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All Journal Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Jurnal Penelitian Teknologi Informasi dan Sains
Lazuardi Fatahilah Hamdi
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering

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Sistem PPDB Online Berbasis Web di MTs Negeri 3 Purworejo Imam Tri Suryadin Imam; Anwar, Aang Anwarudin; Wahyu, Wahyu Ikhsanudin; Lazuardi Fatahilah Hamdi
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 3 No. 1 (2025): JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v3i1.2987

Abstract

Acceptance of new students is one of the processes that exist in educational institutions such as schools which are useful for screening prospective students who are selected according to the criteria determined by the school to become their students. In general, the process of admitting new students is carried out through the stages of registration, selection tests, and announcement of student acceptance. In this research, its application to MTs Negeri 3 Purworejo, which has been done manually or online so far, but only uses the PPDB link, which allows for a lot of data shortages. The formulation of the problem of this research is how to create an information system for new student admissions at MTs Negeri 3 Purworejo. The methods used are literature, observation, analysis, design, testing and implementation. This new student admissions information system was built using the PHP programming language and utilizes the MySQL database as a database server. The result of this research is a web-based new student admissions information system that has the ability to make it easy for parents of prospective new students to obtain all information about new student admissions and carry out the online registration process.
A Lightweight Machine Vision Pipeline for Screen-Printing Defect Detection in MSMEs Using Low-Cost Image Acquisition Galih Mahardika Munandar; Tiyan Fatkhurrohman; Lazuardi Fatahilah Hamdi
JTI: Jurnal Teknik Industri Vol 12 No 1 (2026): Juni 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study addresses the need for affordable visual inspection support in micro, small, and medium enterprises (MSMEs) engaged in screen-printing production. Although machine vision and deep learning have been widely applied in manufacturing quality control, many existing systems are designed for relatively controlled industrial settings and require stable cameras, lighting, computing resources, and technical expertise. This condition limits direct adoption by small MSMEs, where image acquisition is often performed with operator-level devices under variable lighting and background conditions. This study designed and evaluated an initial low-resource visual inspection pipeline consisting of low-cost image acquisition, five-class defect labeling, MobileNetV3-based transfer learning, performance evaluation, and TensorFlow Lite conversion. The dataset consisted of 160 screen-printing images grouped into five classes: good, misalignment, bleeding, pinholes, and ghosting. The preliminary evaluation yielded 24.38% multiclass accuracy and a loss of 2.5635, indicating that the model could not yet reliably distinguish detailed defect categories. The converted TensorFlow Lite model was 5.43 MB, indicating that the technical conversion path was feasible. A binary quality-control interpretation produced 75.63% accuracy, but 27 defective images were still predicted as pass QC. Therefore, the pipeline cannot be treated as a final quality-control decision system. The main contribution of this study is empirical evidence that image-acquisition quality, dataset sufficiency, class separability, and training configuration are critical bottlenecks in developing lightweight deep-learning-based inspection for low-resource MSME environments.
Co-Authors Anwar, Aang Anwarudin Galih Mahardika Munandar Imam Tri Suryadin Imam Tiyan Fatkhurrohman Wahyu, Wahyu Ikhsanudin