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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 122 Documents
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Comprehensive Modelling of a Capacitor Charger Boost Converter with PID Control Wicaksono, Ilham Agung; Adhim, Fauzi Imaduddin; Aliffianto, Lutfir Rahman; Arif, Rezki El
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 6 No 2 (2023): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v6i2.6217

Abstract

The DC-DC converter is used to convert the DC voltage, and the conversion is carried out to increase or decrease the voltage. This paper discusses the modeling of a boost converter for charging high-capacity capacitors using PID control. PID control is used to overcome changing voltages and long times to steady-state due to capacitor changes. The boost converter has a 24 V input voltage and a 350 V output voltage. Boost converter modeling is performed by electrically reviewing the circuit and then converting the differential equation from the electrical analysis into a state space that depicts the characteristics of boost converter. The simulation was performed using MATLAB software. For the initial conditions, a simulation was carried out using the calculated critical capacitor value to determine whether the boost converter output voltage reached 350 V. Furthermore, when the boost converter was replaced with a 4700 uF capacitor, the boost converter experienced underdamp oscillations with a settling time of up to 9 s. Therefore, PID control was used to overcome oscillations and long settling times. The Ziegler–Nichols 2 and Routh–Hurwitz stability methods were used to determine the parameters Kp, Ki, and Kd. From the calculation results, the parameters Kp = 0.6, Ki = 21.39, and Kd = 0.0042 are obtained. The output voltage response still has a high undershoot when using the parameters obtained from Ziegler–Nichols 2 tuning, but it already has a fast-settling time. By fine-tuning the PID control, the values Kp = 2, Ki = 90, and Kd = 0.09 were obtained. With these values, the boost converter voltage response has an overshoot of 388 V, an undershoot of 312 V, and a settling time of 0.6 s at 351 V.
NEURO-LINGUISTIC PROGRAMMING-BASED COUNSELING ASSESSMENT APPLICATION DESIGN Utomo, Prabowo Budi; Akhsani, Rafika; Muluk, Muchammad Saiful; Moch. Kholil
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 6 No 2 (2023): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v6i2.5404

Abstract

The non-optimal use of technology in counseling assessment makes the problem identification less accurate. Counseling assessment applications that are able to identify problems precisely are a necessity for counselors / counseling guidance teachers. The use of Google API speech to text allows the development of web applications that produce text outputs from voice input that can facilitate the process of diagnosing modalities and sub modalities of counselees / students. The method used in system development is Prototype which has advantages in faster development time. Combined with the Neuro-Linguistic Programming method, it will increase the accuracy of the diagnosis of modalities and sub modalities of counselees/students. The developed application has been tested functionally with a value of 65.63% which is included in the interpretation of 'Feasible', so it can be stated that the developed counseling assessment application is in accordance with user needs and feasible to use.
Implementation of PHP Based E-KKN at Universitas Islam Lamongan Bagus Reknadi, Danang; Munif, Munif; Mustain, Mustain
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

The college is an educational institution that aims to create scientist environmentally sensitive and able to solve the problems that arise in society and have a responsibility to produce graduates who are professionals so that they can support and handle the problems that arise in the community. In order to realize the objective and responsibility, each college must carry out activities KULIAH KERJA NYATA (KKN). KKN is a form of community service, which is special, because there is an element in the Tri Dharma of Universities, namely: Education and Teaching, Research, Service society. The rapid development of science and technology and the progress of communication tools. The need for a renewal of KKN administrative system, called e-KK. These features are provided runs in accordance with has been drafted, with the SIAKAD data as a reference database. The purpose of the development of e-KKN is to accommodate the needs of the KKN administrative process neat for processing data quickly, accurately, and effectively at the Islamic University of Lamongan. In the design, e-KKN has several features that have been prepared in accordance groove mechanism of opening the registration portal, process list, the small groups at random, until data printing user.
Performance Comparison of Convolutional Neural Network with Traditional Machine Learning Methods in Adult Autism Detection Wijaya, Nurhadi; Muliani, Sri Hasta; Nurain, Maisarah
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

