Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Prosiding National Conference for Community Service Project Bulletin of Information Technology (BIT)
Lie, Joen
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan dan Implementasi Website Tracer Study di Smak Yos Sudarso Batam Liang, Suwarno; Lie, Joen; Siahaan, Mangapul
National Conference for Community Service Project (NaCosPro) Vol. 7 No. 01 (2025): The 7th National Conference for Community Service Project 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v7i01.10842

Abstract

Di era digital saat ini, keberadaan sistem pelacakan alumni (tracer study) sangat dibutuhkan oleh institusi pendidikan untuk mendukung evaluasi kurikulum dan proses akreditasi. SMAK Yos Sudarso Batam mengalami kendala dalam pengelolaan data alumni karena belum diimplementasikan sistem tracer study. Oleh karena itu, dilakukan kegiatan kerja praktik yang bertujuan untuk merancang dan mengimplementasikan website tracer study sebagai solusi dari permasalahan tersebut. Proses pengembangan dilakukan mulai dari analisis kebutuhan, perancangan antarmuka dengan Figma, pengembangan sistem menggunakan Laravel, HTML, CSS, dan MySQL, hingga tahap pengujian sistem. Hasil dari kegiatan ini adalah sebuah website tracer study yang responsif dan fungsional, dilengkapi dengan fitur login untuk admin dan alumni, manajemen data, dashboard statistik, serta fitur impor data menggunakan file Excel. Website ini telah berhasil diimplementasikan, dihosting, dan kini dapat diakses serta digunakan secara online oleh pihak sekolah untuk mendukung proses pelacakan alumni.
Implementing Mobile-based AI in Household Waste Type and Condition Classification Suwarno, Suwarno; Lie, Joen; Siahaan, Mangapul
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2504

Abstract

Urbanization and population growth have significantly increased waste generation, creating challenges for effective waste management and recycling. Improper waste sorting and management often results to unrecyclable waste contaminating recycling streams or recyclable waste ending up in landfill. This research presents a mobile-based waste classification application that integrates YOLOv11n for real-time object detection, and uses TensorFlow Lite with a Flutter-based user interface. The model was trained on a dataset of 4,410 images, which combines self-gathered images and images from Kaggle dataset. The images are then augmented to 10,936 images covering 23 waste classes, including organic, inorganic, hazardous, and residual types, with their recyclability conditions. The application allows users to detect objects using their phone camera, to identify their classification and condition, as well as receive actionable 3R (Reduce, Reuse, Recycle) recommendations. Evaluation results show a precision of 0.5963, recall of 0.60563, mAP@0.5 of 0.62246, and mAP@0.5:0.95 of 0.5279, indicating decent classification despite challenges posed by visually similar objects and variable backgrounds. Overall, the system demonstrates the feasibility of deploying a lightweight AI model on mobile devices in hopes of supporting proper waste segregation, increase user awareness, and potentially reduce contamination in recycling streams through practical waste classification.
Co-Authors Liang, Suwarno Mungkap Mangapul Siahaan Suwarno Suwarno