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PELATIHAN IMPLEMENTASI KURIKULUM MERDEKA BAGI GURU-GURU MATEMATIKA DI KABUPATEN TIMOR TENGAH UTARA, PROVINSI NUSA TENGGARA TIMUR Cecilia Novianti Salsinha; Chatarina Enny Murwaningtyas; Marcellinus Andhy Rudhito; Hongki Julie; Amsikan Stanislaus; Selestina Nahak; Hendrika Bete
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 1 (2024): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i1.204-214

Peluncuran Kurikulum Merdeka di Indonesia telah menandai perubahan penting dalam sektor pendidikan, khususnya bagi para guru matematika di Kabupaten Timor Tengah Utara. Tantangan baru dan peluang untuk pengembangan profesional ini menuntut pemahaman yang lebih mendalam tentang kurikulum dan adaptasi metode pengajaran yang sesuai. Untuk mendukung transisi ini, sebuah program pelatihan khusus telah dirancang, bertujuan untuk memperdalam pemahaman guru tentang berbagai aspek Kurikulum Merdeka dan meningkatkan keterampilan praktis mereka dalam penerapannya. Melalui diskusi interaktif dan tugas-tugas kreatif, fokus pelatihan diberikan pada pengembangan Alur Tujuan Pembelajaran (ATP) dan modul pembelajaran yang inovatif, mendorong pendekatan pengajaran yang lebih efektif dan kreatif. Hasilnya, terlihat peningkatan signifikan dalam kemampuan guru untuk menerapkan strategi dan metode yang diajarkan, menciptakan lingkungan pembelajaran yang lebih dinamis dan menarik. Kemajuan ini tidak hanya memperkuat keterampilan pedagogis guru tetapi juga meningkatkan kualitas pengalaman belajar siswa, memberikan dampak positif pada pengajaran matematika secara umum.

Perbandingan Metode Monte Carlo Antithetic Variate dan Control Variate dalam Penentuan Harga Opsi Barrier Knock-Out Murwaningtyas, Chatarina Enny; Haryono, William Saputra; Uge, Maria Andriani; Kristofel, Tedi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25128

This study aims to examine the effectiveness of the Monte Carlo antithetic variate and control variate methods in pricing knock-out barrier options compared to the standard Monte Carlo method. The main problem in barrier option pricing is the high variance of estimates, which can reduce the accuracy and efficiency of results. The standard Monte Carlo method often requires a very large number of simulations to achieve stable results, which is computationally inefficient. To address this issue, this study employs variance reduction techniques, antithetic variate, and control variate. The findings indicate that both methods offer higher accuracy in price estimation compared to the standard Monte Carlo method. Further analysis reveals that the control variate method is more effective for pricing up and out barrier call options and down and out barrier call options, while the antithetic variate method excels in pricing up and out barrier put options and down and out barrier put options. This study underscores the importance of selecting the appropriate method according to the type of option involved to achieve accurate and efficient estimations.

Efektivitas Problem Based Learning Terhadap Literasi Matematika Siswa Introvert dan Ekstrovert Murwaningtyas, Chatarina Enny; Ayuwanditya, Caecillia Berta
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 3 (2024): Jurnal Derivat (Desember 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/j.derivat.v11i3.7007

This study aims to evaluate the effectiveness of problem-based learning in improving mathematical literacy, particularly in statistics, in classes dominated by introverted and extroverted students. The research employs a mixed-methods approach, combining quantitative and qualitative analysis, conducted at a private high school in Bogor. Data were collected through pretests and posttests to measure learning improvements, as well as observations during the learning process to examine interactions and student responses. The results showed that the implementation of problem-based learning significantly enhanced mathematical literacy in both classes. Although there were variations in interaction dynamics based on students' personality types, no significant difference was found in learning improvement between the introverted and extroverted classes. These findings indicate that problem-based learning can be effectively implemented for students with different personality types. Keywords: Problem Based Learning, Mathematical Literacy, Introvert, Extrovert.

PELATIHAN INOVASI PEMBELAJARAN STEAM MELALUI PENDEKATAN PROYEK DAN KAJIAN MASALAH BERBASIS KEARIFAN LOKAL Enny Murwaningtyas, Chatarina; Tiara Gunawan, Monica; Maharani, Wayan; Marfiani Tapo, Maria; Turnip, Grace; Andy Rudhito, Marcellinus; Julie, Hongki
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 1 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i1.309-321

