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Pengaruh Desain Pesan Pembelajaran Prinsip Persepsi dan Prinsip Memori terhadap Motivasi Intrinsik Belajar Mahasiswa Sudiksa, I Made; Permana, Gusi Putu Lestara; Gama, Adie Wahyudi Oktavia
EDUKATIF : JURNAL ILMU PENDIDIKAN Vol 7, No 5 (2025): Oktober
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edukatif.v7i5.8572

Abstract

Pembelajaran di era digitalisasi yang semakin pesat, desain pesan pembelajaran digital menjadi komponen krusial dalam menciptakan pengalaman belajar yang efektif, interaktif, dan relevan bagi mahasiswa guna menumbuhkan motivasi belajar mahasiswa. Penelitian ini bertujuan untuk menganalisis pengaruh desain pesan pembelajaran prinsip persepsi dan prinsip memori terhadap motivasi belajar mahasiswa. Penelitian ini menggunakan pendekatan kuantitatif dengan analisis Partial Least Square–Structural Equation Modeling (PLS-SEM) dengan software SmartPLS 4.1. Subjek penelitian adalah 177 orang mahasiswa Universitas Pendidikan Nasional yang dipilih melalui teknik probability sampling. Hasil penelitian menunjukkan prinsip persepsi dan prinsip memori berpengaruh positif serta signifikan terhadap motivasi intrinsik belajar mahasiswa. Hal tersebut ditunjukkan dari hasil analisis, di mana nilai P sebesar 0,000 lebih kecil dari tingkat signifikansi 5%. Ini berarti desain pesan pembelajaran yang dirancang berdasarkan prinsip persepsi dan prinsip memori merupakan faktor penting dalam meningkatkan motivasi intrinsik belajar dan efektivitas pembelajaran di era digital. Secara praktis, hasil ini dapat menjadi dasar bagi pendidik dan perancang pembelajaran untuk merancang media dan materi ajar digital yang lebih menarik, mudah dipahami, dan mampu mempertahankan perhatian mahasiswa
Analisis Determinan Karakter Siswa Menggunakan Explainable Machine Learning (SHAP) dan Klasterisasi Profil Sekolah Studi Kasus Rapor Pendidikan Provinsi Bali Dananjaya, Md. Wira Putra; Krisnawijaya, Ngakan Nyoman Kutha; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Gama, Adie Wahyudi Oktavia
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1988

Abstract

Strengthening student character is a key performance indicator in the Merdeka Belajar curriculum, but the identification of the school environment's most influential determinants of character achievement is often assumed. This study aims to quantitatively deconstruct the relationship between school climate and student character quality in Bali Province. Using the Indonesian Education Report dataset released by the Ministry of Primary and Secondary Education (Kemendikdasmen) for the 2023-2025 period with a total of 727 data entries, this study applies the Educational Data Mining methodology with the Random Forest algorithm enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to address data inequality. The novelty of this study lies in the use of SHapley Additive exPlanations (SHAP) for model transparency and K-Means Clustering for zoning mapping. Experimental results show the model is able to predict character achievement with 77.03% accuracy. The SHAP analysis revealed the interesting finding that Climate for Diversity (influence score of 0.45) and Climate for Gender Equality (0.22) were the strongest predictors, far exceeding the influence of Climate for Security (0.13). This finding challenges the common assumption that physical security is the single most important factor. Furthermore, the clustering analysis identified three school typologies in Bali, including one "Vulnerable" cluster that scored critically on gender equality and diversity despite having adequate security scores. This study recommends shifting the focus of education policy in Bali from a physical security approach to strengthening tolerance and gender equality programs, which have been shown to have a more statistically significant impact
Optimalisasi Kunjungan Industri sebagai Sarana Transfer Pengetahuan untuk Penguatan UMKM Swari, Luh Gede Widi; Negara, Komang Ayu Aprillia Puspa; Gama, Adie Wahyudi Oktavia
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 16, No 4 (2025): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v16i4.24673

