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All Journal Jurnal Ilmu Komputer dan Informasi IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL PENDIDIKAN TAMBUSAI PRIMA : Portal Riset dan Inovasi Pengabdian Masyarakat Tsaqofah: Jurnal Penelitian Guru Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Jurnal Pendidikan Dasar Islam PAUDIA: JURNAL PENELITIAN DALAM BIDANG PENDIDIKAN ANAK USIA DINI Jurnal Cakrawala Akademika
Ramadhani, Dian
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Veterinary Other

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Pengaruh Biaya Lingkungan dan Kinerja Lingkungan terhadap Profitabilitas (Studi Kasus Perusahaan Manufaktur Sektor Industri Dasar dan Kimia yang Terdaftar di Bursa Efek Indonesia Tahun 2021-2024) Ramadhani, Dian; Sumarni, Nini
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.37746

Abstract

Kajian ini dimaksudkan untuk menginvestigasi sejauh mana pengeluaran yang dialokasikan untuk kepentingan ekologis serta performa keberlanjutan lingkungan berdampak terhadap tingkat keuntungan finansial perusahaan manufaktur yang bergerak di sektor industri esensial dan kimia, yang terdaftar di Bursa Efek Indonesia (BEI) selama kurun 2021–2024. Pendekatan kuantitatif diterapkan dengan memanfaatkan data sekunder yang diperoleh dari situs resmi BEI (www.idx.co.id). Penentuan sampel dilakukan dengan metode purposive sampling, menghasilkan 11 entitas usaha yang memenuhi kriteria. Analisis dilakukan melalui pengujian asumsi klasik, regresi linear berganda, serta evaluasi terhadap koefisien determinasi dan hipotesis. Hasil pengujian parsial (uji t) menunjukkan bahwa biaya lingkungan berpengaruh signifikan terhadap profitabilitas, dengan nilai signifikansi 0,001 < 0,05 dan t_hitung 2,01954 berada di atas t_tabel 3,411. Sedangkan , kinerja lingkungan tidak menunjukkan relasi yang signifikan terhadap keuntungan, karena nilai_t_hitung (1,587) < _t_tabel (2,01954). Namun, secara simultan, biaya dan kinerja lingkungan menunjukkan pengaruh yang berarti terhadap profitabilitas. Uji F mengindikasikan nilai signifikansi 0,006 < 0,05 serta F_hitung 5,852 yang melebihi F_tabel 3,226, sehingga hipotesis alternatif diterima dan relasi gabungan tersebut dinyatakan signifikan.
Development and Optimization of YOLOv12 for Autonomous Vehicle Navigation Systems Ramadhani, Dian; Muttakin, Muhammad; Irsyad, Hidayat Hatta; Susilo, Edi; Andi, Rahmat Rizal
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2634

Abstract

This study develops and enhances a YOLOv12-based object detection model for autonomous vehicle perception on Indonesian highways, addressing limitations of earlier research that lacked realistic traffic scenarios and field validation. The Roboflow dataset contains 29 object classes, including vehicles, pedestrians, and traffic signs, with existing annotations. Preprocessing included data quality assessment, image resizing, dataset split validation, annotation format conversion, and data augmentation to improve training performance. Eight training configurations were evaluated by varying learning rate, batch size, and optimizer. Initial comparisons showed YOLOv12 significantly outperformed SSD, achieving mAP50 of 0.978 and mAP50–95 of 0.831, compared to SSD’s 0.816 and 0.639. SGD consistently provided more stable and accurate performance than Adam. The best model used SGD with a learning rate of 0.001 and batch size of 16, achieving precision of 0.952, recall of 0.955, mAP50 of 0.974, and mAP50–95 of 0.834. Field testing confirmed strong detection of pedestrians and traffic signs, although challenges remained with small and overlapping objects. Future work should improve small-object detection, expand dataset diversity, and explore advanced architectures or hybrid optimization strategies. These findings support YOLOv12 as a reliable foundation for safer, more efficient self-driving perception systems tailored to Indonesia’s complex road environments in real conditions.
Co-Authors Andi, Rahmat Rizal Andreas, Prima Anggraeni, Fitriya Helen Aprilina Tarigan, Mida Arum, Shaka Arwinence Pramadewi Ayu Wulandari Budiarti, Ajeng Cahyani, Maharani Desita Eka David Sukardi Kodrat Dewita Suryati Ningsih Edi Susilo Erianti, Nagita Evelin, Farrel Farid Abdillah, Rachmat Fazya, Shanaya Fabitha Herry Irawan, Herry Honest, Baldwine Irsyad, Hidayat Hatta Ismayanti, Zahra Dwi Kusuma, Risna Andara Marzuki Marzuki Maslakah, Likfatmawati Muttakin, Muhammad Oktasia Nasution, Hafiza Rofidah, Arini Nur Safitri, Novia Salsabillah, Rifda Nur Sari, Fa'izah Dwi Kumala Sariah, Ananda Putri Sri Purwanti sumarni, nini Suyadi, Naella Wiyarti Utami, Amira Vitriana, Bety Yusza Reditya Murti