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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business JUITA : Jurnal Informatika International conference on Information Technology and Business (ICITB) Annual Research Seminar Journal of Information System JPSriwijaya JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal ULTIMATICS Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Informatik : Jurnal Ilmu Komputer IJID (International Journal on Informatics for Development) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) SEINASI-KESI Jurnal Riset Informatika CSRID (Computer Science Research and Its Development Journal) Jurnal Informatika Global CCIT (Creative Communication and Innovative Technology) Journal JSAI (Journal Scientific and Applied Informatics) Journal of Information Systems and Informatics Mulia International Journal in Science and Technical Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Generic Jurnal AbdiMas Nusa Mandiri Jurnal Pendidikan dan Teknologi Indonesia ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Proceeding of International Conference Health, Science And Technology (ICOHETECH) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Abdimas Maduma JUKEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Aplikasi
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Improving the transfer learning for batik besurek textile motif classification Utami, Marissa; Ermatita, Ermatita; Abdiansah, Abdiansah
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i4.pp3172-3181

Abstract

This proposed research discussion is a new combination model for classifying batik besurek fabric from the implementation transfer learning with mixed contrast enhancement, activation function, and optimizer method. The size of the batik besurek fabric motif image as an input image is 250×250 with three channels consisting of red, green, and blue totaling five classes, namely kaligrafi, rafflesia, burung kuau, relung paku and rembulan. All images in the dataset will be divided into train data (1540 images), validate data (380 images), and test data (480 images) that are taken directly from the batik store in Bengkulu. The division method used is stratified random sampling to take all the data, shuffles it, and divides the data sets for each class. Based on the experiment results, ResNet50 obtained the best performance compared to MobileNetV2, InceptionV3, and VGG16, with a training accuracy of 99.60%, a validation accuracy of 97.44%, and a testing accuracy of 98.12%. In the improvement experiment phase, the ResNet50 model with Adam optimizer, rectified linear unit (ReLU) activation function and contrast limited adaptive histogram equalization (CLAHE) as the contrast enhancement method obtained the highest test accuracy (98.75%), showing that CLAHE was very effective in improving performance on batik besurek data.
Sistem Informasi Kerja Praktik Berbasis Website Untuk Optimasi Program Kerja Praktik Pada Perpustakaan Nasional Republik Indonesia Albert, Albert; ., Ermatita
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 1 No 1 (2023): Jurnal Sistem Informasi dan Aplikasi
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v1i1.5909

Abstract

Di era modern Revolusi Industri 4.0 saat ini, hampir semua aktivitas kehidupan manusia tidak terlepas dari teknologi informasi seperti berbagai aktivitas organisasi, perusahaan, serta berbagai lembaga telah menggunakan teknologi informasi. Penerapan perkembangan teknologi ini menjadi sebuah peluang bagi Perpustakaan Nasional Republik Indonesia dalam melakukan optimasi pada program kerja praktik lapangan melalui sebuah website. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi kerja praktik berbasis website yang dapat mengefisiensikan dokumentasi kerja praktik seperti kegiatan kerja praktik, laporan kegiatan, serta pemberian e-sertifikat dalam sebuah website. Sehingga memberikan kemudahan baik bagi mahasiswa maupun pegawai dalam program kerja praktik. Metode penelitian yang digunakan dalam penelitian ini menggunakan metode Waterfall yang lebih umum dikenal dengan Classic Life Cycle. Diharapkan website ini dapat membantu peserta dalam melakukan kegiatan kerja praktik dengan menggunakan fitur yang tersedia pada website ini
SISTEM BASIS DATA PERPUSTAKAAN Dwi Lestari, Rizky; Tryadriani, Rasqia Nurzulia; Dominica, Alviona Terry; ., ermatita
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 1 No 1 (2023): Jurnal Sistem Informasi dan Aplikasi
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v1i1.6449

Abstract

Sistem Basis Data Perpustakaan adalah sebuah sistem informasi yang dirancang untuk mengelola dan menyimpan informasi mengenai koleksi buku, data peminjaman, anggota perpustakaan, dan informasi lainnya. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem basis data perpustakaan menggunakan XAMPP. Metodologi penelitian melibatkan identifikasi kebutuhan dan tujuan sistem informasi perpustakaan, analisis dan perancangan basis data, penentuan skema basis data, implementasi basis data, pengujian dan validasi, serta evaluasi dan pemeliharaan. Dalam perancangan basis data, entitas-entitas seperti peminjaman, buku, peminjam, dan lain-lain diidentifikasi dengan atribut- atribut yang relevan. Skema basis data ditentukan dengan menentukan tipe data, kunci primer, kunci asing, dan indeks yang diperlukan. Implementasi basis data melibatkan pembuatan tabel-tabel dan pengaturan relasi antar tabel. Pengujian dan validasi dilakukan untuk memastikan kinerja dan kesesuaian basis data dengan kebutuhan sistem. Evaluasi dan pemeliharaan dilakukan secara berkala untuk menjaga integritas basis data. Penelitian ini memberikan kontribusi penting dalam pengelolaan informasi perpustakaan melalui penggunaan sistem basis data. Diharapkan hasil penelitian ini dapat membantu perpustakaan dalam meningkatkan efisiensi operasional, pelayanan kepada pengguna, dan akses terhadap informasi yang relevan
Comparative Analysis of Explainable AI Models for Pneumonia Detection in Chest X-rays Using Grad-CAM Richardo, M Denny; Ermatita, Ermatita; Satria, Hadipurnawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2450

