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G-Tech : Jurnal Teknologi Terapan
Published by Universitas Islam Raden Rahmat Malang
ISSN : 25808737     EISSN : 2623064X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering
Jurnal G-Tech bertujuan untuk mempublikasikan hasil penelitian asli dan review hasil penelitian tentang teknologi dan terapan pada ruang lingkup keteknikan meliputi teknik mesin, teknik elektro, teknik informatika, sistem informasi, agroteknologi, dll.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 897 Documents
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Optimalisasi Akurasi Algoritma C4.5 dengan Metode Adaptive Boosting Memprediksi Siswa dalam Menerima Dana Pendidikan Wahyu Aji Tri Riswandhana; Alva Hendi Muhammad
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5612

Abstract

The importance of increasing accuracy for educational institutions in predicting the provision of educational financial assistance. To make decisions about who deserves education funding. Data processing on aid recipients can be processed into information. This study aims to improve the accuracy of the C4.5 algorithm by using adaboost to determine whether students deserve to receive educational assistance funds or not by comparing the results before and after implementing adaboost. Predicting students' eligibility for obtaining educational assistance funds using decision trees. The dataset was collected from 414 students of SMK Muhammadiyah 1 Ngoro, used for this research. The research results show an increase in accuracy of 2.69% with the application of the C4.5 algorithm which has an accuracy of 77.31%, while the accuracy with the application of Adaboost reaches 80%.
Preparing Better Data for Oil Price Prediction Using Long Short-Term Memory Raymond Sunardi Oetama
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5668

Abstract

Fluctuating oil prices require a prediction model that can capture complex patterns more accurately than traditional methods. This study aims to apply the Long Short-Term Memory (LSTM) model to predict crude oil prices by assessing the effect of the training-test data ratio and window size on model performance. Daily data from 2000 to 2023 were taken from Yahoo Finance, which was then trained and tested on five data ratios and various window sizes. The evaluation was carried out using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R². The results show that the 90:10 ratio with a window size of 3 provides the best performance, with an MSE of 6.2100, RMSE of 2.4920, MAE of 1.8430, MAPE of 2.1363%, and R² of 0.9606. These findings confirm that LSTM can effectively capture temporal dependencies and outperform traditional statistical methods.
The Effect of Fermentation Time on the Chemical and Sensory Characteristics of Yoghurt Containing Eucheuma cottonii Seaweed Deden Yusman Maulid; Nusaibah Nusaibah; Arpan Nasri Siregar; Muhamad Hikam Idris; Endah Yuniarti
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6329

Abstract

Eucheuma cottonii is a seaweed from the Rhodophyta group (red algae) which is widely used in the food industry, especially carrageenan. The fiber content in seaweed comes from cellulose and hemicellulose, carrageenan, agar, and alginate. Yogurt has an important role in the body, such as maintaining stomach health, preventing cancer, and improving the digestive tract. This study aims to determine the effect of fermentation time on the chemical and sensory characteristics of yogurt added with Eucheuma cottonii seaweed. The long fermentation treatment used consisted of P1 = 12 hours; P2= 14 hours; and P3 = 16 hours. The addition of E. Cottonii seaweed to yogurt with a 12-hour fermentation time showed the highest parameters by SNI yogurt compared to other treatments. These parameters consist of moisture content, fat content, and ash content. The fermentation time of 16 hours in yogurt containing E. Cottonii seaweed showed the highest effect on sensory characteristics compared to other treatments. Based on this, yogurt fermentation time can determine the chemical and sensory characteristics of the product.
Usability Evaluation Satu Sehat Application Using Heuristic Evaluation Method Amalia Agung Septarina; Abdul Aziis Arifiyanto; Usman Usman; Muhammad Irsyad Farikhin
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6332

