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All Journal ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Hafizah, Rini
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Social Sciences

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Upaya SDN Pangeran 3 Banjarmasin dalam Mengadaptasi Kurikulum Merdeka di Tengah Tantangan Internal dan Eksternal Siregar, Rima Nur Azizah; Firlana, Hanif; Hafizah, Rini; Noorkhalisah, Noorkhalisah; Pratiwi, Diani Ayu
ARZUSIN Vol 5 No 3 (2025): JUNI
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/arzusin.v5i3.5660

Abstract

The implementation of the Merdeka Curriculum at the primary education level is a strategic step in addressing the challenges of 21st-century education, which demands flexibility, creativity, and contextual learning. This study aims to examine the adaptation process of the Merdeka Curriculum at SDN Pangeran 3 Banjarmasin, a school with limited resources but showing strong initiative in its implementation. Using a qualitative approach with a case study method, data were obtained through in-depth interviews, direct observations, and documentation. The results indicate that although the teachers at this school face challenges such as limited access to training, inadequate facilities, and restricted financial support, they are able to creatively and contextually implement the Merdeka Curriculum. Strategies such as project-based learning (P5), utilizing local learning resources, and informal collaboration among teachers have been key to success. Supportive leadership from the principal and a culture of gotong royong (collective work practice) in the school environment also contribute to the sustainability of this curriculum adaptation. These findings emphasize that the success of the Merdeka Curriculum implementation relies not only on the availability of infrastructure but also on the resilience, creativity, and collaboration of educators. This study recommends strengthening flexible and equitable teacher training, as well as affirmative policies for small schools, so that curriculum implementation can proceed in a more inclusive and sustainable manner.
Machine Learning Implementation for Sentiment Analysis on X/Twitter: Case Study of Class Of Champions Event in Indonesia Hafizah, Rini; Saragih, Triando Hamonangan; Muliadi, Muliadi; Indriani, Fatma; Mazdadi, Muhammad Itqan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.81

Abstract

Sentiment analysis on social media is becoming an important approach in understanding public opinion towards an event. Twitter, as a microblogging platform, generates a large amount of data that can be utilized for this analysis. This study aims to evaluate and compare the performance of three classification algorithms, namely Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), in sentiment analysis related to the Clash of Champions event in Indonesia. To represent the text data, two feature extraction techniques are used, namely Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). In addition, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle data imbalance, while model optimization is performed using GridSearchCV. The research dataset consists of 1,000 tweets collected through web scraping, then manually processed and labeled before model training and testing. The results showed that the TF-IDF technique provided superior results compared to BoW. The Random Forest model with TF-IDF achieved the highest accuracy of 91%, while XGBoost with TF-IDF had the highest Area Under the Curve (AUC) of 0.91. The findings confirm that the selection of appropriate feature extraction techniques and algorithms can improve accuracy in sentiment analysis. This study can be applied in public opinion monitoring and data-driven decision-making. Future research can explore word embedding techniques and transformer-based deep learning models to improve semantic understanding and accuracy of sentiment analysis.
Co-Authors Diani Ayu Pratiwi Fatma Indriani Firlana, Hanif Muhammad Itqan Mazdadi Muliadi Noorkhalisah, Noorkhalisah Saragih, Triando Hamonangan Siregar, Rima Nur Azizah