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All Journal KOMPUTEK Scientica: Jurnal Ilmiah Sains dan Teknologi
Ansori, Yulian
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mechanical Engineering Social Sciences Other

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RANCANG BANGUN SISTEM ABSENKUY BERBASIS IOT (INTERNET OF THINGS) MENGGUNAKAN METODE PROTOTYPING PADA PERUSAHAAN CUBICART Hidayatulloh, Syarif; Najmuddin, Najmuddin; Ansori, Yulian
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 12 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v2i12.3431

Abstract

Perkembangan teknologi informasi dan komunikasi (TIK), terutama Internet of Things (IoT), telah memberikan dampak signifikan dalam berbagai aspek kehidupan, termasuk dunia bisnis. Kemajuan ini mendorong kreativitas manusia untuk memaksimalkan kinerja teknologi yang ada. Salah satu aplikasi inovatif adalah sistem absensi berbasis IoT, yang memungkinkan perangkat elektronik berkomunikasi dan bertukar data melalui internet. Absensi adalah aktivitas pelaporan dan pendataan kehadiran dalam suatu institusi. Di perguruan tinggi, sistem absensi tradisional biasanya manual, di mana mahasiswa mengisi formulir absensi dengan tanda tangan. Penggunaan teknologi IoT dapat meningkatkan efisiensi dan akurasi proses ini. Di perusahaan percetakan Cubicart, absensi karyawan tidak diatur oleh sistem dan hanya mengandalkan kepercayaan atasan, yang sering disalahgunakan. Untuk mengatasi masalah ini, dikembangkan sistem absensi otomatis menggunakan teknologi RFID (Radio Frequency Identification). RFID memungkinkan pengiriman data identitas objek secara nirkabel menggunakan gelombang radio, bagian dari teknologi Automatic Identification (AutoID). Sistem ini terdiri dari microcontroller, modul RFID, modul ESP8266, dan komputer server. Microcontroller mengolah data dari modul RFID melalui komunikasi Transmission Control Protocol/Internet Protocol (TCP/IP). Pada Metode Penelitian sendiri penulis menggunakan metode kualitatif di manah penulis melakukan observasi dan wawancara langsung dengan narasumber terkait untuk keperluan pengumpulan data.
Perbandingan Algoritma LBP dan Cascading LBP-GLCM untuk Ekstraksi Fitur pada Citra Beras Rahman, Arief; Darnis, Febriyanti; Ansori, Yulian
KOMPUTEK Vol 8, No 2 (2024): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i2.2962

Abstract

This study compares two image feature extraction algorithms: Gray Level Co-occurrence Matrix (GLCM) and a combination of Local Binary Pattern with GLCM (LBP GLCM), for rice image classification. The objective is to evaluate the effectiveness of both methods in generating features such as ASM, contrast, correlation, entropy, and energy, as well as to measure the computational time. The results show that the LBP GLCM algorithm significantly improves classification accuracy compared to pure GLCM, but requires 13-17 times longer computational time. While GLCM is more efficient in terms of time, its classification accuracy is relatively lower. These findings align with previous studies indicating that adding LBP to GLCM enhances classification performance. In conclusion, LBP GLCM is superior in accuracy, making it a better choice for applications that prioritize precise classification results. However, the trade-off in computational time should be considered, especially for applications requiring fast processing. These findings are relevant for further development in agriculture and image processing. 
Perbandingan Algoritma LBP dan Cascading LBP-GLCM untuk Ekstraksi Fitur pada Citra Beras Rahman, Arief; Darnis, Febriyanti; Ansori, Yulian
KOMPUTEK Vol. 8 No. 2 (2024): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i2.2962

Abstract

This study compares two image feature extraction algorithms: Gray Level Co-occurrence Matrix (GLCM) and a combination of Local Binary Pattern with GLCM (LBP GLCM), for rice image classification. The objective is to evaluate the effectiveness of both methods in generating features such as ASM, contrast, correlation, entropy, and energy, as well as to measure the computational time. The results show that the LBP GLCM algorithm significantly improves classification accuracy compared to pure GLCM, but requires 13-17 times longer computational time. While GLCM is more efficient in terms of time, its classification accuracy is relatively lower. These findings align with previous studies indicating that adding LBP to GLCM enhances classification performance. In conclusion, LBP GLCM is superior in accuracy, making it a better choice for applications that prioritize precise classification results. However, the trade-off in computational time should be considered, especially for applications requiring fast processing. These findings are relevant for further development in agriculture and image processing. 
Co-Authors Arief Rahman Darnis, Febriyanti Najmuddin, Najmuddin Syarif Hidayatulloh