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 10 Documents
 1 
Search results for , issue "Vol 23 No 1 (2026)" : 10 Documents clear
Penerapan Metode Inverse Distance Weighted (IDW) dalam Interpolasi Curah Hujan (Studi Kasus: Kabupaten Langkat) Tahitu, Jose Carlos; Titaley, Jullia; Manurung, Tohap
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.61-72

Abstract

Penelitian ini bertujuan untuk menentukan nilai power paling optimal yang akan digunakan dalam interpolasi curah hujan periode tahun 2023 di Kabupaten Langkat dengan menggunakan metode Inverse Distance Weighted (IDW) dengan variasi nilai power yang digunakan adalah 1 sampai 6,8 serta menjelaskan hasil interpolasi curah hujan di Kabupaten Langkat. Data yang digunakan dalam penelitian ini adalah data curah hujan Kabupaten Langkat yang bersumber dari Badan Pusat Statistik Kabupaten Langkat dengan jumlah titik pengamatan yang digunakan sebanyak 25. Hasil penelitian ini menyatakan bahwa variasi power mendekati keadaan sebenarnya adalah variasi nilai power 6,8 yang memiliki nilai Root Mean Square Error (RMSE) yaitu 49,99 mm/tahun. Berdasarkan hasil interpolasi dengan metode IDW, titik pengamatan yang memiliki curah hujan yang paling tinggi terletak pada Kecamatan Salapian dan curah hujan terendah terletak di Kecamatan Pangkalan Susu.
Sistem Informasi Gereja terpadu berbasis web dengan framework CodeIgniter 4 Norotama, Unggul Prabowo; Chernovita, Hanna Prillysca
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.14-30

Abstract

Penelitian ini berfokus untuk menghasilkan sistem informasi dengan dasar aplikasi web bagi Father House Community Church (FHCC) di Salatiga menggunakan framework CodeIgniter 4. Sistem ini dirancang untuk memfasilitasi pengelolaan data jemaat, penyebaran informasi gereja, pemetaan kelompok sel (iCare) dan penyediaan renungan harian secara digital. Metode penelitian melibatkan pengembangan sistem dengan arsitektur Model-View-Controller (MVC) yang memisahkan logika bisnis dari tampilan untuk mempermudah pemeliharaan dan pengembangan aplikasi. Hasil penelitian menunjukkan bahwa sistem ini berhasil meningkatkan efisiensi pengelolaan data jemaat, memperlancar komunikasi antara gereja dan jemaat serta memungkinkan akses informasi secara real-time tanpa memerlukan instalasi perangkat tambahan. Selain itu, sistem ini meningkatkan kenyamanan pengguna dalam mengakses informasi dan pelayanan gereja secara lebih cepat, efektif, dan efisien.
Analisis manajemen risiko pada e-learning SMAN 1 Ambarawa dengan menggunakan ISO 31000:2018 Isabel, Hesni Joice Isabel Br; Tanaamah, Andeka Rocky; Muttamaqin , Akhmad
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.1-13

Abstract

Penelitian ini bertujuan mengidentifikasi risiko-risiko utama yang dihadapi, menganalisis sejauh mana risiko-risiko tersebut dikelola, dan menyusun rekomendasi untuk mengatasi hambatan serta risiko yang terjadi pada SMAN 1 Ambarawa, menggunakan rangkaian kerja ISO 31000:2018. Penelitian menggunakan metode kualitatif, yaitu pengumpulan data melalui wawancara untuk mengetahui permasalahan yang akan diteliti. Dengan metode kualitatif, didapatkan risiko dan kemungkinan risiko. Terdapat tiga tingkatan risiko, diantaranya risiko rendah, risiko sedang dan risiko tinggi. Melalui analisis risiko yang didapat, maka diberikan pula penanganan terhadap risiko-risiko tersebut. Perlakuan risiko ini dapat dimanfaatkan oleh pihak SMAN 1 Ambarawa dalam mengatasi dan mencegah risiko-risiko yang dapat terjadi.
Klasifikasi motif Batik Keraton menggunakan arsitektur fine-tuning ResNet-50 Budhi Santosa, Stefaron; Rachmat Chrismanto , Antonius; Susanto, Budi
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.46-60

Abstract

Batik is an Indonesian cultural heritage known for its diverse motifs; however, manual classification of these motifs remains a significant challenge. This study aims to develop a batik motif classification model using the ResNet50 architecture enhanced with data augmentation to improve model accuracy. The dataset consists of four batik motif classes: Kawung, Mega Mendung, Parang, and Truntum. In this research, the model was trained using fine-tuning on ResNet50, with additional CNN layers for feature extraction. The results demonstrated that the proposed model achieved a highest accuracy of 97.80% on test data and 96.80% on validation data, significantly outperforming methods without data augmentation. This study concludes that applying fine-tuned ResNet50 with additional CNN layers and data augmentation effectively classifies batik motifs, offering substantial potential for automating the classification process in the batik industry.
Penerapan Desain Interaksi dalam Perancangan E-commerce pada Pabrik Sepatu Lokal LeePoure Sebagai Strategi untuk Meningkatkan Penjualan Supono, Intan Marcelia
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.31-45

