bluuebunny/crossref_metadata_2025 huggingface dataset:
METALLOPHYTES IN THE LJUBIJA MINE AREA AND THEIR POTENTIAL IN PHYTOREMEDIATION
Authors:   University of Banja Luka, Faculty of Natural Sciences and Mathematics, Banja Luka, Bosnia and Herzegovina, MAKSIMOVIĆ Tanja , MIHAJLOVIĆ Dijana ,   Faculty of Agriculture, University of Banja Luka, Bosnia and Herzegovina, CVIJETINOVIĆ Mile ,   University of Banja Luka, Faculty of Natural Sciences and Mathematics, Banja Luka, Bosnia and Herzegovina, LUBARDA Biljana ,   University of Banja Luka, Faculty of Natural Sciences and Mathematics, Banja Luka, Bosnia and Herzegovina;, BOSANČIĆ Borut , HASANAGIĆ Dino
Published: February 2025

This study focused on a landfill flotation tailing pond of the “Ljubija” iron ore mine, which is habitat covered by sparse vegetation consisted by specific plant groups. The following plant species were selected: Rumex hydrolapathum, Silene vulgaris, Tussilago farfara, Arabis hirsuta, Acer pseudoplatanus and Populus alba. The plants were collected and processed for the determination of the content of toxic metals (Fe, Zn, Cu, Mn, Ni, and Cr) aiming to evaluate their phytoremediation potential. At the same time, the content of toxic metals in the soil was analyzed to determine the degree of loading and the possibility of cleaning the area with phytoremediation techniques. The sequence of bioaccumulation in the tissue of all examined species decreased in the order Fe > Mn > Zn > Cu > Ni > Cr. The values of phytoaccumulation factors BCF and BAC for all investigated elements were below 1, except for Fe and Mn, which indicates a medium to poor accumulation potential of the investigated species. An exception was recorded in the plant A. hirsuta where BCF > 1 for Cr. The translocation coefficient (TA > 1) for Cu and Mn was determined for R. hydrolapathum, for Cu and Zn in A. pseudoplatanus, for Cu, Zn, and Ni in T. farfara, and Zn in A. hirsuta, which classifies these species as effective in techniques of phytostabilization of the researched area. These data are the first research in the area of the Iron Ore Mine Ljubija, which is of great interest to determine the level of contamination of soil and plants with toxic metals, and to remediate the same with phytoremediation techniques.
DAMMING EFFECTS ON RIVER SILICON BIOGEOCHEMICAL CYCLE IN A SOUTH MEDITERRANEAN BASIN (SEYBOUSE, NORTHEAST ALGERIA)
Authors:   Faculty of Natural and Life Sciences, Abbes Laghrour University, Khenchela, Algeria, ZIOUCH Omar Ramzi ,   Laboratory of Bio-Composites, Bio-Molecules, Surface Treatment and Water Treatment. Abbes Laghrour University, Khenchela, Algeria, DAIFALLAH Tarek ,   Faculty of Natural and Life Sciences, Abbes Laghrour University, Khenchela, Algeria;    Laboratory of Biotechnology, Water, Environment, and Health, Abbes Laghrour University, Khenchela, Algeria;, ALLEGUE Nour El Houda ,   Faculty of Natural and Life Sciences, Abbes Laghrour University, Khenchela, Algeria;, LASKRI Hadjer ,   Laboratory of Biogeochemical and Ecological Analyses of Aquatic Environments, Department of Marine Science, Badji Mokhtar University, Annaba, PO Box 12, Sidi Ammar 23000, Algeria;, DALI Naouel ,   Faculty of Natural and Life Sciences, Abbes Laghrour University, Khenchela, Algeria;        Laboratory of Biotechnology, Water, Environment, and Health, Abbes Laghrour University, Khenchela, Algeria;, ZEBSA Rabah ,   Laboratory of Biology, Water and Environment, University of 8 May 1945 Guelma, BP 4010 Guelma 24000, Algeria;, BENSAKHRI Zinette ,   Laboratory of Biology, Water and Environment, University of 8 May 1945 Guelma, BP 4010 Guelma 24000, Algeria;
Published: February 2025

Silicon (Si) is the most abundant element in the earth's crust after oxygen; it is found in aquatic environments as orthosilicic acid. This study concerned for the first time the distribution and flux of dissolved silicates (SiO4) in strategic sites (Charef dam, Bouhamdane dam, and estuary) from the upstream of the Seybouse basin to its mouth to understand silicon biogeochemical transformations under dams and estuarine effects. Surface water sampling was taken and analyzed in the dry (September 2016) and wet (April 2017) seasons. In parallel to sampling dates, physical parameters, and water discharge data were recorded. The highest SiO4 levels were recorded upstream and weakened towards the mouth. It was reduced between 32% and 54% for the Charef and Bouhamdane dams respectively, while the retention rate of the estuary was on average 44%. In terms of flux, the Charef and Bouhamdane dams produced 2 t/yr and 17 t/yr respectively, whereas the estuary produced only 54 t/yr to the coastal zone. Dams and estuarine part play a crucial role in changes in the biogeochemical silicon cycle and are expected to have severe impacts on the river system and the receiving coastline functioning.
THE INFLUENCE OF PROJECT MANAGEMENT KNOWLEDGE ON THE SUCCESSFUL CONSTRUCTION IMPLEMENTATION
Authors: Giovan M Fiecky Zahrandy , A.A BGS. Dinariyana Dwi Putranta
Published: January 2025

