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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 248 Documents
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Analysis of Stress Level Prediction Affecting Employee Performance at Limas Dimensi Caraka Company Using the Support Vector Machine Method Alma Shafira; Oloan Sihombing
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9180

Abstract

Work-related stress is the result of actions, situations, and external events that place excessive psychological or physical demands on individuals. Work-related stress indirectly reduces employee performance, leading to a high turnover rate among outsourced employees on a large scale. A problem that almost always arises every year is the demand from outsourced employees for wage increases; this occurs because the cost of living continues to rise every year. The peak of this wage-related issue usually takes the form of strikes carried out by employees against their companies. Regarding the method, there are various algorithms or techniques that can be used for classifying levels of work-related stress, according to previous research and literature. Some previous studies indicate that the SVM (Support Vector Machine) method yields fairly good accuracy. In this study, the results showed no significant anomalies, and no very strong correlations (approaching 1 or -1) were found
Perancangan Dan Evaluasi UI/UX Aplikasi Fashion Planner Untuk Gen Z Dengan Smart Outfit Recommendation By Occasion, Menggunakan Metode SUS Dan UEQ Rumini Rumini
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9188

Abstract

Tren fashion generasi Z yang dinamis seringkali menyulitkan untuk menemukan kombinasi pakaian yang sesuai untuk berbagai kesempatan, kurangnya platform digital yang praktis menyebabkan pengguna sering mengalami kebingungan, menghabiskan waktu lebih lama, serta kurang percaya diri dalam menentukan pilihan berpakaian. Permasalahan ini berdampak pada pengalaman pengguna dalam memanfaatkan teknologi sebagai sarana pendukung aktivitas sehari-hari, khususnya dalam hal fashion. Menanggapi isu tersebut, studi ini mengembangkan LookSy, sebuah aplikasi untuk mempermudah pemilihan outfit. Metode yang digunakan adalah design thinking yang meliputi tahapan pertama empathize, define, ideate, prototype, dan test, dengan fokus pada kebutuhan dan karakteristik generasi Z. untuk mengukur tingkat kegunaan dan pengalaman pengguna, dilakukan evaluasi menggunakan metode System Usability Scale (SUS) dan User Experience Questionnaire (UEQ) dalam dua tahap pengujian. Hasil evaluasi tahap pertama menunjukkan nilai SUS sebesar 67,58% dan nilai rata-rata UEQ sebesar 1, yang menandakan bahwa aplikasi berada pada kategori cukup baik namun masih memerlukan perbaikan. Setelah dilakukan penyempurnaan desain dan fitur, evaluasi tahap kedua menunjukkan peningkatan dengan nilai SUS sebesar 78,83% dan nilai rata-rata UEQ sebesar 2, yang mencerminkan peningkatan usability dan pengalaman pengguna. Penelitian ini memberikan kontribusi dalam pengembangan aplikasi fashion berbasis UI/UX yang berorientasi pada kebutuhan pengguna dan dapat dimanfaatkan oleh generasi Z sebagai referensi dalam menentukan outfit.
Evaluating Whisper Model on Indonesian Educational Videos Transcription with Varying Audio Conditions Fathia Sabrina; Fitria Nilamsari; Rinny Asasunnaja
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9201

Abstract

The increasing use of video-based learning in digital education highlights the importance of accurate automatic speech recognition (ASR) systems to support accessibility, subtitle generation, and inclusive learning environments. This study evaluates the performance of the Whisper base ASR model on Indonesian educational videos with diverse audio characteristics and production conditions. Several categories of educational videos were analyzed, including classroom lectures, podcasts, interviews, and animated educational content. Audio recordings were converted into WAV format and evaluated using Word Error Rate (WER) and Character Error Rate (CER). Long-duration recordings were additionally segmented into approximately 30-minute chunks to analyze transcription consistency over time. The results showed that transcription performance varied across recording conditions, with WER values ranging from 18% to 47% and CER values ranging from 5% to 23%. The analysis also identified recurring substitution patterns influenced by phonetic similarity, conversational expressions, and culturally contextual phrases. The findings indicate that Whisper base provides reasonably effective transcription capability for Indonesian educational multimedia under realistic recording conditions.
A Comparative Study of Decision Tree, Logistic Regression, and Random Forest Models for Identifying Mental Health Issues Among University Students Ahmad Syafei Nursuwanda; Anindya Ananda Hapsari; Halimatuz Zuhriyah; Devan Junesco Vresdian
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9775

Abstract

Mental health is an important issue in Indonesia, particularly among university students who are vulnerable to anxiety due to academic pressure, life challenges, and emotional instability. This study compares the performance of three machine learning algorithms Decision Tree, Logistic Regression, and Random Forest for detecting anxiety among university students using Python. The results indicate that Logistic Regression achieved the highest accuracy of 90%, while Decision Tree and Random Forest each achieved 80% accuracy. Evaluation using 20% of the dataset for testing and validation with the Taylor Manifest Anxiety Scale (TMAS) showed that Logistic Regression correctly identified 4 out of 5 students with anxiety. These findings demonstrate that Logistic Regression is the most effective algorithm and has strong potential to support early anxiety detection through a data science–based approach.
IoT-Based Water Quality Monitoring System Using ESP32 and K-Nearest Neighbor Algorithm Fiqri Rizaldi Azlin Hasibuan; Man Vredus Zalukhu; Della Agnesia Simamora; Muhammad Irfan Fahmi
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9233

