bluuebunny/crossref_metadata_2025 huggingface dataset:
Small Public Exponent Brings More: Improved Partial Key Exposure Attacks against RSA
Authors: Feng Yansong , Nitaj Abderrahmane , Pan Yanbin
Published: October 2024

Let (N,e) be a public key of the RSA cryptosystem, and d be the corresponding private key. In practice, we usually choose a small e for quick encryption. In this paper, we improve partial private key exposure attacks against RSA with a small public exponent e. The key idea is that under such a setting we can usually obtain more information about the prime factor of N and then by solving a univariate modular polynomial with Coppersmith's method, N can be factored in polynomial time. Compared to previous results, we reduce the number of d's leaked bits needed to mount the attack by log_2 (e) bits. Furthermore, our experiments show that for 1024-bit N, our attack can achieve the theoretical bound on a personal computer, which verified our attack.
Reinventing BrED: A Practical Construction
Authors: Majumder Avishek , Mukherjee Sayantan
Published: October 2024

Broadcast Encryption (BE) allows a sender to send an encrypted message to multiple receivers. In a typical broadcast encryption scenario, the broadcaster decides the set of users who can decrypt a particular ciphertext (denoted as the privileged set). Gritti et al. (IJIS'16) introduced a new primitive called Broadcast Encryption with Dealership (BrED), where the dealer decides the privileged set. A BrED scheme allows a dealer to buy content from the broadcaster and sell it to users. It provides better flexibility in managing a large user base. To date, quite a few different constructions of BrED schemes have been proposed by the research community.

We find that all existing BrED schemes are insecure under the existing security definitions. We demonstrate a concrete attack on all the existing schemes in the purview of the existing security definition. We also find that the security definitions proposed in the state-of-the-art BrED schemes do not capture the real world. We argue about the inadequacy of existing definitions and propose a new security definition that models the real world more closely. Finally, we propose a new BrED construction and prove it to be secure in our newly proposed security model.
An analysis of the Crossbred Algorithm for the MQ Problem
Authors: Vidal Damien , Delaplace Claire , Ionica Sorina
Published: October 2024

The Crossbred algorithm is currently the state-of-the-art method for solving overdetermined multivariate polynomial systems over

𝔽
2

. Since its publication in 2017, several record breaking implementations have been proposed and demonstrate the power of this hybrid approach. Despite these practical results, the complexity of this algorithm and the choice of optimal parameters for it are difficult open questions. In this paper, we prove a bivariate generating series for potentially admissible parameters of the Crossbred algorithm.
Non-interactive Private Multivariate Function Evaluation using Homomorphic Table Lookup
Authors: Li Ruixiao , Yamana Hayato
Published: October 2024

To address security issues in cloud computing, fully homomorphic encryption (FHE) enables a third party to evaluate functions using ciphertexts that do not leak information to the cloud server. The remaining problems of FHE include high computational costs and limited arithmetic operations, only evaluating additions and multiplications. Arbitrary functions can be evaluated using a precomputed lookup table (LUT), which is one of the solutions for those problems. Previous studies proposed LUT-enabled computation methods 1) with bit-wise FHE and 2) with word-wise FHE. The performance of LUT-enabled computation with bit-wise FHE drops quickly when evaluating BigNum functions because of the complexity being O(s·2^d·m), where m represents the number of inputs, d and s represent the bit lengths of the inputs and outputs, respectively. Thus, LUT-enabled computation with word-wise FHE, which handles a set of bits with one operation, has also been proposed; however, previous studies are limited in evaluating multivariate functions within two inputs and cannot speed up the evaluation when the domain size of the integer exceeds 2N, where N is the number of elements packed into a single ciphertext. In this study, we propose a non-interactive model, in which no decryption is required, to evaluate arbitrary multivariate functions using homomorphic table lookup with word-wise FHE. The proposed LUT-enabled computation method 1) decreases the complexity to O(2^d·m/l), where l is the element size of FHE packing; 2) extends the input and output domain sizes to evaluate multivariate functions over two inputs; and 3) adopts a multidimensional table for enabling multithreading to reduce latency. The experimental results demonstrate that evaluating a 10-bit two-input function and a 5-bit three-input function takes approximately 90.5 and 105.5 s with 16-thread, respectively. Our proposed method achieves 3.2x and 23.1x speedup to evaluate two-bit and three-bit 3-input functions compared with naive LUT-enabled computation with bit-wise FHE.
Side-Channel Linearization Attack on Unrolled Trivium Hardware
Authors: Kobayashi Soichiro , Ueno Rei , Todo Yosuke , Homma Naofumi
Published: October 2024

