Publication Search

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

This research was designed to explore the extent to which public beliefs influence the implementation of Digital traceability  systems in the halal industrial sector. The approach used was quantitative with a survey method, where questionnaires were distributed to 60 respondents who were consumers of halal products in Indonesia. Data were analyzed using simple linear regression via Microsoft Excel. Research findings indicate that public confidence has a positive and significant influence on the adoption of Digital traceability  systems, with a regression coefficient of 0.476 and a significance level of 0.000 (<0.05). In addition, the coefficient of determination (R Square) value of 0.219 indicates that public confidence contributes 21.9% to the implementation of the Digital traceability  system, while the rest is determined by other factors that were not researched. These findings confirm that public trust is an important element in encouraging acceptance of digital technology, especially in the halal industry which relies heavily on transparency and consumer confidence. Thus, implementing a Digital traceability  system that is supported by information openness and easy access to technology can be an effective strategy to strengthen consumer trust while expanding technology adoption.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 byToni Firmansyah, S. Farm., Apt. and Asrianty Salam, Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The enactment of Indonesia's Law Number 20 of 2025 on the Code of Criminal Procedure (KUHAP 2025), effective January 2, 2026, introduces a paradigmatic shift in the coordination between investigators and public prosecutors: Article 58 mandates active coordination from the investigation stage, fundamentally departing from the sequential-passive model of the former KUHAP, while Article 70 imposes a strict seven-day deadline for indictment drafting after case files are declared complete. This study examines two interconnected questions: (1) how the legal framework governing investigator–prosecutor coordination is structured under KUHAP 2025 and related legislation; and (2) how that framework is implemented in practice at the Purwakarta District Prosecutor's Office. A normative–empirical mixed-method design was employed, integrating statutory, conceptual, and case-study approaches. Data were gathered through in-depth interviews with prosecutors and investigators at Purwakarta District Prosecutor's Office and Purwakarta Police Resort, case document analysis, and field observation. The theoretical framework combines Lawrence M. Friedman's Legal System Theory and Soerjono Soekanto's Law Enforcement Theory. Findings reveal that KUHAP 2025 delivers substantial normative advancement yet harbours three critical regulatory gaps: the absence of binding technical protocols for implementing mandatory active coordination, the lack of uniform and measurable case-file completeness standards, and no formal mechanism for resolving institutional disagreements on legal interpretation. On the ground, coordination at Purwakarta still operates under the old sequential-passive pattern despite the new law: case-file returns (P-19) remain frequent, driven primarily by absent expert testimony, insufficient factual narration in examination records, and mismatches between charged articles and legal facts. A Friedman–Soekanto diagnostic reveals simultaneous dysfunction across all three legal system components substance, structure, and legal culture with the entrenched 'waiting culture' between the police and the prosecution identified as the most resistant obstacle to reform.

Postpartum depression is a postpartum mental health disorder that significantly impacts maternal well-being, infant development, and family functioning. The high prevalence of postpartum depression in Indonesia is due to limited access to health services, low mental health literacy, and social stigma in the community. This indicates a significant gap between the need for maternal mental health services and the availability of existing interventions, making education a crucial component in efforts to prevent postpartum depression early. This study aims to analyze the prevention of postpartum depression in postpartum mothers through telenursing-based education and screening using the Edinburgh Postnatal Depression Scale (EPDS) in the community. A descriptive case study design was used, involving one respondent, a 25-year-old primigravida mother residing in the Bantul area. The intervention was implemented online via WhatsApp and video calls, including structured health education on postpartum psychological changes, adaptive coping strategies, and the importance of social support. The intervention also included daily remote monitoring of the respondent's condition via the WhatsApp mobile application. The EPDS was administered as a pre-test and post-test to evaluate changes in the respondent's psychological condition. The findings showed a significant decrease in the EPDS score from 16 (moderate depression) to 6 (minimal depression), indicating significant psychological improvement. These results imply that integrating EPDS screening, structured health education, and daily monitoring is an effective and accessible community-based approach to preventing postpartum depression, particularly for mothers with limited mobility and access to health services.

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

Jakarta, the capital of Indonesia, is known for its high congestion levels. Data from the TomTom Traffic Index shows that Jakarta ranked 30th in the world in 2023 as one of the most congested cities, with a congestion level reaching 53% during peak hours. Pisangan Lama in East Jakarta is one of the densely populated areas, adjacent to busy roads. The main campus of STIKOM CKI, also located in East Jakarta, is situated along a route prone to heavy traffic. Given the congestion issues and the lack of information on the nearest routes, this study aims to implement the A* algorithm to find the shortest route from Pisangan Lama, East Jakarta, to the main campus of STIKOM CKI. The A* algorithm is chosen for its optimal routing capabilities. Based on research on three routes (Jl. I Gusti Ngurah Rai, Jl. Basuki Rachmat, and Jl. Raya Kalimalang), the results show that the route via Jl. Basuki Rachmat is the shortest, with a distance of 7.7 km. The implementation of the A* algorithm is expected to provide an efficient solution for the community in finding the nearest route.

