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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Telematika JUITA : Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) POSITIF Annual Research Seminar JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Ilmiah Matrik Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) Jurnal Informatika Global Journal of Information Systems and Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal of Computer and Information Systems Ampera Jurnal Pengembangan Sistem Informasi dan Informatika Journal of Applied Computer Science and Technology (JACOST) Jurnal Mahasiswa Sistem Informasi (JMSI) Jurnal Nasional Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Bina Komputer International Journal of Scientific and Professional Indonesian Journal of Innovation Multidisipliner Research Ngabdimas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Indonesian Journal of Innovation Multidisipliner Research Journal of Computer Science and Technology Application
Yesi Novaria Kunang
Universitas Bina Darma
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Prapemrosesan untuk Klasifikasi Gambar Aksara OKU Timur Prasetya, M. Iqbal; Yadi, Ilman Zuhri; Kunang, Yesi Novaria; Permatasari, Susan Dian
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1629

Abstract

This study investigates methods to enhance the quality of OKU Timur script images through preprocessing techniques utilizing Adaptive Thresholding. The OKU Timur script, significant for daily communication and traditional ceremonies, encounters challenges such as skew, rotation, and low resolution in image processing. The proposed preprocessing approach includes contrast normalization to improve image clarity, noise reduction to eliminate unwanted artifacts, and feature extraction to emphasize critical image characteristics. These steps are designed to enhance the accuracy of character recognition. The findings indicate that proper preprocessing is crucial for effective recognition of OKU Timur script and holds substantial potential for preserving this cultural heritage through modern technological applications.
ANALISIS MANAJEMEN RESIKO TEKNOLOGI INFORMASI INSTITUSI KESEHATAN (Studi Kasus Rumah Sakit Muhamadiyah Palembang) Egy Septian; Yesi Novaria Kunang
Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer Vol 6 No 1 (2024): April
Publisher : Program Studi Teknik Informatika Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/restikom.v6i1.293

Abstract

Rumah Sakit merupakan sebuah instansi kesehatan yang merupakan pusat pelayanan kesehatan di mana orang sakit dapat ditampung dan diobati dengan baik. Rumah Sakit Muhamdiyah Palembang merupakan bagian dari jaringan pelayanan kesehatan AHSA, dimana pada tahun 1986 telah menggunakan aplikasi CLIPPER untuk penyimpanan data. Penelitian ini bertujuan untuk melakukan manajemen risiko Sistem Informasi Manajemen Rumah Sakit (SIMRS) pada Rumah Sakit Muhamdiyah Palembang. Dengan melakukan wawancara serta membagikan Kuesioner kepada bagian Mirsa sebagai pengelola IT pada Rumah Sakit. Dalam melakukan manajemen risiko, ISO 31000 diterapkan dan disesuaikan untuk semua jenis organisasi dengan memberikan struktur dan pedoman yang berlaku generik terhadap semua operasi yang terkait dengan manajemen risiko. Hasil dari penelitian ini menunjukkan bahwa terdapat 2 risk level tingkatan high, merupakan risiko berbahaya yang harus diatasi secepatnya, dan 13 risk level tingkatan medium yang merupakan risiko yang harus diperhatikan terus-menerus, sehingga setiap risiko harus dilakukan perlakuan risiko yang diharapkan dapat menjadi acuan dalam penanganan dan pemeliharaan terhadap Sistem Informasi di waktu yang akan datang.
REST API BACKEND APLIKASI E-COMMERCE SECONDHAND MENGGUNAKAN FRAMEWORK SPRING BOOT Prilsafira, Tania; Kunang, Yesi Novaria; Putra, Muhammad Hatta
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 8 No 2 (2022): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v8i2.1475