This paper presents a new side-channel attack (SCA) on unrolled implementations of stream ciphers, with a particular focus on Trivium. Most conventional SCAs predominantly concentrate on leakage of some first rounds prior to the sufficient diffusion of the secret key and initial vector (IV). However, recently, unrolled hardware implementation has become common and practical, which achieves higher throughput and energy efficiency compared to a round-based hardware. The applicability of conventional SCAs to such unrolled hardware is unclear because the leakage of the first rounds from unrolled hardware is hardly observed. In this paper, focusing on Trivium, we propose a novel SCA on unrolled stream cipher hardware, which can exploit leakage of rounds latter than 80, while existing SCAs exploited intermediate values earlier than 80 rounds. We first analyze the algebraic equations representing the intermediate values of these rounds and present the recursive restricted linear decomposition (RRLD) strategy. This approach uses correlation power analysis (CPA) to estimate the intermediate values of latter rounds. Furthermore, we present a chosen-IV strategy for a successful key recovery through linearization. We experimentally demonstrate that the proposed SCA achieves the key recovery of a 288-round unrolled Trivium hardware implementation using 360,000 traces. Finally, we evaluate the performance of unrolled Trivium hardware implementations to clarify the trade-off between performance and SCA (in)security. The proposed SCA requires 34.5 M traces for a key recovery of 384-round unrolled Trivium implementation and is not applicable to 576-round unrolled hardware.
Implicit Factorization with Shared Any Bits
Authors: Zhao Chunzhi , Zhang Junqi , Cao Jinzheng , Cheng Qingfeng , Wei Fushan
Published: October 2024

At PKC 2009, May and Ritzenhofen proposed the implicit factorization problem (IFP). They showed that it is undemanding to factor two h-bit RSA moduli N1=p1q1, N2=p2q2 where q1, q2 are both αh-bit, and p1, p2 share uh>2αh the least significant bits (LSBs). Subsequent works mainly focused on extending the IFP to the cases where p1, p2 share some of the most significant bits (MSBs) or the middle bits (MBs). In this paper, we propose a novel generalized IFP where p1 and p2 share an arbitrary number of bit blocks, with each block having a consistent displacement in its position between p1 and p2, and we solve it successfully based on Coppersmith’s method. Specifically, we generate a new set of shift polynomials to construct the lattice and optimize the structure of the lattice by introducing a new variable z=p1. We derive that we can factor the two moduli in polynomial time when u>2(n+1)α(1−α^1/(n+1)) with p1, p2 sharing n blocks. Further, no matter how many blocks are shared, we can theoretically factor the two moduli as long as u>2αln(1/α). In addition, we consider two other cases where the positions of the shared blocks are arbitrary or there are k>2 known moduli. Meanwhile, we provide the corresponding solutions for the two cases. Our work is verified by experiments.
Special Soundness in the Random Oracle Model
Authors: Wikström Douglas
Published: October 2024

We generalize the optimal knowledge extractor for constant-round special sound protocols presented by Wikström (2018) to a knowledge extractor for the corresponding non-interactive Fiat-Shamir proofs in the random oracle model and give an exact analysis of the extraction error and running time.

Relative the interactive case the extraction error and the running time are both asymptotically increased by a multiplicative factor equal to the number of oracle queries made by the prover.