Current scientific developments in project management can be compared with other fields of management science. One of the important points in scientific analysis of project management is the study of the level of success of a project. This paper tries to examine the level of project success based on aspects of project management knowledge. The study was carried out at a company that was working on the construction of a building. The 14 aspects of project management knowledge were developed to see how each aspect contributes to the success of a project. 70 respondents who were involved in construction projects were the objects of this research. PLS SEM is used as an analytical tool to see the contribution of project knowledge aspects to project success. It was concluded that the 3 aspects that contributed most to project success were Project Financial Management, Project Claims Management and Project Communication Management.
Path Planning for Mobile Robots on Dynamic Environmental Obstacles Using PSO Optimization
Authors: Fahmizal Fahmizal , Danarastri Innes , Arrofiq Muhammad , Maghfiroh Hari , Probo Santoso Henry , Anugrah Pinto , Molla Atinkut
Published: January 2025

The increasing integration of mobile robots in various industries necessitates efficient navigation strategies amidst dynamic environments. Path planning plays a crucial role in guiding mobile robots from their starting points to target destinations, contributing to automation and enhancing human-robot collaboration. This study focuses on devising a tailored path-planning approach for a fleet of mobile robots to navigate through dynamic obstacles and reach designated trajectories efficiently. Leveraging particle swarm optimization (PSO), our methodology optimizes the path while considering real-time environmental changes. We present a simulation-based implementation of the algorithm, where each robot maintains position, velocity, cost, and personal best information to converge towards the global optimal solution. Different obstacles consist of circles, squares, rectangles, and triangles with various colors and five handle-points used. Our findings demonstrate that PSO achieves a global best cost of 5.1017, indicative of the most efficient path, minimizing overall distance traveled.
Content-Based Filtering in Recommendation Systems Culinary Tourism Based on Twitter (X) Using Bidirectional Gated Recurrent Unit (Bi-GRU)
Authors: Faadhilah Adhyasta Naufal , Setiawan Erwin Budi
Published: January 2025

To address the challenge of information overload in the rapidly expanding culinary sector, a recommendation system using Content-Based Filtering (CBF) and the Bidirectional Gated Recurrent Unit (Bi-GRU) algorithm was developed. This system can help users to suggest culinary options based on user profiles and preferences. Twitter (X) is frequently used to gather culinary reviews in Bandung, forming the foundation for developing recommendation systems. This research contributes to integrating CBF and Bi-GRU to enhance the relevance of culinary recommendations. The system uses Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for item matching. Research adapting CBF and Bi-GRU methods specifically for culinary recommendations, especially in Bandung, remains limited. This study focuses on evaluating the performance of a culinary recommendation system. Data collected from Twitter (X) and PergiKuliner includes 2,645 reviews from 44 Twitter (X) accounts and on 200 culinary places. The culinary recommendation model, using CBF with TF-IDF and Cosine Similarity, achieved a Mean Absolute Error (MAE) of 0.254 and Root Mean Square Error (RMSE) of 0.425, indicating high accuracy in rating predictions compared to previous studies. From the experiments conducted, the third experiment using Bi-GRU, SMOTE, and the Nadam algorithm showed the best improvement with a learning rate of 0.014563484775012459, achieving an accuracy of 86.8%, precision of 86.3%, recall of 85.2%, and an F1-Score of 85.5%, with a 16.2% increase in accuracy from the baseline. Thus, this system effectively helps users with culinary recommendations in Bandung, providing good performance based on user preferences.
Addressing Overfitting in Dermatological Image Analysis with Bayesian Convolutional Neural Network
Authors: Zulfa Mulki Indana , Aryanto Andreas Sahir , Wijonarko Bintang Abelian , Ahmed Waleed Ali
Published: January 2025

