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 185 Documents
 15   16   17   18   19 
Penilaian karyawan kontrak di RS Bhayangkara Denpasar menggunakan Simple Additive Weighting dan Weighted Product Dewa Ayu Putri Novita Sari; Dewa Gede Hendra Divayana; Luh Joni Erawati Dewi
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.v23i3.420-433

Abstract

Evaluasi kinerja karyawan kontrak di Rumah Sakit Bhayangkara Denpasar POLDA Bali selama ini masih dilakukan secara manual, sehingga rentan terhadap subjektivitas, inkonsistensi, dan ketidakefisienan dalam mengambil suatu keputusan. Tujuan dari penelitian ini untuk membuat sebuah sistem pendukung keputusan yang mampu meningkatkan tujuan dan efektifitas proses evaluasi dengan mengimplementasikan metode SAW dan WP. Sebanyak 117 data karyawan kontrak dievaluasi berdasarkan sembilan kriteria penilaian. Sistem dirancang menggunakan pendekatan Software Development Life Cycle (SDLC) model waterfall dan diuji dengan black-box testing. Hasil perhitungan menunjukkan bahwa metode Simple Additive Weighting (SAW) mendapatkan nilai sebesar 3,53 dan Weighted Product (WP) sebesar 0,062, yang digunakan sebagai dasar perangkingan dalam kelanjutan kontrak kerja. Untuk mengukur tingkat akurasi, digunakan metode Mean Absolute Percentage Error (MAPE) dengan hasil sebesar 0,63% untuk SAW dan 0,65% untuk WP, yang menunjukkan bahwa kedua metode memiliki tingkat kesalahan sangat rendah. Sistem ini terbukti dapat memberikan informasi penilaian yang lebih akurat, adil, dan terstruktur. Diharapkan penerapan sistem ini mampu memperkuat pengelolaan SDM, meningkatkan transparansi serta kualitas dalam pengambilan keputusan di rumah sakit tersebut.
Model spatial analysis identifikasi risiko tsunami Pantai Selatan Jawa menggunakan Data Built-up Index Wiwin Sulistyo; Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak
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.v23i3.465-481

Abstract

Wilayah Indonesia, khususnya sepanjang pantai selatan Pulau Jawa, memiliki tingkat kerawanan gempa dan tsunami karena berada pada zona patahan dan memiliki sejarah kejadian tsunami. Potensi berulangnya bencana dalam kurun waktu tertentu memerlukan perhatian melalui identifikasi risiko dan langkah mitigasi yang tepat. Penelitian ini bertujuan mengidentifikasi dan mengklasifikasikan wilayah berisiko tinggi terhadap gempa dan tsunami dengan memanfaatkan citra satelit dan analisis spasial. Data citra satelit diolah untuk memperoleh Spectral Vegetation Indices (SVI) serta sejumlah indeks berbasis lahan terbangun dan karakteristik permukaan menggunakan komputer 3Drecons. Indeks yang digunakan meliputi Index-based Built-up Index (IBI), Urban Index (UI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI), Normalized Built-up Area Index (NBAI), dan New Built-up Index (NBI). Selanjutnya, data dianalisis menggunakan pendekatan klasterisasi dan klasifikasi spasial untuk mengidentifikasi pola dan tingkat risiko pada setiap wilayah studi. Hasil analisis menghasilkan pengelompokan wilayah berdasarkan karakteristik spasial yang dapat digunakan untuk menggambarkan status risiko gempa dan tsunami. Pendekatan ini dapat mendukung identifikasi wilayah prioritas serta penyusunan strategi mitigasi bencana berbasis data spasial.
Analisis faktor penerimaan pengguna terhadap adopsi Artificial Intelligence di Perguruan Tinggi melalui Extended UTAUT2 Jessica Nathania Kho; Hindriyanto Dwi Purnomo; Yessica Nataliani
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.v23i3.449-464

Abstract

Artificial Intelligence (AI) has increasingly been used to support scientific writing, offering various benefits while simultaneously raising ethical challenges for higher education institutions. This situation highlights the need for targeted AI integration based on a comprehensive understanding of students’ acceptance and actual use. This study investigates the factors influencing users’ acceptance and actual use of AI in research and scientific writing using the Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework and Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected through a survey of 129 respondents from the Faculty of Information Technology, Satya Wacana Christian University (SWCU). The results indicate that Trust has the strongest influence on Behavioral Intention, followed by Habit and Social Influence. Meanwhile, Habit is the strongest predictor of Use Behavior, whereas Facilitating Conditions and Behavioral Intention do not significantly influence actual use. These findings provide evidence of an Intention–Behavior Gap, suggesting that actual AI use in scientific writing is influenced more strongly by established usage habits than by conscious behavioral intentions alone.
Perbandingan Random Forest dan XGBoost dengan indikator teknikal untuk prediksi harga saham Bank Mandiri Muhammad Wahyu Tri Ferdiyanto; Nur Wakhidah
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.v23i3.434-448

Abstract

The volatility of PT Bank Mandiri (Persero) Tbk stock prices during 2020–2025, influenced by the COVID-19 pandemic and monetary policy dynamics, presents challenges for investors in making optimal investment decisions. This study aims to compare the performance of Random Forest and XGBoost in predicting Bank Mandiri stock prices using technical indicators. The study used 1,152 daily observations after preprocessing and incorporated 10 technical indicators: Simple Moving Average, Exponential Moving Average, Relative Strength Index, Moving Average Convergence/Divergence, Bollinger Bands, Stochastic Oscillator, Average True Range, On-Balance Volume, Rate of Change, and Momentum. The dataset was divided into 80% training (921 observations) and 20% testing (231 observations) using a time-series split. Model performance was evaluated using MAPE, RMSE, MAE, and R². The results show that XGBoost outperformed Random Forest, achieving an RMSE of 161.97 and a MAPE of 2.42%, compared with 182.61 and 2.65%, respectively. XGBoost also demonstrated 38.79% faster computational performance. Feature importance analysis identified the daily low price (61.75%), the closing price (19.55%), and the 5-day SMA (3.68%) as the most influential features. These findings indicate that XGBoost provides more accurate and efficient stock price prediction for volatile Indonesian banking stock data.
Analisis pengaruh sentimen terhadap akurasi prediksi harga close market cryptocurrency menggunakan Deep Learning berbasis GRU Muhammad Alif Fadillah; Fauzi Niko Allianzah; Arya Maulana; Agam Gunawan; Fajar Delli Wihartiko
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.v23i3.496-510

Abstract

The volatile, dynamic fluctuations in cryptocurrency prices make it difficult to predict closing prices in time-series modeling. In addition to historical price data, the dynamics of the crypto market are also influenced by investor sentiment shaped by digital sources such as news and online media, reflecting their perceptions, expectations, and reactions to market conditions that impact price direction. This study develops a cryptocurrency closing price prediction model using a deep learning approach with Gated Recurrent Units (GRU) that integrates market price and sentiment data into a single integrated framework. The research stages include data collection via APIs, selection of relevant data, preprocessing such as normalization and the creation of time-series sequences, construction of the prediction model, and evaluation and visualization of results. The findings indicate that the model effectively captures the temporal patterns of price movements, generating predictions that align with the actual trends of Bitcoin and Ethereum. The inclusion of sentiment data provides supporting signals to represent market dynamics shaped by investor behavior. Thus, the integration of price and sentiment data yields predictions that are more accurate and better representative of the cryptocurrency market.

Page 19 of 19 | Total Record : 185

 15   16   17   18   19 

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