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INDONESIA
Jurnal Ilmiah Teknologi dan Rekayasa
Published by Universitas Gunadarma
ISSN : 14109093     EISSN : 20898088     DOI : http://dx.doi.org/10.35760/tr.
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
Jurnal ini diterbitkan secara berkala tiga kali dalam setahun, April, Agustus, dan Desember. Artikel yang dimuat dalam jurnal ini merupakan artikel ilmiah hasil penelitian tentang teknologi dan rekayasa yang meliputi teknik informatika, teknik elektro, teknik mesin, dan teknik industri. Artikel dapat ditulis dalam bahasa indonesia maupun bahasa inggris.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 7 Documents
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Search results for , issue "vol. 31 no. 1 (2026)" : 7 Documents clear
Pengaruh variasi laju kendaraan dan beban muatan terhadap karakteristik emisi gas buang mesin diesel Kurniawan Dwi Alfiyan; Ethys Pranoto; Riza Pahlevi Marwanto
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.144

Abstract

This study aims to analyze the influence of variations in vehicle speed and payload on exhaust gas emission characteristics as well as combustion behavior in a diesel engine. Tests were conducted on a pickup vehicle with speed variations of 20, 30, 40, and 50 km/h and payloads of 0 kg, 587 kg, and 700 kg. The emission parameters analyzed include carbon monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), oxygen (O2), and smoke opacity, measured using a gas analyzer and smoke tester. The results show that increases in vehicle speed and load indicate a tendency in the combustion process from relatively stable conditions toward incomplete combustion, as indicated by significant increases in CO, HC, and opacity. The observed findings suggest a tendency toward over-fueling conditions at high loads, where increased fuel supply is not matched by adequate air supply in a naturally aspirated diesel engine, resulting in locally rich zones within the combustion chamber. This condition reduces combustion quality and increases the formation of incomplete oxidation products. The increase in CO₂ emissions reflects higher fuel consumption due to greater power demand, but does not necessarily indicate improved combustion efficiency. Meanwhile, relatively stable O₂ levels suggest that combustion inefficiency may be influenced by limitations in air–fuel mixing and reaction time, rather than a global oxygen deficiency. These results indicate an indirect decrease in energy conversion efficiency and an increase in pollutant emissions. Therefore, controlling vehicle operating conditions is important to improve energy efficiency and reduce diesel engine exhaust emissions.
Klasifikasi tingkat stres berbasis fuzzy mamdani menggunakan indikator aktivitas harian Sandi Badiwibowo Atim; Erin Eka Citra
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.180

Abstract

Peningkatan beban kerja dan penurunan kualitas tidur dalam pola hidup modern telah memicu peningkatan tingkat stres individu, yang sering kali dinilai secara subjektif dan kurang akurat menggunakan pendekatan konvensional. Penelitian ini mengusulkan model klasifikasi tingkat stres berbasis Fuzzy Inference System (FIS) Mamdani untuk menangani ketidakpastian dan subjektivitas pada indikator gaya hidup. Model ini menggunakan empat variabel input: sleep duration, sleep quality, physical activity level, dan daily steps, dengan tingkat stres sebagai variabel output. Data penelitian bersumber dari sleep health and lifestyle dataset sebanyak 374 data. Sistem dirancang menggunakan 20 aturan fuzzy dan metode defuzzifikasi centroid yang diimplementasikan melalui aplikasi berbasis web interaktif. Hasil pengujian menunjukkan performa model yang sangat baik dengan nilai Root Mean Square Error (RMSE) sebesar 1,671 dengan akurasi sebesar 88,50% dan Mean Squared Error (MSE) sebesar 1,671. Hasil Ini menunjukkan bahwa logika fuzzy efektif dalam memberikan hasil klasifikasi yang stabil dan memiliki interpretabilitas tinggi dalam merepresentasikan kondisi manusia.
Pendekatan computer vision untuk analisis fitur visual dalam estimasi produktivitas tanaman kopi Jenie Sundari; Ahmad Sinnun; Fuad Nur Hasan; Mulia Rahmayu
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.182

Abstract

Coffee productivity is an important factor in supporting the sustainability of smallholder plantations, particularly in the Sukabumi region. However, conventional estimation methods that rely on manual observation tend to be subjective and inefficient. Although previous studies have applied computer vision and machine learning for yield prediction, most approaches depend on large-scale datasets and expensive sensing technologies, and often do not integrate multiple visual plant features comprehensively. This indicates a research gap in the use of simple RGB-based imaging for productivity estimation. This study aims to analyze visual features of coffee plants and develop a coffe productivity estimation model using the Random Forest algorithm. The dataset consists of 10 coffee plant images collected directly from field observations, with extracted features including fruit count, fruit maturity percentage, canopy area, leaf color, and leaf texture. Model evaluation is performed using Leave-One-Out Cross Validation (LOOCV) method to optimize data utilization on a limited dataset. The results show that the model achieves a Mean Absolute Error (MAE) of 0.06, a Root Mean Square Error (RMSE) of 0.07, and a coefficient of determination (R²) of 0.91. These results indicate good predictive performance within the available dataset. Feature importance analysis reveals that fruit count and fruit maturity percentage are the most influential factors in determining coffee productivity. This study contributes to the development of a low-cost image-based estimation approach that is practical and potentially applicable for smart agriculture in smallholder coffee plantations, although the findings remain preliminary due to the limited dataset size.
Implementasi model support vector machine sebagai sistem prediksi penyakit ginjal kronik berbasis data klinis Rama Dian Syah; Diandra Hasna Widianto; Bhakti Gunawan; Hadyanto Caputra; Widya Silfianti; Rena Fuji Erin Setyawati
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.218

