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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 984 Documents
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Rainfall Classification in Malang Regency Using Artificial Neural Networks with Boolean Logic-Based Feature Engineering Selina Ayuningtyas; Zainal Abidin; Yunifa Miftachul Arif
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10263

Abstract

This study classifies monthly rainfall in Malang Regency using an Artificial Neural Network (ANN) with the Backpropagation algorithm and Boolean logic approaches (AND, OR, and AND-OR). The dataset consists of 144 monthly climatological records (2012–2023) obtained from the East Java Climatology Station, with three input variables: rainfall, minimum temperature, and relative humidity. Rainfall was grouped into three categories: Low (0–100 mm; 39.58%), Moderate (101–300 mm; 37.50%), and High (301–500 mm; 22.92%). Boolean logic features were generated using the mean values of relative humidity (77.34%) and minimum temperature (17.89°C). The ANN model was tested with three hidden-layer configurations containing 5, 8, and 10 neurons. Data were divided into 70% training and 30% testing sets using a random state of 42. The results show that the 10-neuron configuration achieved the best performance, with 75.00% accuracy, 75.97% precision, 75.00% recall, and 75.00% F1-score. In comparison, the 5-neuron and 8-neuron models achieved accuracies of 68.18% and 65.91%, respectively. The AND-OR Boolean logic approach provided more stable feature representation than the AND or OR approaches alone by combining multiple atmospheric conditions. These findings indicate that ANN with an appropriate architecture can effectively classify rainfall patterns in Malang Regency.
Enhancing Phishing Website Detection Using Artificial Neural Network with Logic Gate-Based Feature Interaction Modeling M. Halvi Rahman; Zainal Abidin; M. Amin Hariyadi
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10280

Abstract

Despite advances in machine learning-based phishing detection, existing Artificial Neural Network (ANN) models operate as black boxes with no interpretable explanation of feature interactions—a critical limitation for security analysts. Furthermore, most approaches deploy large feature sets without investigating whether a minimal subset achieves equivalent performance. This study develops a phishing detection system combining ANN with Logic Gate-Based Feature Interaction Modeling (LGFIM), a novel framework that characterizes ANN decisions through AND, OR, and XOR Boolean operations, addressing both accuracy and interpretability gaps. Using the PhiUSIIL dataset (235,795 instances), Pearson correlation identified URLSimilarityIndex (r=0.8604) and HasSocialNet (r=0.7843) as the two most discriminative features. An ANN (2-64-32-16-1, ReLU, Adam) trained on an 80/20 split achieved 99.63% accuracy, 100% recall, 99.68% F1-score, and 99.91% AUC-ROC with zero false negatives. The LGFIM analysis reveals the classification boundary follows a predominantly AND-type Boolean structure: the AND gate achieves 99.67% accuracy against true labels, while ANN predictions align with AND for 42.48% of samples and XOR for 57.52%, together accounting for 100% of all predictions. This is the first study to comprehensively characterize ANN phishing decisions through logic gate interaction patterns, providing a zero-cost interpretability layer for cybersecurity operations.
Comparison of Boolean OR, AND, and OR–AND Models for Monthly Rainfall Classification in Bawean Island Rudi Kasianto; Zainal Abidin; Totok Chamidy; Mochamad Imamudin
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10298

Abstract

Rainfall classification plays an important role in climate monitoring, water resource management, agricultural planning, and hydrometeorological disaster mitigation. While machine learning techniques have been widely used for rainfall classification, they often require substantial computational resources and complex training processes. This study proposes a simple, interpretable, and computationally efficient Boolean-based framework for monthly rainfall classification on Bawean Island, East Java, Indonesia. Monthly climatological data from 1972–2023, including rainfall, rainy days, mean temperature, and minimum temperature, were analyzed, yielding 624 observations. Rainfall was classified into three categories: low (<100 mm), moderate (100–299 mm), and high (≥300 mm). Rainy days were converted into ordinal scores, while mean and minimum temperatures were transformed into binary scores. Three Boolean-based rainfall classification models were developed and evaluated using confusion matrices, accuracy, precision, recall, and F1-score. Correlation analysis showed that rainy days had the strongest relationship with rainfall (r = 0.858), followed by minimum temperature (r = −0.592) and mean temperature (r = −0.463). The hybrid OR–AND model achieved the best overall performance, with 66% accuracy, 71% precision, 62% recall, and 61% F1-score, outperforming both the OR and AND models. These results demonstrate that the proposed Boolean-based framework provides an effective, transparent, and computationally efficient approach for monthly rainfall classification.
Germination of Expired Spinach (Amaranthus hybridus L.) Seeds with Various Coconut Water (Cocos nucifera L.) Concentrations Nur Malinda; Elvi Rusmiyanto Pancaning Wardoyo; Dwi Gusmalawati
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10300

