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PENGEMBANGAN KEWIRAUSAHAAN KELOMPOK TANI JAGONG FAMILY MELALUI PELATIHAN MANAJEMEN USAHA DAN PEMASARAN ONLINE
Get PDFThis community service program focused on the entrepreneurial development of the Jagong Family Farmer Group in Tebing Tinggi. The program was designed to address the group's limited knowledge and skills in modern business management and low digital literacy for online marketing. The methodology involved intensive training and mentoring in corn emping production, financial, and marketing management, along with fundamental digital training encompassing social media utilization and e-commerce introduction. The results indicate a quantifiable and significant improvement: the average knowledge score of the group members regarding business management and digital marketing increased by 63.46% (from 52 to 85). This enhanced capacity also resulted in better management of production, finance, and marketing, with social media utilization successfully increasing product promotional reach by 35%. In conclusion, the training successfully transformed the Jagong Family Farmer Group from traditional farmers into professional, economically independent, and digitally competitive business actors. This increase in digital literacy and business management skills provides a direct and positive contribution to the Sustainable Development Goals (SDGs), specifically in food security and economic growth
PENINGKATAN KAPASITAS KWT SERUNI MELALUI PARTICIPATORY ACTION RESEARCH DALAM URBAN FARMING DAN HIDROPONIK
Get PDFThis community service program aims to enhance the capacity and self-reliance of the Seruni Women Farmer Group (KWT Seruni) in supporting food security through training on urban farming and hydroponic cultivation, implemented using a Participatory Action Research (PAR) approach. The program engaged lecturers, students, and group members in stages of socialization, training, technology implementation, mentoring, and sustainability planning. Evaluation results indicated a 12% improvement in participants’ understanding based on pre-test and post-test scores, demonstrating the program’s effectiveness in strengthening technical competence. Technological implementation, including the construction of a seed house and the use of pH and TDS meters, resulted in 1,500 high-quality seedlings and a hydroponic pakcoy harvest of 18.4 kg. Participants successfully applied practical skills independently, particularly in nutrient management and the cultivation of economically valuable crops. The program also fostered an internal training system for new members and generated socio-economic benefits, such as increased household income and strengthened women’s roles in agriculture. Overall, this activity aligns with SDGs 1, 2, 5, and 12
Pengaruh Content Marketing pada Instagram terhadap Customer Engagement dan Brand Awareness Miniso Indonesia
Get PDFThis study is motivated by the low level of audience interaction with the Instagram content of @minisoindo, despite the account having a large number of followers. This shows that there are challenges in utilizing social media optimally to increase customer engagement and build brand awareness. The purpose of this study is to analyze the influence of content marketing on customer engagement and brand awareness, as well as identify the most effective content strategy elements in supporting these two variables. This study uses a quantitative approach with an explanatory method. Data was collected through an online survey of 100 respondents who were followers of the @minisoindo Instagram account using a non-probability sampling technique with a purposive sampling approach. Data analysis was carried out with SEM-PLS. The findings show that content marketing has a positive and significant effect on customer engagement by 77,9% and on brand awareness by 57,3%. In addition, the study found that easy-to-understand, easy-to-find, and accurate content is the most effective element of strategies in building customer engagement and strengthening brand awareness. Future research is recommended to examine the effectiveness of content marketing on other platforms such as TikTok, YouTube, or X by employing a mixed methods approach to gain deeper insights.Penelitian ini dilatarbelakangi oleh rendahnya tingkat interaksi audiens terhadap konten Instagram @minisoindo, meskipun akun tersebut memiliki jumlah pengikut yang tinggi. Hal ini menunjukkan adanya tantangan dalam memanfaatkan media sosial secara optimal untuk meningkatkan customer engagement dan membangun brand awareness. Tujuan penelitian ini adalah menganalisis pengaruh content marketing terhadap customer engagement dan brand awareness, serta mengidentifikasi elemen strategi konten yang paling efektif dalam mendukung kedua variabel tersebut. