cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dr. Indrastanti R. Widiasari
Contact Email
editor.aiti@adm.uksw.edu
Phone
-
Journal Mail Official
editor.aiti@adm.uksw.edu
Editorial Address
Kantor Fakultas Teknologi Informasi Jl. O. Notohamidjojo 1-10 Salatiga, Jawa Tengah 50711
Location
Kota salatiga,
Jawa tengah
INDONESIA
Aiti: Jurnal Teknologi Informasi
Published by Universitas Kristen Satya Wacana
ISSN : 16938348     EISSN : 26157128     DOI : https://doi.org/10.24246/aiti
Core Subject : Science,
Computer Science & IT
AITI: Jurnal Teknologi Informasi is a peer-review journal focusing on information system and technology issues. AITI invites academics and researchers who do original research in information system and technology, including but not limited to: Cryptography Networking Internet of Things Big Data Data Science Software Engineering Information System Web Programming Mobile Application Service System Artificial Intelligence Digital Image Processing Machine Learning Deep Learning Geographic Information System Context Aware System Management Information System Software-defined Network
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 176 Documents
 14   15   16   17   18 
Ekstraksi informasi leksikal bahasa Kerinci dari kamus dwibahasa untuk klasifikasi Part-of-Speech otomatis Agung Kharisma Hidayah; Dandi Sunardi; Sri Handayani
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.334-347

Abstract

Bahasa Kerinci merupakan salah satu bahasa daerah di Indonesia yang tergolong low-resource language, sehingga ketersediaan sumber daya linguistik digital sangat terbatas. Penelitian ini bertujuan untuk mengekstraksi informasi leksikal dari kamus dwibahasa Indonesia–Kerinci dan melakukan klasifikasi kelas kata (Part-of-Speech/POS) secara otomatis menggunakan pendekatan berbasis aturan (rule-based). Proses penelitian dimulai dengan mengonversi kamus dwibahasa Indonesia–Kerinci ke dalam format dataset terstruktur yang memuat lema, padanan kata, serta informasi linguistik terkait. Selanjutnya, dilakukan klasifikasi kelas kata secara otomatis menggunakan pendekatan rule-based dengan memanfaatkan padanan bahasa Indonesia sebagai acuan untuk menentukan POS pada lema bahasa Kerinci. Hasil penelitian menghasilkan dataset digital terstruktur beserta label POS otomatis yang dapat dijadikan sumber daya awal untuk pemrosesan bahasa alami (Natural Language Processing/NLP) dalam bahasa Kerinci. Kontribusi utama penelitian ini adalah menyediakan sumber daya linguistik digital awal untuk bahasa daerah yang masih minim data, serta menawarkan strategi klasifikasi POS berbasis leksikal yang sederhana dan dapat direplikasi pada bahasa daerah lain.
Rancang bangun sistem FlowTrackr: Aplikasi manajemen test case dan bug report berbasis web Desril Fatra Aditama; Imelda Imelda
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.290-304

Abstract

Software testing is an essential stage to ensure application quality before deployment. Manual testing practices by Quality Assurance (QA) teams often face challenges, such as scattered test results and bug reports across multiple media, making them difficult to trace and prone to miscommunication. This study designs FlowTrackr, a web-based integrated application for centralized test case management and bug reporting. The development followed the Waterfall methodology, covering requirements analysis, system design, implementation, and testing. Functional testing across 10 scenarios confirmed that all features operated according to specifications, while User Acceptance Testing (UAT) involving 5 QA testers demonstrated that the system was easy to use and more efficient than manual methods. Compared with tools such as Jira, TestRail, and Bugzilla, which tend to be more complex, FlowTrackr offers a lightweight, practical solution for small teams. Therefore, FlowTrackr can serve as an alternative to support manual QA processes, making them more structured and well-documented.
Lung cancer prognosis based on salivary biomarkers using Graph Convolutional Networks Gilang Raka Rayuda Dewa; Raisa Imani Sani; Ady Syamsuri; Charles Agustin; Muhammad Agni Catur Bhakti; Ariana Tulus Purnomo
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.245-260

Abstract

Lung cancer remains the leading factors that incur cancer-related deaths worldwide, mainly due to late-stage detection. In 2022, lung cancer affected almost 2.5 million people, with mortality of more than 1.8 million. However, existing prognostic methods are typically invasive, costly, and time-consuming, hindering effective early detection. Therefore, this research proposes a non-invasive prognostic approach using salivary biomarkers to detect lung cancer via Graph Convolutional Networks (GCNs). By transforming features into graph node representations, the proposed algorithm can model feature dependencies and topological relationships, enabling more effective pattern recognition than conventional classifiers. The proposed algorithm also applies feature selection to reduce computational complexity. The evaluation results show that the proposed algorithm achieves 95.65% accuracy, a macro F1-score of 95.62%, and a Matthews Correlation Coefficient of 0.9434. A comparative analysis shows that the proposed algorithm outperforms other graph-based architectures in terms of classification performance and computational complexity.
Implementasi YOLOv11 untuk pengenalan nominal uang Rupiah dengan konversi suara otomatis Muhamad Alvin Andriyanto; Yani Parti Astuti
AITI Vol 23 No 3 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i3.348-363

