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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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Determining VAK Learning Styles Using SAW and TOPSIS Methods Pradana, Asep Doni; Wahyudin, Wahyudin; Putro, Budi Laksono
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.v5i1.6293

Abstract

This study aims to identify the dominant learning styles of 10th grade PPLG SMKN 1 Talaga students and analyze the application of the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in supporting adaptive learning. Data were collected through the VAK (Visual, Auditory, Kinesthetic) learning style questionnaire, structured observation, and student learning outcomes. The SAW method was used to calculate the preference weights from the three data sources with the same weight (0.33). Vague or combined learning styles were further analyzed using the TOPSIS method for data normalization and grouping. The results showed that the kinesthetic learning style was dominant based on the questionnaire (56.25%) and learning outcomes (40.63%). However, observations showed that the visual learning style was more prominent (32.81%), revealing differences between measurement methods. These findings emphasize the importance of direct practice-based learning, especially for kinesthetic students. Learning media such as videos, direct practice, and visual-based modules are recommended to support learning effectiveness. This study proves that the integration of SAW and TOPSIS produces a comprehensive learning style evaluation, providing guidance for teachers in designing more adaptive learning strategies. Further research on a larger scale is needed for further validation.
Spatial Analysis of Random Forest Classification Model for Availability Mapping of Sports Facilities in Jakarta Candra, Hansen; Andrianingsih, Andrianingsih
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.v5i1.6556

Abstract

This research analyzes the distribution of sports facilities in DKI Jakarta Province using spatial modeling and Machine Learning Random Forest algorithm in order to support Indonesia Emas 2045. The goal is to classify areas based on the level of availability of sports facilities into low, sufficient, and high categories, and evaluate the accuracy of the Random Forest algorithm in the classification. CRISP-DM methodology is used in this research, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data analyzed includes spatial sub-district areas and attributes of sports facilities in DKI Jakarta. Random Forest was chosen because of its ability to classify complex data and identify feature importance. The results show that the distribution of sports facilities is uneven, with low categories more in Central Jakarta and North Jakarta, while high categories are scattered in other areas. Random Forest accuracy reached 89%, with high precision and recall in the high category.
Artificial Intelligence for a Digital Technology Smart Society in the Era of Society 5.0 Sawitri, Dara
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.v5i1.6441

Abstract

Memasuki Era Society 5.0 memperkenalkan perspektif   baru mengenai teknologi digital dan artificial intelligence yang dipadukan sehingga dapat mewujudkan masyarakat cerdas yang berpusat pada manusia. Dimana konsep masyarakat cerdas merupakan masyarakat yang memanfaatkan teknologi digital, data, dan artificial intelligence untuk meningkatkan taraf hidup, efektivitas, dan keberlanjutan dalam berbagai bidang kehidupan. Dengan penerapan artificial intelligence untuk mewujudkan masyarakat cerdas di Era Society 5.0 akan memberikan berbagai dampak positif yang berarti, baik dari sisi aspek kemasyarakatan, faktor ekonomi, maupun aspek ekologis. Artificial intelligence  memungkinkan peningkatan taraf hidup melalui automatisasi proses, layanan berbasis preferensi, dan penentuan kebijakan yang lebih efisien. Penelitian ini akan membahas peran artificial intelligence dalam mendukung transformasi digital masyarakat cerdas sehingga dapat diwujudkan efisiensi dalam sistem administrasi, tata kelola pemerintahan, memaksimalkan pengelolaan kota, mengakselerasi transformasi ekonomi digital, memajukan edukasi, inovasi serta memperbaiki kualitas kesehatan dan kehidupan dan lain sebagainya. Dapat dikatakan dengan memanfaatkan kolaborasi  teknologi yang berorientasi pada manusia, era society 5.0 mengutamakan  pada kesejahteraan manusia dengan artificial intelligence sebagai pemacu  utama dalam menciptakan solusi yang cerdas, menyeluruh, serta berkelanjutan.
Implementation SEO and SEM to Enhance Brand Awareness and Consumer Loyalty Rozaliana, Rozaliana; Azzahari, Muhammad; Khaldun, Ibnu
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.v5i1.6646

