https://ejournal-rmg.org/index.php/JETI/issue/feedJournal Of Engineering And Technology Innovation ( JETI )2025-11-19T08:27:47+00:00Open Journal Systems<p>Journal Of Engineering And Technology Innovation( JETI ) is a scientific publication periodically to accommodate the research for lecturers who want to publish the results of scientific work in the form of literature, research, and technological development as a form of application of methods, algorithms, or framework that includes a variety of topics related to engineering studies, science or other topics related to the field of engineering.<br /><br /><strong>E-ISSN : 2828-1209<br />P-ISSN : 2828-1462<br /></strong></p>https://ejournal-rmg.org/index.php/JETI/article/view/449Kuat Tekan Paving Block dengan Produksi Manual Di Kota Padangsidimpuan2025-06-19T11:34:10+00:00Julianto Lubisjuliantolubis3@gmail.com<p>Paving block banyak digunakan dalam bidang konstruksi, seperti pavement, jalan raya dan juga lahan perparkiran perumahan. Kemudahan dalam pemasangan, perawatan yang murah serta memenuhi aspek keindahan menyebabkan paving block banyak disukai. Banyak pembuatan paving block belum optimal dari sisi kekuatan, dibuat dalam skala kecil sebagai produk home industri / industri rumahan. Dari hasil pengujian dengan ketebalan 7 cm untuk keua jenis jenis type Paving Block yang paling besar kuat tekannya adalah type hollad block, dengan variasi campuran 1:2 = 459,63 kg/cm2, σ=459,63 kg/cm2, camp.1:3 = 397,01 kg/cm2, σ = 397,01 kg/cm2; camp.1:4=196,88 kg/cm2, σ= 196,88 kg/cm2; camp.1:5 = 156,38 kg/cm2 , σ= 156,38 kg/cm2. Penggunaannya untuk perkerasan jalan, Paving Block bagus digunakan pada campuran 1:2 dan 1:3.</p>2025-06-26T00:00:00+00:00Copyright (c) 2022 Journal Of Engineering And Technology Innovation ( JETI )https://ejournal-rmg.org/index.php/JETI/article/view/504Development of Deep Learning Techniques for Dental Caries Detection: A Systematic Literature Review2025-11-19T08:27:47+00:00Sherly Agustinisherly@gmail.comNofri Yudi Arifinnofri.yudi@uis.ac.id<p>Dental caries remains one of the most prevalent oral health problems worldwide, requiring early and accurate detection to prevent extensive damage and reduce treatment costs. Recent advances in artificial intelligence particularly deep learning have led to significant improvements in diagnostic accuracy and consistency compared to traditional visual or radiographic assessments. This systematic literature review evaluates the development of deep learning techniques for dental caries detection based on studies published between 2020 and 2024. Following the PRISMA 2020 guidelines, six eligible studies were identified from Scopus, PubMed, IEEE Xplore, and ScienceDirect databases. The review synthesizes findings related to model architectures, imaging modalities, dataset characteristics, and performance metrics. The results show that models such as CNN, YOLO, U-Net, and EfficientNet consistently demonstrate high accuracy in identifying carious lesions, with bitewing and panoramic radiographs producing the most reliable diagnostic outcomes. However, limitations remain, including dataset variability, limited sample sizes, and reduced sensitivity for early-stage lesions. This review highlights current progress, methodological challenges, and potential research opportunities, emphasizing the need for standardized datasets, improved clinical validation, and stronger multidisciplinary collaboration to support the integration of deep learning into dental diagnostic workflows.</p>2025-11-19T00:00:00+00:00Copyright (c) 2025 Journal Of Engineering And Technology Innovation ( JETI )