Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : bit-Tech

Design and Development of Web-Mobile Application for Housing Project Management Using KNN for Prediction Salsabila Arifa; Fawwaz Ali Akbar; Chrystia Aji Putra
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2546

Abstract

Project management in housing development is essential to ensure timely completion, budget efficiency, and market alignment. However, many small to medium sized property developers still use manual systems, causing inefficiencies in monitoring, documentation, and sales planning. PT Bakti Luhur Abadi is one such company that still relies on Microsoft Excel for recording project progress and housing unit sales. This study aims to develop an integrated project management system equipped with a sales prediction feature using the K-Nearest Neighbors (KNN) algorithm. The goal is to improve operational efficiency, streamline decision making, and support strategic sales forecasting. The system was developed using the Waterfall method, comprising requirement analysis, system design, implementation, and testing. A key novelty of this research is the dual platform implementation web for administrators and mobile for directors and field teams enabling real time access, structured documentation, and effective communication. The KNN algorithm was tested with 30 test data and 114 training data using K values of 3, 5, and 7. The best result was achieved at K = 7 with an accuracy of 86.7%. Functional validation using black-box testing confirmed all web and mobile features operated as expected. In conclusion, the proposed application effectively automates project management and enables accurate sales prediction. It provides practical benefits for small and medium-scale property developers by increasing efficiency, improving internal coordination, and supporting data driven planning through an accessible and intelligent solution.
Sistem Absensi Desktop Menggunakan Face Recognition dan Pendekatan Adaptive Attendance Monitoring Imam Afandy; Fawwaz Ali Akbar; Retno Mumpuni
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3487

Abstract

Manual attendance processes in higher education often face severe constraints regarding time inefficiency and vulnerability to data manipulation, specifically the prevalent issue of proxy attendance. Although Face Recognition technology has been widely adopted, most existing systems utilize a "once recognition" method, which fails to validate the student's presence throughout the entire lecture duration. This study aims to bridge this gap by developing an automatic desktop-based attendance system that integrates Face Recognition with a novel Adaptive Attendance Monitoring (AAM) approach. The proposed system utilizes a robust deep learning pipeline employing the Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection and alignment, followed by FaceNet for generating 128-dimensional feature embeddings. To ensure real-time performance, the processing is accelerated by CUDA GPU technology on an NVIDIA RTX 4060 Ti. The system architecture follows a decoupled Client-Server model based on REST API, ensuring scalability and low-latency data transmission. The primary novelty of this research is the AAM algorithm, which continuously calculates the cumulative duration of a student's presence. A student is validated as "Present" only if they maintain visibility for at least 80% of the total session duration, effectively eliminating the "check-in and leave" loophole. Experimental results demonstrate that the system achieves a 100% recognition accuracy at an optimal distance of 1.0 meter under normal lighting conditions, with a processing latency consistently maintained under 100ms. These findings confirm that the proposed desktop-edge architecture significantly outperforms traditional mobile-based solutions in terms of stability, security, and continuous monitoring capabilities.
Co-Authors -, Moslim Wahyu S B Ade Fathoni Prastya Akbar, Muhammad Fadel Akbar, Rizky Januar Aldito Restu Wintama Ana, Vika Rafi Anggraini Puspita Sari annisaa sri indrawanti annisaa sri indrawanti Anwar Sanusi Aprilyan, Verdiansyah Ayus Ardisty Palvelus Jumala Arhinza, Rayhan Saneval Awandi, Nadhif Mahardika Azzahra, Rahma Adisty Muffid Basuki Rahmat Benny Danendra Hadi Bhakti, Mulyani Satya Bimantara, Candra Kusuma Muhammad Budi Nugroho Budi Nugroho Burhanuddin Muhammad Zhirof Cahyas, Jerry Ramadhani Chrystia Aji Putra Dewanto, Fuad Kurnia Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fadhilasari, Annisa Fahturohman, Ridho Fajar Faisal Muttaqin Faisal Muttaqin Farhana, Hafi Ihza Faris Munir Fauzan, Daffa Athallah Fetty Tri Anggraeny Firdausi, Putri Aulia Citra Firmantara, Wahyu Firza Prima Aditiawan Fisena, Muhammad Reyhan Dwi Gabriel, Paskalis Reynaldy Elroy Ghifari, Farhat Ibad Al Ginting, Fitznigel Diamond Daniel Habibi, Nabil Henni Endah Wahanani Henni Endah Wahanni Hutagaol, LeonHoss I Gede Susrama Mas Diyasa Ibad, Farhat Ida Ayu Putu Sri Widnyani Ika Nur Habibah Imam Afandy Iriansah, Ogy Rachmad Kasyfillah, Muhammad Rohan Kasyfillah, Rohan Kurniawati, Felliani Kusuma, Steven Nathan Lina Nurlaili, Afina Lugito, Lugito Michael Imanuel Prasetya M. Miftachul Anwar Made Hanindia Prami Swari Mahnedra, Zenryo Yudi Arnava Darva Mandyartha, Eka Prakarsa Martoni Martoni Matondang, Natalia Maulana, Dimas Octa Maulana, Hendra Mayya, Kalfin Syah Kilau Mohamad Ilham Prasetyo Raharjo Muhammad Agung Shobirin Muhammad Reyhan Dwi Fisena Muttaqin, Faisal Nadia, Prasinta Hari Natalia Matondang Noor Imansyah Basoeki, Dandy Nugroho, Iliochiesa Hilmi Nur Fadillah Dwi Rahma Nurlaili, Afina Lina Parlika, Rizky Paskalis Reynaldy Elroy Gabriel Prastya, Ade Fathoni Putra, Chrystia Aji Putri, Angie Nurshabrina Rabbani, Rafi Rahma, Nur Fadillah Dwi Ramadhana, Fitranda Ramadhani, Neo Retno Mumpuni Reza Aminullah Rifan, Triyono Romadhoni, Firman Ronggo Alit Salsabila Arifa Salsabila, Belia Putri Sandy Rizkyando Saputra, Bayu Rachmawan Saputra, Bayu Rahmawan Saputra, Ersa Valerian Siran, Timothy Ueldy Siti Rochimah Sri Indrawanti, Annisaa SUBAGIA, RONNY Subagio, Moh. Mario Sugiarto Sujatmoko, Amanda Widya Indah Susrama Mas Diyasa, I Gede Syahbana, Ahmad Nadhif Fikri Wardana, Anak Agung Ngurah Wisnu Wintama, Aldito Restu Yisti Vita Via Yudi, Zenryo Ziidan, Muhammad Fattah