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksplanatori. Data dikumpulkan melalui survei online terhadap 100 responden yang merupakan pengikut akun Instagram @minisoindo dengan teknik non-probability sampling menggunakan pendekatan purposive sampling. Analisis data dilakukan dengan SEM-PLS. Hasil penelitian menunjukkan bahwa content marketing berpengaruh positif dan signifikan terhadap customer engagement maupun brand awareness. Selain itu, penelitian ini menemukan bahwa konten yang mudah dipahami, mudah ditemukan, dan akurat merupakan elemen strategi yang paling efektif dalam membangun interaksi audiens dan memperkuat kesadaran merek. Penelitian selanjutnya disarankan untuk meneliti efektivitas content marketing pada platform lain seperti TikTok, YouTube, atau X dengan menggunakan metode campuran agar hasil yang diperoleh lebih mendalam
PERCEPTION AND BARRIERS TO MOOC ADOPTION: A CASE STUDY OF KARTU PRAKERJA RECIPIENTS
Get PDFThe Indonesian government launched the Pre-Employment Card (Kartu Prakerja) program to enhance workforce skills and address economic challenges. This program provides training through online platforms, including Massive Open Online Courses (MOOCs). The UTAUT2 model was employed as a framework to understand the factors influencing the acceptance and use of educational technology in this context. This study examines the effects of UTAUT2 variables—performance expectancy, effort expectancy, habit, traditional barriers, platform content, access limitations, interaction limitations, facilitating conditions, hedonic value, price value, and social influence—on the intention and adoption of MOOCs among Pre-Employment Card participants. The sample consisted of 222 respondents who were users of the Prakerja platform. Data were collected using a questionnaire and analyzed through Structural Equation Modeling (SEM) with the support of PLS-SEM software. In addition, a sentiment analysis was conducted on comments posted on the official Instagram account @prakerja.go.id to explore public perceptions of the program. The findings reveal that 46.2 percent of public sentiment was negative, particularly related to the program implementation and the use of partner MOOC platforms. SEM analysis further indicates that hedonic value, habit, and social influence have positive and significant effects on the intention and adoption of MOOCs. The moderation analysis by gender shows that performance expectancy, hedonic value, and social influence are stronger among males, whereas effort expectancy, habit, and platform content are stronger among females
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ
Get PDFCustomer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was 48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods
ASSESSING SMPIT AJIMUTU GLOBAL INSANI WEBSITE QUALITY USING THE WEBQUAL 4.0 METHOD
Get PDFDigitalization in the world of education encourages schools to have quality websites to provide online information and learning services. This study aims to measure the quality of the SMPIT Ajimutu Global Insani website using the Webqual 4.0 method, which involves three main dimensions: usability quality, information quality, and service interaction quality. This research method involves a survey of 50 respondents consisting of teachers, students, and parents of students. Data were analyzed descriptively using a Likert scale to evaluate the level of user satisfaction. The results showed that the information quality dimension had the highest score (4.2), followed by service interaction quality (4.0), while usability quality scored the lowest (3.8). These findings indicate that the website content is relevant, but navigation and interface design need improvement. Recommendations are given to improve the quality of the website, including optimizing interactive features and adding multimedia content. The implementation of the results of this study is expected to support the digital transformation of schools more effectively
ENHANCED FLOWER IMAGE CLASSIFICATION USING MOBILENETV2 WITH OPTIMIZED PERFORMANCE
Get PDFFlower classification is an essential activity in multiple fields, including healthcare, cosmetics, agriculture, and environmental monitoring. Deep learning has achieved notable success in intricate picture categorization problems, especially through the utilization of lightweight convolutional neural network (CNN) architectures like MobileNet and MobileNetV2. This work assesses and contrasts the efficacy of four prevalent optimizers Adam, RMSProp, SGD, and Nadam on datasets of flower and herbal leaf images. Experiments were performed using a uniform training configuration on a CPU-based system devoid of GPU acceleration, evaluating both model efficacy and computational efficiency. Evaluation criteria including accuracy, precision, recall, F1-score, and loss were utilised, augmented by confusion matrix analysis. The findings indicate that MobileNetV2 regularly surpasses the baseline MobileNet, with RMSProp attaining the highest accuracy (99.52%) and the lowest loss (0.0126) on the herbal dataset. In the flower dataset, RMSProp achieved the highest accuracy of 96.67%. Moreover, MobileNetV2 necessitated increased memory and extended training duration, while delivering superior classification performance overall. These findings underscore the significance of optimizer selection and model architecture in lightweight deep learning applications, especially for deployment on resource-limited devices