Abstract

Spinach (Amaranthus hybridus L.) productivity in Indonesia declined from 170,688 tons in 2023 to 168,400 tons in 2024, a decrease partly attributed to the use of expired seeds with reduced physiological viability. Coconut water (Cocos nucifera L.), which contains auxin and cytokinin, has potential as a natural priming agent to restore the germination capacity of expired seeds, yet its effect at different concentrations on expired spinach seeds using the top-of-paper method has not been established. This study aimed to determine the effect of various coconut water concentrations on the germination of expired spinach seeds. A Completely Randomized Design (CRD) with five treatmentsK0 (control/distilled water), K1 (25%), K2 (50%), K3 (75%), and K4 (100% coconut water)was applied, each replicated five times, and data were analyzed using ANOVA followed by Duncan's Multiple Range Test (DMRT). Coconut water soaking significantly increased germination percentage and germination ability, and significantly reduced abnormal sprouts and dead seeds (p < 0.05). The highest germination percentage (96.8%) and germination ability (92%) were both recorded at 75% coconut water concentration (K3), statistically comparable to 50% (K2) and 100% (K4). The control treatment (K0) produced the highest abnormal sprouts (8.8%) and dead seeds (15.2%), while the 25% concentration (K1) gave the fastest average germination time (2.04 days). These findings indicate that soaking in 50–75% coconut water for 6 hours is an effective, low-cost priming method for restoring the viability of expired spinach seeds and offers a practical recommendation for small-scale seed quality improvement.
Application of the K-Nearest Neighbor Algorithm for Performance Classification Based on Students' Academic Performance and Lifestyle Zaehol Fatah
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10315

Abstract

Conventional student performance evaluation mechanisms often fail to detect a decline in performance in a timely manner because they overlook non-academic factors. This study aims to build a comprehensive student performance classification model by simultaneously integrating academic performance and lifestyle patterns using the k-Nearest Neighbor (k-NN) algorithm. The research method utilizes 1,000 student datasets from Kaggle, which were divided using the Stratified Split method into 80% training data and 20% test data. Simulations were run using RapidMiner Studio software with the Euclidean Distance metric and varying k values of 3, 5, and 7. Experimental results show the model achieved a peak global accuracy of 97% at the optimal k value of 5, where the model successfully classified 194 out of 200 test data samples correctly. Lifestyle attributes independently influence students’ academic performance, with the number of study hours per day being the most significant predictor (47.94%). Sensitivity analysis confirms that k=5 is the optimal value to avoid data noise at k=3 (94.50%) and oversmoothing at k=7. In conclusion, this k-NN-based model is highly suitable for application as an early warning system for schools to provide proactive and personalized pedagogical interventions.
Performance Evaluation of Carbon Block Electrodes in a Dual-Chamber Microbial Fuel Cell Using Rice Paddy Sludge for Bioelectricity Generation Septiana Ambarwati; Henny Parida Hutapea; Ameilia Wahyu Banuwati; Sri Jangkung Laksono Sukardi
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10319

Abstract

Microbial Fuel Cell (MFC) are a promising technology for converting organic biomass into renewable electricity through the activity of electroactive microorganisms. Rice paddy sludge is an abundant biomass resource containing organic matter and naturally occurring electroactive microorganisms, making it a potential substrate for sustainable bioelectricity generation. This study developed a dual-chamber MFC using carbon block electrodes and rice paddy sludge as both the substrate and microbial inoculum. After three days of operation, the system produced a maximum voltage of 0.22 V. Under an external resistance of 1000 Ω and a total anodic surface area of 0.02 m², the calculated power output and power density were 0.0484 mW and 2.42 mW/m², respectively. These findings demonstrate that rice paddy sludge can generate bioelectricity in MFC systems. Although the power density was lower than that reported for modified electrodes, carbon block electrodes provide advantages such as low cost, availability, and durability, highlighting their potential for sustainable MFC applications.
Classification of Hotel Maintenance Levels Using Principal Component Analysis and Support Vector Machine Bagus Gilang Pratama; Sely Novita Sari; Rizal Maulana; Zainul Arifin; Annisa Fauziah
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10360

