Jurnal Pekommas is a free open-access journal that publishes research, development, and scientific studies in communication, media, and informatics conducted by researchers, academics, practitioners, and observers. Jurnal Pekommas is currently managed by Sekolah Tinggi Multi Media "MMTC" Yogyakarta. From 2022, Jurnal Pekommas will publish bi-annually in June and December. Based on the Decree of the Director General of Higher Education, Research, and Technology Number 10/C/C3/DT.05.00/2025 dated March 21, 2025, concerning the Accreditation Ranking of Scientific Journals for the First Period of 2025, Jurnal Pekommas has been re-accredited as the 4th rank of SINTA (Science and Technology Index of Indonesia) from Volume 9 No. 1 2024 to Volume 13 No. 2 2028.
Sekolah Tinggi Multi Media "MMTC" Yogyakarta
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Forecasting of Rice Agricultural Production in Lamongan Regency Using the Brown's Double Exponential Smoothing Method
Rice production data from 14 sub-districts in Lamongan Regency during the 2008–2021 period, comprising 196 observations, exhibited variations over time. These fluctuations need to be anticipated because changes in rice production may affect rice availability. Therefore, a forecasting model is required to estimate rice production for the subsequent period as a basis for formulating agricultural and food security policies. The forecasting results can be utilized by the local government to plan rice reserve management, maintain price stability, optimize food distribution, and develop preventive measures in the event of a projected decline in production. This study applied the Brown Double Exponential Smoothing method to determine the smoothing parameter that produces the lowest forecasting error. Based on the evaluation of several alpha (α) values, α = 0.5 was identified as the optimal parameter, yielding the lowest Mean Absolute Percentage Error (MAPE) of 2.66%, indicating that the model has a relatively low forecasting error. Using this parameter, the forecasting model was obtained as: F_(t+m) = 791904.36 + 20071.34(m). The model was then used to forecast rice production in Lamongan Regency for 2022, resulting in an estimated production of 811,975.71 tons. In addition to evaluating the forecasting model, this study also assessed the developed system through questionnaire-based usability testing. The evaluation produced a feasibility score of 83.76%, which falls into the highly feasible category, indicating that the system is easy to learn, user-friendly, satisfying to use, and suitable for supporting decision-making in rice production planning.
Copyright (c) 2026 Rika Yunitarini, Bagus Dwi Prasetyo, Yudha Dwi Putra Negara https://creativecommons.org/licenses/by-nc-sa/4.0
Self-Disclosure in Managing the Mental Health of Generation Z Following Parental Divorce Through Anonymous TikTok Accounts
Parental divorce can affect Generation Z's mental health, emotional communication, and self-disclosure. Limited communication within post-divorce families encourages adolescents to seek alternative spaces to express their emotional experiences, one of which is through anonymous social media accounts. This study aims to understand the experiences of Generation Z individuals with divorced parents in utilizing anonymous TikTok accounts as a means of self-disclosure for mental health management through the perspective of Communication Privacy Management (CPM). This research employed a qualitative approach using Interpretative Phenomenological Analysis (IPA) involving four Generation Z participants who actively use anonymous TikTok accounts. The findings reveal that anonymous TikTok accounts serve as a space for expressing emotions, personal experiences, and psychological distress that are difficult to communicate directly. Self-disclosure practices function as a coping mechanism that helps participants achieve emotional release, while anonymity is utilized as a privacy management strategy that enables them to remain open without revealing their personal identities. These findings indicate that anonymous TikTok accounts enable Generation Z individuals with divorced parents to negotiate their need to express emotional experiences while maintaining control over their privacy in digital spaces.
Copyright (c) 2026 Aditya Andira Mayashifa, Devi Wening Astari https://creativecommons.org/licenses/by-nc-sa/4.0
Pertamina’s Crisis Communication Response Strategy in The Adulterated Fuel Case: A Quantitative Content Analysis of CNN Indonesia and Kompas.Com News
This study is motivated by a public trust crisis toward PT Pertamina following allegations of fuel adulteration from Pertalite to Pertamax. This incident triggered massive financial losses and a severe reputational threat. This research aims to describe Pertamina's crisis communication response strategy based on the Situational Crisis Communication Theory (SCCT) and analyze differences in tendency of implementing these strategies in CNN Indonesia and Kompas.com. The method used is quantitative content analysis with purposive sampling. The total sample includes 50 news articles (25 from CNN Indonesia, 25 from Kompas.com) during the peak crisis period of February-March 2025. Data were analyzed using descriptive statistics and the nonparametric Mann-Whitney U test. The results show a deviation from the ideal assumptions of SCCT. In a preventable crisis requiring a rebuild (accommodative) strategy, Pertamina predominantly used the Denial strategy at 60% (15 articles) in each media outlet. The Mann-Whitney U test yielded a significance value of 0.861 (Sig. > 0.05), thus the null hypothesis (H0) is accepted. In conclusion, there is no significant difference regarding the tendency of Pertamina’s crisis response strategies in both media. Both outlets uniformly display the dominance of Pertamina’s defensive communication approach (denial posture) in managing public perception.