Abstract

The development of digital technology has changed the way micro, small, and medium enterprises (MSMEs) market their products and build relationships with consumers. Digital marketing is one of the main strategies in increasing brand awareness and consumer loyalty. This study aims to analyze the effectiveness of digital marketing strategies in increasing brand awareness and consumer loyalty. The research methods used are literature studies and surveys of consumers who actively use digital platforms to interact with brands. The results of the study show that effective digital marketing strategies, such as the use of social media, content-based marketing, and service personalization, can increase brand awareness and build strong loyalty in consumers. By utilizing Search Engine Optimization (SEO),    Search Engine Marketing (SEM), and influencer marketing techniques, MSMEs can reach target markets more widely and effectively. These findings underline the importance of the role of digital marketing in the era of digital transformation that continues to grow. Therefore, an integrated and sustainable digital marketing strategy is needed to increase the company's competitiveness and maintain customer loyalty.
RDBMS Scoring Analysis: Billing System Efficiency Solution (Case Study at PT. XYZ) Jaswadi, Jaswadi; Dwi Putranto, Bambang Purnomosidi; Redjeki, Sri
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.v5i1.6372

Abstract

Penggunaan Oracle exadata pada sistem billing di PT. XYZ membutuhkan biaya ATS (Annual Technical Support) dan ACS (Advanced Customer Service) yang besar sehingga dibutuhkan pencarian software database RDBMS yang sesuai untuk penyimpanan dan pengolahan data dalam proses generate billing di PT. XYZ. Oleh karena itu pada penelitian dilakukan analisis penentuan pemilihan database RDBMS (Relational Database Management System). Metode yang digunakan adalah metode scoring untuk membandingkan 3 database RDMS (Oracle non-exadata, EDB Postgres, dan 11DB Postgres) berdasarkan 5 kriteria yang meliputi penyiapan infrastruktur, migrasi data, tingkat akurasi hitung billing, durasi hitung billing, dan waktu pengerjaan. Hasil penelitian menunjukkan bahwa score database Oracle memiliki nilai paling tinggi di antara database yang lain yaitu sebesar 95, kemudian disusul oleh EDB Postgres dan 11DB Postgres dengan score secara berurutan yaitu sebesar 84 dan 57. Berdasarkan hal tersebut, maka dapat disimpulkan bahwa Oracle non-exadata memiliki performance yang paling tinggi di antara database yang lain. 
Implementation of Network Performance Monitoring (NPM) in Nagios for Sending Alert Via Telegram Notification Munawir, Munawir; Muttaqin, Khairul; Rezeki, Weldyan
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.v5i1.6516

Abstract

Stabilitas dan kinerja jaringan komputer menjadi elemen penting yang mendukung berbagai kegiatan. Oleh karena itu, pemantauan kinerja jaringan jaringan atau Network performance Monitoring (NPM) menjadi aspek yang krusial untuk mengidentifikasi dan mengatasi masalah yang dapat mempengaruhi jaringan. Namun salah satu tantangan dalam sistem pemantauan ini adalah cara penyampaian notifikasi yang efektif kepada tim yang bertugas. Penggunaan Telegram sebagai media notifikasi pada Penelitian ini karena Telegram memiliki fitur notifikasi yang lebih cepat dan fleksibel. Penelitian ini bertujuan untuk menerapkan NPM menggunakan Nagios dan mengintegrasikannya dengan Telegram guna mengirim notifikasi alert secara real-time. Penelitian ini menggunakan pendekatan eksperimen dan pengamatan langsung terhadap implementasi Nagios pada tiga server Linux yang dijalankan menggunakan virtual machine yang melibatkan instalasi dan konfigurasi Nagios untuk memantau host dan service, serta mengintegrasikannya dengan bot Telegram. Hasil pengujian menunjukkan bahwa sistem berhasil mengirim notifikasi alert ke grup Telegram dalam waktu rata-rata 1 sampai 5 menit setelah perubahan status terdeteksi. Uji coba pemantauan membuktikan kemampuan Nagios dalam mendeteksi status host dan service yang sedang down atau mengalami masalah, sementara Telegram memungkinkan penyampaian informasi secara cepat dan efesien. 
Workshop Management Application Design At Pt Abc Using Rational Unified Process Method Markopa, Andre; Rais, Falatehan; Dafid, Dafid
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.v5i1.6468