Abstract

Chronic Kidney Disease (CKD) is one of the global health problems with increasing prevalence and mortality rates, requiring accurate early detection methods to support timely treatment and prevention. Various previous studies have applied machine learning techniques for CKD prediction; however, most studies are still limited to basic model implementation without systematic parameter optimization or real-time web-based prediction system deployment. This study aims to develop a CKD prediction model using the Support Vector Machine (SVM) algorithm optimized through GridSearchCV to improve classification performance. The research was conducted based on the CRISP-DM framework using the CKD dataset from the UCI Machine Learning Repository. The preprocessing stage included categorical data transformation, missing value handling using median imputation, and feature standardization using StandardScaler. Parameter optimization was performed by testing several SVM parameter combinations using a 5-fold cross-validation approach. The results showed that the optimized SVM model achieved an accuracy of 98.75%, with high precision, recall, and F1-score values in CKD and non-CKD classification. These results indicate better performance compared to several previous studies using similar datasets with accuracy below 98%. Furthermore, the model was implemented in a web-based application using Gradio and Hugging Face Spaces to support real-time prediction. Initial validation by an internal medicine specialist indicated that the system predictions were consistent with medical interpretation, suggesting that the proposed model has potential as a decision-support tool for early CKD detection
Pengembangan sistem pakar diagnosis tingkat depresi mahasiswa semester akhir berbasis android menggunakan metode certainty factor Maximilianus Amasanan; Yoseph Pius Kurniawan Kelen
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.143

Abstract

Depression is increasingly experienced by final-year university students due to academic pressure, with symptoms such as prolonged sadness, loss of motivation, sleep disturbances, and difficulty concentrating. Early detection remains limited because of restricted access to mental health professionals and the high cost of consultations. This study aims to develop an Android-based expert system for diagnosing depression levels using the Certainty Factor (CF) method. The research employed the Research and Development (R&D) method with the Rapid Application Development (RAD) model, which consists of requirements planning, design, construction, and implementation stages. The system utilizes 15 symptoms and classifies four levels of depression: mood disorder, mild depression, moderate depression, and severe depression. The evaluation was conducted on 20 respondents by comparing the system’s diagnostic results with expert analysis. The evaluation results showed that 17 out of 20 system diagnoses were consistent with the expert’s analysis, resulting in an accuracy rate of 85%. An example of the calculation process using the CF method produced a diagnostic value of 0.95 (95%), which falls into the severe depression category. The developed system is capable of supporting early detection of depression in a faster, more practical, and easily accessible manner through Android devices, and it can serve as an initial consultation tool for final-year students. The system can assist early detection more efficiently and with greater accessibility.
Evaluasi keamanan sistem e-journal berbasis web menggunakan framework NIST SP 800-115 dan analisis berdasarkan OWASP top 10 Ibnu Ajis; Tri Rochmadi; Yanuar Wicaksono; Dadang Heksaputra
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.158

Abstract

The use of web-based information systems in higher education institutions, particularly e-journal platforms, continues to rise. However, this is often not accompanied by adequate security measures, thereby threatening Confidentiality, Integrity, and Availability (CIA). This study aims to evaluate the security of a proceedings system based on Open Journal Systems (OJS) at the Adisutjipto Institute of Aeronautical Technology (ITDA) in Yogyakarta. The method used was penetration testing based on the NIST SP 800-115 standard via a black-box approach, which included the planning, discovery, attack, and reporting phases. Initial identification using the OWASP ZAP automated scanner identified 11 vulnerability indicators with estimated severity levels ranging from low to medium. However, after conducting the exploitation validation (proof-of-concept) phase and assessment using the OWASP Risk Rating method, it was found that some vulnerabilities posed a higher risk impact than the initial scan results indicated. The final research results confirmed the presence of 3 high-risk findings and 1 medium-risk finding. These high-risk vulnerabilities include Host Header Injection, which enables Reflected XSS attacks; a Security Misconfiguration on the /server-status endpoint; and the absence of a rate-limiting mechanism in the authentication feature. This study concludes that the system has significant security vulnerabilities that require immediate mitigation. Key recommendations include updating the OJS platform version, implementing server configuration hardening, and enhancing login security mechanisms to continuously protect the Integrity and Confidentiality of the institution’s academic data.
Sistem pakar diagnosis stadium rabies pada manusia menggunakan metode certainty factor berbasis web: studi kasus Puskesmas Oelolok Maria Gradiana Misa; Yoseph Pius Kurniawan Kelen; Krisantus Tey J Seran
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.167

Abstract

Delays in the early diagnosis of rabies remains a significant issue due to the limited of public knowledge in recognizing the symptoms of the disease, resulting in delayed medical treatment. This study aims to develop a web-based expert system designed to assist in the early diagnosis of rabies in humans using the Certainty Factor (CF) method. This method is used to calculate the confidence level of the diagnosis based on the symptoms selected by the user. The system knowledge base was obtained through expert interviews and literature studies, which were represented in the form of diagnostic rules. The system is capable of providing rabies diagnosis results along with their corresponding confidence values based on the symptoms entered by users. Functional testing using the Black Box Testing method showed that all system features functioned properly. In addition, system validation was carried out by comparing the system diagnosis results with expert diagnoses through 30 testing scenarios using different symptom combinations. The test results showed 27 matching data and 3 non-matching data, resulting in a system accuracy rate of 90%. This research contributes to the implementation of the CF method in a web-based expert system to support early rabies diagnosis in a fast and measurable manner.

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