Copyright (c) 2026 Risma Nurbani Ilham, Dian Purworini https://creativecommons.org/licenses/by-nc-sa/4.0
Exploring Social Media Use through Uses and Gratifications Theory: A Systematic Literature Review
This research aims to analyze the application of the Uses and Gratifications Theory (U&G) in social media studies thru the systematic literature review (SLR) method with a PRISMA approach. A total of 21 journal articles published between 2022 and 2025 were analyzed to see how this theory is used. The research results show that U&G is still relevant in explaining social media user behavior, especially because it emphasizes that users are active in choosing media to meet specific needs. The research findings categorize gratifications into four main categories: motives-based, personal, social, and applied gratifications, which indicate the diverse purposes of social media use. Additionally, the types of gratifications are also influenced by the characteristics of each platform such as TikTok, Instagram, Facebook, LinkedIn, and X. However, existing research is still dominated by quantitative approaches, with limited geographical coverage and minimal use of mixed methods and studies related to the role of algorithms or new technologies. Therefore, future research is recommended to use a more diverse methodological approach and expand theoretical perspectives in order to understand the relationship between users and social media more comprehensively.
Copyright (c) 2026 Salsabila Nafa Nikita https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence–Driven News and Public Trust: Examining the Relationship Between Automation and Engagement Production
This study aims to analyze the impact of AI automation in news production on public trust and engagement, as well as explore public perceptions of ethics and transparency in the use of AI in the media sector. The research is based on the increasing use of AI in journalism, which has sparked various public reactions concerning ethical and transparency issues. The research method employed is multiple regression, with the population consisting of Yogyakarta residents who consume AI-generated news. The sample comprises 222 respondents selected through Simple Random Sampling. The results indicate that AI automation positively impacts public trust, with a coefficient of 0.185, a t-value of 2.290, and a significance of 0.023. Conversely, transparency negatively affects public trust, with a coefficient of -0.559, a t-value of -3.512, and a significance of 0.001. Ethics positively and significantly influence public trust, with a coefficient of 0.394, a t-value of 2.526, and a significance of 0.012. These findings underscore the importance of implementing robust ethical practices in the use of AI to maintain public trust.
AI Automation, News Production, Public Trust, Public Involvement, Transparency Ethics
Copyright (c) 2025 Jurnal Pekommas https://creativecommons.org/licenses/by-nc-sa/4.0
Leadership Communication and Employee Performance: An Empirical Study at PT Herba Emas Wahidatama Purbalingga
Effective communication is a critical leadership competence that influences organizational performance. This research investigates how participative, directive, and supportive leadership communication contribute to employee performance while controlling for employees' educational background, work experience, and age. A quantitative research design was adopted, using a census (saturated sampling) method involving all 96 employees in the production workforce. Descriptive findings indicate that the participative communication style has the highest mean score (3.96), followed by directive (3.53) and supportive (3.37). Regression analysis demonstrates that participative and directive communication significantly improve employee performance, whereas supportive communication does not exhibit a statistically significant effect. Education level has a positive effect, whereas work experience and age show no significant impact. The model explains 73.9% of performance variation (R² = 0.739). Overall, the findings suggest that participative and directive communication practices play an important role in improving employee coordination, motivation, and productivity, highlighting the importance of strengthening leaders' communication capabilities through continuous organizational development.
Copyright (c) 2026 Khoiriyatun Widyastuti, Muhammad Izzy https://creativecommons.org/licenses/by-nc-sa/4.0
Communication Model for Improving Digital Literacy in The Community of Labuan Bajo, East Nusa Tenggara
This study aims to identify an appropriate communication model to improve digital literacy among the community in Labuan Bajo, East Nusa Tenggara (NTT), one of Indonesia's Super Priority Tourist Destinations. NTT has a Digital Society Index (IMDI) score of 39.34, below the national IMDI average of 43.18, placing it among the five lowest-ranked provinces out of 38 in Indonesia. This research adopts a descriptive qualitative approach with data collected through interviews, observations, and document analysis. The findings indicate that Lasswell's communication model, integrated with the Ministry of Communication and Digital Affairs’ four pillars of digital literacy, provides a relevant strategic framework when adapted to the socio-cultural context. The proposed digital literacy communication model is participatory and adaptive, involving key communicators such as local youth leaders, community groups, educators, MSME activists, local universities, religious leaders, and government agencies. The messages focus on the four pillars of digital literacy, prioritizing digital security and ethics, delivered through various social media platforms supported by face-to-face training that incorporate sociocultural approaches. The target audiences are segmented into MSME actors, youth, parents, and village officials. The expected outcome is to improve digital literacy among the people of Labuan Bajo.