Pembelajaran Science, Technology, Engineering, Arts, and Mathematics (STEAM) merupakan pendekatan terintegrasi dalam pendidikan yang bertujuan untuk meningkatkan kemampuan berpikir kritis, pemecahan masalah, dan kreativitas siswa. Pendekatan ini menjadi lebih relevan ketika dikombinasikan dengan pembelajaran berbasis proyek dan kajian masalah yang berakar pada kearifan lokal, memungkinkan siswa untuk menghubungkan konsep-konsep ilmiah dengan konteks kehidupan nyata di lingkungan mereka. Untuk mendukung inovasi pembelajaran ini, sebuah program pelatihan khusus telah dirancang dengan tujuan memperdalam pemahaman guru mengenai pembelajaran STEAM melalui pendekatan proyek, meningkatkan keterampilan praktis dalam penerapannya, serta mengintegrasikan kajian masalah berbasis kearifan lokal. Melalui diskusi interaktif, tugas proyek kreatif, dan fokus pelatihan pada integrasi STEAM dengan kearifan lokal, program ini mendorong kolaborasi dan kreativitas di kalangan guru. Hasil pengabdian menunjukkan peningkatan signifikan dalam kemampuan guru untuk menerapkan STEAM dalam pembelajaran serta merancang proyek yang relevan dengan kearifan lokal, menciptakan lingkungan belajar yang kolaboratif dan menarik. Kemajuan ini tidak hanya meningkatkan motivasi guru dan kemampuan mereka dalam menyelesaikan masalah secara kreatif, tetapi juga meningkatkan kualitas pengalaman belajar siswa serta memberikan dampak positif pada pembelajaran matematika secara umum. Dengan integrasi STEAM dan kearifan lokal, pembelajaran menjadi lebih kontekstual, menarik, dan bermakna bagi siswa, mendukung perkembangan pendidikan yang lebih holistik dan kontekstual di Indonesia.

Model decision tree untuk prediksi prestasi akademik matematika siswa kelas VIII SMP Frater Don Bosco Manado Gunawan, Monica Tiara; Tine, Jeane Yosefa; Murwaningtyas, Chatarina Enny
Jurnal Pendidikan Informatika dan Sains Vol. 13 No. 2 (2024): Jurnal Pendidikan Informatika dan Sains
Publisher : IKIP PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v13i2.7696

Penelitian ini bertujuan untuk mengembangkan model Decision Tree yang dapat memprediksi prestasi akademik matematika siswa kelas VIII di SMP Frater Don Bosco Manado, serta untuk mengidentifikasi dan menganalisis faktor-faktor penting yang perlu diperhatikan oleh orang tua dalam upaya meningkatkan prestasi akademik anak mereka. Data dikumpulkan melalui dokumentasi nilai akademik siswa, catatan kehadiran, dan kuesioner yang diisi oleh siswa untuk memperoleh informasi tentang dukungan keluarga, banyaknya kegiatan ekstrakurikuler yang diikuti, lama belajar, dan tingkat pendidikan orang tua. Data tersebut dianalisis menggunakan pendekatan data mining dengan model Decision Tree. Dua model dikembangkan dan dibandingkan: model pertama tanpa seleksi fitur dan model kedua dengan seleksi fitur menggunakan metode SelectKBest. Model tanpa seleksi fitur mencapai akurasi 93,33%, sementara model dengan seleksi fitur mencapai akurasi 95,56%. Evaluasi terhadap pentingnya fitur menunjukkan bahwa tanpa seleksi fitur, nilai rapor matematika semester sebelumnya menjadi fitur yang paling dominan, diikuti oleh nilai ulangan harian dan banyaknya kegiatan ekstrakurikuler yang diikuti. Sebaliknya, dalam model dengan SelectKBest, durasi belajar menjadi fitur yang paling signifikan, diikuti oleh tingkat pendidikan ayah, dukungan keluarga, dan nilai ulangan harian. Temuan ini menunjukkan bahwa penggunaan seleksi fitur tidak hanya meningkatkan akurasi prediksi tetapi juga membantu mengidentifikasi faktor-faktor kunci yang perlu difokuskan oleh orang tua, seperti durasi belajar, pendidikan orang tua, dukungan keluarga, partisipasi dalam kegiatan ekstrakurikuler, dan nilai akademik sebelumnya, untuk meningkatkan prestasi akademik siswa.

Data Mining Analysis of Moodle Learning Data and Student Perceptions During and After the Covid-19 Pandemic Murwaningtyas, Chatarina Enny; De Jesus, Maria Fatima Dineri
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84005

This study examines the academic performance of students from the 2020 and 2023 cohorts, highlighting differences in activity, attendance, task completion, midterm and final exam scores, and perceptions of educational metrics. A data mining approach was applied to predict students' GPA using Decision Tree, Random Forest, Multinomial Naïve Bayes, and Gaussian Naïve Bayes algorithms. The Gaussian Naïve Bayes model showed the highest accuracy of 0.93 for the 2020 cohort and 0.92 for the 2023 cohort, with the lowest error rate making it the most effective predictor. Feature importance analysis revealed that task completion and exam scores were the most influential factors, while students' perceptions had a lesser impact. The findings suggest that direct academic metrics should be the focus for improving student performance. This study emphasizes the need for further refinement of predictive models and suggests incorporating both academic metrics and student perceptions for a holistic understanding of student performance.