VGG, ResNet, and DenseNet are popular convolutional neural network (CNN) designs for transfer learning (TL), aiding dermatological image processing, particularly in skin cancer categorization. These TL-CNN models build extensive neural network layers for effective image classification. However, their numerous layers can cause overfitting and demand substantial computational resources. The Bayesian CNN (BCNN) technique addresses TL-CNN overfitting by introducing uncertainty in model weights and predictions. Research contributions are (i) comparing BCNN with three TL-CNN architectures in dermatological image processing and (ii) examining BCNN ability to mitigate overfitting through weight perturbation and uncertainty during training. BCNN uses flipout layers to perturb weights during training, guided by the KL divergence and Binary Cross Entropy (BCE) loss function. The dataset used is the ISIC Challenge 2017, categorized as malignant and benign skin tumors. The simulation results show that three TL-CNN architectures, namely VGG-19, ResNet-101, and DenseNet-201, obtained training accuracies of 96.65%, 100%, and 97.70%, respectively. However, all three were only able to achieve a maximum validation accuracy of around 78%. In contrast, BCNN can produce training and validation accuracy of 81.30% and 80%, respectively. The difference in training and validation accuracy values produced by BCNN is only 1.3%. Meanwhile, the three TL-CNN architectures are trapped in an overfitting condition with a difference in training and validation values of around 20%. Therefore, BCNN is more reliable for dermatological image processing, especially for skin cancer images.
Control and Navigation of Differential Drive Mobile Robot with PID and Hector SLAM: Simulation and Implementation
Authors: Fahmizal Fahmizal , Pratikno Matthew Sebastian , Isnianto Hidayat Nur , Mayub Afrizal , Maghfiroh Hari , Anugrah Pinto
Published: January 2025

Navigation technology is essential in fields like transportation and logistics, where precise mapping and localization are critical. Simultaneous Localization and Mapping (SLAM) technologies, such as Hector SLAM, enable robots to map environments by detecting and predicting object locations using sensors like LiDAR. Unlike other SLAM methods, Hector SLAM operates without odometry, relying solely on LiDAR data to produce accurate maps. This study investigates the application of Hector SLAM in a differential drive mobile robot controlled via the Robot Operating System (ROS), with PID control managing the motor speeds. The research contribution is the integration of Hector SLAM with PID control to enhance mapping accuracy in environments without odometry data. The method involves testing the robot's mapping performance in an indoor environment, focusing on the impact of varying linear and angular velocities on the quality of the generated maps. The PID control was tuned to ensure stable speed values for the robot's differential drive motors. Results show that Hector SLAM, when combined with well-tuned PID control, generates highly accurate maps that closely match the actual environment dimensions, with minimal errors. Specifically, the mapping error was found to be within 0.10 meters, validating the effectiveness of this approach in non-odometric systems. In conclusion, the study demonstrates that Hector SLAM, supported by PID-controlled motor stability, is an effective solution for mapping in differential drive mobile robots, particularly in scenarios where odometry is unavailable.
A Comparative Study of Modern Activation Functions on Multi-Label CNNs to Predict Genres Based on Movie Posters
Authors: Al Wafi Ahmad Zein , Nugroho Anan
Published: January 2025

Categorization of images based on their visuals into various genres has a crucial role in the recommendation system. However, multilabel classification poses significant challenges due to the complexity of assigning multiple labels to each instance. This study contributes to the understanding of how activation functions influence the efficiency and accuracy of multilabel CNNs and provides practical insights for selecting appropriate functions in movie poster classification tasks. This investigation focused on identifying the activation function that provided the fastest convergence, highest accuracy, and lowest computational cost or training time. The results show that the Leaky ReLU activation function achieved the fastest convergence and highest training accuracy with an top accuracy of 99.7% and GELU demonstrated the highest validation accuracy at 91.5% across the training iteration. Softplus showed convergence characteristics at epoch 14 while other in 30. The computational cost analysis revealed that ReLU was computationally efficient with training time of 1896 seconds. Overall, the Leaky ReLU activation function is identified as the most effective for multilabel CNNs, balancing convergence speed, accuracy, and computational cost.
Image-Based Position Control for Three-Wheel Omni-Directional Robot
Authors: Sukamta Sri , Nugroho Anan , Subiyanto Subiyanto , Rezianto Rizal , Soambaton Muhammad Febrian , Ardiyanto Agus
Published: January 2025

Robotic technology continues to advance, but real-time position tracking of robot movements still faces challenges, especially in dynamic and irregular environments. Sensors often fail to maintain accuracy due to environmental disturbances. This study introduces an innovation using omnidirectional wheels on a three-wheeled robot and image-based position control to improve maneuverability and precision. This method utilizes an infrared camera mounted on the ceiling to detect the robot's position. Image processing algorithms are used to determine waypoints and direct the robot. Omni-directional wheels allow the robot to move freely in all directions, which is important for accurate navigation on complex trajectories. The robot was tested on the "X" and "Square" trajectories in a 1.8-meter x 1.8-meter room to rotate vertical, horizontal, and diagonal movements. The test results showed that on the "X" trajectory, the second movement had the most significant error with a Mean Absolute Error (MAE) of 134.96, while the third movement had the slightest error with an MAE of 52.49, with an average error of 91.36. The first and third movements in the “Square” trajectory showed more significant errors than the second and fourth movements, with MAE of 105.37 and 100.47, respectively. The second and fourth movements had MAE of 67.20 and 59.65, with an average error of 83.17. These results indicate that the image-based control system and omnidirectional wheels improve accuracy compared to conventional methods, which is important for robot navigation applications. Practical implications of this technology include potential applications in the robotics and automation industry. Future research should focus on developing more precise control algorithms and sensors to improve accuracy. Directions for future research include exploring more sophisticated image processing techniques and applying this technology to various industrial scenarios.
Optimization of Wind Farm Yaw Offset Angle using Online Genetic Algorithm with a Modified Elitism Strategy to Maximize Power Production
Authors: Kurniawan Kurniawan , Triwiyatno Aris , Setiawan Iwan
Published: January 2025