Abstract

The advancement in information technology is currently rapidly. Information technology can make it easier for humans to work or interact with other people. Technology applications have many various uses. One of the uses is that it can be a means of buying and selling goods or what we often call e-commerce. The SECONDHAND application developed in this study focuses on API Backend selling used goods using the Spring Boot framework. The method used is the SCRUM method which is a type of System Development Life Cycle (SDLC) design method. This research produces a REST API-based backend system.
Besemah Language Translation Machine Model Based on Machine Learning with Recurrent Neural Network (RNN) Model Algorithm Andika, Muhamad; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Purnamasari, Susan Dian
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2614

Abstract

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.
The Memory Efficiency in a Receptionist Robot's Face Recognition System Using LBPH Algorithm Yudi, Endang Darmawan; Yesi Novaria Kunang; Zarkasi, Ahmad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6048

Abstract

This research aims to develop a memory-efficient face recognition system for a receptionist robot using the Local Binary Patterns Histogram (LBPH) algorithm. Given the computational limitations of the Raspberry Pi, the system utilizes optimization techniques including grayscale conversion, noise reduction, and contrast adjustment to enhance processing efficiency. Testing demonstrates that the face recognition accuracy achieves 80.5% to 85.5% in offline mode, and 72% to 81% in real-time mode, with variations due to lighting conditions and facial expressions. The robot's servo motors exhibit a response time between 1.945 and 3.561 seconds, enabling responsive and interactive user engagement. The results suggest practical benefits for deploying face recognition in resource-constrained environments, enhancing the efficiency of robotic receptionist applications.
Clustering OKU Timur Script Images using VGG Feature extraction and K-Means Toriko, Liu; Purnamasari, Susan Dian; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Andri, Andri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2292

Abstract

This study focuses on the utilization of clustering models to group manuscript images from the OKU Timur region based on specific characteristics. OKU Timur is rich in cultural heritage, including a unique writing system known as the OKU Timur script. The development of intelligent systems technology can be employed to recognize the OKU Timur script. For this purpose, a dataset of OKU Timur script is needed, which will later be used for classifying script images. One of the challenges in preparing the dataset is grouping a large number of script image samples according to the number of characters. A proposed solution in this research is to automatically group script images by applying the K-Means algorithm. The dataset comprises 2,280 images, representing 19 characters and 228 variations with different diacritics. Features are extracted using the VGG16 model, which are then clustered with the K-Means algorithm. Clustering performance is evaluated based on the percentage of correctly grouped characters. For 19 groups (character count), the model achieves an accuracy of 82.6%. For 228 groups (variations and diacritics), it correctly groups 48.16% of characters. Despite the challenges, the results demonstrate the model’s potential for further refinement. This study’s contribution lies in introducing an efficient clustering approach for cultural manuscripts, supporting digital preservation, and advancing automatic recognition of the OKU Timur script. These efforts aim to preserve the script for future generations.
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm Firdaus, Firdaus; Nurmaini, Siti; Yusliani, Novi; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Kunang, Yesi Novaria; Fachrurrozi, Muhammad; Armansyah, Risky
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9670

Abstract

Text clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope.
ANALISIS PENINGKATAN KUALITAS LAYANAN E-LEARNING PALCOMTECH MENGGUNAKAN PENDEKATAN WEBQUAL 4.0 DAN E-LEARNING MATURITY MODEL(EMM) maria, Fitri; Herdiansyah, M. Izman; Sutabri, Tata; Kunang, Yesi Novaria
Jusikom : Jurnal Sistem Komputer Musirawas Vol 9 No 1 (2024): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i1.2343

Abstract

E-Learning is a very important learning method, especially in the context of course and training institutions (LKP) such as PalComTech. The study will be conducted to analyze the improvement of the quality of the LKP PalComTech e-learning system using the Webqual 4.0 approach. and E-learning Maturity Model (eMM). Thus the main objectives of this thesis are: Analyzing the Quality of e-Learning Services: Conducting an in-depth evaluation of various aspects of the quality of e-learning services provided by LKP PalComTech, including content, usability, interactivity, and reliability The abstract must be clear, descriptive and must provide a brief overview of the problem being studied. Applying the Webqual 4.0 approach to analyze the quality of e-learning services, thus providing a comprehensive understanding of the performance of the e-learning platform. Providing concrete recommendations for the improvement and development of the LKP PalComTech e-learning platform.
The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models Sapitri, Wulan; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Mahmud, Mahmud
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1073