Development of IoT System for Weight, Height, Temperature and Heart Rate Monitoring with Health Recommendations
Health facilities such as community health centers, clinics, and hospitals play a crucial role in providing basic health check-up services, including measuring weight, height, body temperature, and heart rate. This examination is important as an initial step in early detection of disease and routine monitoring of patient health conditions. This study aims to develop an Internet of Things (IoT)-based health monitoring system that can automatically measure these body parameters, display data in real-time, print test results via a thermal printer, and provide simple health recommendations. This study uses the Research and Development (R&D) method with a modified Borg and Gall model approach. The system trial was conducted at the UI student dormitory in May 2025, involving 38 student respondents as samples. The results of the study showed that the developed system was able to perform measurements with high accuracy and fast response time. In addition, the features of printing results and providing health recommendations have been shown to increase service efficiency and user satisfaction. This system has the potential to support remote health services (telemedicine) and increase public awareness of the importance of independent and regular health monitoring
Prediction of Stunting Prevalence in Toddlers Using Support Vector Machine Algorithm and Synthetic Minority Oversampling Technique (SMOTE)
Stunting in toddlers represents a condition where isa nutritional deficiency. This becomes more complex when there is insufficient information regarding stunting in toddlers available. Predicting the prevalence of stunting in toddlers involves studying a dataset of stunting prevalence among toddlers through a supervised learning model using Support Vector Machine (SVM) and synthetic minority oversampling technique (SMOTE). The use of SMOTE serves as a data balancing method, while exploratory data analysis (EDA) acts as the preprocessing method for the toddler dataset. From the research implementation on a dataset consisting of 6879 toddlers, an accuracy of 94% was obtained for predictions. This accuracy is comprised of 94% accuracy, 95% precision, 94% recall, and a 94% F1-score.
stunting, toddlers, support vector machine, synthetic minority oversampling, exploratory data analysis
Single Neuron PID Controller for Speed Regulation of Induction Motor on Gamma Irradiator Characterization Prototype and Its Comparison with Classical PID Controller Using the Modified Ziegler-Nichols Method
Induction motor control is a crucial part in the operation of gamma irradiator characterization prototypes, which are used for component testing and operator training. In this system, the induction motor functions as the primary actuator within the automated transport mechanism, therefore the motor's speed and stability greatly affect the accuracy of sample positioning relative to the radiation source. Proportional-Integral-Derivative (PID) controllers are widely used due to their simple structure but have limitations in responding to complex and time-varying system dynamics. To improve control performance, artificial intelligence-based control methods such as the Single Neuron PID (SNPID) have been developed as alternatives, offering adaptive characteristics through dynamic parameter adjustment. This study presents a comparative performance analysis between a PID controller tuned using the Modified Ziegler-Nichols (MZN) method and a SNPID controller in regulating the speed of an induction motor in a gamma irradiator system. Evaluation results show that the SNPID controller with a uniform learning rate of 0.5 and a Hebbian constant of 0.0018 produces faster, more stable, and more adaptive response compared to the MZN-tuned PID controller with a 30° phase margin. These results indicate that the SNPID controller is a more effective choice for motor control systems in gamma irradiation applications that require fast response, high precision, and robustness against disturbances.