Abstract

Pneumonia is one of the main reasons why young children die around the world, so it's essential to detect it early and make sure the methods used are straightforward to understand for doctors. This study aims to analyze and compare pneumonia detection systems based on Explainable Artificial Intelligence (XAI) using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique across four Convolutional Neural Network (CNN) architectures: VGG16, DenseNet, MobileNet, and EfficientNet-B0. The dataset used consists of approximately 5,800 chest X-ray images from Kaggle, split into training, validation, and test sets. The dataset underwent preprocessing, augmentation, and filtering. Each model was trained and tested using the accuracy, precision, recall, and F1-score measures. Additionally, the models were analyzed for explainability using Grad-CAM heatmaps. The results showed that MobileNet achieved the highest classification performance, attaining 99.6% accuracy, precision, recall, and F1-score, while EfficientNet-B0 demonstrated the highest explainability in a visual evaluation by medical practitioners. Explainability was assessed through a survey distributed to four medical professionals—two radiologists, a general practitioner, and a radiology technologist—using a Likert scale (1–5) to rate aspects such as focus accuracy, heatmap clarity, consistency of the area, and interpretability. EfficientNet-B0 achieved the highest average explainability score of 41.50, followed by MobileNet at 40.50. Thus, MobileNet is recommended for accuracy, while EfficientNet-B0 is the best choice if visual interpretability is a priority. This research underscores the importance of integrating explainability into the development of AI-based disease detection systems to enhance trust and safety in clinical applications.
PEMODELAN DAN SIMULASI DAFTAR ULANG PPDB PADA SEKOLAH MENENGAH ATAS DENGAN METODE MULTIPLE CHANNELS SINGLE PHASE (M/M/1) Meizalina, Mutiara Amalia; Ermatita, Ermatita; Ibrahim, Ali
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 3 (2024): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i3.21788

Abstract

The New Student Admission (PPDB) process in high schools is complex and requires efficient management to avoid long queues and errors in data processing. Until now, the Re-registration of New Student Admissions in high schools has always involved hundreds of prospective students in one school, resulting in long service and waiting times. Therefore, a solution is needed to address these issues and optimize available resources. This study uses the Multi Channels Single Phase (M/M/c) method with the Multi Channels Multi Phase (M/M/s) method. Modeling and simulation can help identify factors affecting latency, service time, and the number of students that can be served within a specific period. This can assist schools in planning their student population and optimizing the use of available resources. Based on data analysis, discussion, and conclusions from the comparison of two Modeling and Queue Simulation Methods in the re-registration process of New Student Admissions at SMA Negeri 10 Palembang, namely the Multi Channel-Single Phase and Multi Channel-Multi Phase Methods.
Prediksi Kelulusan Mahasiswa Menggunakan Metode K-Means dan Random Forest Ermatita, Ermatita; Hafyz Sytar, M.
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.577

Abstract

Keberhasilan dalam menghasilkan lulusan tepat waktu di institusi pendidikan tinggi mencerminkan kualitas akademik yang baik. Prediksi kelulusan mahasiswa tepat waktu menjadi tantangan penting dalam mendukung keputusan strategis bagi mahasiswa dan institusi. Penelitian ini bertujuan untuk mengembangkan model prediksi kelulusan mahasiswa tepat waktu pada program pascasarjana menggunakan algoritma K-Means dan Random Forest. K-Means digunakan untuk mengelompokkan mahasiswa berdasarkan atribut-atribut penting seperti IPK, jumlah kredit, dan lama studi, sedangkan Random Forest diterapkan untuk klasifikasi status kelulusan berdasarkan pola-pola yang dihasilkan dari pengelompokan. Kombinasi metode ini diharapkan dapat memberikan hasil prediksi yang lebih akurat dan dapat diterapkan dalam mendukung pengambilan keputusan terkait kelulusan mahasiswa tepat waktu. Hasil evaluasi menunjukkan bahwa model prediksi yang dibangun memiliki tingkat akurasi tinggi, dengan Random Forest menunjukkan akurasi sebesar 98.41% dalam membedakan antara mahasiswa yang lulus tepat waktu dan yang terlambat, serta model K-Means mampu mengelompokkan data mahasiswa dengan baik berdasarkan pola yang ditemukan dalam dataset.
Pelatihan Sistem Informasi Untuk Pendataan UMKM di Indramayu Ermatita, Ermatita; Adrezo, Muhammad; Matondang, Nurhafifah; Irmanda, Helena Nurramdhani
ABDI MOESTOPO: Jurnal Pengabdian Pada Masyarakat Vol 7, No 1 (2024): Januari 2024
Publisher : Universitas Prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/abdimoestopo.v7i1.3467

