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All Journal Jurnal Pengabdian Masyarakat Khatulistiwa Journal of Applied Data Sciences Journal of Artificial Intelligence and Digital Business
Ade Trya Amanda
Universitas Labuhanbatu
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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PEMANFAATAN OLAHAN TAHU MENJADI BROWNIES SEBAGAI INOVASI EKONOMI KREATIF Di DESA KAMPUNG BARU JANJI Fauziah Hanum; Ade Trya Amanda; Ibnu Rasyid Munthe; Kamsia Dorliana Sitanggang; Siti Hartati Yusida Saragih; Novi Fitriandika Sari
Jurnal Pengabdian Masyarakat Khatulistiwa Vol 9, No 1 (2026): APRIL
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jpmk.v9i1.6579

Abstract

ABSTRACTTofu is a long-known and popular soybean product in Indonesia. It boasts high nutritional value, is readily available, and offers potential as a substitute ingredient in brownies. This community service program employs a participatory and collaborative approach, encompassing outreach, technical production training, hands-on practice, mentoring, and product evaluation. This product not only offers nutritional advantages in the form of increased vegetable protein and reduced saturated fat, but also opens up opportunities for home businesses and increases community income. Socially, this activity strengthens the role of housewives, teenagers, and village youth as entrepreneurs while enhancing self-confidence and a spirit of mutual cooperation.Keywords: Tofu, brownies, economic innovation ABSTRAKTahu merupakan salah satu produk olahan kedelai yang sudah lama dikenal adan sangat populer di Indonesia. Tahu memiliki  kandungan gizinya yang tinggi, mudah diperoleh, serta potensinya sebagai bahan substitusi dalam pembuatan brownies. Metode yang digunakan dalam pelaksanaan pengabdian kepada masyarakat ini dengan menggunakan pendekatan partisipatif dan kolaboratif, meliputi tahapan sosialisasi, pelatihan teknis produksi, praktik langsung, pendampingan, dan evaluasi produk. Produk ini tidak hanya menawarkan keunggulan gizi berupa peningkatan protein nabati dan penurunan lemak jenuh, tetapi juga membuka peluang usaha rumah tangga serta meningkatkan pendapatan masyarakat. Dari sisi sosial, kegiatan ini memperkuat peran ibu rumah tangga, remaja dan  pemuda desa sebagai pelaku usaha sekaligus meningkatkan rasa percaya diri dan semangat gotong royong.Kata Kunci : Tahu, brownies, inovasi ekonomi
Peran Orang Tua dalam Membentuk Kesadaran Sosial Anak Nur'Ainun Gulo; Ade Trya Amanda
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.6482

Abstract

Keluarga merupakan lingkungan pendidikan pertama yang berperan dalam membentuk cara anak memahami diri sendiri, orang lain, serta kehidupan bermasyarakat. Penelitian ini bertujuan untuk menganalisis peran orang tua dalam membentuk kesadaran sosial anak, terutama melalui proses pendidikan, pembimbingan, pengawasan, penyediaan fasilitas, dan hubungan orang tua sebagai teman bagi anak. Penelitian menggunakan pendekatan deskriptif kualitatif dengan metode studi literatur. Data diperoleh dari buku, artikel ilmiah, dan hasil penelitian yang membahas keluarga, pola asuh, perkembangan sosial emosional, komunikasi keluarga, serta pembentukan perilaku sosial anak. Data dianalisis melalui tahap pemilihan sumber yang relevan, pengelompokan informasi berdasarkan tema, penyajian secara naratif, dan penarikan kesimpulan. Hasil kajian menunjukkan bahwa orang tua merupakan agen sosialisasi utama yang menanamkan nilai empati, kepedulian, tanggung jawab, toleransi, kerja sama, dan penghargaan terhadap orang lain. Keteladanan dan pembiasaan sehari-hari menjadi cara yang paling nyata bagi anak untuk mempelajari perilaku sosial. Peran sebagai pembimbing membantu anak memahami konsekuensi tindakannya, sedangkan pengawasan yang proporsional memberikan perlindungan tanpa menghambat kemandirian. Penyediaan lingkungan bermain dan komunikasi yang terbuka juga memperkuat kemampuan anak dalam berbagi, menyelesaikan konflik, mengelola emosi, dan membangun hubungan sosial. Dengan demikian, konsistensi perilaku, kualitas komunikasi, dan keterlibatan aktif orang tua menjadi fondasi penting dalam membentuk kesadaran sosial anak sejak usia dini secara berkelanjutan.
Implementation of Stacking Technique Combining Machine Learning and Deep Learning Algorithms Using SMOTE to Improve Stock Market Prediction Accuracy Ibnu Rasyid Munthe; Bhakti Helvi Rambe; Fauziah Hanum; Ade Trya Amanda; Anita Sri Rejeki Hutagaol; Richi andrianto
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.421

Abstract

This study introduces a stacking technique that integrates machine learning (ML) and deep learning (DL) algorithms to enhance the accuracy of stock market trend predictions. The stacking model utilizes XGBoost and Random Forest as base models from the ML domain, while Logistic Regression and LSTM (Long Short-Term Memory) function as meta models to optimize predictive accuracy. A significant challenge in stock market data is class imbalance, where certain trends, such as stock price drops, are underrepresented. To mitigate this, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic data for the minority class. This approach helps the model better capture patterns from the underrepresented data while preserving essential information from the majority class. The implementation of SMOTE, coupled with the stacking technique, yielded a substantial improvement in prediction accuracy. The results showed that the Random Forest algorithm achieved an accuracy of 85% with precision, recall, and F1-score all at 85%, while XGBoost and Logistic Regression achieved accuracies of 82% and 81% respectively. For the deep learning models, LSTM reached an accuracy of 83%, while the Stacking Meta Model with LSTM achieved an accuracy of 83% with slightly better precision and recall at 84%. The stacking model, with Logistic Regression as the meta model, ultimately achieved the highest accuracy of 86%, outperforming individual models such as SVM (Support Vector Machine), LSTM, Random Forest, and Logistic Regression (LR). These findings demonstrate the efficacy of combining SMOTE with stacking to address data imbalance and improve stock market predictions. The novelty of this study lies in the integration of advanced ML and DL models within a stacking framework to handle class imbalance in financial datasets. Future research will explore the deployment of this model in a real-time web-based application to support investor decision-making in stock market trend analysis.
Co-Authors Anita Sri Rejeki Hutagaol Bhakti Helvi Rambe Fauziah Hanum Ibnu Rasyid Munthe Kamsia Dorliana Sitanggang Novi Fitriandika Sari Nur'Ainun Gulo Richi Andrianto Siti Hartati Yusida Saragih