Abstract

Digital transformation in the health sector aims to improve efficient, effective, and integrated services. The government has started building mobile-based applications in various fields. Satu Sehat application is one of mobile-based application developed by the Indonesian Ministry of Health. As an integrated health platform in Indonesia, faces challenges in ensuring optimal user experience. User experience is greatly influenced by a good user interface. User interface must have good usability aspect so that users will feel comfortable when using the application. This study aims to evaluate the usability of the application using the Heuristic Evaluation method, by identifying problems based on Nielsen's 10 heuristic principles. Evaluation was conducted by 5 evaluators who have expertise in interaction design, health technology, and usability analysis. The results reveal two problems in error prevention and one problem in help and documentation, which are the basis for recommendations for further development. Recommendations are given on several aspects to improve the user experience.
Unleashing the Power of Deep Learning: Revolutionizing Facial Recognition with GhostFaceNets Ariana Tulus Purnomo; Edrick Hansel Limantoro; Muhammad Nafis Aimanurrohman
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6459

Abstract

Facial recognition technology has advanced significantly due to the development of deep learning algorithms. This paper explores deep learning, a branch of machine learning that employs multi-layered neural networks to simulate human decision-making processes in facial recognition. It provides a brief literature review of significant works in various deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The core of the study is the implementation of the GhostFaceNets model, an enhancement of GhostNets, which is specifically designed for efficient and accurate facial recognition. By using Ghost Modules, this model reduces computational redundancy in generating additional feature maps through linear operations. An integrated attention mechanism is used in this study to emphasize critical facial features. Additionally, this study also employs the ArcFace loss function to improve class separation accuracy. The VGG2-FP dataset was used to train and evaluate this model and achieved an accuracy of 94.45%. This study contributes to the evolution of facial recognition systems, particularly in constrained computational environments.
ANALYSIS OF GT 2.1 TRANSFORMER OIL TESTING USING THE DISSOLVED GAS ANALYSIS (DGA) METHOD AT PT PLN NUSANTARA POWER Amsal Aritonang; Soni Prayogi
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6481

Abstract

This study analyzes transformer oil from the GT 2.1 transformer at PT PLN Nusantara Power using the Dissolved Gas Analysis (DGA) method. Transformers are crucial for power systems, and their performance relies on the condition of their oil, which acts as both an insulator and a coolant. The DGA method detects gases like hydrogen, methane, ethylene, ethane, and acetylene in the oil, which indicate electrical or thermal faults. Oil samples were analyzed using diagnostic techniques such as the Rogers ratio, Duval triangle, and IEC standards to assess potential faults. The results revealed faults like thermal overheating and electrical discharges, offering valuable insights into the transformer’s health. The study highlights the importance of routine oil testing and DGA for predictive maintenance, enabling early fault detection. This proactive approach helps prevent failures, reduces outages, and extends transformer lifespan, ultimately enhancing the reliability and efficiency of power delivery at PT PLN Nusantara Power. The findings support the integration of advanced diagnostic methods in maintaining critical electrical infrastructure.
Metode Machine Learning dan Deep Learning dalam Prediksi Kinerja Siswa: Tinjauan Sistematis Desty Yani; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6501

Abstract

Research on student performance prediction has advanced rapidly in recent years, driven by the increasing volume of educational data generated by digital learning platforms. This data can be analyzed using Machine Learning (ML) and Deep Learning (DL) techniques, integrated with feature management strategies tailored to specific needs. However, selecting the most relevant features and optimizing predictive models remain significant challenges. Different studies apply various feature selection and engineering techniques, leading to inconsistent results and limited generalizability. This study conducts as a Systematic Literature Review (SLR) to explore ML and DL approaches for student performance prediction, emphasizing their relationship with feature management techniques. The reviewed studies span publications from 2019 to 2024. This SLR aims to assist researchers in identifying effective strategies for predicting student performance, including the selection of methods, datasets, or feature management techniques.  Most studies utilized publicly available datasets due to their accessibility and ease of use. Among ML methods, Random Forest emerged as the most frequently applied, achieving an F-measure of 99.5% integration of filter-based and wrapper-based feature selection techniques. Among DL approaches, the ANN-PCACSN model, employing Principal Component Analysis (PCA) for dimensionality reduction, achieved the highest accuracy of 99.32%. These findings highlight the importance of aligning preprocessing strategies with dataset properties and algorithm capabilities to enhance predictive performances.
Quality Control on Packing Process of Willarine Margarine Products at PT. XYZ with FMEA and FTA Methods Ramadhan Argaputra Novantya; Andesta Deny; Hidayat Hidayat
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6508