Abstract

UMKM menjadi salah satu bagian dari kegiatan yang memberikan solusi dalam mengatasi perekonomian masyarakat di Indonesia khususnya di Kabupaten Indramayu. Dinas Koperasi dan UMKM Indramayu saat ini dalam melakukan pendataan UMKM masih manual,  pendataan masih menggunakan Ms. Excel. Dengan system yang masih manual ini menimbulkan resiko terjadinya human error saat pencatatan, tidak ada histori pada perubahan data, data yang kurang akurat, dan butuh waktu lama untuk rekonsiliasi. Dari kekurangan tersebut sering terjadi pada saat melakukan pendataan jumlah dan jenis UMKM. Oleh karena itu maka dibutuhkan sistem yang dapat mendata UMKM secara akurat. Penggunaan sistem ini butuh pelatihan bagi pengguna yang akan bertugas mendata UMKM yang ada didesa untuk dapat mendata UMKM secara akurat di Kabupaten Indramayu. Pelatihan penggunaan sistem UMKM diberikan kepada perwakilan pengelola data UMKM di desa, sehingga dari desa dapat didata UMKM secara akurat.Dengan pelatihan ini dapat membantu pihak pemerintah Kabupaten indramayu untuk mendata jumlah, lokasi dan jenis UMKM yang ada. Sehingga mempermudah magi pihak pemerintah untuk melakukan pembinaan UMKM yang dapat meningkatkan perekonomian masyarakat. Pelatihan penggunaan sistem UMKM diberikan kepada perwakilan pengelola data UMKM di desa, sehingga dari desa dapat didata UMKM secara akurat.
Enhancing Weather Prediction Models through the Application of Random Forest Method and Chi-Square Feature Selection Irmanda, Helena Nurramdhani; Ermatita, Ermatita; bin Awang, Mohd Khalid; Adrezo, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2356

Abstract

This study discovers weather forecast methodologies, concentrating mainly on the climatic issues faced by Indramayu Regency and its considerable impact on agriculture, specifically rice production and national food security. The study emphasizes the crucial need for accurate weather forecasting, especially in the context of ongoing climate change, by highlighting the region's vulnerability to weather anomalies and their possible disruption of crop output. To solve these issues, the study investigates machine learning techniques, particularly ensemble learning methods such as Random Forest in conjunction with Chi-Square feature selection. The article thoroughly outlines the research approach, including data collection from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), data pre-processing, feature selection processes, and data splitting. Notably, the methodology integrates the Synthetic Minority Over-sampling Technique (SMOTE) to adjust imbalanced data and uses key weather attributes for model construction (humidity, wind speed, and direction). The resulting Random Forest model performs well, with an accuracy rate of 87.6% in forecasting different types of rainfall. However, the study indicates potential overfitting in some rainfall classes, implying the need for additional data augmentation or modeling technique refining. In conclusion, this study demonstrates the potential efficacy of ensemble learning techniques in weather prediction, focusing on the Indramayu Regency. It emphasizes the need for exact forecasts in the agricultural and fisheries industries and suggests possibilities for additional investigation, such as research into alternative prediction approaches such as deep learning.
An Integrated Depok Smart City Evaluation Arista, Artika; Ermatita, Ermatita; Bunga Wadu, Ruth Mariana
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2316