Abstract

Deep learning technology is widely used for recognizing character images, including various regional characters and diverse ancient scripts. Deep learning models require large data sets to recognize images accurately. However, creating a dataset has limitations in terms of quantity, including the Komering script dataset used in this study. Data augmentation techniques can be applied to expand the dataset by modifying existing images to increase data diversity. This study aims to investigate the impact of augmentation techniques on the performance of deep learning models in the case of Komering script recognition. The dataset consists of 500 images for five classes of Komering script characters. Three augmentation techniques, namely random rotation, height shift, and width shift, were applied to the five characters, which were then used to test the model trained to recognize characters in the Komering dataset. This research contributes to providing insights into the effect of augmentation techniques on robust confidence prediction of deep learning models for recognizing newly augmented data. The results demonstrate that the deep learning model can recognize modified data using augmentation techniques with an average accuracy of 80.05%.
Besemah Language Translation Machine Model Based on Machine Learning with Recurrent Neural Network (RNN) Model Algorithm Andika, Muhamad; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Purnamasari, Susan Dian
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2614

Abstract

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.
Co-Authors Adam Prasetya Afiyudi, Afiyudi Afriyudi Agus Setiawan Agus Setiawan Ahmad Zarkasi Andika, Muhamad Andri Andri Anggie Khristian Ari Muzakir Arief Algiffary Armansyah, Risky Atmojo, Toni Tri Beni Brahara Bhakti Yudho Suprapto Damayanti, Nita Rosa Darmawahyuni, Annisa Dedy Syamsuar Dedy Syamsuar Deris Stiawan Dinata, Aria Dzakwan, Fadhlur Rahman Edi Surya Negara Egy Septian Eka Puji Agustini Endang Etriyanti Fajarino, Aldo Ferdiansyah Ferdiansyah Fernandy Jupiter Firdaus Firdaus Firdaus Gllen yusuf abbel Hadinata, Novri Hamanrora, Muhammad Dio Hellen Puspita Sari Hendra Marta Yudha Herdiansyah, Izman Herdiansyah, M. Izman Herdiansyah, M. Izman Herferry, Ibrahim Ade Ilman Zuhri Yadi Ilman Zuhriyadi Inda Anggraini Irwansyah Ibrahim Kurniawan Kurniawan Kurniawan, Tri Basuki Lang Dimas Perkasa Leon Andretti Abdillah Liza Fahreni M Fikri M Izman Herdiansyah Mahmud Mahmud Mahmud Mahmud Maria, Fitri Muhammad Fachrurrozi Muhammad Finaldo Muhammad Hatta Muhammad Izman Herdiansyah Muhammad Naufal Rachmatullah Netti Herawati Novi Yusliani Novifika, Seva Parker, Jonathan Permatasari, Susan Dian Prasetya, M. Iqbal Pratiwi, Ayu Okta Prilsafira, Tania Putra, Muhammad Hatta Putri Ramadhani, Nasywa Ramadhan, Dwi Nur Ramadhona, Nuzulur Rianda, M. Rianda Rio Ananda Fitriansyah Sapitri, Wulan Sari, Tia Permata Siti Nurmaini Sri Murniati Suryayusra - Susan Dian Purnamasari, Susan Dian Taqrim Ibadi Tata Sutabri Toriko, Liu Tri Basuki Kurniawan Usman Ependi Via Sukma Cendanie Widya Cholil Widya Putri Mentari Winoto Chandra Wulandari, Intan Fitriana Yanti Pasmawati Yayuk Ike Meilani Yudi, Endang Darmawan Yunike, Yunike Yustida Bellini Ziqrullah, Muhammad Hafiz Zulkifli Harahap Zulkifli Harahap