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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 14 Documents
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Search results for , issue "Vol 5, No 1 (2025): Maret" : 14 Documents clear
Irrigation Data Inventory Using Web-Based Geographic Information System to Support the Water Discharge Distribution in Belu Regency Tetik, Fransiskus Rendi; Kelen, Yosep Pius Kurniawan; Lestari, Anastasia Kadek Dety; Manek, Siprianus Septian
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6451

Abstract

The development of information technology in the era of globalization has become an integral part of human life, especially in providing fast, accurate, and easily accessible information. BeluRegency, located in East Nusa Tenggara Province, has challenges in managing air resources, especially related to agricultural irrigation. Inaccurate and incomplete irrigation data hinders the efficiency of water discharge distribution, which is very important or most of the population who make a living in the agricultural sector. This research aims to apply the Geographic Information System (GIS) in the inventory of distributed data, in order to improve the efficiency and effectiveness of air resource management. By using GIS, it is hoped that accurate mapping of the location of irrigation canals can be carried out, making it easier to make decisions regarding the distribution of water discharge. The results of this research are expected to increase agricultural productivity, maintain environmental curiosity, and improve the welfare of farmers and local communities in Belu Regency. This research is entitled Irrigation Data Inventory Using Web-Based Geographic Information System to Support the Water Discharge Distribution in Belu Regency".
EDA and Tableau Analysis for Identification of Heart Disease Risk Factors Silmina, Esi Putri; Perkasa, Legawan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6389

Abstract

Heart disease is one of the leading causes of death worldwide, influenced by various risk factors such as high blood pressure, cholesterol levels, and lifestyle. This study analyzes the risk factors for heart disease using the Heart Disease Dataset which includes more than 1,000 records with variables such as age, blood pressure, cholesterol, and alcohol consumption. Exploratory Data Analysis (EDA) was applied to identify patterns and relationships between variables, while Tableau was used to present the results visually and interactively. The results showed that high blood pressure was the main risk factor, with the majority of patients having blood pressure in the range of 130-135 mmHg, which is considered high risk. In addition, high cholesterol levels (200-205 mg/dL) also contributed significantly to the increased risk of heart disease, while alcohol consumption in the "Heavy" category worsened heart health conditions. Data visualization shows an increasing trend in heart disease cases, especially in individuals with a combination of these risk factors. Therefore, this study emphasizes the importance of routine blood pressure and cholesterol monitoring, implementing a healthy diet, regular physical activity, and health education to reduce the incidence of heart disease in the future.
Design Of Decision Support Systems Determining Priority Customers PT. XYZ with Topsis Method Web Based Mayrizkiy, Muhamad Dafhi; Mulyati, Mulyati
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6464

Abstract

The priority customer assessment process at Company XYZ has a direct influence on increasing the company's operational profits. There are problems in determining priority customers by companies resulting in reduced efficiency and the potential for financial losses due to the use of random, manual and individual opinion-based selection methods, causing significant waste of time. The aim of this research is to design a Decision Support System (DSS) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) which can help the priority customer assessment process effectively. This system has several assessment indicators that are used to assess priority customers, namely number of orders, length of subscription, amount/nominal payment, payment history. The results of testing and system implementation show that Customer D's alternative received the highest score, namely 0.839, followed by other alternatives with appropriate assessments. With the TOPSIS method, company XYZ can make better decisions related to the weight of the criteria based on the provisions of the criteria that have been determined and can determine the weight of the criteria objectively, prioritize criteria based on customer eligibility levels, and speed up the assessment process. As a result, assessments become fairer, more accurate and in line with company expectations.
Development of Quadcopter Drone and IoT Module Technology in Geospatial-Based Air Emission Monitoring Hapsari, Anindya Ananda; Vresdian, Devan Junesco; Dionova, Brainvendra Widi; Andreansyah, Teddy
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6300

Abstract

his research introduces an innovative drone-based air quality monitoring system, equipped with multi-parameter sensors to measure temperature, humidity, CO2 concentration, and PM2.5 in real-time. The system is designed with integration to the Internet of Things (IoT), enabling direct data transmission to smart devices for instant access and quick analysis. This system provides a practical solution for monitoring air quality flexibly, especially in areas that are difficult to reach with conventional monitoring devices. Simulations showed that the drone canoperate for 4.8 to 14.77 minutes with an 80% battery depletion, depending on environmental conditions. Calibration testing of the sensors showed high accuracy, although data variations were significantly affected by temperature changes. The system underwent trial and calibration as an initial step to ensure device reliability and open opportunities for broader application development, including air quality monitoring in urban areas, industrial zones, and natural environments. The main advantage of this system lies in its ability to provide real-time data and flexibility in data collection from various locations. However, the study also identified several challenges, such as battery capacity limitations and communication range, which will be the focus of further development. Potential improvements include optimizing battery capacity, expanding communication range, and developing predictive
Fruit Image Classification Using Naive Bayes Algorithm with Histogram of Oriented Gradients (HOG) Feature Extraction Saputra, Andika Jodhi; Andriyani, Widyastuti
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6536

Abstract

A classification system using Naïve Bayes algorithm was developed to distinguish between fresh and rotten fruits, specifically apples, bananas and oranges. This research utilized a dataset consisting of 13,599 images and applied the Histogram of Oriented Gradients (HOG) technique for feature extraction, followed by model training and evaluation. The results showed that the Naïve Bayes algorithm achieved an accuracy of 87%, with the highest precision in the fresh apple class (0.9792) and the highest recall in the rotten apple class (0.9843). The rotten banana class showed a balanced performance with the highest F1-score of 0.9085. Although there were some misclassifications, especially in the rotten citrus fruit category, this study shows that image processing techniques have great potential and are reliable for assessing fruit quality based on visual characteristics.
KerjaKarya: An Inclusive Digital Solution to Expand Access for the Disabled Labour Force Mohaimin, Susetiyo; Aditya, Addin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6429