Abstract

Given the complicated pressures brought on by the fast pace of urbanization, innovative and long-lasting solutions to the issues arising from urban expansion are needed. To ensure a greater standard of life for their citizens and make sustainable growth one of their long-term goals, cities will need to make more inventive, persistent, and successful changes to their infrastructure. Nonetheless, smart cities require complex solutions to problems involving ICT, economics, government, social issues, the environment, and transportation. The sustainability of smart cities is now a topic that academics, environmental policymakers, and governmental organizations are more interested in. Depok's smart city must be evaluated to determine its capacity to fulfill the desired vision to help implement the Movement Towards 100 Smart Cities. This study offers an evaluation approach for the Depok smart city. Three indices were used to construct an integrated evaluation approach: the IMD Smart City Index 2023, The Cities of the Future Index, and the Global Power City Index. None of the indexes' results include all six of the Depok Smart City's necessary dimensions. Thus, the advice was to merge the three indices into an integrated evaluation approach for evaluating the six primary dimensions of the Depok Smart City. The results of this study also offer a sample measurement statement according to Depok Smart City. Furthermore, follow-up actions that the government or stakeholders can take to improve Depok's smart city performance include implementing the integrated matrix indicators and evaluating their validity and relevance in the real world. 
Handwritten Kaganga script classification using deep learning and image fusion Dwika Putra, Erwin; Ermatita, Ermatita; Abdiansah, Abdiansah
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8747

Abstract

Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.
Co-Authors Abdiansah, Abdiansah Adi Sutrisman Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Sanmorino Aidil Putrasyah Al Farissi Albert Albert Aldin, Moehammad Alfarezy, Reza Ali Amran Ali Bardadi Ali Bardardi Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Andini Dwi Lestari Anita Desiani Apriansyah Putra Arnelawati Artika Arista Ayuputri, Niken Bambang Suprihatin Barlian Khasoggi Barlian Khasoggi Belly, Belly Nagustria bin Awang, Mohd Khalid Budi Prayoga, Muhamad Hafiz Cindo, Mona Dafid Dedik Budianta Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dien Novita Dominica, Alviona Terry Dwi Asa Verano Dwi Lestari, Rizky Dwi Meylitasari Br. Tarigan Dwi Rosa Indah Endang Lestari Ruskan Endy Suherman Erwin, Erwin Eva Darnila Eva Darnila Fajriana, Fajriana Fajriana, Fajriana Falih, Noor Fathiyah, Alyssa Fathoni - Fauza Adelma Syafrizal Fuadi, Wahyu Geovani, Dite Gumay, Naretha Kawadha Pasemah Hadipurnawan Satria Hafyz Sytar, M. Hartini Hartini Hijriani, Nurul Huda Ubaya Huda Ubaya Husnawati Husnawati Ika Oktavianti ina aisyah handayani Indra Maulana Irmanda, Helena Nurramdhani Ispramono Hadi, Sigit Iwan Pahendra Iwan Pahendra Anto Saputra Jaidan Jauhari Johannes Petrus Joko Purnomo Kareen, Pamela Ken Dhita Tania Khairun Nisak, Novrinda Kurniawan, Mochamad Aryo Aji Kurniawan, Rizky Fariz Andry Lovinta Happy Atrinawati M Fariz Januarsyah M. Fariz Januarsyah M. Miftakul Amin Madya, M Miftahul Matondang, Nurhafifah Mauliza Mauliza, Mauliza Megah Mulya Meizalina, Mutiara Amalia Mgs Afriyan Firdaus Mira Afrina Mohammed Y. Alzahrani Mona Cindo Monterico Adrian Muhammad Adrezo Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Muhammad Sadli Muhammad Sadli, Muhammad Muhammad, Duwen Imantata Mutammimul Ula Mutia Fadhila Putri Neni Alyani Noprisson, Handrie NUNI GOFAR Nurul Chamidah Nurul Mufliha Eka Putri Nurul Mufliha Eka Putri Octaria, Orissa Osvari Arsalan Pacu Putra Pahendra, Iwan Parwito Patimah, Endah Pratama, M Octaviano Primanita, Anggina Purwita Sari Purwita Sari, Purwita Putra, Erwin Dwika Rachma nia Rahman, Puti Ayu Andhini Rahmat Budiarto Rahmat Izwan Heroza Rahmat Izwan Heroza Rendra Gustriansyah Reza Firsandaya Malik Richardo, M Denny Richki Hardi Rifkie Primartha Rizka Dhini Kurnia Rizka Dhini Kurnia Rizki Kurniati Royan Dwi Saputra Rudhy Ho Purabaya Ruth Mariana Bunga Wadu Safithri, Selviana Rizki Salamah, Fitri Samsuryadi Samsuryadi Shinta Puspasari Soraya, Atika Suci Destriatania Suci Destriatania Sukemi Sukemi Susan Dwi Saputri Susan Dwi Saputri Sytar, M. Hafizh Terttiaavini, Terttiaavini Tjahjanto, Tjahjanto Tryadriani, Rasqia Nurzulia Verano, Dwi Asa Verlly Puspita Wahyu Fuadi Wahyu Ningsih Yadi Utama Yadi Utama Yudha Pratomo Yudha Pratomo Yudha Pratomo Yundari, Yundari Zalika, Indah Zulkardi