Motor Induksi, Irradiator Gamma, PID, Single Neuron PID
Improving the Accuracy of Deep-Learning Models for Coconut Maturity Classification Using Acoustic Signals
Nowadays, the development of signal processing technology, advanced intelligent systems and deep learning have become commonplace in agricultural technology. One of the agricultural commodities that are widely available in the world is coconut. Unfortunately, the application of post-harvest agricultural technology is very minimal in coconut farming. So far, farmers have determined the maturity of coconuts by listening to the sound of the coconut being knocked so that it takes a lot of time to check the maturity level. This paper will discuss improving the accuracy of deep learning models in classifying coconuts based on acoustic signals. For the trial of the classification of the maturity level of coconuts, a deep learning method consisting of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) was used. And using an open access dataset consisting of samples used of 129 coconuts with three classification levels - premature, mature and overmature (Caladcad, 2023). In this paper, we propose an improvement in the deep learning model architecture with learning rate parameters of 0.0001, epoch 500, Batch-Normalization and Dropout 0.3 with the results of the coconut acoustic signal classification test having a data testing accuracy of 98.36% and an F1 Score of 99%. The results of this trial are better than the previous trial (Caladcad, 2024) with an accuracy of 97.42% and an F1 Score of 97.20%. and shows that the improvement of the deep learning combination method produces a more reliable coconut ripeness classification system, and is free from class bias
Agency Visualization: Empowering Sexual Violence Survivors (A Feminist Study of Visual Rhetoric)
News coverage of sexual violence on mainstream media portals often objectifies women as victims, both in the narrative and the visualization presented through supporting illustrations. Media that continuously objectify women will reinforce public perception in understanding the issue of sexual violence as detrimental to women as the majority of victims. To address this, Remotivi organized an illustration competition to challenge all creators taking their side with victims and survivors of sexual violence. This study aims to explore how illustrations can become a counter-narrative to those objectifying and visualizing women as the suffering ones. This study uses a descriptive qualitative method with feminist visual rhetoric analysis. The authors found that illustrations depicting their creators’ side with survivors of sexual violence display various elements we barely find on mainstream media: representation of empowered women as subjects, active gestures resisting perpetrators, a view point that comes from women as subjects, and things that cultivate a new perspective. It is expected that the new coming of illustrations using feminist perspective will change the public in understanding the issue of sexual violence in a deep and empathic way, no longer merely viewing victims as objects of news coverage.
The Relationship Between Social Media and Customer Visits at Tourist Destinations: Case Study of Bali Indonesia
This study examines the relationship between social media use by zoos in Bali, Indonesia, and tourist visits. The study investigates how effectively these platforms engage visitors and disseminate information by analyzing the official websites, Instagram accounts, and Twitter accounts of five zoos, combined with visitor surveys. The method used was to conduct a survey of visitors to tourist destinations. The survey results were processed using tools to determine the preferences of visitors to tourist destinations. The survey results showed that visitor preferences for communication channels differed between zoos, highlighting the importance of tailoring social media strategies to target audiences. By mapping visitor expectations against actual social media management practices, the study identifies areas for improvement and proposes a new framework for analyzing the effectiveness of digital communication media in parks and recreation. This framework allows zoo management to better align their social media strategies with visitor preferences, ultimately improving communication and potentially increasing tourist visits.
Applying the Analytical Hierarchy Process to Select Strategic Locations for Culinary MSMEs in Tomohon City
Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in Indonesia's economy, making up over 99% of business units. In Tomohon City, located in North Sulawesi, Indonesia, MSMEs, especially in the culinary sector, have faced challenges due to the COVID-19 pandemic, which led to significant revenue losses and business closures. However, recovery efforts are underway, prompting the development of a web-based system to aid culinary businesses in selecting optimal locations. Utilizing the Analytic Hierarchy Process (AHP) method, the system evaluates potential sites based on five criteria: completeness of infrastructure, population density, competition, community incomes, and business capital. Each criterion is assessed using sub-criteria such as very good to very inadequate, providing tailored recommendations for Downtown District, Dotu Tololui Tua Statue, Kakaskasen Highway Area, and Beriman Terminal. Among these criteria, infrastructure completeness (40.4%) is prioritized most, followed by population density (26.2%), competition (18.1%), community incomes (10.4%), and business capital (4.9%). The sensitivity analysis indicates that changes in criteria weights significantly impact the rankings of alternatives, with infrastructure and community incomes causing minor changes but preserving the top-ranked options, while population density and business capital lead to more substantial shifts, including changes in the top position.
