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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 198 Documents
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Mobile Application Development For Digitalization In The Service Sector “ReHome” Alfarizi, Muhammad Fadhillah; Nasir, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7128

Abstract

This research presents the development of ReHome, an Android-based mobile application designed to support the digitalization of architectural design services. The application provides various features that enable users to access house designs, save references, and communicate directly with architects. ReHome was developed using the Rapid Application Development (RAD) methodology to ensure rapid and adaptive system development according to user needs. The application interface was designed using Figma and implemented in Android Studio environment with Java/Kotlin programming languages, integrated with Firebase for authentication and data storage. The application features include user login and registration, main dashboard with design categories and search functionality, detailed house design views, user-architect communication system, architect portfolio management, and admin dashboard for design verification. Testing was conducted using black box testing methods for functional validation. The results show that ReHome successfully achieves its main objective of supporting the digitalization of the architectural design service industry by providing an efficient, modern, and interactive platform. The application enables users to access house design services efficiently while allowing service providers to reach more clients through digital platforms. ReHome demonstrates the practical application of digitalization concepts in the Industry 4.0 era and has great potential for further development with additional features such as integrated service booking systems and more complex admin dashboards.
Comparative Analysis of Random Forest Algorithms, Artificial Neural Networks, and Logistic Regression in Breast Cancer Prediction with Machine Learning Approach M. Ali, Rahmadi; Nurdin, Nurdin; Khaidar, Al; Azzanna, Maghriza; Rusadi, Athirah
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7028

Abstract

Perkembangan teknologi informasi khususnya kecerdasan buatan dan machine learning, telah meningkatkan efektivitas deteksi dini penyakit seperti kanker payudara. Namun, tingginya angka kejadian dan kematian akibat kanker payudara di Indonesia masih menjadi tantangan besar, terutama karena rendahnya tingkat deteksi dini dan banyak pasien datang dalam stadium lanjut. Penelitian ini membandingkan performa tiga algoritma machine learning, yaitu Random Forest, Artificial Neural Network (ANN), dan Logistic Regression, dalam memprediksi diagnosis kanker payudara berdasarkan akurasi, efisiensi komputasi, dan kestabilan kinerja. Evaluasi dilakukan dengan classification report dan validasi silang 10-Fold Cross Validation. Hasil menunjukkan Logistic Regression memiliki akurasi rata-rata tertinggi sebesar 77,56% dan waktu eksekusi tercepat, yaitu 0,024897 detik, menandakan efisiensi dan kestabilan yang baik. Random Forest memberikan akurasi classification report 80% dan nilai AUC tertinggi 0,89, menunjukkan keunggulan dalam diskriminasi kelas. ANN memiliki performa terendah dengan akurasi validasi silang 74,64% dan recall rendah untuk kelas positif. Logistic Regression direkomendasikan sebagai model paling seimbang, sementara Random Forest sebagai alternatif akurasi tinggi.Kata Kunci: Random Forest, Artificial Neural Networks, Logistic Regression, Breast Cancer Prediction, Machine Learning
Implementation of the Analytic Hierarchy Process Method for Performance-Based Rewards for Lecturers Ruskan, Endang Lestari; Seprina, Iin; Ariani, Ardina; Indah, Dwi Rosa; Athalina, Githa
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7600

Abstract

Rewards are carried out by the faculty as an effort to motivate lecturer performance. The obstacle is that the system has not accommodated all lecturer performance on the required indicators, the supporting files are also a problem, and the final results of many performance scores are the same, potentially due to subjective decision-making. There is no scientific formulation, no common standard, and no direct file collection system as the cause. The research method used is identifying problems and collecting data, analyzing data, designing decision support systems, and making prototypes. The research objective is to apply the analytical hierarchical process method to the application of a decision support system for determining lecturer rewards.  The contribution of research on performance-based criteria instruments applied to dynamic applications in determining indicators, so that they can be adjusted to the performance report indicators of higher education institutions. The results are in the form of lecturer performance criteria weights based on key performance. The highest lecturer performance ranking is 0.0880, the second is 0.067, and the third is 0.0663, with detailed lecturer performance scores for each criterion. These digital files can be used at any time, helping management make objective and transparent decisions.
IoT-Based Real-Time Security System Implementation at Kaka Game Center Internet Café Prastya, Alvian Bagus; Maulindar, Joni; Purwanto, Eko
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7327

Abstract

Security in public spaces such as internet cafés (warnet) is a major concern, especially during non-operational hours. This study develops a real-time security system based on the Internet of Things (IoT) by integrating the ESP32 and ESP32-CAM microcontrollers to enhance surveillance effectiveness in warnet environments. The system uses a PIR sensor to detect motion, then automatically sends a notification to the Telegram application and triggers image capture via the ESP32-CAM. In addition to automatic detection, users can also manually control the system via Telegram, such as activating the buzzer, requesting snapshots, or accessing real-time video streaming using ngrok. The system was developed using the Waterfall method, which includes requirement analysis, design, implementation, and testing. The results show that all system features operate properly, are responsive, and support efficient remote monitoring. With its low cost and optimal technology integration, the system is a viable alternative to conventional security systems for small-scale businesses.
Sentiment Analysis of Fans Toward Brand Merchandise Releases Using Support Vector Machine (SVM) Munaiseche, Christian Imanuel; Nurchim, Nurchim; Cipto Utomo, Bangun Prajadi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7264