Abstract

PT. XYZ is a company engaged in the crude palm oil processing sector, one of which is willarine margarine which is exported to various countries. In maintaining global competitiveness, the company strives to produce high-quality products. However, in practice there are still obstacles that need to be overcome. One of them is during the packing process there are three types of product defects identified, namely leakage in the pouch packaging, lack of margarine volume, and damage to the pouch carton. So this research uses the FMEA (Failure Mode and Effect Analysis) method used to determine and transfer the severity, occurrence, and detection levels, so that it is obtained with the highest total RPN, namely leaky pouch packaging of 904, and FTA (Fault Tree Analysis) as a clue with the root of the problem tree, so that it is known that the root causes are lack of rest time, lack of supervision, no concern for the importance of track setting instructions, and no worker concern for cleanliness. After the results are known, improvement proposals are given, namely providing sufficient rest time and always rechecking, providing track setting instructions, and always doing routine cleaning at the beginning and end of work.
Application of K-Means and Naïve Bayes Algorithms for Prediction Model of Student Interest Concentration (Case Study: Amikom University Yogyakarta) Danang Eko Prayogo; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6523

Abstract

Amikom University Yogyakarta, has a Master of Informatics Engineering study program with three concentrations of specialization: Business Intelligence, Digital Information Intelligence, and Intelligence Animation. The choice of concentration by prospective students has been based on subjectivity, not on competence or work experience. To overcome this, this research proposes an algorithm-based concentration prediction and recommendation model to help prospective students choose the appropriate concentration. The dataset is obtained through questionnaires collected from active and inactive students. This research uses the K-Means algorithm for clustering raw data (unsupervised) in order to generate target classes, which are then classified using Naïve Bayes. The clustering process determines concentration labels such as Business Intelligence and others, while the SMOTE technique is used to balance the dataset to avoid data imbalance problems. This approach aims to produce more objective and accurate recommendations in determining student concentrations, reducing the tendency of subjectivity, and increasing the relevance of student competencies to the chosen field of specialization. From this research, the K-Means DBI score is 0.277 and the Naïve Bayes prediction accuracy score is 89%. This research aims to produce more objective and accurate recommendations in determining student concentrations, reducing subjectivity, and increasing the relevance of student competencies to the chosen field of specialization. The proposed model is expected to help universities in designing a more targeted admission strategy, as well as supporting students in making academic decisions that are in accordance with their abilities and interests, thereby increasing the effectiveness of the learning process and the suitability of graduates to the needs of the world of work.
Analysis of Employee Mental Workload Using The NASA-TLX Method (Case Study at PT. ABC) Reza Saputra; Nana Rahdiana; Karnadi Karnadi
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6536

Abstract

Human resources are the most important assets for a company. Each worker has their own role and obligations which will result in their own workload. PT. ABC is one of the companies that provides surface finishing services, electroplating services and electrode position painting. From the observation results, there are complaints about insufficient time adjustments to meet targets and demands from superiors for these targets, resulting in increased mental workload. In order to overcome this problem, it is necessary to measure the mental workload felt by employees using the NASA-TLX method, this method measures 6 dimensions of workload including Mental Demand, Physical Demand, Temporal Demand, Performance, Effort and Frustration Level. This study shows the acquisition of workload scores, it is known that 75% of employees have a high category workload and 25% of employees have a very high category workload. The dominant aspects that cause the amount of mental workload of PT.ABC employees are Mental Demand of 19.62%, Effort of 19.40% and Physical Demand of 17.93%. In order to minimize the level of mental workload, namely by adding some workers in the production and quality control sectors.

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