Abstract

Kegiatan company visit merupakan salah satu metode pembelajaran yang efektif untuk memberikan pengalaman langsung kepada mahasiswa dalam memahami industri kreatif dan kewirausahaan lokal. Penelitian ini bertujuan mendeskripsikan proses transfer pengetahuan dari hasil kunjungan ke industri kuliner Bakpia Wong Keraton Yogyakarta ke UMKM lokal, yaitu Pia Karomah di Pasuruan. Pendekatan yang digunakan adalah experiential learning (Kolb, 2015), yang menekankan pada keterlibatan aktif mahasiswa dalam mengamati, merefleksikan, dan mengimplementasikan hasil pembelajaran di lapangan. Data dikumpulkan melalui wawancara, observasi, dan kuesioner pre-test dan post-test. Hasil menunjukkan adanya peningkatan signifikan pada tujuh indikator, meliputi pemahaman SOP produksi, strategi pemasaran digital, kreativitas desain kemasan, integrasi nilai budaya dalam promosi, pemahaman target pasar, efisiensi proses produksi, dan inovasi varian produk. Rata-rata skor keseluruhan meningkat dari 2,8 sebelum kegiatan menjadi 4,3 sesudah kegiatan. Hasil ini membuktikan bahwa sinergi antara dunia akademik dan praktik lapangan mampu memperkuat kapasitas kewirausahaan lokal sekaligus melestarikan nilai budaya daerah.
Analisis Performa XGBoost dan Gaussian Naive Bayes untuk Klasifikasi Dini Penyakit Hipertensi Ni Made Ochiana Septhi Pratiwi; Adie Wahyudi Oktavia Gama
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2119

Abstract

Hypertension is one of the leading causes of premature death globally that often goes undetected due to minimal clinical symptoms, earning it the nickname “silent killer.” The application of artificial intelligence (AI), particularly Machine Learning, is a strategic approach to early detection, but the main challenge lies in balancing diagnostic accuracy with detection sensitivity so that no patients at risk are overlooked. This study aims to analyze and compare the performance of the Extreme Gradient Boosting (XGBoost) algorithm with the Cost-Sensitive strategy compared to Gaussian Naive Bayes (GNB) as a baseline in hypertension risk classification. The dataset used included 1,985 electronic medical records with 9 clinical attributes, which were evaluated using the 10-Fold Cross-Validation method to determine model validity. The test results showed that XGBoost consistently outperformed GNB across all evaluation metrics. XGBoost recorded superior performance with an Accuracy of 92.19% and an AUC of 0.9752, far surpassing GNB, which obtained an Accuracy of 84.13%. The application of Cost-Sensitive Learning in XGBoost proved effective in overcoming performance trade-offs by producing a Recall of 91.26% and a Precision of 93.53%. Furthermore, Feature Importance analysis identified Blood Pressure History, Smoking Status, and Family History as the most dominant risk factors, which is in line with global medical guidelines. Based on these results, it is concluded that XGBoost is a more reliable and accurate method to be applied in early detection systems for hypertension compared to classical probabilistic approaches.
ANALISIS KOMPARATIF METODE DEMPSTER-SHAFER DAN CERTAINTY FACTOR PADA SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT DIABETES I Nyoman Rizky Anggika; Adie Wahyudi Oktavia Gama
Berajah Journal Vol. 6 No. 2 (2026): Berajah Journal
Publisher : CV. Lafadz Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47353/bj.v6i2.370

Abstract

This study aims to analyze and compare the performance of the Dempster-Shafer and Certainty Factor methods in expert systems for diagnosing diabetes. The research uses a qualitative approach with a literature study method by reviewing various scientific publications related to both methods in medical expert systems. The analysis focuses on key aspects such as diagnostic accuracy, ability to handle uncertainty, computational complexity, and ease of implementation. The results show that the Certainty Factor method is more efficient and easier to implement, making it suitable for structured data with lower uncertainty, while the Dempster-Shafer method is more effective in handling complex uncertainty and incomplete data due to its evidence-based approach, although it requires more complex computations. The study concludes that no single method is universally superior, as each method has its own strengths depending on data characteristics and system requirements, and suggests that combining methods could improve the performance of expert systems in diabetes diagnosis.
Comparative Performance of Machine Learning Algorithms for Diabetes Prediction Sudestra, I Made Ardi; Gama, Adie Wahyudi Oktavia; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Hakimi, Musawer
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1195

Abstract

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.
RAINFALL ANALYSIS AND FORECASTING USING THE PROPHET METHOD ON TIME SERIES DATA Ni Komang Sintya Dewi; Adie Wahyudi Oktavia Gama
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 4 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