The wake interaction in a wind farm occurs when the front turbines block the flow of wind to the turbines behind them, causing a total power loss of approximately 10–25%. Wake interactions can be redirected to reduce bad impacts by optimizing the yaw offset angles. Optimization of the yaw offset angle can increase the total power of the wind farm by approximately 6–9%. However, the fluctuating wind flow angle in the environment causes the behavior of the wake interaction to change, making it difficult to optimize the yaw offset angles. Therefore, this study proposes an online genetic algorithm with a modified elitism strategy to overcome this problem. The contribution of this study is to improve the performance of the genetic algorithm by modifying the elitism strategy in order to optimize the yaw offset angle for each turbine adaptively to a wind farm operating in a dynamic environment. The optimal yaw offset angles are stored in the elite population for various wind flow angles and then reinserted into the search population in each generation according to the actual wind flow angles. A Gaussian-based analytical wake interaction model under a yawed condition developed by Shapiro is employed in this study to evaluate the total power of a wind farm. This study resulted in a convergence speed that was 3.8 times faster than the classical elitism strategy. At several wind flow angles of 270°, 315°, and 360°, an average power increase of 10.52% was obtained. This study shows that the modification of the elitism strategy can increase the convergence speed to adaptively track the optimal yaw offset angle at various wind flow angles, so that the average increase in wind farm power is 1.94% higher than in previous studies.
Gazebo Semar: An Android-based Farmer Education Platform for Agricultural Waste Management
Authors: Dharmawan Mahdaviqia , Sari Lusia Dara , Gunawan Jericho Pandita , Antriyandarti Ernoiz , Duong An
Published: January 2025

Agricultural waste and lack of knowledge about agricultural waste management is an environmental problem in Karanganyar Regency. Farmers in Karanganyar only handle agricultural waste, such as rice straws, husks, and corn stalks by burning them. Therefore, this study attempts to create innovation by educating farmers on properly treating agricultural waste. This study implemented mix method, namely application development and application evaluation by conducting survey. The Gazebo Semar application is built using a Kodular service begins with concept planning, interface design, features, and coding. Application evaluation were collected from a survey using a questionnaire of 120 farmers in Karanganyar Regency to evaluate the usability testing using USE (Usefulness, Satisfaction, and Ease to use) questionnaires and use the Likert scale for measurement. Gazebo Semar is a solution to provide information on agricultural waste types, waste management, and marketing of processed agricultural waste products. This application allows the users to easily access information about agricultural waste and Zero Waste without visiting multiple websites or blogs. The Gazebo Semar App has nine main features: Home Screen, Zero Waste, SDGs, Waste Source, Waste Classification, Waste Management, Gazebo Semar Store, Quiz, and About Us. Gazebo Semar provides a number of novelties in terms of substance and features, so that it is expected to have an impact on local farmers especially in the field of agricultural waste processing. The results show that the score of Usefulness, Satisfaction, and Ease of Use is above 71%, which means Gazebo Semar has provided the application that fits with the needs of farmers. The research contribution is to improve the mindset of farmers in processing agricultural waste into other forms that are more economically valuable. The significance of this study is to increase public knowledge about agricultural waste, zero waste, and waste management.
Dynamic Voltage Restorer for Mitigation of Voltage Sags Due to 3 Phase Motor Starts Based on Artificial Neural Networks
Authors: Sujito Sujito , Gumilar Langlang , Ridzki Imron , Syah Abdullah Iskandar , Falah Moh. Zainul
Published: January 2025

The Direct On-Line (DOL) process of starting a high-power 3-phase induction motor causes voltage sags in the distribution system that is connected to one point of common coupling (PCC). Voltage sag can cause damage and failure of sensitive loads. This article analyzes and proposes a simulation of voltage sag recovery using a Dynamic Voltage Restorer (DVR) based on an Artificial Neural Network (ANN). ANN is used as a detector and regulator of the voltage compensation value. In this study, a 3-phase induction motor will be connected to a sensitive load, and the DVR will be placed in series with a voltage source or PCC with a sensitive load. The simulation test system uses Simulink-Matlab R2016a with different configurations of induction motor parameters. Based on the simulation results show that the parameters of the 3-phase induction motor cause the depth and duration of the voltage sag. DVR with ANN control can detect and compensate for a voltage sag of 0.5 pu so that the voltage will be normal to 1 pu.
Slaging Analysis Based on Boiler Wall Temperature at PLTU Paiton Unit 3
Authors: Basri Muhammad Hasan , Tijaniyah Tijaniyah , Moyo Risto
Published: January 2025

