cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
ekrek@gunadarma.ac.id
Editorial Address
Jalan Margonda Raya 100
Location
Kota depok,
Jawa barat
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 8 Documents
 1 
Search results for , issue "Vol 30, No 2 (2025)" : 8 Documents clear
Evaluating Logistic Regression and SVM for Image Analysis Using VGG-16, VGG-19, and Inception V3 Features Habibi, Wildan; Yuadi, Imam
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.14056

Abstract

This paper presents a comparison of the classification accuracy of Logistic Regression (LR) and Support Vector Machine (SVM) classifiers on facial expression classification based on image embeddings obtained from pre-trained models like VGG-16, VGG-19, and Inception V3. Facial expression classification has relevance in emotion analysis, human-computer interaction, and security. The dataset consisted of five expressions: Angry, Fear, Happy, Neutral, and Sad. Feature embeddings were extracted by using CNN models, which are said to learn spatial features, and were classified using LR and SVM. Performance metrics like accuracy, precision, recall, and F1-score were evaluated. Inception V3 topped with 89.3% accuracy on SVM, followed by VGG-19 (87.6%) and VGG-16 (85.4%). Inception V3 was best in discriminating fine-grained expressions, as confirmed through confusion matrix analysis and visualization techniques like MDS and t-SNE. In contrast to earlier works on individual models or conventional approaches, this work emphasizes the merits of fusing powerful CNNs with strong classifiers. Limitations encompass a limited dataset and just five expressions, indicating that future research should address larger, varied datasets and real-time responsiveness for enhanced system robustness.
Optimalisasi Kontrol Lingkungan Kandang Ayam Tertutup Menggunakan Sensor DHT22 dan MQ135 dengan Analisis Ambang Batas Adaptif Setyawan, Galih; Attaqi, Muhammad Ilham
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.13947

Abstract

This study develops an automatic system to manage the closed poultry house environment, focusing on temperature control and ammonia mitigation, for improved animal health and productivity. Conventional static threshold-based systems often lack efficiency and are prone to excessive actuator activations. The proposed innovation utilizes a DHT22 temperature sensor and an MQ135 gas sensor, selected for their accuracy and responsiveness in monitoring critical parameters. The system integrates with an Arduino Uno, an AC Dimmer module for fan speed control, and a water spraying pump. Fan speed regulation is adjusted based on temperature using a three-level logic: slow mode (30^oC). Meanwhile, the activation of the water spraying pump for ammonia mitigation is controlled by a static threshold of ≥550 ppm. In-depth analysis of MQ135 sensor data from three ammonia testing experiments reveals significant potential for implementing more adaptive pump activation thresholds, specifically using the moving average method. Comparison between the existing static threshold-based control system and this adaptive scenario consistently demonstrates that applying an adaptive threshold can significantly reduce pump activation frequency, by an average of 50% to 60% (from 6-7 activations to 3 activations per experiment). This reduction indicates great potential for mitigating pump chattering, optimizing energy consumption, and extending the operational lifespan of actuators. These results confirm that the developed system effectively operates in maintaining poultry house environmental conditions, while also paving the way for smarter and more adaptive control in the future that can enhance farm operational efficiency.
Penerapan Total Productive Maintenance (TPM) Pada Mesin Building RTBC1 Di PT ASWX Wijaya, Astika Sari; Wahyudi, Rizqi
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.14069