Through carefully chosen notation, novel concepts, and a technical lemma, we effectively recast the extraction problem of the notoriously complex non-interactive case to the interactive case. Thus, our approach may be of independent interest.
Attacking trapdoors from matrix products
Authors: Decru Thomas , Fouotsa Tako , Frixons Paul , Gilchrist Valerie , Petit Christophe
Published: October 2024

Recently, Geraud-Stewart and Naccache proposed two trapdoors based on matrix products. In this paper, we answer the call for cryptanalysis. We explore how using the trace and determinant of a matrix can be used to attack their constructions. We fully break their first construction in a polynomial-time attack. We show an information leak in the second construction using characteristic polynomials, and provide two attacks that decrease the bit security by about half.
Key Rank Estimation Methods: Comparisons and Practical Considerations
Authors: Hay Rebecca , Oswald Elisabeth
Published: October 2024

New proposals for scalable key rank estimation methods have appeared recently, in particular the sampling based approach MCRank. The idea is that one can consistently estimate the key rank by sampling only a small portion of the key space as a “proxy”, leading to both an accurate and scalable approach, at least in comparison with another approach based on histograms. We show that the (earlier) GEEA algorithm is in fact a sampling based algorithm, and provide an in-depth comparison between GEEA (when adapted to produce rank estimates rather than guessing entropy estimates), GM bounds, MCRank and the currently most performant counting based rank estimation as implemented in the Labynkyr library. We find that although MCRank does live up to the promised accuracy and scalability for probability-based distinguishers, it fails to handle cases with unusual distinguisher distributions.

Furthermore, we put forward a novel proposal for a highly scalable key rank estimation method by introducing the notion of an “attacker budget”. Our proposal is based on the idea that, in particular for very long keys, the exact key rank is less important than the knowledge whether a key is within a certain bound. Thus our “budget approach” is based on efficiently checking if the result of an attack is such that the attacker's budget suffices for successful enumeration. Our budget approach scales linearly with the key size and thus enables security estimations even for post-quantum key lengths.
Matching radar signals and fingerprints with MPC
Authors: Mortensen Benjamin , Nordal Mathias , Strand Martin
Published: October 2024

Vessels can be recognised by their navigation radar due to the characteristics of the emitted radar signal. This is particularly useful if one wants to build situational awareness without revealing one's own presence. Most countries maintain databases of radar fingerprints but will not readily share these due to national security regulations. Sharing of such information will generally require some form of information exchange agreement.

However, all parties in a coalition benefit from correct identification. We use secure multiparty computation to match a radar signal measurement against secret databases and output plausible matches with their likelihoods. We also provide a demonstrator using MP-SPDZ.
Detection of Road Damages Using Machine Learning Methods with Data Collected from Various Geographies: A Study on Türkiye
Authors: Kavcı Ahmet Cihangir , Cansız Ömer Faruk
Published: October 2024

Road damage seriously affects the comfort and safety of drivers. The detection of road damage is of great importance not only for transportation safety, but also in terms of cost. The detection of road damage is critical for enabling early intervention and repair. In this study, the road damage detection performance of the YOLO (You Only Look Once) v8 algorithm was evaluated using datasets obtained from different geographies, including Czechia -Türkiye, India-Türkiye, USA-Türkiye, and Japan-Türkiye. The findings revealed both the capabilities of the algorithm in damage detection and the challenges it faced in distinguishing certain types of damage. For the creation of the Türkiye dataset, images of roads in the province of Hatay were recorded. These images were labeled using Microsoft's VoTT application. Comparisons and evaluations were made among the developed models. Among these models, the Japan-Türkiye model yielded the best results with a 0.55 mAP and 0.54 F1 score. The results of the models indicated that the appearance of damage varies according to the geographical location and the quality of road data. It was observed that data consisting of local images and uncertain damage types were important in training.
An Application of DC Motor Modelled Classical Sliding Mode Control with Moving Sliding Surface to Rotary Inverted Pendulum System
Authors: Aydın Muhammet , Yakut Oğuz
Published: October 2024