Implementation of Genetic Algorithm for Teenager Nutrition Management
Adolescence is an important moment in human life marked by growth, emotional, and psychosocial. During adolescence, a healthy diet becomes very crucial to support adolescent development and prevent future health problems. Therefore, this study aims to implement the Genetic Algorithm for Teenager Nutrition Management in an expert system. This system is designed to provide recommendations for food menus that are in accordance with the nutritional needs of a teenager, which include protein, carbohydrates, energy/calories, and fat. Genetic algorithms are used so that the food recommendation process can be in accordance with the daily nutritional needs of teenagers. The variables in this study are age, gender, weight, height, and physical activity. Meanwhile, the results of this system are in the form of total energy or calorie needs, protein, fat, and carbohydrates included in the daily food menu according to the nutritional needs of teenagers. This system can provide daily food menu recommendations in the form of a menu list using a genetic algorithm. The best fitness value is 0.0098 at a generation size of 100 and 670 dataset of food
Traffic Signs Detection System Based on You Only Look Once (Yolov8) using Raspberry Pi
Autonomous vehicle (AV) is projected to become a part of land transportation in the New Capital City of Nusantara (IKN). . This opens up opportunities for related research to be conducted prior to actual implementation.. One of the capabilities of AV is recognizing different types of traffic signs. Therefore, this study aims to design a traffic sign detection system as an insight and support for the implementation of autonomous vehicles in IKN Nusantara. To achieve this, a total of 11,157 images containing 30 types of traffic signs were collected as the primary dataset along the roads of the Special Region of Yogyakarta. Â Variations of the dataset were also added in the form of noise, blur, and dark. During the model training, hyper-parameter configurations such as learning rate, epoch, and image size were performed. In this study, the You Only Look Once v8 method is used. The results of testing with daytime data showed an accuracy of 80%, recall of 83%, and precision of 96%. In contrast, tests with night data showed 93% precision, 70% recall, and 67% accuracy. This test works well for cars moving at speeds below than 40 km/h because of the Raspberry Pi hardware's computing speed constraints
Integration of Smart Sensors with IoT Systems to Monitor Nitrogen Gas Levels in Biogas Reactors
Renewable energy is a type of energy derived from natural processes and can be used continuously. The use of new and renewable energyrepresents a societal effort to reduce reliance on fossil fuels. Biogas is one of the newest sustainable energy sources in the home.One such renewable energy source applicable at the household level is biogas, a gas produced by microorganisms through the anaerobic fermentation of organic matter. This study presents an Internet of Things (IoT)-based nitrogen gas monitoring system for determining the quantities of nitrogen gas in biogas. The hardware system for detecting nitrogen gas levels utilizes an MQ-135 sensor and an ESP8266 microcontroller for processing data via the internet or in real-time. Â Monitoring results indicate that the nitrogen gas concentration in biogas does not exceed 20,000 ppm, which aligns with the established standard range of 2%–5% nitrogen, or approximately 20,000 ppm. Based on these findings, the biogas is deemed to meet the required quality criteria.
User Acceptance and Effectiveness of AutoML Systems in Predicting Training Success: A Case Study of the DTS Program
The advancement of digital technologies has transformed workforce demands, emphasizing digital literacy, adaptability, and innovation. Indonesia’s Digital Talent Scholarship (DTS) program, launched in 2018, has trained over 500,000 participants in IT skills. However, completion rates vary significantly (57%–96%), highlighting challenges in participant engagement and program effectiveness. This study integrates the DeLone and McLean Information Systems Success Model and the Technology Acceptance Model (TAM) to evaluate system effectiveness and user acceptance. Variables from the DeLone model—System Quality, Information Quality, and Service Quality—were incorporated into TAM constructs, influencing Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and User Satisfaction, which shape Attitude Toward Use (ATU) and Behavioral Intention (BI). Survey data from 342 DTS administrators were analyzed using Structural Equation Modeling (SEM). Results show PEOU significantly influenced PU (path coefficient = 0.900) and ATU (0.594), while PU and ATU collectively explained 70.3% of BI. High PEOU (0.91) and PU (0.87) scores highlight the importance of system usability and utility. However, low ATU stems from organizational misalignment, as the system’s focus on participant quality contrasts with the DTS priority on participant numbers. Addressing this misalignment, enhancing system features, and improving service quality can boost adoption and foster a digitally skilled workforce aligned with Indonesia’s evolving demands.