Abstract

The rapid growth of the e-commerce industry in Indonesia is driven by several factors, such as increased internet access and consumer behavior. As a local shoe manufacturer, LeePoure is faced with the challenge of developing and transforming their e-commerce platform to improve user experience, sales conversion, and brand reputation. The research objective was to analyze and design an effective interaction design for LeePoure's independent e-commerce platform with a user-centered design (UCD) approach. The process involved user interviews, persona analysis, prototyping and, and usability testing. The results show that intuitive navigation, responsive display, and secure can improve user satisfaction and LeePoure's competitiveness in the e-commerce market.
Sistem pakar deteksi dini kesehatan mental mahasiswa dengan metode Forward Chaining: Sistem pakar deteksi dini kesehatan mental mahasiswa dengan metode Forward Chaining Izdiana, Alvi Zumaela; Tikaridha Hardiani
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mental health is a crucial aspect that has a significant impact on the daily lives of individuals. Data from the 2023 mental health screening conducted by the Psychology Service Bureau (BLP) of Universitas 'Aisyiyah Yogyakarta revealed that 16.8 percent of students experienced mental health disorders out of a total of 857 participants. Screening activities that have been carried out through Google Forms have limitations, because screening cannot directly determine the condition of mental health due to the lack of feedback after the test. This study aims to develop a web-based expert system that utilizes the Forward Chaining method to determine a person's mental condition. The system was tested directly by expert and compared the results with manual diagnoses Based on the test, the expert stated that the feature requirements of the system for mental health screening tests have been fulfilled.
Pemanfaatan Algoritma Convolutional Neural Network (CNN) untuk Klasifikasi Jenis Noken Rosyidah, Wahyuni Fajrin
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Noken is a traditional bag from Papua that holds high cultural value and has been recognized as an Intangible Cultural Heritage by UNESCO. The diversity of noken types based on motifs, shapes, and regions of origin presents a challenge in the identification process, which until now is still carried out manually. This study aims to develop an automatic noken image classification system using Convolutional Neural Network (CNN) with a transfer learning approach. Three CNN architectures used in this study are VGG16, InceptionV3, and MobileNetV2. The dataset consists of 250 noken images, comprising two types of noken, Bitu Agia and Junum Ese. The training process was conducted using the TensorFlow library with the best parameters, namely 50 epochs, a batch size of 32, the Adam optimizer, and a learning rate of 0.0001. Evaluation was carried out using accuracy, precision, recall, and F1-score metrics, as well as confusion matrix visualization. The results showed that MobileNetV2 achieved the best performance with an accuracy of 97 persen, followed by InceptionV3 with 93 persen, and VGG16 with 87 persen. This study demonstrates that the deep learning approach is effective in the image classification of cultural objects and can support the digital preservation of Papuan culture.
Analisis Komparatif Kinerja HAProxy dan Zevenet pada Infrastruktur Web Server Bare-Metal Linux Nursafaat, Maulachusnan Nursafaat
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

As system demands for speed, stability, and traffic handling continue to grow, selecting the right load balancing solution becomes increasingly critical. This study compares the performance of two open-source load balancers, HAProxy and Zevenet, on a Linux-based bare-metal web server infrastructure. The experiment was conducted using two identical backend servers and the wrk benchmarking tool, configured for five-minute tests, 1000 concurrent connections, and twelve repetitions per platform. The results show that HAProxy achieved a lower average latency (261.97 ms), higher throughput (1076.68 RPS), and fewer timeout errors (15,952) compared to Zevenet. While Zevenet offers a more user-friendly graphical interface, HAProxy proved to be more efficient and stable in high-traffic conditions. This study provides practical insights for implementing effective load balancing in non-virtualized systems with limited resources and high-performance demands.
Pemanfaatan AI dalam Evaluasi Pembelajaran Kognitif dan Kreativitas Musik Gen Z: Pendekatan Difusi Inovasi Charlie Christofel Watofa; Ratna Juita; Dedi I Inan; Muhamad Indra
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini mengkaji pemanfaatan alat musik berbasis kecerdasan buatan (AI) dalam evaluasi hasil belajar kognitif dan kreativitas musik pada Generasi Z. Menggunakan pendekatan Diffusion of Innovation (DOI), studi ini memungkinkan adopsi dan persepsi Gen Z terhadap teknologi AI dalam penciptaan dan produksi musik digital. Meski penggunaan AI dalam pendidikan berkembang pesat, kajian tentang dampaknya terhadap pembelajaran musik berbasis kognisi masih terbatas, khususnya di Indonesia. Data dikumpulkan melalui survei berani terhadap responden Gen Z di Papua. Penelitian memberikan pengaruh karakteristik inovasi seperti keunggulan relatif, kemudahan penggunaan, dan kesesuaian teknologi terhadap hasil belajar. Analisis R-square menunjukkan kontribusi moderat AI dengan nilai CA = 0,354; CL = 0,473; CP = 0,489; dan LO = 0,591. Hasil menunjukkan bahwa AI tidak hanya meningkatkan keterlibatan kognitif tetapi juga memperkuat kreativitas peserta musik. Temuan ini mendukung pengembangan sistem pembelajaran berbasis teknologi dalam pendidikan musik digital yang lebih adaptif dan inovatif.
Pengembangan Model Klasifikasi Kualitas Sarang Burung Walet Berbasis CNN dengan Transfer Learning MobileNetV2 anas, hasni
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The quality of edible bird’s nests is a crucial factor in determining their market value, thus requiring an accurate and automated classification system. This study aims to develop a quality classification model for edible bird’s nests using a Convolutional Neural Network (CNN) algorithm with a transfer learning approach based on the MobileNetV2 architecture. The dataset consists of 3,406 bird’s nest images collected directly from farmers, which were processed through background removal and aggressive augmentation to highlight the main object. The data were evenly split into 2,723 training images and 683 validation images, covering three quality classes: high, medium, and low. The model was trained in two stages: initial training with a frozen base and subsequent fine-tuning. Evaluation results showed an improvement in accuracy from 93% to 97% after fine-tuning, with average precision, recall, and F1-score values of 0.97. The confusion matrix indicated high classification accuracy. This study contributes to the development of an image-based classification model with high accuracy, offering potential for efficient and objective industrial sorting.

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue 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