Abstract

Proses bisnis yang berjalan pada PT ABC saat ini masih memiliki kekurangan yang dapat menghambat berjalannya proses bisnis dan pengambilan keputusan. Oleh karena itu, perancangan aplikasi ini dilakukan dengan bertujuan untuk membantu proses pencatatan dan perhitungan laporan serta transaksi agar pencatatan serta perhitungan laporan harian dan bulanan menjadi lebih cepat dan efisien serta tidak rentan terhadap kesalahan. Metode Pengembangan yang digunakan pada perancangan ini adalah metode RUP (Rational Unified Process), yang dimana prosesnya memiliki empat tahap utama yaitu inception, elaboration, construction, dan transition. Perancangan ini menghasilkan sebuah aplikasi manajemen bengkel yang dapat membantu proses pencatatan dan perhitungan laporan serta transaksi agar pencatatan serta perhitungan laporan harian dan bulanan menjadi lebih cepat, efisien dan tidak rentan terhadap kesalahan.
Implementation of the SMART (Simple Multi-Attribute Rating Technique) Method in a Web-Based Decision Support System for Job Promotion Proposals at XYZ Company Fadhillah, Aginda Noor; Mulyati, Mulyati
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.v5i1.6417

Abstract

Proses kenaikan jabatan karyawan di perusahaan XYZ merupakan langkah strategis yang mempengaruhi perkembangan organisasi dan kesejahteraan karyawan. Namun, tantangan berupa penilaian yang tidak terstruktur dan potensi ketidakkonsistenan dalam pengambilan keputusan dapat menghambat proses ini. Penelitian ini bertujuan untuk merancang Sistem Pendukung Keputusan (SPK) berbasis metode Simple Multi Attribute Rating Technique (SMART) guna mendukung proses pengusulan kenaikan jabatan karyawan. Sistem yang dirancang mengintegrasikan berbagai kriteria, seperti masa kerja, pendidikan terakhir, capaian kinerja, serta penilaian perilaku kerja. Berdasarkan implementasi dan pengujian sistem, hasil evaluasi menunjukkan bahwa alternatif keempat mendapatkan skor tertinggi sebesar 85,65, diikuti oleh alternatif lainnya dengan rekomendasi yang sesuai. Sistem ini terbukti efektif dalam memfasilitasi proses penilaian yang lebih terukur dan konsisten, sehingga memudahkan manajer dalam mengambil keputusan yang lebih cepat dan tepat. Dengan demikian, penerapan sistem ini diharapkan dapat memberikan kontribusi positif terhadap pengembangan sumber daya manusia dan mendukung efektivitas organisasi secara keseluruhan.
Optimization of the Naïve Bayes Classifier Algorithm Using Cost-Sensitive Learning to Detect Lung Diseases with an Imbalanced Dataset sarwani, mohammad zoqi; Khoiron, Mohamad; Udin, Muhammad
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.v5i1.6474

Abstract

Lung diseases are one of the global public health issues that continue to be a primary concern in the medical field. According to data from the World Health Organization (WHO), 91% of the world’s population lives in areas with poor air quality. Continuous exposure to dust, cigarette smoke, air pollutants, and toxic chemicals can increase the risk of developing lung diseases. In efforts to reduce the health impacts on the lungs and assist doctors in classifying lung diseases, a method is needed to predict lung diseases. Naïve Bayes is a classification technique that uses probability and statistics. This research uses a dataset of 30,000, which is divided into training data and testing data, with 80% allocated for training and 20% for testing. The results of this study show that optimization performed on the Naïve Bayes algorithm using cost-sensitive learning achieved an accuracy of 79.6%, which represents a 12% improvement in accuracy compared to the previous result without optimization.
Comparison Of The Effectiveness Of K-Nearest Neighbor (KNN) And Naive Bayes Algorithms In Identifying Diabetes Patients Susanto, Eko Budi; Anzila, Anin Naba; Ismanto, Bambang
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.v5i1.6275

Abstract

Delay in diagnosis of diabetes is one of the causes of increasing mortality due to complications before diagnosis is made. In the medical field, the application of machine learning models has opened up significant opportunities in improving the accuracy of early diagnosis of diabetes. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naive Bayes Classifier (NBC) algorithms using a secondary dataset of 128 data records and containing 10 data variables relevant to the prediction of diabetes. The results of the analysis show that the KNN algorithm with parameters K = 21 based on the evaluation of the confusion matrix obtained an accuracy of 76.92%, recall 100%, precision 72% and F1-Score 84%. Meanwhile, the naïve Bayes algorithm obtained an accuracy of 65.63%, recall 52%, precision 100% and F1-Score 69%. In the evaluation using the k-fold cross validation method with K = 10, the average accuracy for the KNN algorithm was 73% and for the Naïve Bayes algorithm was 70%. Thus, the KNN algorithm is superior and recommended for diabetes disease classification.

Page 10 of 22 | Total Record : 215

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