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Sanriomi Sintaro
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Jalan Gatot Subroto No 47 LK I, Desa/Kelurahan Tanjunggading, Kec. Kedamaian, Kota Bandar Lampung, Provinsi Lampung, Indonesia
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INDONESIA
CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Published by Tech Cart Press
ISSN : 29642450     EISSN : 29642485     DOI : https://doi.org/10.58602/chain
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
CHAIN: Journal of Computer Technology, Computer Engineering and Informatics is a peer-review journal focusing on Computer Technology, Computer Engineering and Informatics. CHAIN invites academics and researchers who do original research in computer technology, computer engineering and informatics. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics are published by Tech Cart Press in January, April, July, and October every year. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics accept articles in Bahasa Indonesia and English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 103 Documents
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Pengembangan Sistem Computer Based Test (CBT) Berbasis React.js untuk Menjaga Integritas Evaluasi Akademik di SMP Al-Ishlah Afriza Ikhsan Muzaki; Rima Nursyifa; Thomas Aninditya Widianto; Verra Sofica
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.363

Abstract

Pengembangan teknologi informasi di bidang pendidikan mendorong transformasi sistem evaluasi dari metode konvensional menjadi Computer Based Test (CBT) berbasis web. Namun, implementasi CBT masih menghadapi permasalahan terkait integritas akademik, khususnya potensi kecurangan seperti perpindahan tab, akses aplikasi lain, dan pencarian jawaban melalui internet selama ujian berlangsung. Penelitian ini bertujuan untuk merancang dan membangun sistem CBT berbasis React.js dengan implementasi mekanisme keamanan berupa kiosk mode dan deteksi kehilangan fokus layar sebagai upaya mitigasi kecurangan ujian pada SMP Al-Ishlah Cintaraja. Metode pengembangan yang digunakan adalah Agile Scrum dengan teknologi React.js, Node.js, Express.js, dan Firebase. Sistem dilengkapi fitur pengacakan soal, pembatasan waktu, fullscreen mode, serta deteksi perpindahan tab. Pengujian dilakukan menggunakan Black Box Testing, User Acceptance Testing (UAT), dan Security Testing. Hasil penelitian menunjukkan bahwa sistem CBT yang dikembangkan mampu meningkatkan keamanan, efektivitas, dan integritas pelaksanaan ujian digital, serta memudahkan pengelolaan ujian oleh guru dan pengawas.
Implementasi Sistem Pakar Rekomendasi Jurusan Smk Menggunakan Metode Forward Chaining Pada Smk Bina Mandiri Bekasi Maratus Sholiha; Ari Nurul Alfian
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.370

Abstract

Pemilihan jurusan di Sekolah Menengah Kejuruan (SMK) merupakan salah satu tahapan penting yang memengaruhi proses belajar dan pengembangan kompetensi peserta didik. Namun, proses penjurusan di SMK Bina Mandiri Kota Bekasi masih dilakukan secara manual sehingga calon siswa sering mengalami kesulitan dalam menentukan jurusan yang sesuai dengan kemampuan akademik, minat, dan bakat yang dimiliki. Penelitian ini bertujuan mengembangkan sistem pakar berbasis website yang mampu memberikan rekomendasi jurusan secara cepat, objektif, dan sesuai dengan karakteristik siswa menggunakan metode Forward Chaining. Metode pengembangan sistem yang digunakan adalah Rapid Application Development (RAD) yang meliputi tahapan perencanaan kebutuhan, perancangan, konstruksi, dan implementasi. Basis pengetahuan sistem diperoleh melalui observasi, wawancara dengan pihak sekolah, serta studi pustaka yang menghasilkan data mata pelajaran, minat, bakat, dan 28 aturan (rule) sebagai dasar proses inferensi. Sistem dibangun menggunakan PHP dengan framework CodeIgniter 3 dan MySQL sebagai basis data. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan rekomendasi jurusan sesuai dengan aturan yang telah ditentukan serta seluruh fungsi sistem berjalan dengan baik berdasarkan pengujian Black Box Testing. Selain itu, hasil User Acceptance Testing (UAT) terhadap 23 responden memperoleh tingkat penerimaan pengguna sebesar 87,11% yang termasuk dalam kategori baik. Dengan demikian, sistem yang dikembangkan dapat membantu calon siswa memperoleh rekomendasi jurusan secara efektif dan mendukung proses pengambilan keputusan penjurusan di SMK.
Integration of Data Assessment Method Weighting and Proximity Indexed Value for Best Employee Selection in Decision Support Systems Setiawansyah Setiawansyah; Dyah Ayu Megawaty; Faruk Ulum; Agung Deni Wahyudi; Fadila Shely Amalia
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.378

Abstract

The selection of the best employee is a multi-criteria decision-making problem because employee performance is evaluated using several criteria with different levels of importance. Subjective criterion weighting may not fully represent the characteristics of the assessment data and can influence the resulting decision. This study proposes the integration of Data Assessment Method (DAM) Weighting and Proximity Indexed Value (PIV) within a Decision Support System for best employee selection. DAM Weighting is employed to determine objective criterion weights based on the information contained in the assessment data, while PIV is used to evaluate the relative proximity of each employee alternative to the reference condition. The results show that the criterion weights are relatively balanced, with R1 obtaining the highest weight of 0.1494 and R4 the lowest weight of 0.1381. The PIV calculation produces different proximity values among the nine employee alternatives, with Alt-08 obtaining the lowest value of -2.2864, followed by Alt-09 at -1.8580 and Alt-06 at -1.7148. Based on the resulting ranking, Alt-08 is identified as the best employee. These findings indicate that the integration of DAM Weighting and PIV can provide an objective, quantitative, and data-oriented mechanism for supporting best employee selection in a Decision Support System.

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