The rotary inverted pendulum system (RIP), which is highly favored in control applications, is examined in this work. By determining the coordinates of the Rip elements' centers of gravity, the system's total kinetic and potential energies were determined. The kinetic and potential energy expressions were used to generate the Lagrangian function. Expressions providing the system's equations of motion were discovered by taking the Lagrangian approach into consideration. The motor's equations, which will initiate the system, have also been considered. Through the use of state variables and a Matlab program, the system's pendulum angle was managed by using a moving sliding surface and the traditional sliding mode control technique. Based on the dynamics, the slip surface's slope was computed. The sliding surface's slope was computed based on the system's dynamics. The genetic algorithm was utilized to determine the ideal values for the coefficients employed in the control structure. The findings showed that the inaccuracy was roughly zero and that the pendulum angle took around 1.5 seconds to reach the intended reference value. Furthermore, it noted that the motor torque and current values are 12 Nm and 2.5 amps, respectively. The findings show that the motor values are reasonably similar to the values seen in real-world applications. Control in real-time applications won't be an issue if the motor is chosen based on these values.
Development of a Production Process for Biomass-Based Activated Carbon via Hydrothermal Carbonization Method
Authors: Katı Nida , Uçar Ferhat
Published: October 2024

This study presents a process development including detailed characterization of hydrochars and activated carbons produced by hydrothermal carbonization and chemical activation methods using apricot kernel shells (AKS). Initially, the AKS were processed by grinding, followed by subjecting them to hydrothermal carbonization at 240ºC for 24, 36, and 48 hours, resulting in three distinct hydrochars. Subsequently, these hydrochars were mixed with KOH for 3 hours and subjected to a carbonization process at 700°C for 1 hour to obtain activated carbons. Various characterization methods such as Brunauer-Emmett-Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS), X-Ray Diffraction (XRD) analysis, and Fourier-Transform Infrared spectroscopy (FTIR) measurements were employed to determine the properties of the activated carbons. The results obtained indicate that the duration of the hydrothermal reaction increases the carbon content and leads to the formation of porous structures. Particularly, the chemical activation process was found to be effective in pore formation, as evident in SEM images. In conclusion, this study provides a detailed description of the characteristic properties of hydrochars, and activated carbons derived from AKS.
Effect of Ground Diatomite on Early Strength of Self-Compacting Mortars
Authors: Karabulut Büşra , Şahin Yön Merve , Karataş Mehmet
Published: October 2024

Portland cement fabrication is a significant factor that increases the amount of carbon dioxide released into nature. For this reason, it is very important to use natural and waste materials with pozzolanic properties instead of portland cement. In this article, the usability of diatomite rock, a natural pozzolanic material that can be substituted with portland cement, in the manufacture of self-compacting mortar was studied. In the experimental study, prismatic specimens with dimensions of 40 × 40 × 160 mm were used to examine the impact of ground diatomite on the early age mechanical properties of self-compacting mortar; it was produced by replacing 0%, 5%, 10%, 15%, 20% of diatomite with portland cement, respectively. The slump-flow test to obtain self-compacting mortar was conducted according to the European Federation of Specialized Construction Chemicals and Concrete Systems guidance. Specimens prepared using 0%, 5%, 10%, 15% and 20% diatomite were cured in water at 23±2 ̊C temperature for 3 days. 3-day (early age) flexural and compressive strength worths were gained for the samples whose curing period was completed. As a result of this experimental study, it was specified that the highest strengths were in the series containing 5% diatomite, exceeding the reference samples. Additionally, it has been determined that mechanical strength decreases when the diatomite ratio in mixtures is more than 5%.
Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM
Authors: Praha Tohpatti Crippa , Widodo Widodo , Nugraheni Murien
Published: October 2024

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.
Enhancing Novice Developer Efficacy through UX Journey: Integrating User Experience and User Requirement to Develop Developer Skills
Authors: Kusuma Wahyu Andhyka , Jantan Azrul Hazri , Admodisastro Novia Indriaty , Norowi Noris Mohd
Published: October 2024