Diagnosing Autism Spectrum Disorder (ASD) in adults is a challenging task, requiring precise and efficient early detection methods. However, there is limited research in this area. Hence, this study seeks to address this gap by evaluating the effectiveness of Convolutional Neural Networks (CNNs) compared to traditional machine learning techniques for detecting autism in adults. The study introduces a CNN-based model and conducts a performance comparison with conventional algorithms such as Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The objectives are to evaluate the efficacy of CNNs in adult autism detection, identify algorithm strengths and weaknesses, and explore healthcare implications. The research utilizes the Autism Screening on Adults dataset, with 704 records and 21 features, employing preprocessing steps to optimize data quality. The proposed CNN model encompasses convolutional layers, max-pooling, dropout, and dense layers, while baseline algorithms serve as benchmarks. Evaluation metrics include the Confusion Matrix and Classification Report. The CNN model achieved remarkable accuracy (99%) and precision in adult autism detection, outperforming traditional algorithms. SVM emerged as the closest competitor but fell short. This study underscores CNN's potential for precise autism detection in adults, with implications for early intervention and telehealth applications. The research highlights CNNs' effectiveness and superiority over traditional machine learning algorithms, suggesting their promise for accurate diagnosis. Future research opportunities include expanding datasets, optimizing model parameters, and addressing ethical considerations for practical healthcare implementation
Disease Segmentation in Purple Sweet Potato Images Using Yolov7 khusniyatul latifah; Aviv Yuniar Rahman; Istiadi
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v7i1.6023

Abstract

Purple sweet potato is a very important plant in many parts of the world and a major crop in tropical and subtropical climates. Its cultivation can significantly increase production and consumption, and it is beneficial for the nutritional status of people in both rural and urban areas. However, purple sweet potatoes are susceptible to disease outbreaks, which can cause substantial losses to the agricultural industry. To prevent the spread of these diseases and minimize financial losses, it is crucial for farmers to identify purple sweet potato diseases as early as possible. Utilizing deep learning technology to separate areas of purple sweet potatoes marked with disease can effectively address this problem. In this study, researchers employed a segmentation method using the YOLOv7 algorithm. The study's results demonstrated a mean Average Precision (mAP) value of 98.6% from a dataset of 1500 images, divided into two classes: healthy sweet potatoes and diseased sweet potatoes with tuber rot. The mAP value for healthy sweet potatoes was 96.1%, while the mAP for diseased sweet potatoes with tuber rot was 98.6%. The YOLOv7 method, therefore, produces high accuracy values for the segmentation of purple sweet potato diseases. This research significantly contributes to agriculture by enhancing the productivity and quality of sweet potato harvests and can assist farmers in improving the efficiency and sustainability of purple sweet potato production.
Implementation Of Decision Support System Topsis Fuzzy MCDM Method For Web-Based Cafe Recommendation Kholis, Mukhamad Jainur; Dr. Istiadi S.T.,M.T.; Aviv Yuniar Rahman, S.T., M.T
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v7i1.6037

Abstract

The purpose of this research is to provide recommendations for cafes using the TOPSIS Fuzzy MCDM method and implementing it in a web-based system. This system aims to assist consumers in choosing a cafe based on various factors such as location, facilities, rating, price, and service quality. The TOPSIS Fuzzy MCDM method was selected for its ability to handle uncertainty and produce a more accurate ranking of alternatives. The research demonstrates that this system can deliver recommendations aligned with predetermined preferences, thereby facilitating better decision-making. The study was conducted by researchers in the Malang City area, but the system is accessible to users from any location based on their coordinates. The test results show that the system successfully provides recommendations with scores of 100%, 73.07%, 53.92%, and so forth. These findings indicate that the system can effectively help users decide on their preferred cafe by offering reliable and accurate recommendations. Moreover, the integration of real-time data and Google Maps ensures that the recommendations are up-to-date and relevant. This implementation is expected to enhance the consumer decision-making process, providing a practical tool for selecting cafes that meet specific user criteria. This research underscores the potential of using advanced decision-making methods like TOPSIS Fuzzy MCDM in real-world applications, demonstrating its effectiveness in improving the user experience in the cafe selection process. The system's capability to integrate additional information, such as social media presence and customer reviews, could further enhance its utility in future developments.
Prototype SMS and GPS based Otoped Rental Tracking System for Car Free Day Darmo Surabaya kridoyono, agung; Yunanda, Anton Breva; Sidqon, Mochamad; Hartono, Elvianto Dwi; Sudaryanto, Aris; Yuwono, Istantyo
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