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

Abstract

Climate change has increased rainfall variability, making it more difficult to predict rainfall patterns in terms of intensity, duration, and spatial distribution. This study aims to develop a daily rainfall forecasting model using the Prophet method, which is capable of handling seasonal patterns and long-term trends in time series data. The data used consist of daily rainfall records from 2015 to 2025 across nine regions in Bali Province, obtained from the NASA POWER platform. The research methodology includes data collection, data preprocessing, exploratory data analysis (EDA), Prophet model development with parameter optimization, cross-validation, and forecasting. Model performance is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics on test data. The results indicate that the Prophet method is capable of effectively modeling seasonal patterns and rainfall trends, producing stable predictions for future periods. This forecasting system is expected to serve as a decision-support tool in agriculture, water resource management, and hydrometeorological disaster mitigation.
Co-Authors Adhiya Garini Putri, Dewa Ayu Agus Ariana, I Komang Ajeng Ayu Fitri Ariatmaja Ariesta, Ni Luh Wina Sinta Arta, Kadek Ananda Dwi Pebri Arya Putra Sanjaya, I Ketut Gede Bunga, Melania Pritama Danang Utomo Dananjaya, Md. Wira Putra Darma, I Gede Wahyu Surya Darmaastawan, Kadek Davi, Nadine Kalina Dennatan, Monalisa Devi Anggreni, Ni Komang Ayu Devi, Ni Kadek Sintya Dewa Ayu Putu Adhiya Garini Putri Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Puspita Ningrat, Qorry Dharma, I Kadek Dwi Yudiarsana Diantari, Putu Yuliska Dwi Sanjani Mertaningsih, Ni Kadek Gede Hendra Divayana, Dewa Gede Humaswara Prathama Ginanita Utami, Cokorda Istri Ustana Grren, Agustini Degni Melsy Gunanti, A A Istri Indah Paristya Gunawan, Putu Vina Junia Antarista Gusi Putu Lestara Permana Gusti Ngurah Darma Paramartha, I Hakimi, Musawer Hari Putri, Tasya Prajna Pratisthita Hayu Mas Wrespatiningsih I Dewa Putu Arjun Suhartana Wisesa I G. N. Oka Ariwangsa I Gede Artha Negara I Gusti Ayu Cintya Wardani I Gusti Ayu Intan Candra Dewi I Gusti Ngurah Darma Paramartha I Gusti Ngurah Putu Dharmayasa I Gusti Putu Riyan Nugraha I ketut Gede Darma Putra I Made Ardana I Made Riski Aditya Darma I Made Sudiksa I Made Wirya Darma I Nyoman Gde Artadana Mahaputra Wardhiana I Nyoman Hary Kurniawan I Nyoman Rizky Anggika I Putu Agung Bayupati I Putu Wisna Ariawan I Wayan Abimayu Angga Nugraha I Wayan Aditya Suranata I Wayan Dikse Pancane I Wayan Sukadana I Wayan Sukadana I Wayan Sutama I Wayan Sutama Irma Suryanti Ivan Surya Pramana Putra, Kadek Bagus John Junieargo Timotius John Timotius Junieargo Kadek Devi Kalfika Anggaria Wardani Kadek Devi Kalfika Anggria Wardani Kadek Prasilia Candra Dewi Komang Bagus Novan Bayu Pramana Putra Kurniawan, I Nyoman Hary Lin, Fanny Made Jana Narendra Made Widnyani, Ni Maharani, Faradita Putri Aura Maulidan, Bagus Maw, Me Me Negara, I Gede Artha Negara, Komang Ayu Aprillia Puspa Ngakan Nyoman Kutha Krisnawijaya Ngurah Komang Wiradnyana Ni Kadek Nadya Kartika Paramita Ni Komang Sintya Dewi Ni Made Ochiana Septhi Pratiwi Ni Nyoman Triana Margareta Ni Putu Jenifer Febriari Ni Putu Widayanti Ni Wayan Rena Mariani Nilton Da Conceicao Marques Nimadeni Yuniartika Nur Aprilya, Fira Nurullita Wardani, Venti Oktama Setyawan, I Kadek P. WAYAN ARTA SUYASA Permana, Putu Indra Pertama, Gusti Putu Lestara Praditya Maha Wiguna, I Made Putra, Komang Satria Wibawa Putri Prema Paramitha Putu Emy Samiadnyani Putu Purnama Dewi Putu Riska Indah Mentari putu suparna, putu Sastra Dewanti, Wayan Ari Sudestra, I Made Ardi Sugiana, I Putu Sugiharni, Gusti Ayu Dessy Suputra, Komang Yudi Swari, Luh Gede Widi T Krisna Narayana, Made Gede Bagus Wardhiana, I Nyoman Gde Artadana Mahaputra Wardhiana, Nyoman Dana Wayan Sugandini Widnyani, Ni Made Wisesa, I Dewa Putu Arjun Suhartana