User experience and user requirements are two different approaches to software development. User requirements focus on meeting customer expectations and demands for software solutions, while user experience covers all aspects of software interaction with users. To increase the value of the software, the software must have usable and easy-to-use features with an attractive design or work environment that fits the user's behavior. Integrating software requirements and user experience can increase developer productivity by focusing on features that meet user requirements and expectations. This integration can also increase software development efficiency by addressing issues arising during development. This article addresses developers' challenges when addressing user needs and provides practical solutions widely accepted in industry and academia. Combining user experience and user needs into the UX Journey approach can increase developer productivity and confidence in software development. The design of the UX Journey is carried out by evaluating several existing design solution methods such as Design Thinking, IDEO, HPI, and Double Diamond to determine the existing conditions and needs for the problems faced. Then, by mapping the user, context, and domain, the model is obtained. appropriate. The proposed model comprises Discover, Explore, Test, and Listen activities. A trial was carried out on the respondents to test the method, and a feasibility test and an implementation schedule were obtained based on the statistical analysis of the initial user. It took 980-1500 minutes to complete the design solution. Focusing on features that align with user needs and improve problem-solving efficiency throughout development gives developers greater confidence in producing high-quality software.
Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem
Authors: Krisbiantoro Dwi , Waluyo Retno , Hasanah Uswatun , Pratama Irfan , Sarmini Sarmini
Published: October 2024

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.
Collaborative Intrusion Detection System with Snort Machine Learning Plugin
Authors: Priambodo Dimas Febriyan , Faizi Achmad Husein Noor , Rahmawati Fika Dwi , Sunaringtyas Septia Ulfa , Sidabutar Jeckson , Yulita Tiyas
Published: October 2024

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.
Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm
Authors: Binti Mohd Sabudin Umi Syahirah , Makhtar Siti Noormiza , Nor Elya Mohd , Muhamed Siti Anizah , Mohd Sani Fareisya Zulaikha , Kamarudin Nur Diyana
Published: October 2024

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.
UI/UX Redesign of SH-UPI App Using Design Thinking Framework
Authors: Fuada Syifaul , Setyowati Endah , Restyasari Nissa , Heong Yee Mei , Hasugian Leonardi Paris
Published: October 2024

The rise of smart home technology has significantly impacted people’s behavior and lifestyle, especially when controlling household electronics remotely. Smart homes have become increasingly commoditized in the last decades, resulting in several vendors offering various commercial products and their variants. Universitas Pendidikan Indonesia (UPI) has created the smart home platform “SH-UPI,” which includes the Smart LED Bulb RGB. The platform’s mobile app can be downloaded from the Play Store or the official website (www.sh-upi.com). The SH-UPI is one of Indonesia's leading smart homes that adopted Internet-of-Things (IoT). However, customers have reported issues with the app’s user interface (UI) and user experience (UX), prompting a redesign to address these concerns and stay competitive. This study employs the design thinking method of empathizing, defining, imagining, prototyping, and testing. The define stage to prototype stage resulted in a newly designed prototype for the SH-UPI app. Testing involved evaluating the prototype using metrics like the User Experience Questionnaire (UEQ) and System Usability Scale (SUS). The test results showed an increase in the UEQ parameter value, exceeding the initial average scores of -0.8 to 0.8. Additionally, there was an improvement compared to benchmark scores, which initially ranged from below average to poor but now range from above average to excellent. The SUS score also improved, rising from 59.75 (grade D) to 83.625 (grade A). The findings of this study can be a valuable resource for developing SH-UPI apps with significantly enhanced UI/UX.
The Implementation and Empirical Analysis of Android Learning Application toward Performance among Students Electronics Engineering Education
Authors: Hidayat Hendra , Harmanto Dani , Orji Chibueze Tobias , Anwar Muhammad
Published: October 2024