Slagging on boiler surfaces in power plants is still a serious problem to reduce thermodynamic efficiency and threaten the operation of generating units. In this study, an innovative slagging diagnosis method based on analysis of vibration signals from tube panels is proposed to monitor slagging conditions. We analyze boiler wall temperature under various slagging conditions and air velocity at PLTU Paiton unit 3. Root Mean Square (RMS) in the time domain and decomposition in the frequency domain are used to extract features from vibration signals and predict slagging conditions without turning them off in the future. It was found that the RMS value of the tube panel signal decreased with increasing slagging weight, especially at low air speeds. The relative signal energy at a certain frequency will experience significant changes. In order to verify the experimental results on changes in the tube panel vibration signal features under various slagging conditions, we have successfully demonstrated our laboratory results through analysis of the tube panel vibration signal. This indicates that the vibration signal of the tube panel between the heater and the furnace wall panel can be collected and used for the diagnosis of slagging in the walls of a coal boiler. Our study is promising for the prediction of slagging and further mitigating the risk caused by slagging of exchange panels in boilers
Performance Evaluation of Sliding Mode Control (SMC) for DC Motor Speed Control
Authors: Saputra Dimas Dwika , Ma'arif Alfian , Maghfiroh Hari , Baballe Muhammad Ahmad , Tusset Angelo Marcelo , Sharkawy Abdel-Nasser , Majdoubi Rania
Published: January 2025

DC motor is an industrial motor that is practical for many applications and implementations. However, the speed of a DC motor often decreases because of the given load, thus causing it to be unstable and inconstant. In addition, parameter uncertainty is another issue of DC motors. The performance of the system will be impacted by the uncertainty. Therefore, in this study, SMC is used as speed control of the DC motor since it can handle non-linear plants. The performance also compares with PID to know the effectiveness of the SMC method in DC motor speed control. This study proposes a hardware design and implementation of DC motor angular speed control on Arduino UNO as an embedded control system. The performance comparison analysis results proved that both controllers could perform well. However, both controllers need further fine-tuning. There are still overshoot and steady-state errors for PID and SMC, respectively. In future work, the optimization method can be used to find the optimal gain or by combining it with an adaptive algorithm.
Electrical Tomography Sensor Modelling for Detection of Fuel Proportion in Vessel
Authors: Fahrizal Rian , Julianto Jaga Sobar , Maulana Alief , Alfanz Rocky , Ahendyarti Ceri , Rohmadi Rohmadi , Muttakin Imamul
Published: January 2025

Electrical capacitance volume tomography (ECVT) is a method for determining the volumetric distribution of dielectric permittivity using the capacitance measurement principle. The determination of volumetric distribution of dielectric permittivity is important to regulate a process in which quantity of materials is a decisive parameter such as in industrial setting or vehicle sub-system. ECVT is a relatively fast and non-radiating method to observe spatio-temporal phenomena inside a process, making it a valuable technique. Sensor modelling and image reconstruction study are essentials in designing a suitable imaging system based on measurements from plurality of electrodes providing higher degree of information being observed. This research conducts sensor modelling with varying fuel objects in the interior of a cylindrical vessel. The capacitance value was simulated between a combination of eight electrodes mounted encapsulating the tube. Each measured electrode was given an excitation voltage as a source of an electrostatic field, which interacts with the object’s presence. The objects in this study are benzene, kerosene, and diesel fuel, along with reference dielectric values of water and air. Image reconstruction used the linear back projection (LBP) method. Matrix operations between sensor’s pre-defined sensitivity and capacitance values produce data that can be plotted into an image estimating the true distribution of objects. Capacitance values from modelling are proportional to the actual object’s permittivity. The reconstruction provides qualitative information on the proportion of fuel in the vessel based on the capacitance value. Images have distinct values according to the presence of different objects under investigation. The research contribution is a proof of concept in using capacitance tomography to detect different fuels inside an enclosed tank at modelling stage. In addition, this study serves as a guideline for implementing a non-invasive and non-intrusive system for determining proportions of materials of interests inside a certain setup.
Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine
Authors: Melky Franky , Sendari Siti , Elbaith Ilham Ari
Published: January 2025

