Journal of Engineering and Technological Sciences

Modification of Polylactic Acid with Eggshell Filler as Biodegradable Composite

Nonni Soraya Sambudi — 2025-08-29

In this study, polylactic acid (PLA) was proposed as a material for producing bioplastics due to the desirable properties, including high processability, low cost, and good transparency. However, the degradation of PLA as a bioplastic remains a significant challenge. To address this problem, PLA was modified by blending with a bio-filler, in the form of calcium carbonate (CaCO3) prepared from eggshell powder (ESP). The CaCO3 filler in form of ESP was incorporated into PLA using the solution casting method. The parameter being varied was the ESP loading, ranging from 0 wt% to 20 wt%. The results showed that the inclusion of eggshell-derived filler in PLA increased tensile strength and Young’s modulus by 10%, from 24.12 to 26.61 MPa, and 162%, from 3022 to 7932 MPa, respectively. The degradability of composite was done through burial test, which the weight of PLA/ESP-20wt% was decreased by 11.11 wt% after 3 weeks. This suggests that eggshell waste has the potential to serve as an effective filler to improve the mechanical strength and degradation of PLA.

Real-time Assessment of ECG Classification based on Time-series Data and Other Types of Features

Thanh-Luan Tran — 2025-08-29

Cardiovascular diseases are the leading cause of mortality worldwide. An increasing number of studies have applied artificial intelligence (AI) to identify anomalies and classify electrocardiograms (ECGs), supporting early detection and diagnosis. This study proposes and evaluates the classification of ECG signals based on time-series data and features extracted via fast Fourier transform (FFT) and discrete cosine transform (DCT), implemented on resource-limited microcontroller units (MCUs) for selected AI models. Two models, the artificial neural network (ANN) and the convolutional neural network (CNN), were proposed for classifying five common ECG labels. These models were trained and tested with three types of input data: time-series data, FFT features, and DCT features, sourced from an available database. After training, the optimized models were quantized to assess their accuracy before being deployed in real-time to measure inference time on the ESP32 MCU. Before quantization, the ANN model achieved the highest accuracy with both DCT and time-series inputs (98.0%); meanwhile, the CNN model performed best with time-series input (97.0%). After quantization, the ANN maintained the highest accuracy with time-series input (97.1%), followed by the ANN with DCT at 95.6%. CNN models remained stable, with post-quantization accuracy of 95.8% for time-series input, 94.9% for DCT, and 90.0% for FFT. In contrast, ANN with FFT input showed a significant drop to 65.6%.

Design of Solar-powered Automatic Shrimp Feeder based on the Internet of Things Technology

Terapong Boonraksa — 2025-08-29

This paper presents the design and implementation of a solar-powered automatic shrimp feeder utilizing IoT technology to enhance efficiency and precision in aquaculture. The core of the system is the DC motor operated feeder mechanism, and is capable of user defined customization like feeding scheduling, speed control, real time system monitoring etc. from distance through IoT integration via a smartphone. The motor speed can be adjusted to 1000, 1500, and 2000 rpm as per the requirement. Additionally, system can also monitor other water quality parameters like water temperature, pH and DO values, which are displayed via a smartphone. The experimental results confirm that the feeder perform as expected, it is capable of dispensing 5 kg of feed within 54 second, feeding up to 4.5 m depth when operating at 2000 rpm while consuming just 178.92 W power. The feeder is designed to autonomously determine its operating schedule, with users having the flexibility to adjust the timing at any point through the mobile application. This automation ensures consistent and efficient feeding aligned with aquaculture needs. Furthermore, the study includes a comprehensive cost analysis. The use of solar power and automation led to an annual cost reduction of 83.18% compared to manual labor. Over a ten-year period, the system achieved a total cost savings of 96.78%, amounting to 1,460,000 baht. Beyond enhancing feeding efficiency, the integration of IoT in shrimp farming substantially lowers labor expenses, contributing to a more sustainable and economically viable farming model.

Synthesis of Geopolymer from Ferronickel Aluminosilicate Waste

Tjokorde Walmiki Samadhi — 2025-07-25

The nickel industry in Indonesia generates massive volumes of ferronickel slag that may harm the environment. This research evaluates the feasibility of utilizing coal fly ash and slag from a ferronickel smelter in Obi Island in Indonesia to synthesize geopolymer, an environmentally friendly cementitious material. Compressive strength of geopolymer mortars was measured as a function of slag particle size (coarse and fine), fly ash mass fraction in the dry aluminosilicate binder precursor blends (0.4 and 0.8), and thermal curing period (24 and 48 hours). Mortar specimens were produced by mixing ash and slag with activator solution and sand. The activator solution contained Na₂SiO₃ and NaOH at a mass ratio of 2:1. Solid reactants to activator solution mass ratio was 3.33. After heat curing, specimens were held in ambient conditions to an age of 7 days. The compressive strength of the mortars was in the 2.1-24.8 MPa range. Geopolymer mortars were able to comply to Indonesian SNI 15-2049-2004 or US ASTM C1329-05 standards for Portland cement. FTIR and XRD characterizations confirmed the conversion of fly ash and slag into amorphous geopolymers at near ambient temperature. Finer slag particle size increased reactivity, ultimately producing higher compressive strength.

Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks

Wiwit Suprihatiningsih — 2025-07-25

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.

Design and Application of a Kirigami-Based Soft Robotic Gripper using Finite Element Analysis

Efrem Olivio Gomes — 2025-07-25

The demand for adaptable and efficient soft robotic grippers has grown due to their potential applications in industries such as food handling, manufacturing, and logistics. This study explores a Kirigami-based soft robotic gripper, designed to handle a wide range of objects with minimal risk of damage. The gripper utilizes a Kirigami-inspired structure combined with Liquid Silicone Rubber (LSR CN-251), chosen for its flexibility, durability, and food-safe properties. Finite element analysis was conducted to analyze the gripper’s mechanical performance under tensile forces ranging from 0.1 N to 4.3 N, focusing on stress distribution and deformation. Experimental validation was performed to verify the simulated results and assess the gripper’s performance in real-world scenarios. The simulations revealed predictable stress distribution and controlled deformation, with experimental tests demonstrating the gripper’s successful handling of delicate items, irregular objects, heavier item, and others. The Kirigami structure’s passive force distribution enabled a secure yet gentle grip, minimizing the risk of damage. The gripper’s adaptability, flexibility, and lightweight construction were confirmed in these tests. Manufactured from food-safe LSR, the gripper presents a cost-effective and efficient alternative to traditional pneumatic or jamming-based grippers. Limitations in the experimental setup, such as the restricted range of the uArm Swift Pro, were noted, and future research should explore dynamic performance under real-world conditions, enhance the range of motion, and integrate sensory feedback for improved precision.

Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review

St Mardiana — 2025-07-25

Zeolites are widely applied in various fields owing to their outstanding properties. However, our understanding on the nature of zeolite synthesis is not completed yet due to its high dimensional parameters. Machine learning has the ability to unravel fundamental relationships between complex parameters and predict the possible outcomes; thus, it can potentially reveal the nature of zeolite synthesis. This mini review highlights the current use of machine learning to comprehend the black box issue in zeolite synthesis. Conventional syntheses of zeolite were also elaborated to showcase the gap between traditional methods and machine learning approaches in zeolite synthesis. The future prospects of machine learning applications in zeolite synthesis are also discussed. This mini-review may bring crucial insights on the zeolite synthesis process.

Development of Solar-Powered Automatic Pest Trap for Rice Cultivation Plants in Indonesia

Mareli Telaumbanua — 2025-08-14

Rice is the main commodity processed into rice, as a staple food for the people of Indonesia. Pests and diseases can cause decreased production to crop failure. The method used by farmers is pest control by spraying chemical pesticides. However, chemical pesticides have serious impacts on plants, increased immunity for pests, increased chemical residues in crops that threaten human health, and environmental pollution. The objective of this study is to design an integrated high-tech trap that is effective, efficient, cost-effective, durable, safe, environmentally friendly (zero-emission), and low in operational costs, with the ultimate goal of enhancing farmers' income. This automatic pest trap embeds a microcontroller, infrared sensor, fan and solar panel. Observations were made on 4 tools, namely complete lures, yellow LED, lights and pheromones. Based on the observations, Scotinophara coarctata and Nilaparvata lugens were identified as the most commonly trapped insect pests in rice fields. The insect's fall speed was fast, namely 1 minute 4 seconds with good stability. Insect readings came in higher on the complete lure due to the combination of three lures, each of which has its own insect attraction. The effectiveness of insect capture on the complete automatic insect trap was the highest at 84.47%.

Financing Model for Construction and Demolition Waste in Indonesia

Fajar Susilowati — 2025-08-14

Construction waste poses a significant environmental and economic challenge in Indonesia’s rapidly expanding construction sector. This research develops a financing model for managing construction waste throughout the project life cycle, emphasizing the integration of cost components and waste management strategies. Data were collected through surveys, structured interviews, and observations from 80 construction projects across Indonesia. The analysis revealed that while reinforcement, bricks, and split stone have high recycling potential, actual reuse remains limited due to poor planning and insufficient infrastructure. Seven financing components were identified: material loss, production/management, sorting, collection, transportation, recycling, and dumping. Notably, material loss accounts for the largest cost share—up to 10% of project value—while recycling and dumping costs are underfunded at 0.01%–0.5%. A cost-based model was developed to simulate waste-related expenses, ranging from 0.39% to 20.5% of overall project costs. The research also highlights the design stage as a critical leverage point for maximizing waste reduction. By aligning financial planning with life cycle stages, this research provides practical guidance for stakeholders and supports Indonesia’s transition to a circular construction economy through better budgeting, policy development, and waste strategy implementation.