The integration of technology into the realm of education is experiencing exponential growth, and an ever-evolving selection of media formats is being created to facilitate teaching and learning in a more effective manner. The objective of this research endeavor is to ascertain the degree to which the implementation of learning applications influences the academic achievement of students enrolled in electrical engineering-related programs. To accomplish this objective, learning methodologies and self-directed learning must be implemented as variables that impact students' academic performance. To facilitate this inquiry, a total of 339 representative samples of participants were collected. The collected data were subjected to analysis using the SmartPLS 4.0 software and the Structural Equation Model (SEM) with partial least square (PLS) correction. Following a thorough analysis, it was determined that the data provided an accurate representation of the population. The findings of this study have practical implications-students who engage in self-directed learning and implement effective learning strategies will see a substantial improvement in their overall learning outcomes. Students desire easy access to a variety of educational resources and materials, according to the findings. This aspiration motivates the proliferation of mobile media devices. To facilitate students' access to a diverse range of learning strategies, instructors possess the ability to provide accommodation. These applications benefit students by streamlining the process of obtaining access to learning-supporting materials and resources
Participation of Citizen as Social Capital in LAPOR! Application in Indonesia
Authors: Febriani Rika , Luthfi Zaky Farid , Waldi Atri
Published: October 2024

Citizen engagement is crucial for the success of smart cities. In urban areas, communities as social capital has an interpersonal bond that unites people. Due to the complexity of urban issues like pollution, waste, and population density, citizens can monitor the government's work priorities to solve the problem. This article focuses on smart cities from a human-centered perspective, emphasizing the role of information technology. In Indonesia, citizens' rights awareness is facilitated by the National Public Service Complaint Management System (SP4N) or People's Aspiration and Online Complaints Service (LAPOR!), which acts as social capital. This article explores five key aspects of smart cities and smart citizenship: governance, citizen participation, infrastructure, public spaces, and urban livability. While ICT-driven approaches can swiftly transform cities, a community-based approach fosters long-term shifts. Meanwhile, limited citizen involvement contributes to ecosystem degradation in Indonesia and underscores the urgency and importance of citizen engagement in urban planning. In democratic societies, citizens hold power, and concepts of smart citizenship encompass civic intelligence and, at the same time, also value local knowledge. In Indonesia, LAPOR! reflects smart citizenship, enabling transparent reporting of aspirations and complaints. However, factors like limited access and education of citizens affect its use. This research used qualitative methods with content analysis methods. Qualitative content analysis is used to examine the textual content of websites.  Using LAPOR! SP4N in Indonesia to see the implementation of the concept of smart society and smart city. Participatory processes should contribute substantially to urban planning and governance. Information technology serves as a temporary solution for long-term urban planning issues. Broader concerns like public policy and participatory democracy must also be considered. In conclusion, this article explores the dynamic relationship between smart cities and smart citizenship, highlighting the importance of active citizen engagement and the potential of technology to empower communities for positive urban transformations.
Drone Kit-Python for Autonomous Quadcopter Navigation
Authors: Pulungan Ali Basrah , Putra Zaki Yuanda , Sidiqi Adam Rasyid , Hamdani Hamdani , Parigalan Kathleen E
Published: October 2024

Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.
Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression
Authors: Sadek Amjed Mohammed , Mohammed Lekaa Ali
Published: October 2024

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.
Improved Face Image Authentication Scheme based on Embedding in Adjacent Coefficients
Authors: Jawad Asmaa Hatem , Thabit Rasha , Zidan Khamis A.
Published: October 2024

Face image authentication (FIA) schemes have recently been developed using face detection and image watermarking technology. The research in this direction proved the presented schemes' efficiency in accurately detecting the manipulated face regions and recovering the original face region. Recovering the original face region is very important in practical applications. Still, it was at the cost of increasing the secret data that must be embedded in the face image. The increment in the secret data required a large embedding capacity, which was not available in some images. To overcome this limitation, an improved FIA scheme based on a new data embedding algorithm is presented in this paper. The suggested FIA scheme consists of two main algorithms applied at the sender and receiver sides, where both start by detecting the face region and dividing and classifying the image into blocks that belong to the face region or outside the face region. At the sender side, the secret data are generated from the face region and embedded in the blocks outside the face region using the suggested algorithm called Embedding in Adjacent Coefficients (EAC) for three subbands obtained after applying the Slantlet transform of the blocks. On the receiver side, the secret data are extracted from the blocks outside the face region using the suggested algorithm called Extraction from Adjacent Coefficients (ExAC). The extracted data is used to authenticate the face region and recover the original one when manipulations occur. The proposed FIA scheme obtained higher embedding capacity than previous ones, making it applicable to protect more face images that could not be protected using previous FIA schemes.
Fermented and Unfermented Cocoa Beans for Quality Identification Using Image Features
Authors: Basri Basri , Indrabayu Indrabayu , Achmad Andani , Areni Intan Sari
Published: October 2024

