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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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.
Copyright (c) 2025 Jurnal Pekommas https://creativecommons.org/licenses/by-nc-sa/4.0
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
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. 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. 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
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 "Indonesia”, "Prabowo”, 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
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%.
The Implementation of Random Forest Classification for Identifying Infrastructure Priorities in Smart Cities
The main challenge in developing smart cities lies in the difficulty of determining effective and efficient infrastructure priorities. This study aims to implement classification techniques and to identify infrastructure priorities in smart cities. The research method employed is experimental, using the Random Forest classification algorithm and secondary data from various sources related to urban infrastructure. The study results indicate that the experimental method with classification techniques can identify infrastructure priorities with a high degree of accuracy. Data analysis on population density, economic growth, traffic congestion, and other variables reveals a significant relationship between infrastructure needs and these variables. The experimental model developed with the Random Forest algorithm can predict infrastructure needs with high accuracy, making it a valuable tool for city governments in making more precise decisions. The application of the Random Forest algorithm also demonstrates that the identified infrastructure priorities align with real needs on the ground, ultimately improving the efficiency of smart city management. Therefore, this study makes a tangible contribution to supporting smart city development through a more effective data-driven approach
Transformational Leadership, Psychological Safety, and ICT Competencies: Effects on Employee Performance
In the growing digital era, transformational leadership has become one of the key factors in improving employee performance. It motivates employees to achieve organizational goals and creates a psychologically safe environment, where employees feel valued and supported. This study aims to examine the influence of transformational leadership and psychological safety on employee performance, by exploring the mediating role of ICT competence in Subang Regency MSMEs. This study employs a quantitative approach utilizing SEM-PLS methodology to explore the interrelationships among relevant variables. The research was conducted on 55 SMEs, using an online questionnaire as the data collection tool. This study emphasizes that ICT competence acts as a crucial mediating factor in the relationship between transformational leadership and psychological safety on employee performance in MSMEs in Subang Regency. The analysis results show that transformational leadership and psychological safety have a positive and significant impact on employee performance, which is enhanced by ICT competence. Transformational leadership improves employee performance by strengthening technical skills, technology knowledge, and the ability to adapt to new technologies. Similarly, psychological safety, supports employee performance through increased ICT competence, which facilitates productivity, work quality, and innovation.
A Digital Lifeline: User Acceptance of Coronary Heart Disease Detection Apps
Cardiovascular disease, especially coronary heart disease (CHD), is the leading cause of death globally, with Indonesia, particularly North Sulawesi, experiencing significant prevalence due to unhealthy lifestyles. This study aims to assess the adoption and utilization of an early detection application for CHD among residents of the Spiritual Region of the Sacred Heart of Mary Cathedral Parish in Manado. It also seeks to identify opportunities for enhancing user experiences with the application using the Technology Acceptance Model (TAM). TAM includes constructs like perceived usefulness (PU), perceived ease of use (PEU), attitude towards use (ATU), behavioral intention to use (BIU), actual system use (ASU), and three external variables: information quality (PUIQ), service quality (PEUSVQ), and system quality (PEUSQ). This study uses the Partial Least Square (PLS) approach for data analysis. The results confirmed that all measures effectively assess their intended constructs. Strong correlations among the constructs demonstrate solid internal consistency. Regarding reliability, all constructs demonstrate acceptable consistency
The Impact of Social Media on Culinary Consumption Trends Among Young Civil Servants in Major Cities
This research seeks to explore how social media influences and shapes communication behaviors related to culinary consumption among middle-class individuals, particularly young civil servants in major cities. Culinary trends today are predominantly driven by younger generations who are adept with technology. This study examines how young civil servants utilize social media to influence their own culinary habits as well as those of their families. Importantly, previous research has not specifically addressed the social media consumption behaviors of young civil servants or the potential risks they may encounter as public servants. Adopting a qualitative approach, The author conducted in-depth interviews with 10 young civil servants in one civil servant office in Jakarta. The findings indicate that Instagram serves as a primary resource for culinary information, with users frequently engaging with the "save" and "share" features. Some participants reported feeling a sense of addiction to culinary content, which often resulted in impulse purchases and a fear of missing out (FOMO). Additionally, social media-driven culinary choices are made not only to satisfy hunger but also to fulfill other personal motivations
Influence of Communication Using WhatsApp on Cohesiveness of Citra Kinaraya Cooperative Members in Central Java
Information and communication technology is advancing rapidly. As technology advances, social media also grows and develops. WhatsApp is one of the most widely used social media. This study aims to analyse the factors that influence the use of WhatsApp social media as a communication medium on the cohesiveness of Citra Kinaraya Cooperative members. The research was conducted in December - January, which took place at the Citra Kinaraya Cooperative, Mlatiharjo, Demak. The research method used was a survey with a quantitative approach. The number of respondents was 95 cooperative members—data collection methods by interview, observation, and literature review. The data analysis used in this study used multiple linear regression analysis. This study shows that members of the Citra Kinaraya cooperative have characteristics: aged 41-60 years, the majority are male, have a high school education level, and the length of membership in the cooperative is 1-4 years. The results showed that frequency, duration, attention, age, and education level simultaneously influenced group cohesiveness. On the use of WhatsApp, as seen from frequency, attention duration, age, and education level, only attention partially affects group cohesiveness
The #STYOut Phenomenon on X:Social Network Analysis
This research paper analyzes the distribution of information related to Shin Tae-yong controversy, as the coach of the Indonesian National Team, on social media, using the Social Network Analysis (SNA) method. The objective is to identify key actors and user interaction patterns. The findings show that accounts such as @idextratime and @kopex__ have significant influence in the #STYOut network. Identification of these influential actors can help communication and marketing strategies, as well as decision making processes. This study is also relevant for sports team management in understanding the impact of public reaction on the coach's reputation. The study findings supports the Two-Step Flow Model of Communication theory, which shows the role of opinion leaders and the rapid spread of information. This study also provides insights into social interactions on social media, implications for sports management, and contributions to communication theory in the digital era.
