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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 26 Documents
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Search results for , issue "Vol 5, No 1 (2025): March" : 26 Documents clear
Smart Parking Space Detection Using Advanced Deep Learning Techniques Aguswandi, Lalu Heri; Triwijoyo, Bambang Krismono; Martono, Galih Hendro
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6473

Abstract

This study aims to develop an accurate and efficient empty parking slot detection model to assist users in finding parking spaces. The developed model utilizes YOLOv11 as a pretrained model and demonstrates excellent performance with a precision of 99%, recall of 99%, and a Mean Average Precision (mAP) of 99%. These results validate the model's ability to accurately detect empty parking slots with 100 training epochs. Additionally, the model operates in real-time with a frame rate of 25 frames per second (FPS)
The Detection of Objects and Distance for the Visually Impaired by Using Deep Learning ResNet-152 and the Triangulation Method Ichwan, Muhammad; Dewi, Irma Amelia; Salsabilla, Nadiati
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6419

Abstract

This research aims to detect objects and determine the distance from the mobile camera to facilitate and assist visually impaired users in recognizing the surrounding environment using several models made with the RetinaNet Method and Residual Network-152 architecture. Three object detection models were generated by ADAM and SGD parameter optimizers. Object recognition was performed using the TensorFlow framework with a dataset of 2,444 images. The first model with training parameters used is ADAM optimizer, epoch 50, batch size 16, and lr 1e-5. The second model has training parameters, such as ADAM optimizer, epoch 100, batch size 16, and lr 1e-5. The third model uses SGD optimizer training parameters, epoch 50, batch size 16, and lr 1e-5. Based on 250 tests on each model, the results show that the best model is the first model, which shows a precision value of 82%, a recall value of 98%, an f1 score value of 89%, and an accuracy value of 86%. The distance from the mobile camera is tested in multiples of 10 at a distance of 100-300 cm with a camera height of 100-130 cm and a camera angle of 80⁰-90⁰ getting reasonable distance detection results at a camera height of 130 cm because it gets the smallest total difference value of 14.3 cm.
Implementation of Fuzzy Logic in Educational Game on Manners and Morals for Kids Using Godot Engine Kynta, Diva Putri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6458

Abstract

SariAdanya peningkatan signifikan terhadap game yang tidak pantas untuk anakanak yang beredar di internet mengakibatkan penurunan adab dan akhlak anak zaman sekarang. Sehingga dikembangkan lagi game edukasi yang cocok untuk mengatasi masalah ini. Game seharusnya digunakan untuk melatih motorik halus pada anak dan tidak memberikan efek samping buruk yang dapat memengaruhi perilaku anak. Solusi yang ditawarkan pada proyek ini adalah pengembangan game edukasi anak yang mampu mengajarkan adab dan akhlak sejak dini dan dapat digunakan sebagai pengukur perkembangan anak melalui gameplay. Game edukasi yang dihasilkan memiliki persentase kepuasan sebesar 89.7%. Sehingga dapat disimpulkan bahwa game edukasi yang telah dikembangkan dapat berjalan dengan baik. (9 pt).AbstractThe significant increase in inappropriate games for children circulating on the internet has resulted in a decrease in the manners and morals of today's children. So that educational games that are suitable to overcome this problem are developed again. Games should be used to train fine motor skills in children and not give bad side effects that can affect children's behavior. The solution offered in this project is the development of children's educational games that are able to teach manners and morals from an early age and can be used as a measure of children's development through gameplay. The resulting educational game has a satisfaction percentage of 89.7%. So can be concluded that the game that have been developed to perform well. (9 points).
Implementation Of Fuzzy Sugeno Method To Determine The Production Amount Of Android-Based Oyster Mushroom Baglogs Budiarto, Sony Panca; Alfiyan, Faruk Alfiyan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6285

Abstract

The agricultural industry is one of the key sectors of the global economy. Along with the increasing demand for agricultural products, the challenge to improve production efficiency and productivity is also increasingly urgent. Oyster mushroom (Pleurotus ostreatus) is one of the agricultural commodities that has great potential to be developed, has high nutritional value, and growing demand in the global market. In facing the challenges of increasing oyster mushroom production, there are several problems that must be overcome. One of them is determining the right amount of oyster mushroom baglog needed to match the increasing demand and supply of oyster mushrooms. The decision-making process in determining the optimal amount of baglog production is often complex and requires in-depth analysis of various variables. In this study, researchers used the Fuzzy Inference System (FIS) Sugeno method to determine the optimal amount of oyster mushroom baglog production. The Sugeno method was chosen because it can produce clear rule-based solutions by better considering the correlation between input variables. The FIS will be developed based on previous baglog demand, inventory, and production data. The system is developed based on Android Mobile using React Native. The results of measuring the validation value and accuracy of Sugeno fuzzy applications in predicting the number of oyster mushroom baglog needs get a MAPE value of 1.88%; the prediction method used is in the very good category.
Implementation Of A Simple Linear Regression Method For Webgis Based Prediction Of North Timor Central Timor District Perumda Water Use Conceicao, Helidora Jelia Sasiana; Rema, Yasinta Oktaviana Legu; Lestari, Anastasia Kadek Dety
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6476

