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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Teknik Informatika (JUTIF) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Abdimas Maduma Media Elektrik Jurnal Ilmiah Dinamika Rekayasa
Primanita, Anggina
Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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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%.
Rancang Ulang Antarmuka Human Capita Management System dengan Metode Design Thinking (Studi Kasus: PT. Mitra Solusi Telematika) Gandi Subara; Osvari Arsalan; Anggina Primanita
JSI: Jurnal Sistem Informasi (E-Journal) Vol 18 No 1 (2026): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v18i1.228

Abstract

Human Capital Management System (HCMS) merupakan solusi digital yang membantu departemen Sumber Daya Manusia (SDM) dalam mengotomatisasi dan meningkatkan efisiensi berbagai proses terkait pengelolaan karyawan. Di PT Mitra Solusi Telematika (MST), HCMS telah digunakan sejak tahun 2022. Namun, antarmuka yang digunakan telah terlihat ketinggalan zaman dan tidak memenuhi ekspektasi pengguna, terutama generasi muda yang dominan sebagai pengguna utama. Oleh karena itu, penelitian ini bertujuan untuk merancang ulang antarmuka HCMS guna meningkatkan pengalaman pengguna, memperbarui tampilan sistem, serta mempermudah penggunaan bagi lebih dari 100 karyawan MST. Metode Design Thinking dipilih karena memiliki pendekatan yang berpusat pada pengguna sehingga mampu mengidentifikasi kebutuhan, permasalahan, dan preferensi pengguna secara mendalam sebelum menghasilkan solusi desain yang sesuai. Untuk menguji usability dari sistem setelah rancang ulang, dilakukan pengujian dengan System Usability Scale (SUS). Skor SUS dari sistem setelah perancangan ulang adalah 86,375 di mana sistem memiliki adjective rating Excellent. Berdasarkan hasil tersebut, dapat dinyatakan bahwa penelitian ini menghasilkan antarmuka yang lebih sederhana, fungsional, dan estetis, sehingga meningkatkan daya tarik dan kemudahan penggunaan HCMS.
Decision Support System for Determining Priority Pilgrims at PT Hafiz Wisata Darussalam Based on The C4.5 Algorithm and Profile Matching Herlina Dianaria; Ermatita; Anggina Primanita
Jurnal Ilmiah Dinamika Rekayasa Vol. 22 No. 2 (2026): Jurnal Ilmiah Dinamika Rekayasa - Juli
Publisher : Engineering Faculty, UNSOED

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jidr.2026.22.2.136

Abstract

The increasing competition among Umrah travel service providers requires companies to optimize the use of historical data to support decision-making. PT Hafiz Wisata Darussalam possesses approximately 500 historical records of Umrah pilgrims from the 2022–2025 period; however, these data have not been fully utilized, resulting in a subjective process for determining priority pilgrims. This study aims to develop a Decision Support System (DSS) for determining priority pilgrims by integrating the C4.5 algorithm and the Profile Matching method. The study employed 500 historical records consisting of the following attributes: previous pilgrim status, occupation, social media activity, Umrah package, age, and family group departure. The C4.5 algorithm was applied to classify priority pilgrims by identifying the most influential attributes through a decision tree, while the Profile Matching method was used to rank pilgrims based on their similarity to the company's ideal profile. The system was implemented using PHP and MySQL. The experimental results show that the C4.5 algorithm achieved an accuracy of 82.6%, a precision of 81.5%, a recall of 81.2%, and an F1-score of 81.4%. Out of 500 historical records, the system identified 233 priority pilgrims, who were subsequently ranked using the Profile Matching method to generate objective priority recommendations. The proposed system assists the company in identifying appropriate marketing targets, improving alumni pilgrim loyalty, and supporting more effective and data-driven decision-making.
A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network Ahmad Zarkasi; Hadipurnawan Satria; Anggina Primanita; Deris Stiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3903-3918

Abstract

This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Co-Authors Abdiansah Abdiansah Abdiansah, Abdiansah Abdurahman Abdurahman Ahmad Fali Oklilas Ahmad Nizar Ahmad Zarkasi Ahmad Zarkasi Ananda Haykel Iskandar Athalaza, Muhammad Nawar Danny M. Saputra Darmawijoyo, Darmawijoyo Dedy Fitriady Fitriady Deris Stiawan Dian Palupi Rini Egga Asoka Ermatita - Frendredi Muliawan Gandi Subara Hadi Purnawan Satria Hadi Purnawan Satria Hadipurnawan Satria Hadipurnawan Satria Hasby Rifky Herlina Dianaria Idawati Idawati Isa, Indra Griha Tofik Iskandar, Ananda Haykel Jambak, Muhammad Ihsan M. Fachrurrozi . M. Nazhif Rizani Muhammad Afif Muhammad Fachrurrozi Muhammad Nawar Athalaza Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Muhammad Rasuandi Nugraha, Wibisena Osvari Arsalan Rafly Pakomgan Ramadhan, Muhammad Fajar Rizki Kurniati Rusdi Efendi Samsuryadi Samsuryadi Saparudin Saparudin Saputra, Tommy Siswahyudianto Sri Desy Siswanti Sutarno Sutarno Sutarno Sutarno Sutarno Syahroyni, Muhammad Syamsul Arifin Syamsuryadi Syamsuryadi Wibisena Nugraha