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All Journal Abdimas Awang Long: Jurnal Pengabdian dan Pemberdayaan Masyarakat Bulletin of Informatics and Data Science
Rafika Damayanti Sururin Nufus
Telkom University, Surabaya
Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Penerapan Rumah Botol untuk Pengelolaan Limbah Botol Plastik Berbasis Ekonomi Sirkular Berkelanjutan Laila Putri Faradilah; Silvi Istiqomah; Desita Nur Rachmaniar; Rafika Damayanti Sururin Nufus; Ivon Nadhia Aisyah; Inez Rexana; Nafa Naysilla Hilda Amru; Nicho Ali Nasrudin; Kaka Satriya Yudhantara
Abdimas Awang Long Vol. 9 No. 2 (2026): Juni, Abdimas Awang Long
Publisher : Sekolah Tinggi Ilmu Hukum Awang Long

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56301/awal.v9i2.2257

Abstract

Limbah botol plastik menjadi salah satu permasalahan lingkungan yang terus meningkat akibat tingginya penggunaan produk sekali pakai dan rendahnya pengelolaan sampah berbasis masyarakat. Kondisi tersebut mendorong perlunya penerapan konsep ekonomi sirkular melalui sistem pengelolaan limbah yang lebih terstruktur dan berkelanjutan. Kegiatan pengabdian masyarakat ini bertujuan untuk menerapkan teknologi tepat guna berupa Rumah Botol sebagai sarana pengumpulan limbah botol plastik berbasis masyarakat di Desa Sukodono, Kabupaten Sidoarjo, Jawa Timur. Metode pelaksanaan dilakukan melalui enam tahapan, yaitu survei awal dan identifikasi permasalahan, perancangan Rumah Botol, sosialisasi konsep ekonomi sirkular dan closed-loop supply chain, implementasi dan operasional Rumah Botol, pendampingan dan monitoring berkelanjutan, serta evaluasi dampak program. Hasil kegiatan menunjukkan bahwa penerapan Rumah Botol mampu meningkatkan partisipasi masyarakat dalam pemilahan dan pengumpulan botol plastik rumah tangga. Selain itu, program ini juga meningkatkan kesadaran masyarakat mengenai pentingnya pengelolaan sampah berbasis ekonomi sirkular dan menjaga kualitas material plastik sejak tahap pengumpulan. Rumah Botol tidak hanya berfungsi sebagai sarana pengumpulan limbah, tetapi juga menjadi bagian dari sistem pengelolaan material yang lebih terstruktur untuk mendukung keberlanjutan lingkungan dan pengurangan timbulan sampah plastik di masyarakat.
Interpretable Decision Tree for Predicting High Self-Reported Happiness Using Digital Behavior and Lifestyle Indicators Rafika Damayanti Sururin Nufus; Fiandra Lazuart Adi Hafizh Arraszaq
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.153

Abstract

Research on digital well-being is dominated by text-derived features and post hoc explanations, leaving limited evidence on whether compact behavioral data can support an inherently interpretable happiness classifier without obscuring predictive trade-offs. This study develops and audits an inherently interpretable decision tree for classifying high self-reported happiness from age, screen time, sleep quality, stress, offline days, exercise, gender, and social-media platform. The contribution is a leakage-safe evaluation that combines nested cross-validation, comparison with Random Forest, logistic regression, and radial-basis-function support vector machine, out-of-fold discrimination and calibration, a one-standard-error tree-selection rule, bootstrap stability, and sensitivity analyses. Across five outer folds, the decision tree obtained balanced accuracy 0.779 ± 0.049, macro-F1 0.778 ± 0.050, ROC-AUC 0.869 ± 0.045, and Brier score 0.146 ± 0.028. Random Forest achieved the highest mean balanced accuracy (0.820), whereas logistic regression achieved the highest ROC-AUC (0.913) and lowest Brier score (0.122). The selected depth-three tree retained eight leaves and used daily screen time and stress as its primary decision pathways. In 200 bootstrap samples, stress was selected in 100.0% of trees and screen time in 86.5%; however, the root alternated between stress (53.5%) and screen time (45.0%), indicating stable relevance but unstable ordering. Results remained similar with the four-feature subset, while performance changed across happiness thresholds. The model therefore offers transparent decision rules at a modest predictive cost, but the cross-sectional, single-dataset design supports predictive association rather than causal or clinical interpretation
Co-Authors Desita Nur Rachmaniar Fiandra Lazuart Adi Hafizh Arraszaq Inez Rexana Ivon Nadhia Aisyah Kaka Satriya Yudhantara Laila Putri Faradilah Nafa Naysilla Hilda Amru Nicho Ali Nasrudin Silvi Istiqomah