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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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Modeling Of Centralized Exchange (CEX) Crypto Asset Platform Recommendation System Using Collaborative Filtering Utomo, Diva Reihan Ferdian; Nastiti, Faulinda Ely; Suryani, Fajar
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.v5i3.7179

Abstract

The rapid growth of crypto assets and the variety of Centralized Exchange (CEX) platforms make it difficult for traders to choose a platform that fits their preferences. This research aims to model a recommendation system for CEX platforms using Collaborative Filtering. User rating data for several CEX (Binance, Bybit, Bitget, Tokocrypto, Indodax) were collected via questionnaire. The K-Nearest Neighbors With Means (KNN With Means) method with cosine similarity is used to predict ratings based on the similarity of preferences between users. The model was trained and tested with a 75:25 train-test split. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used as evaluation metrics. Test results show low MAE and RMSE values (around below 1.0 on a 1–5 rating scale), indicating that the recommendations generated are quite accurate. It can be concluded that the Collaborative Filtering approach is effective in recommending CEX platforms according to user needs. This recommendation system is expected to assist traders – especially beginners – in choosing the right exchange more objectively.
Comparison of Random Forest, Decision Tree, and XGBoost Models in Predicting Student Academic Success Nurbaeti, Nurbaeti; Sulistiyaningsih, Neny; Rismayati, Ria
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.v5i3.7138

Abstract

Students' academic success is influenced by various academic and non-academic factors. Machine learning (ML) offers an effective approach to predicting academic outcomes by analyzing complex data patterns. However, most previous studies are limited to graduation prediction and rarely incorporate non-academic features or multiple feature selection techniques. This study aims to compare the performance of three ML algorithms Random Forest, Decision Tree, and XGBoost in classifying students’ academic success using a dataset from the UCI Machine Learning Repository, consisting of 4424 records and 37 features. The data underwent cleaning, label transformation, and feature selection using PCA, SelectKBest, and Variance Threshold. Models were trained using a holdout method (80% training, 20% testing) and evaluated based on accuracy, precision, recall, and F1-score. The results show that Random Forest with Variance Threshold achieved the highest accuracy (0.77) and F1-score (0.84) on majority classes. XGBoost followed with 0.75 accuracy, while Decision Tree showed the lowest performance. All models struggled to classify the minority class, indicating challenges related to data imbalance. This research highlights the importance of algorithm choice and effective feature selection in academic classification tasks. It also emphasizes the need for data balancing strategies to reduce class bias. The findings can help educational institutions design data-driven interventions to improve learning outcomes and reduce dropout rates.
Enhancing Resource Efficiency in Urban Agriculture: A GA-Fuzzy Logic IoT-Based Smart Hydroponic Greenhouse Ula, Munirul; Rusadi, Athirah; Daud, 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.v5i3.7799

Abstract

Pertanian presisi berbasis Internet of Things (IoT) menawarkan solusi inovatif terhadap tantangan ketahanan pangan dan keterbatasan lahan di daerah perkotaan. Penelitian ini bertujuan merancang dan mengevaluasi sistem rumah kaca cerdas berbasis hidroponik untuk budidaya tumpang sari anggur dan selada  menggunakan Nutrient Film Technique (NFT). Metodologi penelitian mengintegrasikan Pengendali Logika Fuzzy yang dioptimalkan dengan Algoritma Genetika (GA-FLC) untuk kontrol real-time enam parameter lingkungan: suhu, kelembapan, pH, konduktivitas listrik, intensitas cahaya, dan konsentrasi CO₂. Sistem menggunakan mikrokontroler ESP32 dengan array sensor presisi tinggi dan platform cloud (ThingSpeak, Firebase) untuk monitoring dan kontrol otomatis. Eksperimen dilaksanakan menggunakan Randomized Complete Block Design dengan dua faktor (sistem kontrol GA-FLC vs konvensional; monokultur vs tumpang sari) selama 120 hari di kondisi iklim tropis Bireuen, Aceh. Hasil menunjukkan sistem GA-FLC superior dalam akurasi kontrol dengan Mean Absolute Error suhu 0,7°C (61% lebih baik), response time aktuator 47-53% lebih cepat, dan efisiensi energi 25-30% lebih tinggi. Produktivitas anggur meningkat 27,8% (2,48 kg/tanaman) dan selada 23,7% (245 g/tanaman) dibandingkan sistem konvensional. Efisiensi sumber daya menunjukkan penghematan air 33,3%, energi 32,6%. Water Use Efficiency mencapai 12,4 kg/m³ dengan Energy Productivity 1,85 kg/kWh. Sistem ini memberikan kontribusi signifikan untuk pertanian perkotaan berkelanjutan dengan produktivitas tinggi, efisiensi sumber daya optimal, dan viabilitas ekonomi yang menarik untuk implementasi komersial di daerah tropis.
Comparison of KNN and SVM Performance in 2024 Election Results Sentiment Analysis Bukit, M Iqbal Fahilla; Lubis, Andre Hasudungan
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.v5i3.7659