Abstract

The RTBC1 machine is a building machine used to combine tire components. The problem in this study is that the current condition of the RTBC1 building machine is not yet in a suitable condition to operate effectively. The cause is that there is output from the machine that experiences damage (downtime) and disruptions to the machine due to machine components experiencing problems that impact the speed of machine production. This study aims to analyze the most dominant factors of the Six Big Losses, the implementation of Total Productive Maintenance (TPM) and Maintenance, Repair, and Overhaul (MRO), and provide recommendations for continuous improvement from the results of TPM implementation. The initial research results obtained that the OEE value of the RTBC1 machine is 78% and is still below the international standard (≥85%). After the initial improvement recommendations using one of the TPM pillars, namely autonomous maintenance, a change in the machine's OEE value was obtained, which increased by 5.1% to 83.1%. However, this only slightly reduced the value of losses, there are still two dominant losses, namely reduced speed losses with a value of 20.78% and idling and minor stoppages losses with a value of 14%. The improvement recommendations given to reduce these two losses are to use one of the MRO actions, namely running maintenance.
Sentiment Analysis of Twitter Data on Indonesia’s Cabinet Using Naïve Bayes and Support Vector Machine Algorithms Riyantoro, Riyantoro; Fauziah, Fauziah
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.13953

Abstract

Twitter has become a widely used platform for information dissemination among internet users and it serves as a valuable data source for sentiment analysis and decision-making. In this context, sentiment analysis is used to automatically categorize user tweets into positive or negative opinions. The Indonesia Maju Cabinet, the current administration under President Joko Widodo has emerging various public opinions regarding their performance and responsibilities. Sentiment analysis provides a method to categorize public opinions on social media. This study uses a dataset collected through a crawling process on Twitter with the keyword "Menteri Jokowi" (Jokowi's Ministers). The obtained data was then analyzed using two algorithms: Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM), to compare their cross-validation results. The analysis results show that the Naïve Bayes Classifier algorithm achieved 91.70% accuracy, 91.69% recall, and 91.69% precision. Meanwhile, the SVM algorithm achieved 96.77% accuracy, 96.71% recall, and 96.71% precision. The difference in accuracy is due to NBC’s tendency to misclassify neutral tweets as positive, whereas SVM, despite optimizing class separation, struggled with detecting sarcasm and subtle sentiment shifts, sometimes misclassifying negative tweets as neutral. Based on these results, it can be concluded that both algorithms can be effectively used for classifying opinions about ministers through sentiment analysis, although SVM demonstrates higher accuracy.
Analisis Sentimen Terhadap Aplikasi M-Paspor Menggunakan Algoritma Long Short-Term Memory (LSTM) dan BERT Embedding Hardianto, Bambang Gunawan; Wibisono, Satrio
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.12687

Abstract

The Directorate General of Immigration launched the M-Paspor app for online passport applications. Although it has been downloaded more than 1 million times, its 2.5 rating indicates user dissatisfaction. Review analysis is necessary for developers to understand the issues and improve the app to enhance the overall user experience. Therefore, this study conducts sentiment analysis on M-Paspor app reviews using a combination of Bidirectional Encoder Representations from Transformers (BERT) embedding and LSTM to classify user opinions. The advantage of BERT lies in its ability to understand the deep context of text, while LSTM excels in handling sequential data. LSTM is used as a classification method because it can capture long-term patterns in sequential data through memory management with cell states and three main gates, enabling it to continuously understand sentence context to support sentiment analysis. In this study, labeling consists of three classes: positive, negative, and neutral, using the lexicon method. The LSTM-BERT model shows consistent and higher accuracy values with a smaller proportion of training data. Testing results with a confusion matrix show the highest accuracy of 91.33% on a 70%:30% data split.
Penerapan Teknik Ensemble Learning untuk Deteksi Dini Penyakit Jantung Menggunakan Metode Voting Classifier dan Stacking Classifier Kusuma, Mutiara Romana; Kurniawan, Antonius Angga
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.14826