Fermented cocoa bean products are one of the high-quality requirements of the cocoa processing industry. On an automated industrial scale, early identification of cocoa bean quality is essential in the processing industry. This study aims to identify the condition of quality cocoa beans based on fermentation and non-fermentation characteristics. This study applies analysis based on static images taken using a camera with a distance variation of 5 cm, 10 cm, and 15 cm in both classes, with 500 image data each. The Feature extraction Approach uses the Oriented Gradient (HOG) method with a Support Vector Machine (SVM) classification technique. Image analysis of both object classes was also performed with a color change to show the dominance of the color pattern on the skin of the cocoa beans to be analyzed. The results showed that fermented cocoa beans show a color pattern and texture that tends to be darker and coarser than non-fermented cocoa beans. Computational results with performance analysis using Receiver Operating Characterisic (ROC) on both classes showed the results that the distance of 5 cm and 15 cm has 100% accuracy, but based on the best performance, comprehensively seen in terms of Precision, Recall, and F1-Score shows the best value is at a distance of 15 cm. The results of this research based on the literature review conducted have better achievements, thus enabling further research on the development of conveyor models with real-time video data for automation systems.
Analisis Kebijakan Local Currency Settlement Antara Indonesia Dan China
Authors: Nitasya Filza Diva , Sabila Laura , Felisia Savira
Published: October 2024

Tujuan dari penelitian ini adalah untuk membahas kebijakan Penyelesaian Mata Uang Lokal antara Indonesia dan China. Menggunakan metode analisis SWOT dengan mengumpulkan dan mengevaluasi data dari berbagai sumber publikasi akademik. Hubungan antara Indonesia dan China, keduanya memegang posisi penting dalam tatanan global. Penyelesaian Mata Uang Lokal (LCS) sendiri adalah penyelesaian transaksi bilateral yang dilakukan antara Indonesia dan China untuk dapat menggunakan mata uang lokal yang berlaku di masing-masing negara. Pelaksanaan LCS membawa banyak manfaat bagi Indonesia, termasuk peningkatan volume perdagangan antara Indonesia dan China, kemudahan pembayaran menggunakan mata uang lokal, dan penguatan hubungan bilateral antara Indonesia dalam perdagangan dan investasi.
 
The purpose of this research is to discuss the Local Currency Settlement policy between Indonesia and China. Using the SWOT analysis method by collecting and evaluating data from various sources of academic publications. The relationship between Indonesia and China, both of which hold significant positions in the global order. The Local Currency Settlement (LCS) itself is a bilateral transaction settlement conducted between Indonesia and China to be able to use local currencies that are valid in each country. The implementation of LCS brings many benefits to Indonesia, including increased trade volume between Indonesia and China, ease of payment using local currencies, and strengthening bilateral relations between Indonesia in trade and investment.
Pendidikan Politik Pemilih Muda Oleh Komisi Pemilihan Umum Kota Bandung Melalui Program Rumah Pintar Jelang Pemilihan Umum 2024
Authors: Hartono Syakira Syafiqya Tsabita Putri , Djuyandi Yusa
Published: October 2024