Abstract

Hotel building maintenance in tourism-driven regions such as the Special Region of Yogyakarta is essential for maintaining service quality and building reliability. However, conventional assessment methods often depend on subjective expert judgment, which may be time-consuming and inconsistent. This study evaluates the performance of Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) to classify hotel building maintenance levels in Yogyakarta into five categories. The dataset includes 175 samples and 11 variables covering architectural, structural, mechanical, electrical, outdoor space, and housekeeping aspects, with naturally imbalanced class distribution. Data were normalized using MinMaxScaler and reduced to nine principal components, explaining 93.41% of cumulative variance. Three SVM kernels polynomial, radial basis function (RBF), and sigmoid were tested using a 70:30 training–testing split with default scikit-learn hyperparameters. The sigmoid kernel produced the best performance, achieving 90.57% accuracy, 91.32% precision, 90.57% recall, and 90.53% F1-score, outperforming RBF and polynomial kernels. Stratified 5-Fold cross-validation showed an average accuracy of 81.71% ± 9.66%. The results indicate that PCA-SVM with a sigmoid kernel is effective for automated hotel building maintenance classification.
Production Layout Optimization to Reduce Transportation Waste and Improve Process Cycle Efficiency in Passenger Seat Assembly Amrin Amrin; Feby Gusti Dendra; Ahlandika Wirasatria
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10366

Abstract

Passenger seat assembly is a critical process in bus manufacturing because it directly affects production completion and delivery performance. This study aimed to optimize the production layout to reduce transportation waste and improve Process Cycle Efficiency (PCE) in the passenger seat assembly process of a bus manufacturing company. A quantitative case study was conducted using data collected through direct observation, time studies, interviews, and questionnaires. The analysis integrated Value Stream Mapping (VSM), Waste Assessment Model (WAM), Value Stream Analysis Tools (VALSAT), and Process Activity Mapping (PAM) to identify dominant waste and develop layout improvement strategies. The results showed that transportation waste was the dominant waste, accounting for 18.37% of the total waste score. The proposed layout reduced the number of activities from 36 to 32, decreased transportation time from 72.1 to 14.8 minutes, and eliminated all non-value-added activities. Consequently, lead time decreased from 543.03 to 456.67 minutes, while Process Cycle Efficiency increased from 51.64% to 61.88%. These findings demonstrate that lean-based production layout optimization effectively reduces transportation waste and improves operational efficiency in make-to-order bus manufacturing.
Procedural Controls Associated with OSH Culture among Heavy Equipment and Lifting Workers at PT XYZ, Balikpapan Yifrans Sastra Wiguna; Anis Rohmana Malik; Aulia Rahma
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10374

Abstract

Occupational safety and health (OSH) culture is essential in heavy-equipment and lifting services because safe performance depends on consistent procedural control, communication, and worker participation. This study examined factors associated with OSH culture among all 55 active workers of PT XYZ, representing operational units involved in transportation, lifting, and heavy-equipment services in PT XYZ. A quantitative cross-sectional design was applied using a structured four-point Likert-scale questionnaire. Data were analyzed through descriptive statistics, chi-square tests, and exploratory logistic regression. Overall, 63.6% of workers were categorized as having a good OSH culture. Age, gender, education level, and work tenure were not significantly associated with OSH culture, whereas work unit, worker involvement, OSH knowledge, PPE compliance, management commitment, OSH rules and procedures, leadership, teamwork, and work environment showed significant bivariate associations. In the final regression model, OSH rules and procedures showed the strongest association with good OSH culture (p < 0.001; OR = 136.00; 95% CI = 14.05–1,315.98). Because the model showed sparse-data instability, this result is interpreted as an exploratory association rather than causal proof. The findings support procedural strengthening through SOP monitoring, practical lifting training, direct supervision, discipline, and standardized crane communication.
Determination of Inventory Policy to Address Raw Material Shortages Using Lagrange Multiplier and Capacity Restriction at Coffee Shop X Elieolsa Wanmilsen C. Putri; Fransiska Hernina Puspitasari; Ika Murti Kristiyani; V. Reza Bayu Kurniawan
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10460

Abstract

Managing inventory well in a business is extremely important. It is not uncommon that if this is neglected, it will result in lost sales. A coffee shop in Yogyakarta experienced a similar situation. Some coffee menu items have to be unavailable for order due to a shortage of Arabica and Robusta coffee beans. In addition, this café also faces budget constraints for purchasing coffee beans and limited storage space. Therefore, the Lagrange Multiplier method, supplemented with capacity constraints, is used to solve this problem. Based on the calculation results, the optimum order quantity for each type of coffee bean is 3 kg, which does not exceed the maximum storage capacity of 10 kg and is still within the budget of Rp700,000.00. Additionally, reorder point (ROP) and safety stock (SS) calculations were also performed. The SS value was obtained as 0.32 kg (Arabica) and 0.07 kg (Robusta), while the ROP value was obtained as 0.46 kg (Arabica) and 0.13 kg (Robusta). After conducting the inventory simulation, it was found that there were no stockouts of coffee beans, and the inventory costs incurred were Rp3,942,710.55 for Arabica coffee beans and Rp1,097,546.31 for Robusta coffee beans.

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