Abstract

People with disabilities often face significant challenges in accessing the world of work due to limited inclusive information media and the stigma of low productivity. To address these issues, this research aims to develop and test the KerjaKarya application, a web-based platform specifically designed to facilitate people with disabilities, especially the physically, hearing, and speech impaired, in finding job vacancies, submitting applications, and building communities. This research uses the System Development Life Cycle (SDLC) method with a waterfall approach, involving data collection from literature studies, interviews with people with disabilities, and testing using black box techniques and user trials. The trial involved five job applicants, four companies, and one customer service with a Likert scale-based questionnaire instrument. The results showed that the application successfully met the needs of users with a friendly interface and relevant features, such as registration, application submission, and vacancy management. The implication of this research is that it provides a practical solution to improve accessibility and participation of people with disabilities in an inclusive labour market. This research also opens up opportunities for further development, such as the integration of artificial intelligence and skills training services
Android Based Wedding Organizer Service Ordering Information System Naihely, Stefanus Apriyanto; Kelen, Yoseph Pius Kurniawan; Syarifuddin, Risald
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6463

Abstract

Salon Michelle, as one of the wedding organizer service providers, is experiencing an increase in demand for wedding organizer services. The process of ordering wedding organizer services at Salon Michelle, which is still done manually, presents various challenges, such as the length of time it takes to make an order, difficulties in managing schedules, coordinating with clients, and the risk of communication errors with potential customers. Therefore, to make it easier for prospective brides and grooms to choose wedding packages available at Salon Michelle, an information system for wedding package providers is needed that provides complete information regarding the wedding packages offered by Salon Michelle vendors. By using this information system, it is hoped that customers can adjust their needs and desires to the availability of wedding facilities on offer, as well as see in detail the prices and facilities that can be obtained from Salon Michelle vendors, so that it will be very helpful in organizing wedding receptions.
Smart Lighting System Design at STMIK AMIKOM Surakarta Using Blynk Adristiawan, Ranu Arva; Widiati, Ina Sholihah; Sugiarto, Lilik
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6292

Abstract

The Internet of Things (IoT) is a technology that enables automation in human activities by connecting hardware, software, and the internet for communication and control. This study focuses on an IoT-based smart light control system designed using the Blynk app. The system utilizes the ESP8266 NodeMCU module and a 5V 8-Channel Relay as the primary components. The Blynk app is used as the controller, connecting the hardware to the user through an Android device. The research follows an experimental approach with three phases: tool design, tool construction, and tool testing. A miniature of the STMIK AMIKOM SURAKARTA environment was used as a prototype to simulate the system’s implementation. Test results show that the system operates effectively in remote control with internet coverage of up to 30 meters, achieving a 100% success rate within a range of 5–30 meters. All lights responded correctly to input commands from the Blynk app with 100% success. The monitoring system also showed stable connectivity between the app and hardware, though slight delays occurred due to internet network stability. This system is expected to serve as a practical solution for efficient management of electrical devices in educational settings, improving both energy efficiency and convenience.
Sentiment Analysis of Visitor Reviews on Google Maps at Kampung Coklat Tourism Hamdana, Elok Nur; Nur Wardani, Alifah Okta; Tri Hayati Ririd, Ariadi Retno
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6488

Abstract

Google Maps plays an important role in the tourism industry, allowing visitors to share their reviews widely. These reviews not only influence potential visitors' decisions but also impact the reputation of tourist destinations. However, evaluating service quality based on offline reviews remains suboptimal compared to online reviews, which are more accessible and interpretable. This research focuses on sentiment analysis of visitor reviews on Kampung Coklat in Blitar using the Naïve Bayes algorithm. The goal is to classify reviews into positive, neutral, or negative to understand visitors' perspectives on the tourism services. Data was collected from Google Maps and processed using the Naïve Bayes method, which has proven effective in sentiment classification even with relatively small training data sets. Experimental results showed the highest accuracy of 75% with an 80% training data and 20% testing data ratio. WordCloud analysis also depicts frequently occurring words in positive, neutral, and negative reviews, providing insights into aspects influencing tourists' experiences.
Comparison of K-Nearest Neighbor and Naive Bayes Algorithms for Tuberculosis Diagnosis Classification Setiadi, Dedi; Arif, Alfis; Oktaria, Anik
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6456

Abstract

Tuberculosis is an infectious disease caused by the bacteria mycobacterium tuberculosis. Tuberculosis is a serious global health problem and can cause death if not treated properly. At the Sidorejo Health Center, the current process of diagnosing patients uses several benchmarks of medical history obtained from patients regarding complaints, symptoms, and risk factors, while the results of the diagnosis calculation are not yet known. Comparison of the K-nearest neighbor and naïve bayes algorithms in classifying tuberculosis can provide input for the Sidorejo Health Center in seeing the accuracy of the diagnosis of tuberculosis, with medical information such as symptoms and medical history, where later patient data will be processed using the rapid miner application. The system development method used in this study is CRISP-DM, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The testing method uses a confusion matrix to measure the accuracy of the algorithm model with the results being that the K-nearest neighbor algorithm produces a high accuracy of 98% while the naïve bayes algorithm is the lowest with an accuracy of 0.70%.

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