Abstract

The increasing need for clean water along with population growth is a challenge for the provision of drinking water services, especially in North Central Timor Regency. The Tirta Cendana Regional Drinking Water Company (PDAM) plays an important role in meeting this need, although water availability is influenced by rainfall and population. The aim of this research is to develop an information system that predicts clean water usage based on the number of customers using a simple linear regression method. The system was designed using the waterfall method and integrated into a WebGIS platform to manage water supply and usage data, and also provides a visualization of water demand in different locations. This research aims to assist PDAM Tirta Cendana in planning water production and improving services to customers, as well as supporting water resource management by local governments to meet community needs in the future.
Software Testing in E-Commerce: A Comparison Between Manual and Automated Testing Using Katalon Studio and Python Rakly, Brian Duen; Andriyani, Widyastuti
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6448

Abstract

Software testing is a crucial element in software development to ensure quality and reliability. This study compares manual and automated testing using tools like Katalon Studio and Python. Manual testing is effective for scenarios requiring human judgment, such as user experience (UX) evaluation. In contrast, automated testing is more efficient for routine and repetitive tasks, reducing human error and speeding up the process. This study evaluates the effectiveness, efficiency, and costs of both methods in the context of e-commerce software testing. The results indicate that manual testing is superior in detecting defects before release, while automated testing is more cost-effective and time-efficient for repetitive testing. This guide assists developer for selecting the appropriate testing method based on their project needs.
The Implementation of Proof-of-Work Technology in the Security of Job Training by the Surakarta City Manpower Office Agus Putra, Affriza Brilyan Relo Pambudi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6403

Abstract

This study aims to implement Proof-of-Work (PoW) innovation as a security component in the job training system managed by the Surakarta City Manpower Office. In the rapid development of technology, information security is a very important aspect, especially in the implementation of training activities involving sensitive data from participants and instructors. This study uses a prototyping approach to design, implement and test a PoW blockchain technology-based system to ensure information security in job training. The prototyping approach was chosen because it allows a rapid development process that is immediately applied to perfect the system. This system is designed in such a way that every data entered into the training track record is secured through a mining process, which ensures its integrity and security. The results of PoW technology are able to verify new transactions and data entered so that only valid and encrypted information can be received and stored. In conclusion, the implementation of a PoW-based security system on this job training platform has proven to be successful in increasing data protection and preventing manipulation by irresponsible parties. Thus, PoW technology not only provides a higher level of information security, but also increases the trust of participants and job training organizers at the Surakarta City Manpower Office.
Design of a Traditional Clothing Rental Information System Using the First Come First Serve (FCFS) Algorithm Based on a Website Handoyo, Nuh; Nur Laila, Siti
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6454

Abstract

This study aims to design and develop a web-based traditional clothing rental information system using the First Come First Serve (FCFS) algorithm to enhance rental management efficiency. The background of this research is based on issues in traditional clothing rental management, which is still conducted manually, leading to booking uncertainties and stock management errors. The research methods include interviews, observations, and literature studies, along with system development using the Waterfall model. The results show that the developed system effectively manages rentals by ensuring orders are processed according to arrival time while providing a responsive and user-friendly interface. The conclusion of this study is that the FCFS-based system improves transparency and fairness in traditional clothing rentals while making it easier for customers to place orders.
Implementation of Cyber Threat Intelligence on Intrusion Detection System using STIX Framework Mahardhika, Yesta Medya; Saputra, Ferry Astika; Syarif, Iwan; Wibowo, Prasetyo; Ardhani, Misbahul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6518

Abstract

Cyber threats are complex and diverse issues. Various types of threats emerge daily on the internet. In this research, we proposed a new Cyber Threat Intelligence platform to deal with the challenges above, using Snort as a tool for detecting anonymous network traffic and STIX as a serialization format and standardization of Cyber Threat Intelligence data. As a result, a Cyber Threat Intelligence based on Snort contains Apache Spark as the processing engine, MongoDB as the database, and STIX as the serialization format and data standardization. We test our platform by using two data sources, the CIC-IDS2017 dataset, and the real traffic. We successfully converted the snort alerts to STIX format and visualized them into graph. The graph shows indication of network traffic suspicious, the country of attacker come from, attribute information and attack pattern. The experiment shows that converting Snort data to STIX requires considerable time if the amount of data processed is getting bigger, Real Traffic needs 16 seconds of data preprocessing and 3 minutes of conversion time, while PCAP needs 35 seconds of preprocessing time and 13 minutes of conversion time.
Prediction of Budget Planning Using the Long Short Term Memory Ambari, Nasser; Puspitasari, Novianti; Septiarini, Anindita
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6428

Abstract

Keputusan merupakan elemen kunci dalam manajemen perusahaan, karena perencanaan yang baik menjadi faktor penentu kesuksesannya. Salah satu aspek penting dalam perencanaan adalah prediksi penjualan. Sebuah perusahaan properti dapat mengalami kesulitan aliran kas akibat over budget, sehingga memaksa perusahaan untuk meninjau kembali strategi pemasaran. Penelitian ini menggunakan Long Short Term Memory (LSTM) untuk membantu perusahaan dalam mengurangi risiko over budget di masa depan. Metode Long Short Term Memory (LSTM) mampu menghasilkan model prediksi dengan akurasi tinggi. Data penelitian berupa data pendapatan penjualan properti dari sebanyak 107 data. Hasil pengujian menunjukkan bahwa penggunaan LSTM dengan perbandingan data latih dan data uji sebesar 90:10, 200 epoch, dan learning rate sebesar 0.005 menghasilkan nilai Root Mean Square Error (RMSE) terendah sebesar 0.128883554. Hasil prediksi menunjukkan pendapatan penjualan yang terus menurun selama tiga tahun.

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