Abstract

This study compares the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in sentiment analysis related to the 2024 election results using data from social media. The dataset used consists of 506 public opinion entries categorized into three sentiment labels: positive, negative, and neutral. The data processing involved preprocessing steps such as case folding, tokenization, stopword removal, and stemming, then represented using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The test results showed that both algorithms were able to classify with an accuracy of over 70%. The KNN algorithm produced an accuracy of 75.49%, precision of 71.36%, recall of 75.49%, and an F1-score of 72.88%, while the SVM algorithm showed slightly better performance with an accuracy of 77.45%, precision of 70.59%, recall of 77.45%, and F1-score of 72.15%. Based on the confusion matrix analysis, both models have a high ability to classify positive sentiments, but still face obstacles in recognizing negative and neutral sentiments due to the imbalance in data distribution. Overall, this study indicates that SVM is more suitable for election sentiment analysis on high-dimensional text data.
Development of a Microservice-Based Attendance System with Face Recognition and QR Code at SMK Negeri 2 Cimahi Riyan, Riyan; Sugianto, Castaka Agus
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.v5i3.7417

Abstract

Sistem absensi manual yang masih banyak digunakan di lingkungan sekolah sering menimbulkan permasalahan, seperti ketidakefisienan proses pencatatan, potensi kecurangan kehadiran, dan keterlambatan penyampaian informasi kepada pihak sekolah maupun orang tua. Penelitian ini bertujuan mengembangkan sistem absensi digital berbasis arsitektur microservice dengan mengintegrasikan teknologi pengenalan wajah (face recognition) untuk absensi harian, pemindaian kode respons cepat (quick response code) untuk absensi per mata pelajaran, serta pengiriman notifikasi otomatis melalui WhatsAppApplication Programming Interface (API). Sistem dikembangkan menggunakan metode Waterfall melalui tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Pengujian dilakukan secara langsung dengan fokus pada pengujian fungsional terhadap fitur utama yang telah dirancang. Hasil pengujian menunjukkan bahwa seluruh fitur dapat berjalan dengan baik sesuai kebutuhan pengguna, membantu proses verifikasi kehadiran menjadi lebih cepat dan akurat, serta mempercepat penyampaian informasi ketidakhadiran kepada orang tua. Penelitian ini menunjukkan bahwa implementasi arsitektur microservice efektif dalam meningkatkan kualitas sistem absensi sekolah dan memiliki prospek untuk pengembangan lebih lanjut.
Temperature, Humidity, Air Quality Monitoring System In Laying Chicken Coop Based On IoT Firdaus, Azkha Brilliant; Maulindar, Joni; Muhtarom, Moh.
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.v5i3.7279

Abstract

The health and productivity of chickens in a coop heavily depend on optimal environmental conditions, such as temperature, humidity, and air quality. The ideal temperature for laying hen coops generally ranges between 18-28℃. However, manual monitoring of coop conditions is often ineffective and requires significant resources. Therefore, this research develops a Chicken Coop Monitoring system based on the Internet of Things (IoT) to improve the efficiency of environmental supervision and maintenance within the coop. This system is designed using an ESP32 microcontroller connected to temperature, humidity, and air quality sensors, combined with an internet connection to transmit real-time data to a database storage. The collected data can be accessed via a monitoring dashboard that visually displays information on the coop's environmental conditions, including whether the temperature is within the optimal range. Testing results show that this system is capable of accurately monitoring environmental parameters, detecting non-ideal conditions, and assisting in decision-making related to coop environment regulation. By implementing this technology, chicken health and productivity can be significantly improved through more efficient and effective coop environment management.
Raw Material Stock Prediction Using the Long Short-Term Memory Algorithm Anjarwati, Pipin; Widyaningsih, Pipin; Pramono, Pramono
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.v5i3.7199