Abstract

This research aims to develop a computer vision-based Indonesian rupiah currency denomination detection system that can provide automatic voice output as accessibility support for the visually impaired. The model was developed using the YOLOv11 algorithm, which was customized to recognize seven nominal classes of 2022 issue banknotes. The public dataset was used as training, validation, and test data, which was then processed thru transformations and augmentations to improve model generalization. Training was conducted using a controlled configuration with AdamW optimization and overfitting prevention strategies. Performance evaluation was conducted using the metrics of accuracy, precision, recall, F1-score, mean Average Precision, confusion matrix, and ROC-AUC. The research results show that the model achieved very high performance with an accuracy of 99.64% and mAP of 0.9935, indicating consistent identification capabilities for currency denominations across all classes. The simple OpenCV-based implementation and voice conversion using gTTS prove that the model can operate in real-time and provide direct audio feedback. This finding indicates that YOLOv11 is effective for Indonesian rupiah recognition and has the potential for further development in accessibility applications for the visually impaired.
Deteksi URL phishing menggunakan kombinasi model XGBoost dan Random Forest Ilham Maulana Hadinanda; Sindhu Rakasiwi
AITI Vol 23 No 3 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i3.363-378

Abstract

Phishing merupakan salah satu bentuk dari serangan siber era digital sekarang. Serangan ini memanfaatkan teknik manipulasi untuk menipu, sehingga secara tidak sadar pengguna akan mengungkapkan data sensitif melalui web palsu yang meniru tampilan situs resmi. Deteksi terhadap URL phishing menjadi langkah penting dalam meningkatkan keamanan siber, serta mengurangi kerugian pengguna. Dalam penelitian ini digunakan pendekatan berbasis ensemble learning dengan mengombinasikan dua model, yaitu Extreme Gradient Boosting (XGBoost) dan Random Forest, dalam satu model gabungan. Kedua model selanjutnya dilatih menggunakan dataset yang terdiri dari 11430 URL dan terbagi sama rata antara kelas phishing dan legitimate. Model diuji menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model gabungan XGBoost dan Random Forest menghasilkan akurasi tertinggi, sebesar 0,944. Hasil ini mengungguli hasil yang didapatkan jika menggunakan model tunggal XGBoost (0,942) dan Random Forest (0,928). Temuan ini memperkuat bukti bahwa pendekatan ensemble memberikan generalisasi yang lebih baik dibanding model tunggal untuk deteksi phishing.
Sistem deteksi hama dan penyakit daun kelapa dengan metode CNN menggunakan arsitektur MobileNetV2 Olivia Kanakang; Audy Kenap; Medi Tinambunan
AITI Vol 23 No 3 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i3.378-393

Abstract

Coconut plants are a vital source of income for communities in East Melonguane District, yet they are vulnerable to leaf pests and diseases that disrupt photosynthesis and reduce crop yields. Farmers often detect symptoms too late, resulting in delayed treatment and increased damage. Although previous studies using Convolutional Neural Networks (CNN) have demonstrated promising accuracy, only a few have implemented web-based systems specifically for coconut leaves in remote areas, and many lack detailed hyperparameter reporting for reproducibility. Therefore, this study aims to develop a web-based application for detecting coconut leaf pests and diseases using a CNN with the MobileNetV2 architecture. A dataset consisting of 1,000 images across four categories—Sitora nitens, dry leaves, wilted leaves, and yellowing leaves—was used. The model was trained using transfer learning with 25 epochs, a batch size of 16, a learning rate of 0.0001, the Adam optimizer, and categorical cross-entropy loss. The experimental results show that the proposed model achieved an overall accuracy of 96% (193 out of 200 test samples correctly classified), with a precision of 96%, recall of 96%, and F1-score of 96%, indicating its effectiveness for fast and accurate classification in practical applications.

Page 18 of 18 | Total Record : 176

 14   15   16   17   18 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol 23 No 3 (2026) Vol 23 No 2 (2026) Vol 23 No 1 (2026) Vol 22 No 2 (2025) Vol 22 No 1 (2025) Vol 21 No 2 (2024) Vol 21 No 1 (2024) Vol 20 No 2 (2023) Vol 20 No 1 (2023) Vol 19 No 2 (2022) Vol 19 No 1 (2022) Vol 18 No 2 (2021) Vol 18 No 1 (2021) Vol 17 No 2 (2020) Vol 17 No 1 (2020) Vol 16 No 2 (2019) Vol 16 No 1 (2019) Vol 15 No 2 (2018) Vol 15 No 1 (2018) Vol 14 No 2 (2017) Vol 14 No 1 (2017) Vol 13 No 2 (2016) Vol 13 No 1 (2016) More Issue