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All Journal Jurnal Teknik Informatika (JUTIF) JagoMIPA: Jurnal Pendidikan Matematika dan IPA Takuana: Jurnal Pendidikan, Sains, dan Humaniora
Nurkhodijah, Siti
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Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Mathematics Physics Social Sciences

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Hubungan Resiliensi Matematis dengan Kemampuan Pemecahan Masalah Matematis Siswa Kelas VIII Nurkhodijah, Siti; Abadi, Agung Prasetyo
JagoMIPA: Jurnal Pendidikan Matematika dan IPA Vol. 5 No. 3 (2025): JagoMIPA: Jurnal Pendidikan Matematika dan IPA
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/jagomipa.v5i3.1873

Abstract

Resiliensi matematis adalah perilaku positif yang membantu siswa mengatasi kesulitan dan tekanan ketika menghadapi masalah matematika yang sulit dipecahkan. Untuk dapat memecahkan masalah matematika dengan baik, diperlukan sikap mental yang positif. Tujuan dari penelitian ini yaitu mengkaji adanya keterkaitan antara resiliensi matematis dengan kemampuan pemecahan masalah matematis siswa. Penelitian ini dilaksanakan dengan pendekatan kuantitatif dan menggunakan metode korelasional. Seluruh siswa kelas VIII di salah satu SMP di Kabupaten Karawang menjadi populasi pada penelitian ini, sedangkan sebanyak 62 siswa dijadikan sampel melalui teknik cluster random sampling. Data dalam penelitian ini diperoleh melalui angket resiliensi matematis dan tes kemampuan pemecahan masalah matematis. Data angket diubah melalui teknik Method of Successive Intervals (MSI) sebelum diuji normalitasnya. Uji Rank Spearman digunakan untuk melakukan analisis korelasi karena data tidak memenuhi asumsi normalitas. Penelitian ini memberikan hasil bahwa ada hubungan positif yang signifikan antara resiliensi matematis dengan kemampuan pemecahan masalah matematis siswa, dengan nilai korelasi 0,268 dan signifikansi 0,035 (p < 0,05). Artinya, peningkatan resiliensi matematis sejalan dengan peningkatan kemampuan pemecahan masalah matematis siswa, begitupun sebaliknya. Kesimpulannya, siswa dengan tingkat resiliensi matematis yang lebih tinggi cenderung memiliki kemampuan pemecahan masalah yang baik, meskipun tingkat korelasinya tergolong lemah. 
Strategi Pemasaran Digital Menggunakan Media Sosial Tiktok untuk Meningkatkan Minat Beli UMKM Riani, Winda; Nurkhodijah, Siti; Syahrani, Puput; Olivia, Tiara; Putri, Nadia; Lestari, Aisyah; Junaidy, Junaidy; Darsih, Trysanti Kisria
Takuana: Jurnal Pendidikan, Sains, dan Humaniora Vol. 4 No. 4 (2026): Takuana (January-March)
Publisher : MAN 4 Kota Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56113/takuana.v4i4.377

Abstract

The rapid advancement of digital technology has fundamentally changed the marketing practices of MSMEs in Indonesia. TikTok, as a short-video-based social media platform, is increasingly recognized as a strategic medium for expanding market reach and stimulating consumer purchase intention. This study was designed to systematically investigate the implementation of TikTok-based digital marketing strategies in supporting the growth and sustainability of MSMEs. A descriptive qualitative approach was applied, with data collection techniques including in-depth interviews with five MSME managers as key informants, content observation, documentation, and online surveys of consumers exposed to promotional content. The findings revealed that TikTok features such as live streaming, hashtag challenges, and collaboration with creators effectively increase consumer engagement, build brand awareness, and stimulate purchase intention. However, MSMEs face challenges such as limited human resources and algorithm understanding. This study concludes that TikTok has great potential as a digital marketing medium for MSMEs, provided that creative, consistent, and consumer-oriented content strategies are optimally implemented.
Explainable Ensemble Learning for Depression Risk Classification Using Multidomain Behavioral Features Junianto, Erfian; Nurkhodijah, Siti
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5009

Abstract

Depression is a growing global health concern, particularly among adolescents and university students. Despite the availability of standardized assessments, delays in early detection remain a major barrier to effective treatment. Digital behavioral data holds considerable potential for mental health assessment, but its utilization remains limited due to the absence of integrated and interpretable computational models. This study presents an interpretable machine learning framework for classifying depression risk using multi-domain behavioral features extracted from simulated digital life datasets. Three public datasets were integrated and mapped to five psychological clusters based on DSM-5 criteria: self-regulation, negative affect, cognitive strain, comparison and avoidance, and sleep disturbance. Two ensemble classifiers, Random Forest and XGBoost, were applied and evaluated using 10-fold stratified cross-validation. Depression risk was categorized into three levels: Low, Medium, and High. The Random Forest model achieved the highest accuracy (81%) and macro-averaged F1-score (0.81), showing strong performance especially in identifying transitional Medium-risk users. To enhance transparency, both global and local model interpretations were performed using SHapley Additive exPlanations (SHAP). Results revealed that digital stressors such as excessive screen time and disrupted sleep patterns were prominent in high-risk classifications, while mood stability and mindfulness were protective factors in low-risk groups. The proposed framework offers a scalable and explainable for early depression screening by integrating psychological theory with artificial intelligence methods. The findings contribute to the field of behavioral informatics by demonstrating the practical value of interpretable models in enhancing the reliability, transparency, and applicability of digital mental health systems and personalized behavioral monitoring.
Co-Authors Abadi, Agung Prasetyo Erfian Junianto Junaidy, Junaidy Lestari, Aisyah Nadia Putri Olivia, Tiara Riani, Winda Syahrani, Puput