AutoML, Digital Skills, Technology Adoption, TAM, System Quality
Interference Analysis between 5G Network and LoRa Technology in Wireless Communication System
5G and LoRa technologies have their own advantages in wireless communication. However, when used together, they can experience signal interference, especially at the 925 MHz frequency. This study aims to analyze interference between 5G and LoRa networks using the SEAMCAT simulation tool with a Monte Carlo approach. There are two scenarios used in this study, namely device spacing and transmit power settings. The simulation results show that the transmit power and device distance settings affect the Interference Received Signal Strength (iRSS) value and interference probability. At a distance of 35 km and a transmit power of 40 dBm, the interference probability is up to 0%. Thus the two technologies perform without interfering with each other. This research provides a simple solution to reduce interference and maximize the use of 5G and LoRa technologies in wireless communications
interference, 5G, longe range (LoRa), SEAMCAT, wireless communication
Copyright (c) 2026 Jurnal Pekommas https://creativecommons.org/licenses/by-nc-sa/4.0
Sentiment Analysis and Topic Modeling of Twitter Conversations in Indonesia's 2024 Presidential Election
The 2024 Indonesian presidential election has garnered significant attention owing to the increasing influence of social media on public perceptions of political leaders and information exchange. This study aimed to analyse the online discourse surrounding three prominent Indonesian presidential candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Utilising sentiment analysis, frequency analysis, and the Latent Dirichlet Allocation (LDA) algorithm, this study examines conversations on social media platforms, focusing on word frequency, sentiment, subjects, and entities that emerge in the discussions. The findings reveal that Prabowo Subianto is the most frequently mentioned candidate, followed by Anies Baswedan, and Ganjar Pranowo. Sentiment analysis indicated a predominantly neutral to slightly positive sentiment across conversations. The LDA topic analysis uncovered distinct campaign focuses for each candidate, with Anies emphasising local issues and debates, Prabowo concentrating on national discourse and defense policy, and Ganjar discussing national concerns, justice, and progress. Named Entity Recognition (NER) highlights the prominence of entities such as "Indonesiaâ€, "Prabowoâ€, and "Pilpres2024" in online discussions. This study underscores the crucial role of social media in shaping public opinion and provides valuable insights into the online narratives of presidential candidates. These findings contribute to a deeper understanding of the dynamics of digital elections and offer guidance for political analysts and academics in navigating the evolving landscape of online political discourse in Indonesia.
The Relationship of Digital Literacy, Exposure to AI-Generated Deepfake Videos, and the Ability to Identify Deepfakes in Generation X
The study explores the relationship between digital literacy, exposure to AI-generated deepfake videos, and the ability to identify deepfakes by Generation X in Indonesia who are currently between the ages of 43 and 58. It also analyzes the impact of deepfake identification capabilities on the cognitive, affective, and behavioral aspects of internet users. Through a survey involving 199 respondents taken from a total population of 42 million Generation X internet users in Indonesia, it applied a random sampling method. The sample size was determined by the Slovin formula with a confidence level of 90% and a margin of error of 7.1%. The descriptive analysis shows a moderate level of digital literacy and relatively low exposure to deepfakes. However, the ability to identify deepfakes was found to be low. The results of inferential statistical analysis show that digital literacy and exposure to deepfakes do not have a significant influence on the ability to identify deepfakes. Additionally, the ability to identify deepfakes does not significantly affect cognition, compassion, or behavior. While digital literacy is important, these findings reinforce the assumptions of Generation Theory and Media Dependency Theory. Additionally, it suggests that specific training on media manipulation technologies is needed to improve deepfake detection capabilities. This research implies that efforts to improve digital literacy should be expanded, including technical skills and critical thinking relevant to manipulative media such as deepfakes.
Copyright (c) 2024 Jurnal Pekommas https://creativecommons.org/licenses/by-nc-sa/4.0
The Influence of the Quality of Information on the @taulebih.id Instagram Account on Increasing Followers' Reproductive Health Knowledge
Reproductive health information encompasses knowledge about the functions, systems, and processes of reproduction. Reproductive health information should be understood by families, including parents and adolescents themselves. This study aims to analyze the influence of the information quality provided by the Instagram account @taulebih.id on the reproductive health knowledge of its followers, with a population of 90,000 followers and a sample of 273 individuals. Data collection for this study was conducted using a Google Forms-based questionnaire. The research employed a simple regression analysis method using SPSS 25. The outcomes derived from the basic regression analysis demonstrate support for the alternative hypothesis (Ha) is accepted, indicating that the information quality provided by the Instagram account @taulebih.id exerts a meaningful and favorable influence on the reproductive health knowledge of its followers, with a partial t-value of 12.248 > 1.650 and a significance value of 0.000 < 0.05. Furthermore, the results of the coefficient of determination test demonstrate a significant influence of the information quality provided by the Instagram account @taulebih.id on the reproductive health knowledge of its followers, accounting for 35.4%, while the remaining 64.6% is affected by additional factors not encompassed within the scope of this investigation.
Analysis and Mitigation of DDoS Vulnerabilities in Network Infrastructure using Hierarchical Clustering and IPTables Firewall Techniques
Network Security infrastructure including routers and server devices, which are connected directly to the global internet has become an important issue along with the increase in internet communications in maintaining the confidentiality, integrity and availability of digital communications. The most challenging problem is the network infrastructure for exploiting a monoculture of routers and servers and detecting Distributed Denial-of-Service (DDoS) attacks. This research aims to combine analysis and mitigation techniques with Hierarchical Clustering single linkage, complete linkage, average linkage and ward linkage as well as IPTables firewall filtering mitigation measures, to analyze DDoS logging data on NIDS suricata, with low, medium and high severity levels exploited from the network public. Clusteirng single linkage deployments produces cluster 3 with a DDoS logging intensity level of high severity, on the TCP Sync Flood protocol type. Cluster 3 shows high severity for the source IP address. The complete linkage clustering technique also provides significant results with a large number of potential DDoS logging, found in cluster 1 and cluster 2. The results of the average linkage distribution show a group with a low average severity level for DDoS. The Ward linkage clustering produces a more uniform group of attributes for each n_clusters 1 to cluster 6. Implementation of mitigation techniques with IPSet and firewall scripting IP Tables provides positive results in reducing the workload of router and vServer devices when facing DDoS attacks. After convergence, the running status resulted in the workload of vCPU resources experiencing a decrease in the percentage of vCPU vR1 by 10%, vCPU vR2 by 9% and memory by 11%.
