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JiTEKH (Jurnal Ilmiah Teknologi Harapan)
Published by Universitas Harapan Medan
ISSN : 23385677     EISSN : 25496646     DOI : 10.35447
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering
JITEKH merupakan jurnal ilmiah yang menyajikan artikel orisinal tentang pengetahuan dan informasi riset atau aplikasi riset dan pengembangan terkini dalam bidang keteknikan dan teknologi informasi. Ruang lingkup Jurnal JITEKH meliputi bidang Informatika, Teknik Mesin, Teknik Elektro, Sistem Informasi, Teknik Industri dan Teknik Sipil. Focus and Scope: Design Manufacture Konversi Energi Material Data analysis Natural Language Processing Artificial Intelligence Neural Networks Pattern Recognition Image Processing Genetic Algorithm Bioinformatics/Biomedical Applications Biometrical Application Content-Based Multimedia Retrievals Augmented Reality Virtual Reality Information System Game Mobile Data mining Artificial Intelligence Sistem Pendukung Keputusan IT Bussiness Incubation IT Governance Cloud Computing Dessision Support System Instrumentation and Control Electrical (Power) Electronics Engineering Industrial Engineering Optimasi Sistem Produksi Perencanaan Produksi dan Pengendalian Persediaan Penjadwalan dan Manajemen Operasi/Proyek Computer-Integrated Manufacturing System Sustainable Product Design and Manufacturing Penelitian Operasional dan Decision-Making Model Manajemen Teknologi Pengukuran Kerja, Faktor-Faktor Manusia dan Ergonomi Rekayasa dan Pengendalian Kualitas Sistem Logistik dan Supply Chain Management Perancangan dan Pengembangan Produk Concurrent Engineering Analisis Ekonomi Teknik Pemodelan Sistem dan Analisis Simulasi Perancangan Layout Fasilitas Knowledge Management Reliability and Maintenance Engineering Sistem dan Teknologi Informasi Rekayasa Pelayanan (Service Engineering) Technopreneurship dan Inovasi Aplikasi Model Stokastik dalam Teknik Industri Aplikasi Metode Metaheuristik dalam Teknik Industri Building Materials and Structures Constructions Technology Constructions Economy and Management Earthquake Engineering Environmental Engineering Renovation of Buildings Geotechnical Engineering Highway Engineering Hydraulic and Hydraulic Structures Road and Bridge Engineering Structural Engineering Surveying and Geo-Spatial Engineering Transportation Engineering Tunnel Engineering Urban Engineering and Economy Water Resources Engineering Urban Drainage
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 134 Documents
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Kalibrasi Ambang Batas Kemiripan TF IDF dan Cosine Similarity untuk Deteksi Duplikasi Judul Pengabdian kepada Masyarakat pada Teks Pendek Zurnan Alfian; Asep Erlan Maulana; Syaeful Machfud
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v14i2.1585