Abstract

Conventional water monitoring is often time-consuming and hinders decision-making. Amidst IoT advancements, few systems feature automatic correction mechanisms. This study develops an IoT-based water monitoring system to read pH, temperature, and turbidity in real-time. The NodeMCU ESP32 is used as the main microcontroller connected to pH, DS18B20, and turbidity sensors. Data is collected every 15 minutes, uploaded to Firebase via Wi-Fi, and visualized on a web dashboard. The system implements the K-Nearest Neighbor (K-NN) algorithm with Euclidean Distance to categorize water as good, moderate, or poor. This result triggers an automatic pH-balancing pump actuator if the value falls outside the normal range (6.5–8.5). This laboratory-scale prototype proves that the synergy of ESP32, Firebase, and K-NN overcomes the weaknesses of manual methods, resulting in accurate, efficient, and sustainable water monitoring.
Implementation of the Apriori Algorithm in Determining Menu Bundles at Café XYZ Lusia Evi Sinarudia; Dafid Dafid
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9271

Abstract

Café XYZ is a coffee shop that still faces challenges in menu management, such as the absence of a bundling strategy and the limited use of transaction data, which is currently confined to administrative functions. In fact, transaction data has the potential to be utilized in developing more targeted marketing strategies. This final project aims to implement the Apriori algorithm to analyze sales transaction data in order to identify consumer purchasing patterns and recommend appropriate menu bundles. The analysis process follows the CRISP-DM (Cross Industry Standard Process for Data Mining) approach, while the application development adopts the Waterfall method. The result of this project is a web-based dashboard application built using the Flask framework and Python programming language, which can display transaction summaries and product association analysis results in a direct and interactive manner.
Classification of Taste Levels in Gerga Oranges Using the K-Cluster Classification Tree (K-CT) Method Asep Sayaputra; Febriansyah Febriansyah; Buhori Muslim
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9416

Abstract

ABSTRACTTechnological advancements have accelerated the adoption of various machine learning methods to support decision-making processes in the agricultural and horticultural sectors. One of the persistent challenges in fruit quality assessment is the identification of taste levels, which is commonly performed subjectively based on visual observation and individual experience. Such an approach may lead to inconsistencies in product quality evaluation. Therefore, this study aims to apply the K-Cluster Classification Tree (K-CT) method to classify the taste levels of Gerga oranges based on their physical characteristics. The K-CT method is a hybrid approach that integrates the K-Means Clustering algorithm with a Classification Tree to enhance classification performance while maintaining computational efficiency. The research utilized primary data collected through direct observation of 700 Gerga orange samples obtained from farmers and fruit traders in Tanjung Sakti District, Lahat Regency. Each sample was represented by six physical attributes, namely peel color, pore size, thrips presence, fruit shape, peel texture, and fruit diameter, while taste level served as the target variable. The dataset was divided into 80% training data and 20% testing data. The experimental results demonstrated that the K-CT method achieved a classification accuracy of 94.57%, outperforming Classification Tree, Random Forest, and Gradient Boosting models. Furthermore, the proposed method exhibited competitive computational efficiency and successfully identified fruit diameter as the most influential attribute affecting the taste level of Gerga oranges. These findings indicate that the K-CT method has considerable potential to be implemented as an objective, accurate, and efficient decision support system for fruit quality classification.
Automated Student Card Detection and Identity Verification System for Laboratory Management Using YOLOv8n and OCR Muhammad Raihan Zaldiputra; Raffa Danendra Pramono; Keisyah Zahra Anatasya; Dodik Ariyanto; Faldiena Marcelita; Mayanda Mega Santoni
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9208

Abstract

Pengelolaan peminjaman laboratorium komputer di Sekolah Vokasi IPB University masih dilakukan secara manual, sehingga menimbulkan inefisiensi operasional, ketidakakuratan pencatatan, dan ketidakmampuan pemantauan secara real-time. Penelitian ini bertujuan mengembangkan sistem peminjaman laboratorium berbasis web yang mengotomatisasi proses verifikasi identitas mahasiswa melalui integrasi tiga teknologi utama, yaitu You Only Look Once version 8 Nano (YOLOv8n), Optical Character Recognition (OCR) menggunakan PaddleOCR, dan verifikasi wajah menggunakan ArcFace. Metodologi penelitian meliputi pengumpulan 110 citra Kartu Tanda Mahasiswa (KTM), anotasi bounding box pada empat region, preprocessing dan augmentasi hingga menghasilkan 780 citra, pelatihan model selama 150 epoch menggunakan GPU Tesla T4, serta integrasi ke dalam platform web menggunakan FastAPI dan Next.js. Hasil evaluasi menunjukkan model YOLOv8n mencapai mAP@0,5 sebesar 0,976, precision 0,977, dan recall 0,979. Sistem ini berhasil mengintegrasikan seluruh pipeline verifikasi KTM secara otomatis dan real-time, menghilangkan ketergantungan pada proses manual dalam pengelolaan peminjaman laboratorium.

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