Identification of Transformer Anomalies Utilizing the AdaBoost Machine Learning Algorithm
Electrical energy is one of the most important needs in the local area, concerning the provision of electrical power a transformer is needed to distribute electrical power to each house. The use of transformers in each area requires testing the oil content in them to assess the state of the transformer. The Duval Pentagon Method (DPM) and the Duval Triangle Method (DTM) can be used in tests to identify transformer interference. Owing to the vast quantity of transformers utilized in public energy distribution, the Adaboost machine learning method was applied to identify transformer disruptions. By categorizing test data on a dataset derived from tests conducted with the earlier DTM and DPM techniques, the AdaBoost algorithm predicts transformer disruptions. According to the findings of tests conducted using the best dataset, the division used 80% of the data for training and reserved 20% for testing, using a learning rate of 1 and an estimator of 400 for DTM. This resulted in an accuracy level of 91.1%, which is an excellent classification. In contrast, the DPM approach divides the dataset into 80% training and 20% testing, employs an estimator of 500, and has a learning rate of 0.5. This leads to an excellent classification accuracy rate of 84.9%.
Mapping the Communication Strategy of the Indonesian Feminist Movement in Digital Space
This research aims to explore the communication strategies of the digital feminist movement in Indonesia in using social media as a space for message propagation. In the digital era, social media has become the main instrument for feminist communities to deconstruct common gender narratives and advocate for issues related to gender equality and women's empowerment. Using the Systematic Literature Review (SLR) approach, this research analyzes several relevant literatures to identify the communication strategies applied by Indonesian feminist communities. The results show that these communication strategies include decentralization of information, increased interactivity with audiences, utilization of social media-specific features such as hashtags and highlights, and the use of multimedia content to increase audience appeal. The findings highlight how social media is becoming an influential tool that enables inclusive and collaborative advocacy in the digital space, with challenges of limited internet access and intergenerational differences in understanding how to operate social media features
Corn Leaf Disease Classification Using the EfficientNetB5 Deep Learning Model
The second most important food crop in Indonesia, after rice, is corn, which is highly susceptible to leaf diseases such as common rust, cercospora gray leaf spot, and northern leaf blight. In spite of traditional Machine Learning, in which manual feature extraction must be impeccable for optimal results, a model capable of accurate classification is required. In this investigation, the Deep Learning model EfficientNetB5 is used to classify corn leaf diseases, and the performance model between learning rate and batch size hyperparameters is compared. All models are identical to the dataset, which consists of 3,852 images divided into 4 classifications. The testing results indicate that the combination of learning rate = 0.0001 and batch size = 32 obtains the highest value compared to other models. The obtained evaluation values were 96.27 % accuracy, 90.90 % precision, 97.55% specificity, and 88.13 % sensitivity.
