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Applied Technology and Computing Science Journal
Published by Universitas Nahdlatul Ulama Surabaya
ISSN : 26214458     EISSN : 26214474     DOI : https://doi.org/10.33086/atcsj
Core Subject : Social, Engineering,
Engineering Social Sciences
Applied Technology and Computing Science Journal ( ISSN 2621-4458, E-ISSN 2621-4474) is a journal on all aspect of applied technology natural science that published online by Faculty of Engineering – University of Nahdlatul Ulama Surabaya. This journal published periodically twice in a year (on June and December) to accommodate the researcher from all over the world who want to publish the results of their research and contribution with all variety topics related to Engineering, Applied Computer Modelling and Simulation, Information System, Computer Science, Forecasting, Computer Applications, Expert System, E-Government, E-Business, E-Commerce, Information Security, Big Data, Intelligent System, Data Analysis, Data Mining, Smart City.
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Articles 8 Documents
 1 
Search results for , issue "Vol 8 No 2 (2025): December" : 8 Documents clear
Implementation of a Sales Dashboard Using Metabase for Attendance Analysis and Sales Payroll Systems (Case Study: PT. Fiyansa) Irwana, Galeh Ariya; Budiarti, Rizqi Putri Nourma
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8351

Abstract

In an effort to adopt digital technology, PT. Fiyansa has developed an Android application to monitor sales activities. However, the data generated by this application remains difficult to analyze regarding attendance and is insufficient for supporting decision-making. The primary factor causing this issue is the lack of adequate visualization. To address this problem, this research develops a dashboard to visualize attendance analysis and the payroll system. This dashboard generates visualizations categorized into three groups: attendance summary, sales performance analysis, and sales payroll. The attendance summary category consists of six visualizations, the sales performance analysis category comprises nine visualizations, and the sales payroll category includes three visualizations: payroll details, the three salespeople with the lowest salaries, and the three salespeople with the highest salaries. The results obtained from the dashboard analysis indicate that the level of sales salaries is significantly influenced by work attendance. This is evidenced by the analysis showing that salespeople with the highest salaries tend to have high attendance rates and a low frequency of lateness.
Sentiment Analysis of Digital Ethics in YouTube Islamic Preaching Videos Using Support Vector Machine Rahmah, Arizka Sabilah; Andhyka, Awang; Nugroho, Rizky Aditya
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8422

Abstract

The rapid expansion of Islamic preaching in the digital sphere, particularly through YouTube, calls for a deeper understanding of communication ethics as reflected in user responses. This study analyzes the sentiments expressed in comments on Islamic preaching videos to identify patterns of digital ethics within online communities. The research employs a Support Vector Machine (SVM) classification model with TF-IDF feature representation. Data were collected from YouTube comments and processed through several preprocessing stages, including text cleaning, case normalization, tokenization, stopword removal, and stemming, before being manually labeled into three sentiment categories: positive, negative, and neutral. Testing on 22 data samples shows that the SVM model achieved an accuracy of 77.27%, with the highest performance observed in the neutral category. Misclassification in the positive and negative categories was mainly influenced by data imbalance and linguistic variations commonly found in religious discourse. These findings indicate that SVM combined with TF-IDF is reasonably effective for sentiment analysis in the context of digital Islamic preaching; however, improvements in data balance and the incorporation of contextual features are necessary to enhance classification performance. Overall, this study provides an initial insight into audience response patterns toward digital Islamic preaching and contributes to the development of digital ethics research in Islamic communication studies.
Governance Capability Gap Analysis of SIMLITABMAS: A COBIT 2019-Based Evaluation Methodology and Literature Review Fina Amru Millati; Rahmat, Basuki; Muttaqin, Faisal
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8460

