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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 215 Documents
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Implementation of Cloud Computing in E-Gizi for Recording and Reporting Toddler Nutrition Status in Batuphat Timur Village via Mobile Applications Munanda, Allissa; Hidayat, Hari Toha; Safriadi, Safriadi
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6127

Abstract

Based on existing data in 2022, Aceh still at the fifth highest level in indonesia for the stunting category. At this time, many posyandu still use books to record children’s growth development and this is very ineffective. Based on this fact, a mobile application is needed that can be used for recording and reporting child growth and development. The reseach aims to produce an application that is effective and can be implemented in the East Batuphat posyandu, so that it make easier for posyandu officiers to record and report child growth and development. This reseach used the blackbox testing method to detemine the level of user satisfaction with the feasibility of the application. The result of testing the feasibility of applications using blackbox testing are 95.8% user satisfaction and 4.2% user dissatisfactions and result based on child data obtained from the East Batuphat health center more than 80% of children are in good nutritional status.
A Web-Based Laptop Purchase Recommendation Model Using Natural Language Processing (NLP) on Marketplace Reviews Syahdana, Irham; Hidayat, Rahmad; Khadafi, M
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6133

Abstract

This system will use the Natural Language Processing (NLP) method to analyze user reviews. In addition, the Naive Bayes classification algorithm will be used to provide recommendations based on the analysis. The methods used include collecting laptop user review data from the Shopee platform, Natural Language Processing (NLP) for text analysis, classification with the Naive Bayes algorithm, developing a recommendation system, and evaluating the system using relevant metrics. The results of the study show that this model achieves an accuracy of 0.86 with a precision of 0.93 for positive reviews and 0.69 for negative reviews. Of the total 42 reviews tested, the system provides a recall of 0.87 for positive reviews and 0.82 for negative reviews. The total reviews in the dataset consist of 96 positive reviews and 43 negative reviews. This study is expected to contribute to the development of review-based recommendation systems, so that users can make the right decisions.
Synchronous Multiplayer Shooting in the 2D Game "War Cops" Using Unity Engine Geubrina, Tiara; Husaini, Husaini; Mahlil, Mahlil
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6128

Abstract

In this modern era, the digital game industry continues to grow rapidly with various innovations in game creation. One of the game development platforms that is often used is the Unity Engine. Currently, developed games are only played offline, and users must buy special devices. To overcome this problem, innovation is needed to allow players to play together without these restrictions. In this effort, Unity Engine 2D can be used with Photon Unity Network support for games over different networks. This study aims to determine the response speed of Photon. The results show that the ping test response from the photon obtained an average value of 201 ms.
Smart Infusion Digitalization Based on IoT, Long-Range Communication, and Cloud Ananta, Adam; Nasir, Muhammad; Erdiansyah, Umri
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6135

Abstract

Currently, the monitoring of infusion fluids is performed by periodically checking each patient, regardless of whether there is an obstruction or not. To address this challenge, a system based on the Internet of Things (IoT), Long Range (LoRa) at a 2.4 GHz frequency, and Cloud technology, known as the digital smart infusion system, has been developed. This system aims to enhance the efficiency and safety of infusion fluid delivery, facilitate real-time monitoring by nurses, and provide accurate and up-to-date data. The testing results indicate that the implementation of the MQTT protocol in this system yields positive outcomes, with delay times varying between 42 ms (5 minutes), 84.3 ms (10 minutes), and 73.8 ms (15 minutes), along with very low packet loss rates of 0.03% at 5 minutes, 0.02% at 10 minutes, and 0.01% at 15 minutes. Additionally, the system's throughput remains stable, with values of 92.6 Kbps at 5 minutes, 83.8 Kbps at 10 minutes, and 86.2 Kbps at 15 minutes. In tests of LoRa without obstructions, packet loss percentages remain low up to a distance of 10 meters, with a value of 0%, but then increase to 68.29% at 25 meters. Tests with obstructions show a more drastic decline in signal quality, with packet loss reaching 6.98% at 5 meters and increasing to 70.97% at 25 meters.
Implementation of RFID-Based Attendance Integrated with Management Systems and Notifications via WhatsApp Salsabila, Salsabila; Anwar, Anwar; Syahputra, Guntur
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6130

Abstract

Currently, attendance recording faces challenges, especially with validation using Mac Addresses which is suboptimal due to the dynamic nature of device IP Addresses. To address this issue, the researcher proposes integrating the existing management system with RFID-based attendance. RFID technology enables tracking and identification using radio waves without direct contact, ensuring efficient and accurate attendance recording. The system will send attendance notifications to teachers via WhatsApp and store data in a database, facilitating monthly summaries and ensuring data security and availability for future reference. This research utilizes blackbox testing to evaluate system accuracy, achieving a 95% accuracy rate in RFID tag readings and successful RFID tag scanning processes. Network speed measurement using QoS shows favorable results, with a throughput of 77.4 Kbps, zero packet loss, an average delay of 1.919 ms, and jitter of 1.92 ms in tests involving 15 RFID tags
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.

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