Participation of Indonesian Internet Users in Utilizing LAPOR! as a Public Complaint Service
The People's Online Aspiration and Complaints Service (LAPOR!) was organized as an effort to increase public participation in government administration. LAPOR! is developing quickly, integrated with many central and regional government agencies; and it is officially designated as a general application for public complaint service in Electronic-Based Government System (SPBE). However, with the support of internet-based digital channels, the dominant access channels for LAPOR! users, it is not yet known how the public will participate in using LAPOR! as a public complaint service in various regions in Indonesia. This research aims to reveal the public participation of internet users according to provinces throughout Indonesia, by measuring the participation ratio as a basis for determining the level of participation in each province. Referring to the average national participation ratio (0.277), the research identified 15 provinces with high participation rates and 19 provinces with low participation rates. Among the provinces with high participation rates, five provinces meet the criteria for dominant participation rates, namely DKI Jakarta, South Kalimantan, Banten, DI Yogyakarta, and Central Kalimantan.
Tiktok Impact: Decoding Fashion Purchase Choices In Generation Z Through Social Media Strategies And Product Innovation
This research investigates the impact of TikTok marketing strategies on brand image and purchasing decisions among Generation Z consumers in Indonesia, with a specific focus on the mediating role of product innovation. Utilizing a quantitative approach through an online survey spanning three months, the study collected data from diverse regions without geographical limitations. The sample consisted of 150 Generation Z consumers actively using TikTok and expressing interest in fashion products. Structural Equation Modeling (SEM) analysis using Amos software was employed for data analysis. The results revealed that TikTok marketing strategies significantly influence brand image, supporting Hypothesis 1. Similarly, product innovation has a significant positive effect on brand image, confirming Hypothesis 2. However, TikTok marketing strategies were found not to significantly impact purchasing decisions (Hypothesis 3 rejected), while product innovation significantly influences purchasing decisions among Generation Z consumers (Hypothesis 4 accepted). Additionally, brand image was identified as a significant factor affecting purchasing decisions (Hypothesis 5 accepted). The study concludes that a positive brand image is shaped by effective TikTok marketing and innovative product presentation, influencing the preferences of Generation Z consumers. Despite TikTok marketing's limited impact on purchasing decisions, product innovation plays a crucial role in shaping consumer choices. Practical implications for the fashion industry targeting Generation Z are discussed, emphasizing the need for integrated marketing strategies aligning with innovative product offerings. Recommendations for future research include exploring additional factors and deeper analyses of consumer preferences on the TikTok platform, incorporating demographic and psychographic elements.