Abstract

This study aims to evaluate and enhance the Information Technology (IT) governance capability of the SIMLITABMAS system using the COBIT 2019 framework. By integrating a literature study with a formal evaluation methodology, the research focuses its analysis on the Deliver, Service, and Support (DSS) domain, specifically DSS02 (Managed Service Requests and Incidents), DSS03 (Managed Problems), and DSS05 (Managed Security Services). Literature review results indicate that the DSS domain is a critical juncture in university digital services, which frequently encounters obstacles due to manual procedures. Capability measurement results reveal that SIMLITABMAS is currently at Level 3 (Defined) with a fulfillment rate of 75.2%. This finding confirms a one-level gap toward the target of Level 4 (Measured), driven by a high dependency on informal communication channels such as WhatsApp and low digital literacy among users regarding system manuals. As a strategic solution to bridge this gap, this study recommends the implementation of AI Chatbot technology as a 24/7 automated helpdesk. The integration of the COBIT 2019 methodology with this automation solution is proven to transform service governance from an ad-hoc stage toward a more standardized, secure, and quantitatively measurable support system to sustainably support institutional research productivity.
Development of a Rice Leaf Disease Detection Application Using Python-Based Computer Vision and YOLO Fajar Maulana; Yomei Hendra; Guswita Helmi; Radhiatul Husna; Amma Liesvarastranta Haz; Evianita Dewi Fajrianti
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8486

Abstract

Rice is one of Indonesia’s primary agricultural commodities and is highly vulnerable to various leaf diseases, including blast, blight, brown spot, and tungro, which can significantly reduce crop productivity. To address this issue, an automated and accurate detection system is needed to assist farmers in identifying rice leaf diseases at an early stage. This study aims to develop a rice leaf disease detection application using computer vision technology based on Python and the YOLO (You Only Look Once) algorithm. The research methodology consisted of several stages: problem identification, data acquisition, data exploration, model development, evaluation, and deployment. The dataset was obtained from Roboflow and comprised five classes: blast, blight, brown spot, healthy, and tungro. The YOLO model was trained using Google Colab with optimized parameters to enhance detection performance. Experimental results demonstrate that the proposed model achieved an accuracy of 95% and a mean Average Precision (mAP) of 95%, indicating strong performance in detecting and classifying rice leaf diseases. The system was implemented as a web-based application using Flask and Bootstrap, allowing users to upload images of rice leaves and obtain real-time detection results. This application enables farmers to identify plant diseases quickly and accurately, facilitating timely and effective intervention to minimize crop losses.
Simulation and Optimization of Review Intervals Based on the Mathematical Model of the Ebbinghaus Forgetting Curve Radhiatul Husna; Nabila Gusti Rohima; Alwan Ronan; Mursyid Nur Fahmi; Amma Liesvarastranta Haz; Evianita Dewi Fajrianti
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8491

Abstract

Humans naturally experience memory decay over time due to the brain’s limited capacity, a phenomenon first systematically quantified by Hermann Ebbinghaus in 1885 through the forgetting curve, which illustrates the exponential decline of retention in the absence of reinforcement. This curve demonstrates that newly acquired information fades rapidly unless reviewed, negatively affecting educational outcomes as students struggle to retain knowledge in the long term. Spaced repetition, involving scheduled review sessions, has emerged as an effective strategy to counteract forgetting; however, optimal review intervals are often determined intuitively rather than derived mathematically. This study aims to model memory retention dynamics using an extended Ebbinghaus forgetting curve formulated through a learning and forgetting differential equation model, estimate parameters from empirical data, and optimize review intervals to enhance long-term retention. Data were collected through questionnaires from 15 students in the 2023 Mathematics Study Program at Andalas University enrolled in Real Analysis I, yielding parameter estimates of learning rate (α = 0.70), forgetting rate (λ = 0.30), initial knowledge level (K(0) = 100%), and maximum knowledge capacity (K_max = 100%). The model was solved analytically, and numerical simulations compared three strategies: no review, random review, and optimal review at an 80% retention threshold. The optimal review time was found to be t = 1.098 days (approximately 26 hours and 32 minutes), corresponding to the point at which retention declines to 80%. Simulations showed that no review leads to near-zero retention over time, random review produces inconsistent improvements, and the optimal review strategy maintains retention above 80% efficiently. Overall, the mathematically derived optimal review strategy significantly outperforms alternative approaches, providing a personalized, evidence-based method to improve learning efficiency and long-term memory stability while demonstrating the value of integrating psychological memory theory with mathematical optimization for practical educational applications.
A Dual-Stage Hybrid Vision Framework Using YOLOv8n-Canny Edge Detection for Real-Time Railway Trespassing and Intrusion Monitoring Ciptaningrum, Adiratna; Echsony, Mohammad Erik; Apriyanto, R. Akbar Nur
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8579