During this time, weight point testing is still done manually using a jack until now it has begun to be replaced with hydraulic equipment namely Lift Table Hydraulic (LTH) which is a portable table with a hydraulic system equipped with sensors (Loadcell and LVDT), powerpack control panel, powerpack, relay module and solenoid valve to adjust the table height. This portable table is one component of the heavy point measurement equipment system used for mining and plantation vehicles such as tractors, buses, trucks which are required to have a safe structure in heavy road conditions with rough or uneven surfaces with slopes up to an angle of 15 ° to 20 °. This emphasized research contributes to more accurate testing. Based on these problems, this research was conducted using Support Vector Machine (SVM) for the optimization of heavy point position measurement. The objects used are minibuses with 1 and 19 passengers and buses with 29 and 36 passengers on the proportion of datasets (training: testing) of 80% and 20% using linier kernel. From the experimental results, the accuracy in the condition of 1 passenger is 94.7%; minibus 19 passengers 98%; bus 29 passengers 98.1% and bus 36 passengers 97.4%. The highest accuracy obtains on 29 passengers minibus.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction
Authors: Nikmah Tiara Lailatul , Jumanto Jumanto , Prasetiyo Budi , Fitriani Nina , Muslim Much Aziz
Published: January 2025

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Photovoltaic Generator Approach Model for Characteristic Estimation I-V
Authors: Suwarno Suwarno , Sadiatmi Rini , Dewi Aminah Asmara , Birje Herman
Published: January 2025

Modeling of the photovoltaic (PV) approach is generally described by nonlinear equations with solving the equation using the steps iteratively.  However, this proposed research discusses the monocrystal type PV module approach model to estimate the characteristics of a photovoltaic generator, because it has the advantage that it is good enough to operate in Indonesia. This approach model takes into account the relationship between power, energy, and current to obtain the performance characteristics of the PV generator. This PV generator approach model is compared with PV generator manufacturer data and analyzed to validate the proposed approach model. Approach model with simulation hope this helps to find out IV characteristics according to the data recorded on the PV module and can save time or reduce the time to measurement results. The simulation results obtained the amount of power, energy, and current, 200.475W, 133.65 W/m2, and 7.9 Amperes, respectively with a simulation time of about 1.5 milliseconds. In addition to the above results, a comparison between the current and the power of the PV modules under study is also given and it gives the result that at a certain current the power level will no longer increase, but the power will decrease drastically due to heat from the PV panel module, because the module has the ability to receive heat from outside.
Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework
Authors: Kusnadi Adhi , Arkeman Yandra , Syamsu Khaswar , Wijaya Sony Hartono
Published: January 2025

The demand for halal products in the Islamic context continues to be high, requiring adherence to halal and haram laws in consuming food and beverages. However, individuals face the challenge of distinguishing between haram meat and permissible halal meat. This study aims to answer these challenges by designing an expert system application within the ERP framework to increase the usability functionality of the system that can differentiate between beef, pork, or a mixture of both based on the physical characteristics of the meat. The aim is to determine halal products permissible for consumption by Muslims. The research methodology includes a data collection process that involves taking 30 meat samples from various sources, and the criteria used to classify the meat will be determined based on an analysis of the physical characteristics of the meat. System administrators use expert systems to ensure proper treatment of meat during administration processes, including separating halal beef from pork and implementing different inventory procedures. The Certainty Factor (CF) inference engine deals with uncertainty even though the expert system's accuracy level is relatively good with several rules. However, these results must be studied further because the plan relies on expert opinion. Therefore, it is necessary to set the correct CF value for accurate height classification. The CF inference engine facilitates reasoned conclusions in meat classification. Functional testing confirms the smooth running of the system, validating its reliability and performance. In addition, the expert system accuracy assessment produces a commendable accuracy rate of 90%. In addition, the expert system works powerfully on various meat samples, accurately classifying meat types with high precision. This study explicitly highlights the expert system's design for meat classification in determining halal products by using the Expert System Certainty Factor. In conclusion, this expert system provides an efficient and reliable approach to classifying meat and supports the production and consumption of Halal products according to Islamic principles.
Design of Automatic Organic Fertilizer Processing Tools by Utilizing the Internet of Things (IOT) as a Monitoring System
Authors: Rahmaniar Fadhila Desti , Endri Jon , Salamah Irma
Published: January 2025

This paper aims to design an automatic organic fertilizer processing tool by utilizing the Internet Of Things (IOT) as a monitoring system. The proposed tool system aims to make it easier to sort organic waste or inorganic waste which can then also automatically process organic waste into organic fertilizer. The method used is a literature survey conducted to solve relevant problems. At this stage, a data collection survey is carried out by utilizing previous research sources from books, journals, and the internet. We used this literature review to enable us to update the tool from previous research. The proposed system uses ESP 32 module as a microcontroller that can connect to WiFi. In addition, it has also been introduced in the Internet of Things (IOT) as an automatic monitoring system for organic fertilizer processing plants. Garbage download information is sent via the internet and returned to the Android application. However, in this study, one application, namely WhatsApp, was used. Garbage has long been a social problem, especially in big cities, polluting the environment, endangering public health, and reducing environmental aesthetics. Therefore, waste management is important to minimize the negative impact of waste. Finally, the research results obtained can be useful as technological development in the future of automatic organic waste processing equipment.
Hate Speech Detection Using Convolutional Neural Network and Gated Recurrent Unit with FastText Feature Expansion on Twitter
Authors: Wijaya Kevin Usmayadhy , Setiawan Erwin Budi
Published: January 2025