Based on the 2023 Indonesian Health Survey, the prevalence of overweight among adults reached 23.4% nationally and 24.5% in East Java Province. This condition highlights the need for food innovation, particularly high-fiber snacks suitable for overweight adults. This study aimed to develop cereal based on okara (soy pulp) flour and mung bean flour as an alternative high-fiber food product. Organoleptic testing was conducted on 30 semi-trained panelists evaluating color, aroma, texture, and taste using hedonic and hedonic quality tests. Data were analyzed using the Kruskal–Wallis test followed by the Mann–Whitney test, while nutrient content analysis was analyzed using One Way ANOVA followed by Duncan’s post hoc test. The results showed that in the hedonic test, color, texture, and taste parameters were significantly different (p<0.05), while aroma was not significantly different (p>0.05). In the hedonic quality test, color and taste showed significant differences (p<0.05), whereas aroma and texture did not (p>0.05). Overall, formulation 922 was the most preferred by panelists. The nutritional content of the cereal ranged from 407–410 kcal for energy, 76.44–77.53% carbohydrates, 8.91–9.66% protein, 6.80–7.30% fat, 5.20–5.51% moisture, 1.26–1.41% ash, and 9.72–10.90% total dietary fiber. The developed cereal is classified as a high-fiber food and has potential as a healthy snack for overweight adults.

The development of the digital economy has driven increased customer interaction through online chat services, making customer satisfaction a key factor in business success. Response speed and chat service quality are two important aspects in shaping the customer experience, but previous research has tended to examine them separately. This study aims to analyze the influence of online chat services and response speed on customer satisfaction partially and simultaneously. The method used is a qualitative approach with a literature review of 12 scientific articles from 2020–2025 obtained from academic databases such as Google Scholar and SINTA. The analysis technique used is descriptive-critical through the identification, comparison, and synthesis of previous research findings. The results show that online chat services have a positive effect on customer satisfaction, primarily through interaction quality such as information accuracy, ease of use, and problem-solving ability. Response speed has also proven to be an important determinant, where a fast response significantly increases customer satisfaction. However, speed without quality has the potential to decrease satisfaction. The discussion shows that the two variables have a complementary and inseparable relationship. Online chat services function as a medium for interaction, while response speed is a quality attribute that determines the effectiveness of the service. Therefore, the integration of both in one model is the main contribution of this research in filling the literature gap, especially in the context of e-commerce in Indonesia.

This study investigates the effect of total risk on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Total risk is operationalized through the systematic risk proxy (Beta/β), estimated via the Capital Asset Pricing Model (CAPM) framework as the covariance between individual stock returns and the market return divided by the variance of market returns, using the Jakarta Composite Index (JCI) as the market benchmark. Firm performance is measured through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is operationalized following Gerson et al. (2025) as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies, with model selection guided by the Chow and Hausman specification tests. The Fixed Effect Model (FEM) is adopted for ROA, ROE, and SG, while the Random Effect Model (REM) is applied for Tobin’s Q. Results indicate that systematic risk exerts a significant negative effect on ROA (β = −0.312; p < 0.01) and ROE (β = −0.278; p < 0.01), but is statistically non-significant for Tobin’s Q, suggesting that capital market pricing in Indonesia does not fully incorporate systematic risk information. Critically, systematic risk exerts the largest and most significant negative effect on sustained growth (β = −0.347; p < 0.01), revealing a dual transmission mechanism through which risk suppresses ROE while simultaneously inducing more conservative dividend policies, both of which constrain long-run growth sustainability. These findings carry important implications for corporate risk management strategy and empirically enrich the literature on risk, performance, and growth in emerging capital markets.

The rapid growth of digital technology and social media has reshaped communication practices, consumer behavior, and marketing approaches, particularly among Generation Z, who are widely recognized as digital natives. For this generation, social media serves as a major channel for obtaining information and exploring product references before making purchase decisions. In this environment, influencer marketing has emerged as a prominent promotional approach because it can foster audience interaction, emotional connection, and trust more effectively than conventional advertising. This study examines the role of influencer marketing in shaping the digital behavior of Generation Z in Indonesia. A qualitative literature review method was employed by analyzing secondary sources drawn from academic journals, books, and related publications. The findings indicate that influencer marketing has a meaningful influence on information-seeking behavior, decision-making processes, and the level of digital engagement among Generation Z. Influencers are commonly viewed as more credible, relatable, and authentic sources of information. Furthermore, the effectiveness of influencer marketing is determined more by credibility, authenticity, and the quality of interaction than by follower count alone. These findings suggest that companies should adopt digital marketing strategies that are more interactive, personalized, and relationship-oriented in order to engage Generation Z more effectively.