Abstract

Community Service (PkM) activities constitute a vital pillar of the higher education "Tridharma" (Three Pillars of Higher Education), serving to apply students' technological competencies to address social issues. However, the surge in PkM proposals each semester has created serious administrative challenges, specifically the high frequency of topic repetition and duplication. The manual verification process currently conducted by supervising lecturers faces constraints regarding memory limitations, time inefficiency, and the risk of subjectivity. This research aims to design, implement, and test a web-based automated PkM title selection system utilizing a combination of Natural Language Processing and Information Retrieval. The methodology involves five text preprocessing stages (case folding, cleaning, tokenizing, stopword removal, and stemming using the Sastrawi stemmer), Term Frequency-Inverse Document Frequency (TF-IDF) feature weight extraction, and vector angular distance calculation using Cosine Similarity. The system was developed using the Flask micro-web framework and an SQLite database, implemented in Python 3.11.9. Experiments were conducted on a primary dataset of 839 PkM titles from Informatics Engineering students at Universitas Pamulang (comprising a reference corpus of 641 approved titles and a test set of 198 rejected titles). Threshold tuning established three decision zones: Highly Similar/Saturated (≥0.80), Partially Similar (0.60–0.79), and Unique/Eligible (<0.60). Classification performance testing against expert ground truth yielded an Accuracy of 95.6%, Precision of 92.8%, Recall of 84.5%, and an F1-Score of 88.4%, significantly exceeding the study's minimum key performance indicator (≥85%). The system proved effective as a digital reference for the selection process, reducing verification time and ensuring the originality of student PkM innovation outputs
Internet of Things (IoT) Soil Moisture Monitoring System for Automated Precision Irrigation Alwi Zilva Muhammad; Ratu Mutiara Siregar; Andi Prayogi; Muhammad Akbar Syahbana Pane; Khairul Saleh
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Water availability is one of the important factors affecting the growth of oil palm seedlings, especially during the pre-nursery phase, where the root system is still shallow and sensitive to changes in soil moisture. Manual irrigation practices often result in inaccurate water distribution, which may cause either water deficiency or excess in the growing media. This study aims to design and implement an Internet of Things (IoT)-based soil moisture monitoring and automatic precision irrigation system for oil palm seedlings. The developed system utilizes a soil moisture sensor for detecting soil moisture levels, an ESP32 microcontroller as the main controller, a relay module for controlling the water pump, and an LCD display with cloud monitoring for real-time data visualization. The sensor is installed at a depth of 5–10 cm according to the root zone of the seedlings. The system is configured with moisture parameters where the pump is activated when the soil moisture value is below 40% and stops when the moisture reaches 60–70%. The testing results indicate that the system is capable of performing automatic monitoring and irrigation in a responsive manner with good accuracy. This system is expected to improve water use efficiency and support the optimal growth of oil palm seedlings.
Proof of Concept Digital Twin Berbasis Machine Learning untuk Prediksi dan Optimasi Kinerja Panel Surya Ade Zulkarnain Hasibuan; Irwanda Syahputra
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v14i2.1605

Abstract

This study developed and evaluated a machine learning–based Digital Twin proof of concept for a physical solar panel with a nominal capacity of 100 W. Environmental and electrical data were collected using sensors connected to an ESP32 and transmitted to a server for power prediction using Random Forest Regression. Exhaustive Scenario Search was used to compare panel angles of 25°, 35°, and 45°, while the dashboard presented measured power, predicted power, and angle recommendations. The analysis used 9,720 measurement records divided chronologically into training, validation, and testing datasets. Physical-panel validation was performed by comparing the predicted values with the measured power. For the 1,944 testing records, the model achieved an MAE of 2.769 W, an RMSE of 4.145 W, and an R² of 0.9446. The angle scenario evaluation produced 2,492.97 Wh of energy, which was 3.06% higher than the 2,418.91 Wh obtained with a fixed angle of 35°. These findings support the implementation of a Digital Twin proof of concept for panel-condition monitoring, power prediction, and angle recommendation under the tested conditions.
Prediksi Kepuasan dan Loyalitas Mahasiswa Menggunakan Random Forest dengan Pendekatan Explainable AI Berbasis SHAP Indah Kusuma Dewi; Muhammad Ropianto; Elsha Dellista Ashofie
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v14i2.1591

Abstract

Student satisfaction and loyalty are two strategic indicators of higher education institutions' success in maintaining educational service quality. This study builds prediction models for student satisfaction and loyalty using the Random Forest algorithm, interpreted transparently through the SHapley Additive exPlanations (SHAP) approach. Data were obtained from 437 student respondents covering eight service dimensions with 25 indicator variables. The satisfaction model achieved 94.32% accuracy (AUC = 0.8819), while the loyalty model obtained 87.50% accuracy (AUC = 0.5490). SHAP analysis identified Student financial difficulty solutions, Campus Wi-Fi stability, and Variation in teaching methods as dominant predictors of satisfaction, while Scholarship transparency is the main driver of loyalty.

Page 14 of 14 | Total Record : 134

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