Abstract

Inaccurate management of raw material inventory leads to operational inefficiency and cost overruns in micro, small, and medium enterprises (MSMEs), particularly in the culinary industry where demand is highly fluctuating and difficult to predict. This study develops a raw material stock prediction system using the Long Short-Term Memory (LSTM) algorithm with a Waterfall system development approach, applied to the case of "Mizan and Sunan" grilled bread producers operating across seven branches. The dataset consists of nine months of historical demand data, comprising 5,142 entries with eight main attributes. Data preprocessing includes Min-Max Scaling normalization, sequential data formation using a three-day sliding window, and chronological splitting of training and testing datasets. The LSTM model is trained to predict daily stock requirements, with evaluation conducted using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show an MSE of 403.28, MAE of 10.38, and MAPE of 10.79%, indicating good predictive accuracy. The novelty of this research lies in the application of an LSTM model based on multi-branch MSME culinary historical data characterized by fluctuating demand, along with the development of an adaptive prediction system to support precise procurement decision-making. These findings demonstrate the effectiveness of LSTM as a practical data-driven solution for inventory management in multi-branch MSME operations.
Implementation Of Static Routing And Quality Of Service For Optimization Of Network Traffic Management On Cisco Routers Hermansyah, Hermansyah; Khaidar, Al; Nurdin, Nurdin; Kurnia, Sri
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.v5i3.7381

Abstract

Di era digital, kebutuhan akan jaringan yang andal dan efisien menjadi krusial untuk mendukung pertukaran data yang lancar. Lalu lintas data yang padat dapat menurunkan kualitas layanan, menyebabkan keterlambatan transmisi, dan meningkatkan risiko kehilangan paket. Penelitian ini mengimplementasikan metode static routing dan Quality of Service (QoS) sebagai strategi manajemen lalu lintas jaringan untuk meningkatkan efisiensi dan stabilitas komunikasi pada router Cisco. Metode yang digunakan meliputi konfigurasi static routing untuk mengatur jalur data secara manual dan penerapan QoS untuk memprioritaskan jenis layanan berdasarkan parameter latency dan packet loss. Hasil pengujian melalui simulasi dua router Cisco menunjukkan konektivitas yang stabil, dengan waktu respons rendah dan tanpa kehilangan paket signifikan. Nilai latency tercatat di bawah 150 ms dan packet loss kurang dari 1%, memenuhi kategori “Sangat Bagus” menurut standar TIPHON. Kombinasi static routing dan QoS terbukti efektif dalam mengoptimalkan manajemen lalu lintas jaringan.
Application of K-Means Clustering Algorithm for Disease Grouping at Blessing Dental Care Clinic Fransiska, Cintiya Aulya; Dafid, Dafid
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

Abstract

Blessing Dental Care Clinic is a clinic that provides dental practice services and general practitioner practices located in Palembang. This clinic offers general care and dental care managed by experienced doctors in their fields. The focus of the data used is medical record data, especially from general practice. Grouping large data into several groups based on similar pattern characteristics by utilizing the K-Means Clustering algorithm in CRISP-DM data mining was chosen to be more effective in handling various complaints of various diseases through the Clustering process. The results showed that the form of cluster 1 was 220 dominant data in the respiratory disease category, cluster 2 was 335 dominant data in the cardiovascular disease category, cluster 3 was 584 dominant data in the cardiovascular disease category, cluster 4 was 363 dominant data in respiratory disease, cluster 5 was 70 dominant data in respiratory disease, cluster 6 was 254 dominant data in cardiovascular disease and cluster 7 was 165 dominant data in ENT disease. In cluster 7 with an SSE value of 3189.16, the decrease is getting smaller and the spread pattern is starting to be optimal with a tendency for the pattern to be more spread out.
Implementation of IoT-Based Soil Moisture Monitoring System for Chili Plants Munawaroh, Maysani; Maulindar, Joni; Erlinawati, Mira
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.v5i3.7243

Abstract

Unstable soil moisture is a major challenge in chili cultivation, especially with climate change and limited water resources. To address this, the study developed an Internet of Things (IoT)-based soil moisture monitoring system with automatic irrigation. The system utilizes an ESP32 microcontroller, YL-69 moisture sensor, and the Blynk app for real-time monitoring and control via smartphone. The development followed the Waterfall method, covering requirement analysis, system design, implementation, and testing. Results showed the system accurately detects soil moisture, activates the water pump automatically based on threshold levels, and provides manual control options. The Blynk interface effectively displays real-time data, watering time, duration, and historical graphs with responsive and stable performance.

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