Twitter is a popular social media for sending text messages, but the tweets that can send are limited to 280 characters. Therefore, sending tweets is done in various ways, such as slang, abbreviations, or even reducing letters in words which can cause vocabulary mismatch so that the system considers words with the same meaning differently. Thus, using feature expansion to build a corpus of similarity can mitigate this problem. Two datasets constructed the similarity corpus: the Twitter dataset of 63,984 and the IndoNews dataset of 119,488. The research contribution is to combine deep learning and feature expansion with good performance. This study uses FastText as a feature expansion that focuses on word structure. Also, this study uses four deep learning methods: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and a combination of the two CNN-GRU, GRU-CNN classification with boolean representation as feature extraction. This study uses five scenarios to find the best result: best data split, n-grams, max feature, feature expansion, and dropout percentage. In the final model, CNN has the best performance with an accuracy of 88.79% and an increase of 0.97% from the baseline model, followed by GRU with an accuracy of 88.17% with an increase of 0.93%, CNN-GRU with an accuracy of 87.47% with an increase of 1.86%, and GRU-CNN with an accuracy of 87.55% with an increase of 1.32%. Based on the result of several scenarios, the use of feature expansion using FastText succeeded in avoiding vocabulary mismatch, proven by the highest increase in accuracy of the model than other scenarios. However, this study has a limitation is that the dataset is used in Indonesian.
The Impact of Vegetation on the Performance of Polycrystalline and Monocrystalline Silicon Photovoltaic Modules
Authors: Tanesab Julius , Malelak Monalisa , Beily Marthen , Helle Irvan
Published: January 2025

The performance of a photovoltaic (PV) module decreases with increasing temperature. An emergent method developed to reduce temperature rise is vegetation that refers to cultivating crops under the shade of PV modules. This study aims to investigate the impact of caisim (brassica chinensis var. parachinensis), a popular tropical vegetable, on the performance of two polycrystalline silicon (pc-Si) and two monocrystalline silicon (mc-Si) PV modules. Initially, electrical parameters, solar irradiation, and temperature of the four PV modules were examined without vegetation. Furthermore, the same management was repeated with a treatment of two PV modules (pc-Si 1 and mc-Si 1) were vegetated and the other two modules (pc-Si 2 and mc-Si 2) were designated as reference modules, left without vegetation. Results of the experiments carried out in clear sunny days and analyzed with a least squares method revealed that, for the modules of the same technology, the efficiency of pc-Si 1 (vegetated) was higher than pc-Si 2 (reference), whereas mc-Si 2 (reference) outperformed mc-Si 1 (vegetated). Test results on mc-Si technology indicated that there was no contribution of vegetation to lowering temperature of the vegetated PV module, thereby failing to improve its efficiency. This might be related to the design and material of the mc-Si modules which support conductive heat losses. The conduction effect seemed to be more dominant than the evapotranspiration impact which may be low due to the wind and the greater distance between the vegetation and the modules. The results of this research imply that it is necessary to consider the application of vegetation for pc-Si technology for the design and optimization of the performance of solar power plants in Kupang, Indonesia. This research contributes to shining a light on the intricate relationship between PV module performance and vegetation. In a broader scope, this study provides a motivation for future investigations regarding efforts to overcome land competition to produce energy and food.
Multimedia Forensic Analysis of TikTok Application Using National Institute of Justice (NIJ) Method
Authors: Fauzi Rachmad Nur , Anwar Nuril
Published: January 2025

The advancement of technology, especially in mobile devices like smartphones, has had a significant impact on human life, particularly during the COVID-19 pandemic, leading to the growth of online activities, especially on social media platforms like TikTok. TikTok is a highly popular social media platform, primarily known for its focus on short videos and images often accompanied by music. However, this has also opened up opportunities for misuse, including the spread of false information and defamation. To address this issue, this research utilizes mobile forensic analysis with Error Level Analysis (ELA) to collect digital evidence related to crimes on TikTok. This research contributes by applying digital forensic techniques, specifically Error Level Analysis (ELA), to detect image manipulation on TikTok. By using forensic methods, this research helps uncover digital crimes occurring on TikTok and provides essential insights to combat misuse and criminal activities on this social media platform. The research aims to collect digital evidence from TikTok on mobile devices using MOBILedit Forensic Express Pro and authenticate it with ELA through tools like FotoForensics and Forensically, as well as manual examination. This research follows the National Institute of Justice (NIJ) methodology with ten stages of mobile forensic investigation, including scenario creation, identification, collection, investigation, and analysis. The research yields manipulated digital evidence from TikTok, primarily concerning upload times. Error Level Analysis (ELA) is used to assess the authenticity of images, revealing signs of manipulation in digital evidence. The research's contribution is to produce or collect manipulated digital evidence from TikTok, primarily concerning upload times, and to apply the Error Level Analysis (ELA) approach or technique to assess the authenticity of images, uncovering signs of manipulation in digital evidence.
The Determinant of Indonesian Cocoa Export Growth to the Main Export Destination Countries
Authors: Nisa Dwi Putri Jeng Ivo Nurun , Antriyandarti Ernoiz
Published: January 2025