Abstrak
Pendidikan politik merupakan proses yang krusial dan melekat pada KPU, ditujukan untuk memastikan pemilih memahami esensi dilaksanakannya pemilu dan dampaknya bagi demokrasi. Salah satu segmentasi pemilih yang berperan besar bagi pemilu di Indonesia adalah pemilih muda. Pendidikan politik bagi pemilih muda di Kota Bandung dalam rangka mendorong kesadaran dan kepribadian politik dapat dilakukan dengan berbagai cara, salah satunya adalah dengan pemanfaatan Rumah Pintar Pemilu (RPP) KPU Kota Bandung. Penelitian ini menggunakan metode penelitian kualitatif dengan wawancara sebagai sumber data primer, studi literatur dan dokumentasi sebagai sumber data sekunder. Teknik wawancara menggunakan teknik purposive, informan berasal dari KPU Kota Bandung dan pemilih muda yang pernah berkunjung ke RPP KPU Kota Bandung. Teknik analisis data dilakukan dengan teknik Miles dan Huberman yaitu dengan proses reduksi, penyajian data, dan verifikasi. Proses triangulasi data juga dalam penelitian ini menggunakan triangulasi sumber data. Hasil dari penelitian menunjukkan pendidikan politik melalui RPP KPU Kota Bandung kepada pemilih muda dilakukan dengan pengadaan secara fisik dan juga digitalisasi materi-materi politik. Aktualisasinya, RPP juga menyediakan informasi sesuai kebutuhan pemilih muda. Proses pendidikan politik melalui RPP KPU Kota Bandung memberikan dampak yang cukup signifikan terhadap pemahaman, kesadaran, dan kepribadian politik pemilih muda khususnya dalam agenda pemilu serentak 2024.
 
Kata Kunci: Pendidikan Politik, Rumah Pintar Pemilu, Pemilih Muda, KPU Kota Bandung.
Intralingual Understanding of Zakat, Infaq, alms, and Gifts in Islamic Economics In Aceh
Authors: Faridy Najihatul , Bania Allif Syahputra , Fuadi Raida
Published: October 2024

The implementation of Islamic economics in Aceh has a positive impact on society. Aceh Province is a region in Indonesia that has an influence on poverty issues because it has been hit by social conflict that has been going on for a very long time so that access to work is not conducive for the people of Aceh. ). Aceh Province with the implementation of Islamic law has a role to play in resolving and minimizing poverty problems. The method used by Aceh Province is to foster the management of zakat, infaq and alms because they are sources of eternal funds. Supporting the importance of previous research on zakat, infaq and alms, this study wants to determine the ability to understand interlingual terminology about zakat, alms and infaq plus one other terminology, namely gifts. This research was a descriptive-qualitative research. The data used are 4 terminologies in Islamic economics, namely alms, zakat, infaq and gifts to be translated interlingually. The four terms to be translated were analyzed using the intralingual equivalence method. The sample is 50 students from the Faculty of Economics who have studied science related to Islamic economics courses. The findings obtained that related to the terminology of zakat, all participants as many as 50 students or 100% of participants managed to answer intralingually. Then, there were the same results between the terminology of infak, sedekah, and hadiah, namely 47 students managed to answer correctly but insignificantly there were only 3 students who answered incorrectly.
Influence of Strategic Planning on Banking Performance via Differentiation Strategy
Authors: Waisapi Jeffry Yuliyanto
Published: October 2024

Effective strategic planning plays a crucial role in enhancing the performance of banking businesses. This research aims to analyze the impact of strategic planning on banking business performance through the implementation of differentiation strategy. Utilizing a quantitative approach, primary data was collected through surveys distributed to senior managers from several banks operating in various markets. Multiple linear regression analysis was employed to evaluate the relationship between strategic planning, strategic differentiation, and business performance. The research findings indicate that strategic planning significantly influences strategic differentiation in the context of the banking industry. Additionally, strategic differentiation also positively contributes to banking business performance. The implications of these findings are that banks implementing effective strategic planning tend to adopt better differentiation strategies, thereby enhancing their performance in facing intense market competition. This study provides valuable contributions to understanding the relationship between strategic planning, strategic differentiation, and business performance in the banking context. The results offer guidance for banks to improve their strategic planning practices and implement effective differentiation strategies to strengthen their competitive position in the market. Future research could explore additional factors influencing this relationship and broaden the industry scope to validate these findings more comprehensively.