Abstract

Intrusion and trespasser detection on railway tracks is a crucial safety measure to prevent accidents and maintain operational reliability. This study proposes a hybrid vision-based approach that integrates YOLOv8n, a lightweight real-time object detection model, with the Canny edge detection algorithm to identify and classify unauthorized objects and individuals on railway tracks. In this context, intrusions refer to inanimate objects such as rocks, fallen trees, or construction materials obstructing the tracks, whereas trespassers refer to humans or other living beings engaging in unauthorized activities near or on the railway line. YOLOv8n is employed as a single-stage detector to localize and classify objects, while Canny edge detection is applied to enhance object contours and improve shape-based differentiation between intrusion and trespasser categories. Experimental results show an average accuracy of 52.37%, indicating moderate detection performance. Although the accuracy remains limited, the findings demonstrate the potential of combining deep learning and traditional image processing techniques to develop an automated monitoring system that supports railway safety and surveillance applications. Further optimization of the dataset, model tuning, and feature enhancement are recommended to improve detection performance.
UI/UX Design and Evaluation of Shrouded Buana Game Using Design Thinking and Cognitive Walkthrough Sudaryanto, Aris; Ivaldy, Amsyar Raftan Oskash; Safrodin, Mohamad; Wijaya, Adi
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8598

Abstract

The rapid growth of mobile gaming, which accounts for a significant portion of the global gaming market, necessitates optimal UI/UX design to maintain competitive advantage and player retention. One of the most popular games in Indonesia is Genshin Impact, which recorded 6,857,493 downloads on the Indonesian Playstore and Appstore as of September 2022. Research concerning UI/UX in games like Genshin Impact has become increasingly vital due to the growing emphasis on user engagement and accessibility within the gaming industry. The specific challenges of effectively designing UI/UX for complex open-world games, such as Genshin Impact, present significant opportunities for further exploration. Shrouded Buana is a game developed with Genshin Impact as its primary reference. This research focuses on avoiding various UI/UX-related issues in Genshin Impact. The Design Thinking framework is employed as the methodology for developing the UI/UX of Shrouded Buana. The usability level of the Shrouded Buana UI/UX is measured using the Cognitive Walkthrough (CW) method. the Cognitive Walkthrough is conducted through playtesting, where 30 users are presented with specific scenarios of tasks to complete. The Shrouded Buana's average task completion time (TCT) across all scenarios is 96,71%. The Average of Shrouded Buana's Task Completion Time is 5,15 s. Its mean Shouded Buanas's UI UX are very good so can support respondents to finish all scenarios quickly.
Implementation of Face Recognition-Based Personal Information Management System using Hikvision AI Cameras for UNUSA Community Wibowo, Nanda Dwi Cahyo; Budiarti, Rizqi Putri Nourma
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8621

Abstract

Personal Information Management (PIM) at the University of Nahdlatul Ulama Surabaya (UNUSA) continues to face challenges, particularly in managing attendance within the eSorogan system and the UNUSA Academic Information System (SIM). This study proposes and implements a face recognition-based PIM system designed to automate the identification and recording of members of the UNUSA academic community. The system integrates AI-powered cameras with a web-based dashboard, referred to as the Lobby PIM Dashboard, which displays real-time information related to recognized individuals. The workflow begins with image capture and face detection by the AI camera, followed by data transmission to the server via an API for identification and information retrieval. The system generates detection logs and attendance records, which are stored and presented through the dashboard interface. The results demonstrate that the proposed system can support more efficient attendance management and provide accessible academic information for lecturers and students. Overall, this implementation contributes to improving administrative processes and enhancing the effectiveness of academic services at UNUSA.

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