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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika PUBLIDIMAS Journal of Selvicoltura Asean
Firman Syahputra
Universitas Battuta
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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VEGETATION ANALYSIS ESTIMATING BIOENERGY POTENTIAL AGROFORESTRY RUMBIA VILLAGE BOALEMO DISTRICT Alexander Ruruh; Firman Syahputra; Sukma Ayu Haris; Zeinab Nurlena Y. Suma
Journal of Selvicoltura Asean Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i4.1955

Abstract

Energy is one of the basic needs to support human survival. Along with the increasingly limited availability of energy from fossils, it is necessary to find other alternative energy sources. In the agroforestry area there are Aren plants which are one of the plants that can be processed into an energy source. the data used are primary and secondary data. Data analysis was carried out quantitatively and qualitatively. The results of the data analysis that have been carried out, found a total of 4 species at the tree level and 5 species at the pole level in Agroforestry in Rumbia. The highest important value at the tree level is Aren (Arenga pinnata) with a value of 155.95, the lowest Durian (Durio zibethinus) 21.53. the highest important value index at the pole level is Aren (Arenga pinnata) with a value of 144.42 and the lowest Chocolate (Theobroma cacao) 22.90.  
Pelatihan Penggunaan Aplikasi EcoBinSmart Berbasis IoT Sebagai Upaya Edukasi Pengelolaan Sampah Pada SMK Swasta Perguruan Mabar Ok Muhammad Ihsan; Wiwi Verina Rina; Firman Syahputra; Ermayanti Astuti; Fujiati Fujiati; Elsa Aditya
Publikasi Pengabdian Masyarakat Vol 5 No 2 (2025): PUBLIDIMAS Vol. 5 No. 2 NOVEMBER 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.5.2.2025.19-27

Abstract

Permasalahan pengelolaan sampah di lingkungan sekolah masih menjadi tantangan, khususnya terkait rendahnya kesadaran dan partisipasi siswa dalam memilah serta membuang sampah dengan benar. Berdasarkan hasil penelitian sebelumnya yang telah menghasilkan aplikasi EcoBinSmart berbasis Android yang terintegrasi dengan teknologi Internet of Things (IoT) serta prototipe tong sampah pintar dengan tingkat keberhasilan sistem sebesar 96,8%, diperlukan kegiatan lanjutan berupa pengabdian kepada masyarakat agar hasil penelitian dapat dimanfaatkan secara langsung oleh pengguna. Oleh karena itu, kegiatan pengabdian ini bertujuan untuk melaksanakan pelatihan penggunaan aplikasi EcoBinSmart berbasis Android dan IoT sebagai upaya edukasi pengelolaan sampah pada siswa SMK Swasta Perguruan Mabar. Metode pelaksanaan kegiatan dilakukan melalui pendekatan pelatihan dan pendampingan yang meliputi penyampaian materi pengelolaan sampah dan pengenalan teknologi IoT, demonstrasi penggunaan aplikasi Android EcoBinSmart, praktik langsung oleh siswa, serta diskusi dan evaluasi. Peserta kegiatan merupakan siswa SMK yang dilibatkan secara aktif dalam penggunaan aplikasi Android untuk melakukan pemindaian QR code, pembuangan sampah terpilah, serta pemantauan perolehan poin sebagai bentuk umpan balik terhadap perilaku peduli lingkungan. Hasil kegiatan menunjukkan bahwa siswa mampu memahami dan menggunakan aplikasi EcoBinSmart berbasis Android secara mandiri, serta terjadi peningkatan kesadaran dan partisipasi siswa dalam pengelolaan sampah di lingkungan sekolah. Selain itu, kegiatan ini juga meningkatkan ketertarikan siswa terhadap pemanfaatan teknologi Android dan IoT sebagai solusi permasalahan lingkungan.
Predicting E-Commerce Greenwashing Using Random Forest Machine Learning: Legal Analysis and Marketing Management Strategies Firman Syahputra; Juliya Maria; Hilda Elsera Br Sembiring
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16770

Abstract

The rapid growth of e-commerce and the increasing use of environmental claims in product marketing have created challenges in identifying potentially misleading green marketing communication. This study aims to develop a machine learning framework for predicting greenwashing indications in e-commerce products and interpreting the findings from consumer protection and sustainable marketing perspectives. A dataset of 610 products from Shopee and Tokopedia was independently annotated and validated by two annotators. Product descriptions were processed using Natural Language Processing (NLP) and represented using Term Frequency–Inverse Document Frequency (TF-IDF) with unigram and bigram features. Random Forest was employed as the primary classifier, while Support Vector Machine (SVM) and Multinomial Naive Bayes were used as benchmark models. A leakage-safe feature engineering configuration was additionally evaluated. Model performance was assessed using Accuracy, Precision, Recall, F1-score, confusion matrices, and 5-fold stratified cross-validation. On the independent test set, Random Forest with TF-IDF achieved the highest Accuracy (92.62%) and F1-score (95.81%), while 5-fold cross-validation showed that Random Forest with TF-IDF and Strict Feature Engineering achieved the highest mean Accuracy (95.49%) and F1-score (97.42%). Feature importance analysis identified textual structure and specific lexical patterns as predictive signals associated with model decisions. The findings indicate that the proposed framework can support early screening of potentially misleading environmental claims, consumer protection assessment, and more transparent and verifiable green marketing practices. However, model predictions represent predictive signals rather than causal evidence or legal determinations and require further factual and contextual verification
Co-Authors Elsa Aditya Ermayanti Astuti Fujiati Fujiati Hilda Elsera Br Sembiring Juliya Maria OK Muhammad Ihsan Ruruh, Alexander Sukma Ayu Haris Wiwi Verina Rina Zeinab Nurlena Y. Suma