Penyakit daun jagung, Deep learning, EfficientNetB5, Learning rate, Batch size
Hapsari Peni Agustin Tjahyaningtijas Tjahyaningtijas
Hybrid Intrusion Detection System and Network Infrastructure Vulnerability Mitigation using Active Response (XDR) Technique Wazuh and Suricata
The complexity of cyber threats against the network infrastructure of companies, educational institutions, and government makes protecting network infrastructure a top priority. Router and server devices are highly vulnerable to various types of cyber threats, requiring comprehensive detection and response solutions. This research will implement an intrusion detection system by integrating SIEM technology and Wazuh XDR (Extended Detection and Response). This system analyzes index pattern data from Wazuh agent devices to detect and respond to attacks using the XDR active response firewall. The testing was conducted MikroTik RouterOS, Ubuntu Server 20.04 as Wazuh agent to test reconnaissance attacks, brute force and DoS attacks. The results of the research show Nmap and brute force attacks were successfully detected by Wazuh manager and blocked the attacker IP malicious through active response. Detection of brute force attacks showed an increase in traffic of up to 60 Kbps and CPU usage reached 100%, then decreased after the active response firewall was activated. Authentication failure reached 2198 times in the first hour of the brute force attack. CPU usage increased from 20% to 85% during the attack and decreased to 15% after the active response firewall was activated. DoS attacks, on MikroTik experienced an increase in CPU usage of up to 61% and memory of 67%. After activating the active response firewall, CPU usage decreased to 3%. Traffic on the MikroTik interface increased to 3.3 Mbps during the attack, then decreased to 1 Kbps after the firewall was activated Â
Digital Skills: The Influence of Digital Competence on Online Business Interest
The importance of this research lies in the inevitability of today's digital technological advancements, marking a period where prioritizing work efficiency and effectiveness has become essential, giving rise to what is known as the digital age. A large number of younger individuals are captivated by the complexity of technology, often using it solely for recreational purposes. The impact of such behavior stems from the inappropriate utilization of technology, potentially turning the next generation into mere consumers in a consumer-driven society. The younger generation must be more digitally savvy so as to encourage the utilization of digital technology to be more productive in order to foster the seeds of reliable and skilled young business people who are technologically literate. Digital competence is not only understanding the hardware of a technology, but also the software, and it is inseparable. The purpose of this study is to determine the influence between the digital competence of the young generation of Makassar city on online business interest, as well as what factors that are supporting and inhibiting. The method used was through a quantitative approach which was analyzed through validity, reliability, normality, linearity and simple liner regression tests with the help of the SPSS version 24 application. The respondents of this study were 222 teenagers in Makassar City. The results of the study state that there is a significant influence between digital competence on interest in doing business online by 39.3%, and 60.7% is influenced by other factors. The supporting factors for adolescents include the ability to use hardware such as gadgets/smartphones that are high, as well as the ability to search for information online, while the obstacles are the lack of ability to use commerce platforms and the lack of interest to start an online business, moreover reluctant to run it. Other factors that influence interest in doing business online include the availability of the capital, fear of bearing the risk of failure, etc. The limitations in this study are the lack of respondents who filled out questionnaires and still need to be researched on aspects of adolescent psychology.
Innovating Digestive System Education through Augmented Reality Technology: Enhancing Student Engagement, Understanding, and Motivation
Augmented reality (AR) has emerged as a transformative tool in education, offering immersive and interactive learning experiences. Anatomy education, particularly the gastrointestinal system, often relies on traditional 2D materials that fail to engage students or provide sufficient visualization of its complex structure and functions. To address these challenges, this study developed a mobile AR application featuring 3D visualizations of the gastrointestinal system, designed to enhance student engagement and learning outcomes. The application, created using Unity 3D and Vuforia with the Prototyping method, incorporates interactive features such as animated organ models, quizzes, and multimedia content. Black box testing validated its functionality, while usability testing highlighted improvements in user satisfaction and learning. Quantitative analysis revealed a 30% reduction in quiz completion time and a 25.3% improvement in post-test scores, indicating enhanced comprehension and retention. These results demonstrate the application’s effectiveness in engaging students and simplifying complex concepts. The interactive and immersive AR features significantly boosted understanding and motivation. This research highlights AR's potential to revolutionize educational tools, with future studies focusing on its long-term effects on learning outcomes and broader applications in education.
Implementation of MTCNN Architecture on Different Pixel Dimension Classes and Plotting Multi-Face on Detection Results
Face detection is a computer vision task to identify and verify a person based on a photo of their face. Face detection and alignment in unconstrained environments are very challenging due to various poses, illumination, and occlusions. The human face is difficult to model because there are many variables that can change, such as facial expression, orientation, lighting conditions, and partial occlusions, such as sunglasses, scarves, masks, and others. Recent studies have shown that deep learning approaches can achieve impressive performance on these two tasks. In this paper, face detection on multi-faces will be carried out as well as mapping one by one the results of the face detection obtained (face crop) for the needs of various systems related to face detection using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) approach. This study aims to implement the MTCNN architecture using TensorFlow and OpenCV, with two main benefits. First, this study is expected to provide a pre-training model that performs optimally and strengthens evidence from previous studies that have examined this model. Second, this model can be used as input for other systems. The input variable is a photo image of a face containing one or more to be processed. This photo image will have various pixel dimensions to represent different resolutions. The output variable produced is in the form of coordinates of the detected face location or in the form of landmarks of key facial points, such as the position of the eyes, the corner of the nose, and the mouth. The results of the study showed an average score on various pixel dimensions in the dataset, with an accuracy of 93%, a precision of 95%, a recall of 96%, an F1-score of 95%, and an ROC-AUC of 90.89%.