Optimization of Sentiment Classification on Online Comments using Multinomial Naïve Bayes and TF-IDF Feature Extraction and N-grams
The Naïve Bayes (NB) algorithm is a classifier method that calculates simple probabilities and it is suitable for text classification in the context of sentiment analysis. The classic variant of NB is Multinomial Naïve Bayes (MNB). The weakness of the MNB algorithm is the assumption of feature independence. This research uses a dataset of comments and reviews from various online platforms. This study uses the proposed method to handle the weakness of the MNB algorithm, namely the use of TF-IDF feature extraction and N-grams (1-gram to 5-gram), and the use of Chi-Square feature selection, as well as handling dataset imbalance using SMOTE (oversampling and undersampling method). The results of this study show that the use of pentagram (5-gram) with data that has been oversampled by SMOTE produces the highest accuracy value of 94% and an Area Under Curve (AUC) value of 100%
Optimizing Support Vector Machine Classification with SMOTE: Case Study of Alfagift Application User Reviews
Support Vector Machine (SVM) is a supervised learning algorithm that works by classifying based on classes that refer to patterns resulting from the training process. SVM has several commonly and popularly used kernels, one of which is the linear kernel. The weakness of SVM is in the "parameter selection" and its performance tends to be poor in the case of unbalanced datasets. The purpose of this study is to overcome the weaknesses of the SVM algorithm with the proposed method. This research uses a linear kernel with feature extraction that is Word2Vec with Skip-gram model, and in handling the data imbalance problem using SMOTE (oversampling) technique. The results showed that the unbalanced dataset produced an accuracy of 90% and the balanced dataset (SMOTE) produced an accuracy of 92%, so the SMOTE oversampling technique was proven to increase the accuracy results by 2%.
Interpersonal Communication Relations Of UIN North Sumatra Lecturer And Students In Learning Public Relations
interpersonal communication is an important process in exchanging messages, ideas, emotional expressions and information between individuals who interact directly. In an academic context, the implementation of effective interpersonal communication is becoming increasingly important for lecturers and students. This research aims to determine interpersonal communication relations between lecturers and students as well as students' understanding of public relations learning. The researcsh method used is qualitative case studies with data collection techniques through field observations, interviews and documentation. Communication Accommodation Theory is used as a theoretical framework to explain how individuals adjust their communication style in interactions. The research results show that interpersonal communication between lecturers and students at UIN North Sumatra in the context of Public Relations learning is going very well. Lecturers demonstrate high engagement and the ability to create comfortable and responsive interactions, while students respond positively and enthusiastically. The use of body language and effective verbal communication from lecturers enriches students' learning experiences and lecturers are not only communicators but also communication facilitators for students.
Analysis of Geospatial-Based Road Milestone Clustering Using K-Means and Davies-Bouldin Evaluation
Geographic Information System (GIS) has become a very important tool for spatial analysis and decision making in various fields. In this paper, the analysis process of grouping Kilometer Hectometer (KM/HM) road milestones use the K-Means method in the context of GIS. The KM/HM road milestones are road equipment made of concrete or signboards equipped with texts containing information about the distance and the name of the city to be traveled by road users. The length of the road that has such a long distance will make it difficult to maintain and to manage the road milestones that are scattered throughout the road. Currently, the process of mapping the location of the road milestones is still being carried out using the conventional method, in which the survey officer records the data on paper and measures it with the vehicle's odometer. However, this method often causes data loss, errors in determining the location, and lacks photographic evidence as a reference for assessing the condition of the road milestones. The role of GIS for survey officers is to map the road milestones and to visualize the road milestone data. The main objective is to gain meaningful insights from the spatial distribution of these road milestones, which will assist in better navigation and infrastructure planning. The K-Means method separates clusters from KM/HM road milestones which are identified based on geographical proximity. To assess the quality of these clusters, the evaluation is conducted using the Davies-Bouldin index (DBI) which provides a quantitative measure of inter-group similarity and within-group dissimilarity. For officers, it can be useful to find location points for the road milestones that have high damage conditions to prioritize to be repaired first. Based on the test results using DBI, it produces a value close to zero, which is equal to 0.1656, indicating that the clusters formed have very good quality.
Tradition and Belief: Exploration of Ritual Communication in Knitting Harmony of the Bugis Towani Tolotang Community, South Sulawesi, Indonesia
This research aims to explore the ritual communication experience in knitting the harmony of the Bugis Towani Tolotang community in Amparita Sidenreng Rappang, South Sulawesi, Indonesia. The Bugis Towani Tolotang community has various local traditions and beliefs regarding social care, kinship, diversity, togetherness and equality as solidarity identity which is realized in building harmonious relationships. Data was obtained qualitatively through several informal conversational interviews through ritual communication (Tallotte, 2018) based on ritual theory (Turner 1962, 1969), and phenomenology (Schutz, 1972). The article focuses on the experience of the Bugis Towani Tolotang community, which is a group of communities that believe in worshiping God Dewata Seuwae. Bugis Towani Tolotang constructs ritual communication as a local tradition with its symbols and is considered to have meaning, including the meaning of offerings, requests and hopes. This meaning results from the communication process of subjective interpretation of the rituals mutual cooperation rituals, marriage, sipulung, mappenre inanre, and tudang sipulung. The article contributes to realize harmonious community relations through the discussion of finding the meaning of Bugis Towani Tolotang rituals in the hope of living together peacefully in kinship ties. Strengthening solidarity is essential as it forms bonds of kinship and togetherness. This contribution offers opportunities for traditional accommodation and ways of forming kinship bonds.