The main problems in otoped rentals are congestion, road branches, and internet connectivity. This research aims to design and implement a simple rental tracking system for otoped at Darmo Surabaya Car Free Day, utilizing Arduino technology integrated with SMS, maps, GPS, and the SIM800L module. The system is developed to enhance the efficiency and security of otoped rentals by providing real-time tracking and location updates. The main components include an Arduino microcontroller, GPS module for location tracking, SIM800L module for SMS communication, and a map interface for visualization. The system sends location data via SMS, which can be accessed by users and administrators to monitor otoped positions and usage. The effectiveness of the system is evaluated based on its accuracy, reliability, and user satisfaction. The results indicate that the Arduino-based tracking system performs well in providing real-time updates and accurate location data, significantly improving the management of scooter rentals. This study concludes that the integration of Arduino, SMS, maps, GPS, and SIM800L is a viable solution for a simple and effective rental tracking system for electric scooters.
Identification Types of Plastic Waste Based On The Yolo Method Saputro, Adi Kurniawan; Ulum, Miftachul; Khabibiy, Odiy Syahnurrokhim; Ibadillah, Achmad Fiqi; Alfita, Riza; Hardiwansyah, Muttaqin
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

Plastic is still widely used in the daily lives of Indonesian people. As well as being an inexpensive material, plastic is characterized by weather, lightweight and corrosion resistance. In Indonesia, awareness of waste is still very low, resulting in the indiscriminate accumulation of garbage. Some people don't know the material from the type of plastic used These issues can be solved by using artificial intelligence advancements, such as computer vision.The yolov3 Tiny method was used to identify the type of plastic. The types of plastic used in this study were PET, PP, and HDPE. This method is implemented in a desktop-based application. The system is successful with an average accuracy value of 53.3%. The system easily recognizes the PET type, with an accuracy rating 70% greater than the other varieties. Whereas for the PP type, the result is 60% and for the HDPE type, it gets a value of 30%. For researchers, in turn, it may add a greater number of data slices and vary to be even more accurate in their identification.
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Tuberculosis Detection based on Lung X-Ray Images Using Convolutional Neural Networks (CNN) Kurniawan, Rudi; Badriyah, Tessy; Syarif, Iwan
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v7i1.6448

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, primarily affecting the lungs. Despite being preventable and curable, TB remains a significant global health issue, especially in developing countries. The success of TB treatment heavily depends on the accuracy of the diagnosis, which typically requires expertise from pulmonology or radiology specialists to interpret chest X-ray images.  This study aims to design an assistive tool for TB detection that can automatically diagnose the disease using chest X-ray data.  The study implemented a Convolutional Neural Network (CNN) architecture to analyze the X-ray images. Additionally, image preprocessing and early stopping methods were employed to enhance accuracy performance, optimize computation, and prevent overfitting.  Experiment was conducting using 75% of the data as training data to generate the model and then applied to 25% of the data as testing data. This study comparing image sizes in RGB and grayscale modes. Experimental results show that the use of early stopping has a significant impact on training time, reducing training time substantially in almost all scenarios without drastically sacrificing accuracy. Without early stopping, accuracy does tend to be higher, as seen in grayscale color mode with an image size of 128x128, where the accuracy reaches 0.992, and in RGB mode with an image size of 64x64 which reaches 0.995. However, training time also increases significantly, for example for a 299x299 image with RGB mode, the training time reaches 927 seconds. Therefore, while RGB yields slightly higher accuracy, grayscale is recommended due to significantly faster training times. Additionally, the early stopping mechanism proves effective in reducing computational time, making the training process more efficient.

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