The rapid digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

This study aims to analyze the skills gap in the era of the Industrial Revolution 4.0 and the factors influencing it. The research employed a descriptive-analytical approach based on literature studies and the author’s analysis of the dynamics of formal education curricula and the development of industrial needs. The findings indicate that formal education curricula that are less adaptive to technological developments are the main factor causing the skills gap. In addition, the slow adaptation to technology in educational and industrial environments, as well as the unequal distribution of skills development across various regions in Indonesia, further widen the gap between graduates’ competencies and labor market demands. This condition affects the low readiness of the workforce in facing digital transformation and global competition. The study concludes that the skills gap is a strategic issue requiring serious attention from the government, educational institutions, and the industrial sector. Curriculum reform that is responsive to technological advancements, strengthening digital literacy, and ensuring equal access to training and skills development are essential steps to improve the competitiveness of Indonesian human resources. These efforts are expected to support poverty reduction, reduce social inequality, and achieve the long-term national development goals toward Indonesia Golden Vision 2045.

This study aims to analyze the effect of profitability, company size, and type of public accounting firm on Key Audit Matters (KAM), with audit fees as a moderating variable in manufacturing sector companies listed on the Indonesia Stock Exchange for the 2022–2024 period. This study used a quantitative approach with a purposive sampling method. Data were obtained from audited financial statements and annual reports published on the official Indonesia Stock Exchange website. The study sample consisted of 67 companies, with a total of 201 observations. Data analysis techniques used included descriptive statistical analysis, classical assumption tests, multiple linear regression, and Moderated Regression Analysis (MRA). The results showed that profitability had a significant negative effect on KAM disclosure, while company size had a significant positive effect on KAM disclosure. The type of public accounting firm showed a significant negative effect on KAM disclosure. Furthermore, audit fees did not moderate the relationship between profitability and KAM, but they did moderate the relationship between company size and type of public accounting firm on KAM disclosure. This study demonstrates that company and auditor characteristics play a significant role in determining the disclosure of Key Audit Matters in the independent auditor's report.

This study examines the effect of working capital on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Working capital is operationalized through three distinct proxies derived from Akgün and Memiş Karatəs (2021): the Cash Holding Level (CHL), which measures the proportion of cash and cash equivalents relative to total assets; the Cash Interactive Effect (CIE), which captures the efficiency of converting revenue into operating cash flow; and the Gross Working Capital Ratio (GWCR), which reflects the share of current assets within total assets. Firm performance is assessed through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is measured using the model proposed by Gerson et al. (2025), expressed as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies. The study employs the Fixed Effect Model (FEM) for ROA and ROE, and the Random Effect Model (REM) for Tobin’s Q, as determined by the Hausman specification test. The findings reveal that CHL and CIE exert significant positive effects on ROA and ROE, while CIE is the only proxy to produce a statistically significant positive effect on Tobin’s Q. With respect to sustained growth, CHL and GWCR demonstrate significant negative effects, whereas CIE shows a significant positive effect, indicating that operational efficiency dimensions of working capital actively support long-term growth sustainability. These results reinforce the liquidity management theory and contribute empirical evidence that the structure and efficiency of working capital are strategic determinants of both short-term financial performance and long-term growth sustainability in Indonesia’s consumer goods manufacturing sector.

Stunting remains a persistent nutritional challenge that threatens human capital development in Indonesia. This study examines the effects of exclusive breastfeeding, female education, sanitation, access to safe drinking water, inadequate food consumption, and poverty on stunting prevalence across 34 provinces in Indonesia during 2017–2024. The study employs a random-effects Panel EGLS estimator with Panel Corrected Standard Errors (PCSE) to address heteroskedasticity and cross-sectional dependence in provincial panel data. The findings reveal that exclusive breastfeeding, female education, and adequate sanitation significantly reduce stunting prevalence, while poverty significantly increases it. Interestingly, inadequate food consumption is negatively associated with stunting prevalence, potentially reflecting the contribution of government nutritional assistance and social protection programs. In contrast, access to safe drinking water does not show a statistically significant effect. Among the explanatory variables, female education is strongly associated with reductions in stunting. These findings highlight the importance of strengthening women’s education, improving sanitation quality, and expanding poverty-alleviation and nutrition-sensitive interventions to accelerate reductions in stunting and support the achievement of Sustainable Development Goal 2 in Indonesia.

This study discusses the reform of criminal law regarding acts of abuse of power within the Indonesian legal system. Abuse of power committed by public officials constitutes a serious violation of the rule of law and the principle of equality before the law because it can harm society and weaken public trust in the government. The old Criminal Code inherited from the colonial era was considered incapable of providing an optimal deterrent effect against perpetrators of abuse of authority. Therefore, the enactment of Law Number 1 of 2023 concerning the New Criminal Code became an important step in the reform of criminal law in Indonesia. This study aims to analyze criminal law reform policies in overcoming abuse of power and the effectiveness of their implementation in law enforcement. The results show that the new Criminal Code expands regulations regarding abuse of authority with stricter criminal sanctions and additional penalties in the form of revocation of office rights. However, its implementation still faces challenges such as political intervention, low integrity of law enforcement officials, and the potential overlap with the Corruption Eradication Law. Therefore, the success of criminal law reform requires the support of legal substance, legal structure, and legal culture that work in harmony.