Garuda - Garba Rujukan Digital

Enterprise Resource Planning (ERP) Design Using TOGAF ADM and ACMM (Case Study: PT XYZ) Triyanto, Triyanto; Supriyanto, Aji
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.16

Enterprise Resource Planning (ERP) is software used by companies to unite and manage information from various parts of the company, or a tool that integrates all aspects of business in a company. However, in order to support the operations of PT XYZ, it has not implemented an Information System in its business activities. In order to support the smooth running of business processes at PT XYZ, it is necessary to design a structured Information System with good planning using the TOGAF ADM framework. Before starting the design, the researcher evaluated the maturity level of PT XYZ by comparing the current condition with the desired one using the Architecture Capability Maturity Model (ACMM). The results of this evaluation were recommendations for an ERP architecture that was adapted to PT XYZ's business processes, including business architecture, data, applications, and technology that have reached their respective level 3 maturity levels.

PENERAPAN TECHNOLOGY READINESS ACCEPTANCE MODEL (TRAM) DALAM MENGUKUR KESIAPAN DAN PENERIMAAN TEKNOLOGI CASHLESS Priambodo, Wisnu; Munna, Aliyatul; Pratama, Dicky Yudha; Supriyanto, Aji
SOSCIED Vol 7 No 2 (2024): SOSCIED - November 2024
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v7i2.750

In the era of globalization and industrial revolution 4.0, information and communication technology (ICT) has created major changes, especially in financial transactions. This phenomenon has led to the emergence of cashless payment systems as the dominant trend, including in the education sector. Schools in Semarang, as part of their efforts to adapt to such developments, need to shift from traditional payment methods to cashless payment technologies to improve efficiency and convenience. This study aims to understand the acceptance of Cashless technology among school teachers and employees in Semarang using the Technology Readiness Acceptance Model (TRAM) approach. Through Structural Equation Modeling (SEM) analysis, the results show that insecurity plays a major role in influencing perceived ease of use. Although the insecurity factor did not have a significant effect, security also had a positive effect on users' assessment of the usability of Cashless technology.

DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA Supriyanto, Aji; Zuliarso, Eri; Suharmanto, Eko Taufiq; Amalina, Hana; Damaryanti, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4159

Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.

DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA Supriyanto, Aji; Zuliarso, Eri; Suharmanto, Eko Taufiq; Amalina, Hana; Damaryanti, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4159

Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.

PENERAPAN PEMBELAJARAN BERBASIS KONVENSIONAL DENGAN TEKNOLOGI INFORMASI PADA TPQ RAUDHATUL ‘ULUM MANYARAN KOTA SEMARANG Razaq, Jeffri Alfa; Supriyanto, Aji; Budiarso, Zuly; Suharmanto, Eko Taufiq; Kasprabowo, Teguh
Intimas Vol 5 No 1 (2025)
Publisher : Fakultas Teknologi Informasi dan Industri Unisbank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/intimas.v5i1.9931

One of the places for community-based Islamic Religious Education (PAI) is through the Quran Education Park (TPQ). Not all TPQs implement qualified and modern learning methods and media according to the needs of today's education to realize the goals of PAI. The purpose of this service is to realize the goals of PAI at TPQ Raudahtul 'Ulum with conventional-based learning assistance and training methods with Information and Communication Technology (ICT) and digital. Assistance is carried out by installing ICT and digital devices with TPQ teachers. Meanwhile, training is carried out by combining ICT and digital-based learning media with Reading and Writing the Quran (BTA), books and Educational Game Tools (APE) with Islamic themes. The use of a combination of learning media aims to make learning easy, complete, interesting, creative and innovative, and can be done online and students can learn through their own gadgets. As a result, TPQ teachers have become proficient in installing ICT and digital devices and are able to understand and teach combination learning.

Perbandingan Deep Learning YOLOv5 dan YOLOv8 Untuk Deteksi Penyakit Daun Tanaman Tomat siti choiriyah; aji supriyanto
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 1 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.1.357

Agriculture is one of the mainstays of the country's economy, especially the horticulture sub-sector such as fruits and vegetables. Tomato plants are one of the leading commodities. However, the failure of tomato cultivation due to the many types of diseases that exist is still an obstacle and interferes with plant growth, reduces yields, and even causes tomato plant death. This study aims to detect tomato leaf diseases by comparing the performance of the two YOLOv5 and YOLOv8 models. The purpose of comparing models is to determine the level of accuracy and to conclude which version of YOLO provides a better level of accuracy in the hope of helping to determine which method is most appropriate and appropriate to needs. The results showed that both YOLOv5m and YOLOv8m models performed very well in detection. Both models showed high precision, recall, and mAP values. YOLOv8m is better able to detect all objects in the image where the precision value is superior to YOLOv5m. YOLOv8m is superior in precision with a value of 0.95%, a difference of 0.02% with YOLOv5m and mAP50:95 which is 0.92%, a difference of 0.02% with YOLOv5m which means that YOLOv8m is better at identifying objects very precisely and objects of various sizes, but YOLOv8m requires a slightly longer training time than YOLOv5m. YOLOv8m is better able to detect all objects in the image where the precision value is 0.02% superior to YOLOv5m

Title

Found 36 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 36 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 36 Documents
Search