Abstract

The release of merchandise by idol groups often sparks various emotional reactions among fans, particularly on social media platforms. This study investigates fan sentiment regarding the birthday merchandise release by JKT48 members on the X (Twitter) platform using the Support Vector Machine (SVM) algorithm. A total of 1,062 comments were collected using the Tweet Harvest tool and manually categorized into three sentiment classes: positive, neutral, and negative. The collected data underwent several pre-processing stages, including case folding, data cleansing, tokenization, and stopword removal. The text data were then transformed into numerical features using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To address the class imbalance issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Experimental results show that the SVM model without SMOTE achieved an accuracy of 84.62% and an F1-score of 76.79%. After applying SMOTE, model performance improved significantly, with accuracy reaching 90.09% and F1-score increasing to 90.15%. Furthermore, the results of 5-fold cross-validation confirmed the positive impact of SMOTE in enhancing the model's ability to classify sentiment, particularly for underrepresented classes.
Implementation of the K-Nearest Neighbor (K-NN) Algorithm for Predicting Bestseller of Religious Books Jamil, Alfan; Hermanto, Hermanto; Fajriyanto, Fajriyanto
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7352

Abstract

The development of information technology provides significant opportunities in data management to help make more accurate business decisions. One method used is data mining, especially the K-Nearest Neighbor (K-NN) technique which is quite effective in classification and forecasting based on past data. This study aims to apply the K-NN technique to predict sales of the most popular books and scriptures at the Assyarif Book Store, located at the Salafiyah Syafi'iyah Islamic Boarding School in Sukorejo. It is hoped that this method can help in planning stock and identifying products that are most in demand by customers. This study uses a quantitative approach with observation, interviews, and documentation collection methods. The data used include price, quantity sold, initial stock, and final stock. The results of the analysis using Euclidean Distance show that the K-NN technique is able to predict sales categories (best-selling or not-selling) with an accuracy level of 75% obtained from cross-validation testing, so it can be an effective solution to support sales management and data-based decisions.
Decision Support System for PNBP Proposal Selection Using the Simple Multi-Attribute Rating Technique Suhada, Intan; Putra, Fajri Profesio
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.6964

Abstract

Non-Tax State Revenue (PNBP) is one of the key funding sources at Politeknik Negeri Bengkalis, managed by the Center for Research and Community Service (P3M). The proposal selection process often encounters issues related to objectivity and transparency. This study aims to develop a Decision Support System (DSS) based on the Simple Multi-Attribute Rating Technique (SMART) method to improve the efficiency and accuracy of proposal selection. The system wasdeveloped as a web-based application and tested on 12 proposals. The evaluation criteria include Problem Formulation, Research Output Potential, Research Methodology, Literature Review, and Research Feasibility. The test results showed a compatibility rate of 83.33%,where 10 proposals had identical rankings, and 2 proposals differed by one rank compared to manual assessments by P3M. The scoring was conducted through weight normalization, utility value calculation, and final score aggregation. System evaluation using the Blackbox Testingmethod demonstrated that all core functions operated correctly for three user types (admin, reviewer, and user) without any detected errors. The system successfully produces selection results that closely match real-world conditions and contributes to enhancing objectivity, transparency, and the overall quality of the PNBP proposal selection process.
Developing an NLP-Based Chatbot for Waste Management Education in Sungailiat Wisesa, Bradika Almandin; Mahat Putri, Vivin; Faristasari, Evvin; Jasman Duli, Sirlus Andreanto; Lionza, Rahmat
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7522

Abstract

Penelitianinimemaparkanpengembangan dan evaluasi menyeluruh terhadap chatbot berbasis Natural Language Processing (NLP) yang dirancang untuk meningkatkan pendidikan pengelolaan sampah di Bank Sampah Sungailiat, Indonesia. Dengan mengintegrasikan logika fuzzy untuk pencocokan Pertanyaan yang Sering Diajukan (FAQ) secara akurat dan memanfaatkan model NLP berbasis transformer, DialoGPT-medium, chatbot inimemberikan respons yang relevan secara kontekstual terhadap pertanyaan pengguna mengenaioperasional bank sampah, termasuk pemilahan sampah, proses daur ulang, dan insentif ekonomi. Penelitian ini menangani masalah rendahnya kesadaran masyarakat terhadap praktik pengelolaan sampah yang tepat, yang menghambat partisipasi efektif dalam program daurulang. Sistem hibrida ini mencapai akurasi respons sebesar 85% dalam p engujian pengguna, divalidasi melalui analisis matriks konfusi yang mendetail. Temuan utama menunjukkan peningkatan signifikan dalam keterlibatan pengguna, retensi pengetahuan, dan kesadaran masyarakat, menunjukkan potensi chatbot sebagai solusi pendidikan lingkungan yang berbasis teknologi dan dapat diskalakan untuk konteks serupa di seluruh Indonesia

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