Indonesia remains the sixth largest cocoa producer globally, with exports predominantly comprising cocoa beans (60%).  As global cocoa demand and consumption increase, it is imperative that leading cocoa-producing countries, including Indonesia, capitalize on this trend. To facilitate informed future decisions, Indonesia must identify the key drivers of growth for its cocoa bean exports. This study analyses the factors influencing the growth of Indonesia's cocoa bean exports to five key export destinations, namely Malaysia, America, India, China, and the Netherlands, using panel data analysis. The study found that the growth of Indonesia's cocoa bean exports was significantly impacted by four variables, namely the export volume of cocoa beans, Indonesia's GDP, economic distance, and the population of export destination countries. On the other hand, the variables such as GDP of export destination countries, production, productivity, exchange rates, prices of world cocoa beans, harvested area, and domestic cocoa bean prices did not demonstrate any noteworthy impact on the exports. The development of sustainable cocoa bean exports holds great potential. If the Indonesian government wishes to improve cocoa bean production centers, a collaborative strategy is necessary, particularly among stakeholders to support the development of sustainable cocoa bean exports.
Design Blockchain Architecture for Population Data Management to Realize a Smart City in Cimahi, West Java, Indonesia
Authors: Nugroho Eddy Prasetyo , Afrianto Irawan , Piantari Erna , Anisyah Ani , Al Husaeni Dwi Novia , Bisulthon Ibrahim Danial , Jundurrahmaan Irham
Published: January 2025

Smart city as a concept of city development which integrates information and communication technology with the intention of optimizing city management becomes a major goal for Indonesia, especially through the movement towards 100 Smart Cities. However, population data management is crucial in achieving this for optimal planning and management. Personal data protection becomes a crucial challenge with the rapid population growth and mobility in cities. The need for a more reliable protection system is very necessary. This research proposes a blockchain architecture that not only manages digital identities but also population data. The focus is population administration in Cimahi City, West Java, with the hope of providing security, transparency, and a strong audit trail for all population data. The contribution of this research is to design a blockchain architecture specifically for population data management, meeting the needs of population administration in cities, especially the city of Cimahi. Through a blockchain architecture development approach, this research considers the diverse administrative needs of the population and applies a blockchain model that enables data security and integrity. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This research also shows that the use of blockchain technology specifically for population data management can be a reliable and innovative solution in ensuring the security and reliability of data important for smart city development.However, this research has limited access to central data, so the data obtained is still very limited. Therefore, further research is needed to follow up on these limitations. Apart from that, this research is also expected to provide knowledge and solutions in securing data, especially population data in government environments.
Software Design of Autocorrection Essays on the Website and Application
Authors: Pasaribu Novalanza Grecea , Alif Menara , Budiman Gelar , Akhyar Fityanul
Published: January 2025

Traditional essay assessment methods are often time-consuming and prone to subjectivity. This study proposes a novel Automated Essay Scoring (AES) system, "Essay Mathematic Auto Correction (Emath Toco)," featuring web and mobile app interfaces. Emath Toco leverages visual stimuli and deep learning algorithms like 1D CNN, NasNet Mobile, and GoogleNet to offer objective and efficient essay evaluation. Extensive testing on a 40/60 training/testing data split yielded accurate data classification, validating successful implementation on Flutter-built Android applications and a Firebase-powered web interface. User experience surveys revealed positive feedback on Emath Toco's ease of use, visually appealing interfaces, and effective data collection, confirming its user-friendliness. Emath Toco's innovative use of visual stimuli and deep learning algorithms significantly reduces subjectivity and improves the accuracy of essay evaluation. Emath toco is promising technology with the potential to revolutionize essay assessment and educational methodologies. The research contributes to the field of automated essay scoring in two key ways. First, by integrating visual stimuli as a novel approach, Emath Toco expands the range of factors considered in scoring, potentially leading to more comprehensive and efficient. Second, the successful implementation of the system on both web and mobile platforms demonstrates its flexibility and accessibility, offering educators a versatile tool regardless of technological limitations.