 6 (2), 51-59 , 2023 2023
Mental Health Analysis at the University of Dipa Makassar using Naïve Bayes Classifier AI Maulana, R Rismayani IT Journal Research and Development 8 (1), 72-80 , 2023 2023 Citations: 1
Design of Monte Carlo Simulation Modeling for Determining Favorite Tourist Places in West Sumatera L Efriyanti, L Handoko, N Umar Proceedings of the International Conference on Technology, Education, and … , 2023 2023 Citations: 2
PKM WORKSHOP MICROSOFT OFFICE 2016 UNTUK BAHAN PENGAJARAN DI SMA RAMA SEJAHTERA MAKASSAR ASA Syam, C Susanto, N Nurdiansah, N Tamsir, H Hasriani, R Rismayani JURDIMAS: Jurnal Pengabdian Masyrakat Universitas DIPA Makassar 2 (1), 167-172 , 2023 2023
PKM WORKSHOP TERAPAN APLIKASI PERKANTORAN SDN, MI, MTs, RA DDI LANGKEMME KAB. SOPPENG A Annah, C Susanto, ASA Syam, HSYH SY, S Aisa, H Hasriani, ... JURDIMAS: Jurnal Pengabdian Masyrakat Universitas DIPA Makassar 2 (1), 173-177 , 2023 2023
Aplikasi Rekomendasi Jurusan Bagi Siswa SMA Untuk Lanjut Studi Berdasarkan Nilai Rapor Menggunakan Metode Fuzzy Tsukamoto MSF Pilat, FN Alief, R Rismayani, S Harlina Dipanegara Komputer Teknologi Informatika 16 (1), 184-195 , 2023 2023
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