HYBRID LEARNING STRATEGY COMBINING MODEL-LEVEL TRANSFER LEARNING AND DATA-LEVEL AUGMENTATION FOR BRAIN CANCER CLASSIFICATION
Get PDFDue to the complexity of images, size, and balance of data, brain cancer diagnosis is still one of the most challenging problems to solve. It is shown that traditional classification methods based on 'first principles' do not produce ideal results, often due to different brain tumours. This research uses a hybrid model that leverages transfer learning with data augmentation and AI refinement to categorise three brain tumours: glioma, meningioma, and others. This research aims to improve the classification performance of brain cancer detection using this model. The methodology uses a framework created with a specific dataset, mixed data enhancement, and InceptionV3 model refinement to improve performance. With a validation accuracy of 0.95, the F1 scores for glioma, meningioma, and other brain tumours were 0.98, 0.95, and 0.92, respectively. This hybrid model achieves accuracy without complexity in design while addressing data scarcity and balance issues. The primary focus of this research was to create an effective and robust model for classifying brain cancers that is easy to use in low-resource clinical environments. The results demonstrate how deep learning can improve diagnostic precision and provide a scalable method for detecting brain cancer in the early stages of medical imagin
CLUSTER-BASED MACHINE LEARNING APPROACHES FOR PREDICTING DAILY MAXIMUM TEMPERATURES IN INDONESIA UNDER CLIMATE CHANGE
Get PDFClimate change is increasing the frequency of extreme temperatures in Indonesia, creating significant prediction challenges due to its geographical diversity. To address this, the study proposes a spatially adaptive framework using BNU-ESM and ERA5 data (1980–2005). The Indonesian region was classified into four climate clusters via K-Means, where Support Vector Regression (SVR), Random Forest (RF), and XGBoost models were evaluated. Results show SVR consistently outperformed other models across all clusters. In stable regions, SVR achieved the highest accuracy (RMSE 0.10; MAE 0.08) and remained superior even in the most volatile clusters. The study's novelty is the integration of clustering with comparative model evaluation, offering a robust methodology for precise, regionally adaptive climate early warning systems.Practically, this predictive model can support national mitigation strategies by enabling proactive resource allocation and targeted interventions in high-risk climate zones
IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS
Get PDFGinger (Zingiber offivinale), turmeric (curcuma longa), and galangal (Alpinia galanga) plants are the result of Indonesia's wealth which has high economic and health value. This type of plant has high economic and health value, so its accurate identification is very important in the agricultural and pharmaceutical fields. By combining image classification methods, PCA, KNN, this research aims to develop a system that can identify ginger, turmeric, and galangal automatically and accurately. It is hoped that this system can not only provide a solution for efficient plant identification, but can also contribute to the management of natural resources and the development of herbal plant-based products in Indonesia. Data collected by taking pictures and then processed using MATLAB. This research aims to identify ginger, turmeric and galangal plants using euclidean distance and extract shape and texture characteristics. Shape feature extraction using RGB, HVS, and Area. This research implements the PCA and K-Nearest Neighbor methods in classifying data. Meanwhile, the KNN method is applied by measuring the closest distance between the test data and the training data. In this research there are labels and attributes, labels taken from the level of fruit maturity and attributes obtained from the results of image feature extraction. These attributes are R(red), G(green), B(blue), H(hue), S(saturation), V(value), Area. The accuracy results obtained from the classification of ginger, turmeric and galangal plants using the KNN method were 80% with a K=3 value obtained from 8 test data with accurate classification, and 20% from 2 test data with inaccurate classification