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All Journal International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Pendidikan IPA Buletin KKN Pendidikan Jurnal Ilmiah Kampus Mengajar Jurnal Keilmuan dan Keislaman International Conference on Education for All Indonesian Journal on Learning and Advanced Education (IJOLAE) Jurnal Penelitian Sains Teknologi
Muhammad Syahriandi Adhantoro
Faculty Of Communication And Informatics, Universitas Muhammadiyah Surakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Impact of Adaptive Educational Game Applications on Improving Student Learning: Efforts to Introduce Nusantara Culture in Indonesia Hernawan Sulistyanto; Djumadi Djumadi; Bambang Sumardjoko; Muhammad Izzul Haq; Gamal Abdul Nasir Zakaria; Sabar Narimo; Dyah Astuti; Muhammad Syahriandi Adhantoro; Devary Pradana Setyabudi; Yasir Sidiq; Naufal Ishartono
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 5, No. 3, September 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v5i3.23004

Abstract

This study aimed to introduce Nusantara culture based on educational games by adjusting students' learning styles. Culture as an ancestral heritage tradition needs to be preserved by introducing it to the younger gen-eration from an early age. However, the survey results found that less than 26% of student respondents un-derstood Nusantara culture well. Compared to previous research, the model of cultural introduction through adaptive educational games is more fun because it is adapted to the way students learn. This research was carried out using the Design-Based Research (DBR) method through 4 stages of the procedure. The feasibil-ity test and application effectiveness test were carried out on a group of students from several elementary schools in Indonesia, who were taken using a cluster random sampling technique. The results of media design and content validation obtained an average value of 0.76 and 0.82, which means that the media is declared valid. The feasibility test used the System Usability Scale (SUS) with an average value of 80% in the acceptable category. The results of the research obtained a description of the comparison of the final scores of the control class and the experimental class, which was 55 compared to 75. This study concluded that learning media for introducing Indonesian culture based on adaptive educational games had a positive impact by effectively increasing learning outcomes on students' understanding of Indonesian culture. Further development of this game application can be expanded in the application of animation in more depth.
Students' Perceptions of Scientific Writing Teaching: Implications for Improving Learning Effectiveness Purnomo, Eko; Markhamah, Markhamah; Prayitno, Harun Joko; Adhantoro, Muhammad Syahriandi; Rohmadi, Muhammad; Yusof, Norazmie; Alas, Yabit
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 8, No. 1, January 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v8i1.15023

Abstract

This study aims to analyze students’ perceptions of scientific writing learning within an advanced and innovative learning framework. The research employed a descriptive quantitative approach using a Likert-scale questionnaire completed by 202 students. The data were analyzed descriptively to obtain the mean, standard deviation, and percentage distribution across five main aspects: participation and interaction, clarity of materials and use of examples, feedback and collaborative guidance, development of academic skills, and time management and variation in innovative learning methods. The results indicate that all aspects achieved mean scores above 3.00 (on a 1–4 scale), which are categorized as high. The highest scores were obtained for teaching method variation (3.14) and material implementation support (3.14), while the lowest score was found in learning time allocation (3.06). Although the majority of students selected the “Agree” category, the proportion of “Strongly Agree” responses remained relatively low (13–17%), suggesting the need for improvement in time allocation, feedback quality, and diversity of examples provided. This study underscores the importance of interactive learning design, consistent use of formative feedback, and the implementation of innovative learning methods to enhance the quality of students’ learning experiences. These findings can serve as a reference for teachers and schools in designing more effective and sustainable scientific writing instruction strategies.
IndoBERT-Based Sentiment Analysis of Electric Motorcycle Policy in Indonesia Using Instagram Data Muhammad Syahriandi Adhantoro; Faris Athoil Haq; Dody Hartanto; Aninditawidagda Pandam Sudaryanto
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.17021

Abstract

This study aims to analyze public sentiment toward the procurement of electric motorcycles within the Nutritional Service Fulfillment Unit/ Satuan Pelayanan Pemenuhan Gizi (SPPG) program in Indonesia by utilizing data from Instagram. The approach employed is a deep learning-based sentiment analysis using the IndoBERT model, which has been fine-tuned to classify data into positive, negative, and neutral categories. The research stages include data collection, preprocessing, labeling, model development, and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that public sentiment is predominantly negative at 80%, followed by positive sentiment at 15% and neutral sentiment at 5%. Further analysis reveals that negative sentiment is primarily driven by issues related to budget prioritization, infrastructure readiness, and policy effectiveness, while positive sentiment is associated with environmental benefits and improved service distribution efficiency. The model evaluation demonstrates that IndoBERT achieves high performance, with an accuracy of 0.89, precision of 0.88, recall of 0.90, and F1-score of 0.89. These findings indicate that IndoBERT is effective in capturing the contextual nuances of the Indonesian language in unstructured social media data. This study contributes to the advancement of transformer-based sentiment analysis methods and provides data-driven insights to support more responsive and evidence-based policymaking.
A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques Imaduddin, Helmi; Yusuf, Siti Agrippina Alodia; Adhantoro, Muhammad Syahriandi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11247

Abstract

Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Co-Authors Adi Jufriansah Al-Kubro, Putri Balqis Alas, Yabit Anam Sutopo Aninditawidagda Pandam Sudaryanto Astuti, Dyah Baihaqi, Muhamad Adzib Bakri, Yuhanis Mhd Bambang Sumardjoko Chandra Anugrah Putra D Djumadi Devary Pradana Setyabudi Dian Artha Kusumaningtyas Dody Hartanto Dyah Astuti Eko Purnomo Faris Athoil Haq Fitriany, Devinda Nur Gamal Abdul Nasir Zakaria Hanifah, Dini Haq, Muhammad Izzul Harun Joko Prayitno Helmi Imaduddin Hernanjaya, Arif Nugraha Hernawan Sulistyanto Ishafit Ishafit Jumadi Jumadi Kurniaji, Ganno Tri Buana Kurniaji, Ganno Tribuana Kurniasari, Efi Lenny Puspita Dewi Markhamah Moh. Irma Sukarelawan Muhammad Izzul Haq Muhammad Qolbi Shobri Muhammad Rohmadi Muhroji Mutiara Sari, Ananda Naufal Ishartono Nova Tri Romadloni Pradana, Fajar Gemilang Ratri, Dyan Permata Rhavida Anniza Andyani Sabar Narimo Setyabudi, Devary Pradana Setyawati, Yayuk Sofyan Anif Sopianti, Herlin Ulya, Walidatul Umardhani, Niluh Sekar Zulfa Wahyu Nanda Eka Saputra, Wahyu Nanda Eka YAN ADITYA PRADANA Yasir Sidiq Yusof, Norazmie Yusuf, Siti Agrippina Alodia Zakaria, Gamal Abdul Nasir