Analisis Pengaruh Media Sosial Terhadap Produktivitas Akademik Mahasiswa Menggunakan Metode Decision Tree dan Random Forest Murwaningtyas, Chatarina Enny; Kristiamita, Angel; Putri, Agatha Lintang Antika Ika; Puspaningrum, Fibelia Dwi; Mahanani, Carolina Dhinda Putri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11315

Penelitian ini bertujuan untuk mengevaluasi pengaruh penggunaan media sosial terhadap produktivitas akademik mahasiswa Universitas Sanata Dharma Yogyakarta, diukur melalui Indeks Prestasi Kumulatif (IPK). Metode yang digunakan melibatkan dua model pembelajaran mesin: Decision Tree dan Random Forest. Data diolah menggunakan teknik penskalaan yang tahan terhadap outlier dan penyeimbangan data melalui teknik oversampling. Hasil penelitian menunjukkan bahwa model Random Forest memiliki performa superior dengan akurasi, presisi, recall, dan F1-score masing-masing sebesar 90%. Sementara itu, model Decision Tree menunjukkan akurasi sebesar 80%, dengan presisi 86%, recall 80%, dan F1-score 82%. Analisis pentingnya fitur menunjukkan bahwa 'Fakultas' dan 'Jenis Kelamin' adalah faktor paling signifikan dalam memprediksi IPK mahasiswa. Penelitian ini menyimpulkan bahwa penggunaan Random Forest dengan teknik penyeimbangan data dapat meningkatkan akurasi dan keandalan prediksi, memberikan wawasan tentang pemanfaatan media sosial untuk meningkatkan produktivitas akademik mahasiswa.

Analisis Pengaruh Media Sosial terhadap Produktivitas Akademik Mahasiswa menggunakan Metode Decision Tree dan Random Forest Murwaningtyas, Chatarina Enny; Kristiamita, Angel; Putri, Agatha Lintang Antika Ika; Puspaningrum, Fibelia Dwi; Mahanani, Carolina Dhinda Putri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

This study aims to evaluate the impact of social media usage on the academic productivity of Universitas Sanata Dharma Yogyakarta students, measured through their Grade Point Average (GPA). The methods employed involve two machine learning models: Decision Tree and Random Forest. The data were processed using outlier-resistant scaling techniques and data balancing through oversampling. The results show that the Random Forest model outperformed with an accuracy, precision, recall, and F1-score of 90% each. Meanwhile, the Decision Tree model achieved 80% accuracy, with a precision of 86%, recall of 80%, and F1-score of 82%. Feature importance analysis revealed that 'Faculty' and 'Gender' are the most significant factors in predicting students' GPA. This study concludes that employing Random Forest with data balancing techniques can enhance prediction accuracy and reliability, providing insights into the optimal use of social media to improve students' academic productivity.

Identification of Demographic Factors Affecting Student Performance using Tree-Based Machine Learning Models Murwaningtyas, Chatarina Enny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28815

This study aims to identify key academic and demographic factors influencing student performance in the Logic and Set Theory course, particularly in the context of different learning modes during and after the COVID-19 pandemic. It adopts a quantitative exploratory design involving students from the 2020 to 2023 cohorts at Sanata Dharma University. Academic data (exam and assignment scores, course outcomes) and demographic data (e.g., parental education and income, region of origin, gender, and high school major) were collected from the academic system and supplemented via questionnaires. The dataset was cleaned, encoded, and normalized using RobustScaler, with class imbalance addressed through SMOTE. Descriptive statistics were used to explore initial data characteristics. Five tree-based machine learning models, Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost, were implemented within a pipeline that included preprocessing and model optimization using GridSearchCV with 5-fold cross-validation. Model evaluation employed multiple metrics, including accuracy, precision, recall, F1-score, AUC, and Average Precision. Results showed that XGBoost and CatBoost achieved the best performance (accuracy 92%, AUC 0.99) with balanced precision and recall across all four performance categories. Feature importance analysis indicated that exam and assignment scores were the strongest predictors, while demographic factors such as enrollment year, parental education, and income contributed moderately. Variables like gender, region, and high school major had minimal influence. This research demonstrates how machine learning can effectively integrate academic and demographic data, rather than analyzing them in isolation, to uncover nuanced patterns in student achievement. The findings support the development of data-driven educational interventions, such as preparatory learning modules, peer mentoring for underperforming groups, targeted academic advising for students from low-income or less-educated families, and flexible instructional strategies for cohorts affected by pandemic-related disruptions.

Efektivitas Problem Based Learning Terhadap Literasi Matematika Siswa Introvert dan Ekstrovert Murwaningtyas, Chatarina Enny; Ayuwanditya, Caecillia Berta
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 3 (2024): Jurnal Derivat (Desember 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/j.derivat.v11i3.7007

This study aims to evaluate the effectiveness of problem-based learning in improving mathematical literacy, particularly in statistics, in classes dominated by introverted and extroverted students. The research employs a mixed-methods approach, combining quantitative and qualitative analysis, conducted at a private high school in Bogor. Data were collected through pretests and posttests to measure learning improvements, as well as observations during the learning process to examine interactions and student responses. The results showed that the implementation of problem-based learning significantly enhanced mathematical literacy in both classes. Although there were variations in interaction dynamics based on students' personality types, no significant difference was found in learning improvement between the introverted and extroverted classes. These findings indicate that problem-based learning can be effectively implemented for students with different personality types. Keywords: Problem Based Learning, Mathematical Literacy, Introvert, Extrovert.

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