A The Academic Information System plays a crucial role in efficiently managing student, faculty, and campus administration data. However, system security needs to be a primary concern as it is vulnerable to cyber attacks. This research aims to analyze the security of the Academic Information System at the Muhammadiyah Business Institute Bekasi. The research method used is a comprehensive security analysis based on the OWASP framework. The study includes identifying potential vulnerabilities, penetration testing, and system improvement recommendations. Testing is conducted through simulated attacks based on the OWASP-released security risk list (OWASP Top Ten Most Critical Web Application Security Risks). The analysis results indicate that the system is vulnerable to Broken Authentication due to weak passwords, Sensitive Data Exposure due to URLs pointing to direct directories, and Security Misconfiguration due to open protocols. Furthermore, in CVSS scoring, SQL Injection scored 2.6 (Low), Broken Authentication scored 4.8 (Medium), Sensitive Data Exposure and Security Misconfiguration scored 5.3 (Medium), Cross-Site Scripting scored 2.0 (Low), while XXE, Broken Access Control, Insecure Deserialization, Using Components with Known Vulnerabilities, and Insufficient Logging and Monitoring scored 0.0 (No Vulnerability). Recommendations for future system improvements include regularly updating the system to prevent new security vulnerabilities, tighter server configurations, and routine system monitoring to promptly anticipate suspicious activities.

Image encryption is an indispensable technique in the realm of information security, serving as a pivotal mechanism to safeguard visual data against unauthorized access and potential breaches. This study scrutinizes the effectiveness of merging columnar transposition with the Hill Cipher methodologies, unveiling specific metrics from a curated set of sample images. Notably, employing column transposition with the key "JAYA" and the Hill Cipher with the key "UDINUSSMG," the encrypted images underwent rigorous evaluation. 'Lena.png' demonstrated an MSE of 513.32 with a PSNR of 7.89 dB, while 'Peppers.png' and 'Baboon.png' recorded MSE values of 466.67 and 423.92, respectively, with corresponding PSNR figures of 7.12 dB and 7.31 dB. Across all samples, a consistent BER of 50.00% indicated uniform error propagation, while entropy values settled uniformly at 7.9999, highlighting consistent data complexity. While the findings underscore a consistent error rate and complexity, there's a compelling need for further refinement to enhance image quality and security. Moreover, the study proposes future research avenues exploring a three-layer super encryption paradigm, amalgamating columnar transposition, Hill Cipher, and other robust algorithms. This approach aims to fortify encryption methodologies against evolving threats and challenges in data protection, offering heightened resilience and efficacy in safeguarding sensitive information.

Motor Brushless Direct Current (BLDC) has become the preferred choice in various engineering applications. However, BLDC motor control involves high complexity, and motor performance depends on the control algorithms used. This research discusses the use of sensorless control methods, specifically the Sliding Mode Observer (SMO) for rotor position and speed estimation in BLDC motors within the context of Field-Oriented Control (FOC), validated through simulations using Matlab/Simulink. Simulation results indicate that SMO provides rapid dynamic response to current changes, albeit with slight delays at high speeds. Rotor position estimation with SMO is also reasonably accurate in both steady-state and transient conditions, affirming the iveness of SMO in sensorless control for BLDC motors. SMO can be experimentally implemented to enhance sensorless control in BLDC motors by reducing the cost of installing Hall sensors while maintaining comparable performance.

In this study, MFCC feature extraction and CNN algorithms are used to examine the identification of emotions in the murottal sounds of the Qur'an. A CNN model with labelled emotions is trained and tested, as well as data collection of Qur'anic murottal voices from a variety of readers using MFCC feature extraction to capture acoustic properties. The outcomes show that MFCC and CNN work together to significantly improve emotion identification. The CNN model attains an accuracy rate of 56 percent with the Adam optimizer (batch size 8) and a minimum of 45 percent with the RMSprop optimizer (batch size 16). Notably, accuracy is improved by using fewer emotional parameters, and the Adam optimizer is stable across a range of batch sizes. With its insightful analysis of emotional expression and user-specific recommendations, this work advances the field of emotion identification technology in the context of multitonal music.

Indonesia is a country with the second highest number of Japanese language learners in the world. However, with the main language of Indonesia being derived from the roman alphabets, it makes Indonesian students hard to get used to learning Japanese alphabets, especially Kanji. This study aims to develop a gamified mobile learning application following the Octalysis gamification framework, and assess its impact in garnering student’s acceptance to enhance their Japanese Kanji learning experience. This study was conducted quantitatively using the Technology Acceptance Model, and analyzed through the Structural Equation Model. The data were collected via questionnaires from 194 members of the local Japanese learning community. The variables analyzed in this research are Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using, and Behavioral Intention. All variables are tested for validity and reliability using SPSS Statistics, and structural equation model analysis using SPSS AMOS. The results showed positive significant correlations between Perceived Usefulness and Attitude Towards Using, Perceived Ease of Use and Attitude Towards Using, and Attitude Towards Using and Behavioral Intention. The result also noted a negative correlation between Perceived Usefulness and Behavioral Intention. Each variable contributes to the acceptance of the gamified mobile learning application with a strong emphasis on Perceived Ease of Use, and a mild emphasis on Perceived Usefulness.

Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.

Maximum power point tracking (MPPT) for PV (Photovoltaic) systems is provided in this research using artificial intelligence-based control. The design of MPPT system with Anfis Method on the Zeta Converter with DC Load is used to optimize the work of the Photovoltaic which will be used for DC load sources. The MPPT process consists of four main stages, namely module training data, determining input and output data, determining the number and type of membership functions and ANFIS training data. Zeta converter works like a buck boost, which can increase or decrease the voltage which is an advantage in designing systems with very volatile Photovoltaic sources. Zeta Converter is used to get higher efficiency, smaller input and output current ripple values and smaller core losses in the inductor. To improve the efficiency of system performance, An MPPT algorithm for the adaptive neuro fuzzy inference system (ANFIS) that is programmed into a microcontroller controls the zeta converter. ANFIS control is used because the response is faster and more effective. The combined simulation's findings demonstrate that the ANFIS control was successful, and the system can now produce the best possible power from Photovoltaic ipanelsiiniMPPT mode by boosting efficiency by up to 19.96%.

Today, DC Microgrid gain more attraction due to increasing electronic digital devices application such smart-phones, smart-tvs, and other digital apparatus which are operated in DC form. In the common grid, the electric power from AC source is converted to DC voltage for powering the digital devices as load. Hence, there are power conversions from AC-DC and potentially loss energy during conversions. DC Microgrid consisted of sources, loads, distribution lines and energy storages. In small capacity DC Microgrid, the stability of the system is vulnerable by dynamic load change. During load demands fluctuations, the DC Microgrid voltage is also dynamically fluctuated and can reach over the designated rate. To solve this problem, the PID controller is introduced in the DC-DC converter for maintaining the voltage rate at designated value regardless the load demands. In this paper, the DC Microgrid is consisted of photovoltaics as DC sources, XL-6019 as DC-DC converter, Arduino as controller, voltage and current sensors, distribution lines and loads. The proposed method is evaluated via experimental results. The responses of the proposed method in the DC Microgrid system are presented, evaluated, discussed, and compared between with and without applied method. The experimental results indicate that the proposed method has ability to reduce the voltage profile fluctuations during load demands changes and in short time.

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.