Abstract

Heart disease can be detected early by identifying risk factors that may contribute to its development. The Farmingham Study has conducted research on these risk factors. Machine learning models can be applied to perform early detection automatically based on data from the study. The obtained data is then processed through several pre-processing stages to prepare it for use in the modeling process. Afterward, models are built using the Random Forest, Logistic Regression, and K-Nearest Neighbor algorithms. Models built with individual algorithms show quite good performances, with the highest accuracy value of 0.91 for the Random Forest algorithm and the lowest accuracy of 0.67 for the Logistic Regression algorithm. Ensemble learning techniques such as the Voting Classifier and Stacking Classifier techniques are applied in this study to improve accuracy. The stacking technique successfully increased accuracy to 0.92. However, the voting technique does not outperform the Random Forest model. This is because the voting technique is more suitable for combining algorithms with balanced performance, whereas in this study, the Random Forest and Logistic Regression models have a significant difference in performance.
Desain Sistem Kerja Ergonomis Dalam Pembuatan Sepatu Pada PT Mitra Adiperkasa Tbk (MAPI) Hanifa, Nanda Dafana; Noor, Asep Mohamad
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.13942

Abstract

Tingkat permintaan yang semakin tinggi, perusahaan harus mengeluarkan ide-ide inovatif dalam rangka meningkatkan pemanfaatan sumber daya yang tersedia seoptimal mungkin, untuk menghasilkan tingkat produk semaksimal mungkin, baik dari segi kuantitas maupun kualitas. Untuk dapat melakukan proses penjaitan dengan baik, maka pekerja harus bekerja dalam kondisi nyaman. Tetapi, kondisi aktualnya mereka merasakan keadaan yang menimbulkan keluhan subjektif dalam melakukan pengepakan. Hal ini menyebabkan mereka cepat merasakan lelah dalam bekerja. Untuk mengoptimalkan, maka perlu dibuat perancangan sistem kerja yang sesuai dengan prinsip ergonomik, yaitu suatu sistem kerja yang meningkatkan kenyamanan dan produktivitas kerja. Seperti yang telah diuraikan pada latar belakang, perumusan masalahnya adalah bagaimana cara perancangan sistem kerja di bagian penjaitan agar pekerja tidak dapat cepat lelah. Untuk mengetahui gangguan tesebut maka dipergunakan metode REBA (Rapid Entire Body Asssessment), yaitu metode yang digunakan untuk menganalisa pekerja berdasarkan posisi tubuh. Metode ini didesain untuk mengevaluasi pekerjaan atau aktivitas, dimana pekerjaan tersebut memiliki kecenderungan menimbulkan ketidaknyamanan seperti kelelahan pada leher, bahu, lengan, punggung, pinggang dan kaki. Sehingga berdasarkan pada informasi diatas dirasa perlu bagi peneliti untuk melakukan penelitian terhadap sistem kerja yang ada dan lingkungan pendukungnya, sehingga karyawan jahit dapat bekerja dengan memenuhi target perusahaan serta memperhatikan Kesehatan.
Front Matter Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No 2, Agustus 2025 Jurnal Ilmiah Teknologi dan Rekayasa, Editorial
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

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

Abstract

Page 1 of 1 | Total Record : 8

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 30, No 2 (2025) Vol 30, No 1 (2025) Vol 29, No 3 (2024) Vol 29, No 1 (2024) Vol 28, No 3 (2023) Vol 28, No 2 (2023) Vol 28, No 1 (2023) Vol 27, No 3 (2022) Vol 27, No 2 (2022) Vol 27, No 1 (2022) Vol 26, No 3 (2021) Vol 26, No 2 (2021) Vol 26, No 1 (2021) Vol 25, No 3 (2020) Vol 25, No 2 (2020) Vol 25, No 1 (2020) Vol 24, No 3 (2019) Vol 24, No 2 (2019) Vol 24, No 1 (2019) Vol 23, No 3 (2018) Vol 23, No 2 (2018) Vol 23, No 1 (2018) Vol 22, No 3 (2017) Vol 22, No 2 (2017) Vol 22, No 1 (2017) Vol 21, No 3 (2016) Vol 21, No 2 (2016) Vol 21, No 1 (2016) Vol 20, No 1 (2015) Vol 19, No 3 (2014) More Issue