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All Journal EXPLORE Jurnal Kecerdasan Buatan dan Teknologi Informasi Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Muhammad Masjun Efendi
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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PENERAPAN DIGITAL SIGNATURE SCHEME DENGAN METODE SCHNORR AUTHENTICATION Erfan Wahyudi; Muhammad Masjun Efendi; Moh Subli; Ahmad Subki; Muhammad Rijal Alfian
Jurnal Explore Vol 10, No 1 (2020): JANUARI
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.495 KB) | DOI: 10.35200/explore.v10i1.360

Abstract

Otentikasi (authentication) merupakan identifikasi yang dilakukan oleh masing-masing   pihak  yang  saling  berkomunikasi,   maksudnya  beberapa  pihak yang berkomunikasi harus mengidentifikasi satu sama lainnya. Informasi yang didapatkan oleh satu pihak dari pihak lain harus diidentifikasi untuk memastikan keaslian informasi  yang diterima.  Tanda tangan  digital adalah suatu mekanisme otentikasi yang memungkinkan  pembuat pesan menambahkan  sebuah kode yang bertindak    sebagai   tanda   tangannya.   Skema   yang   dapat   digunakan    untuk melakukan  proses  tanda  tangan  digital  terhadap  suatu  pesan  juga  bermacam - macam.  Skema  otentikasi  dan  tanda  tangan  digital  Schnorr  merupakan  skema tanda tangan digital yang mengambil keamanan dari permasalahan menghitung logaritma diskrit. Masalah   pertama,   membuktikan   keaslian   dokumen,   dapat   dilakukan dengan teknologi  pemberian  cap air dan tanda tangan digital. Pemberian  cap air juga  dapat  digunakan  untuk  menjaga   hak  milik  intelektualitas,   yaitu  dengan menandai  dokumen  atau  hasil  karya  dengan  “tanda  tangan”  pembuat.  Masalah kedua biasanya berhubungan dengan akses kontrol, yaitu berkaitan dengan pembatasan orang yang dapat mengakses informasi. Dalam  hal  ini  pengguna  harus  menunjukkan  bukti  bahwa  memang  dia adalah pengguna yang sah, misalnya dengan menggunakan kata sandi aspek/servis dari security  biometric  (ciri-ciri khas orang),  dan sejenisnya. Dalam penelitian ini melakukan simulasi otentikasi digital signature dengan  menerapkan  metode  hash SHA 1, tanda tangan digital tidak  mudah untuk di kelabui. Kata kunci:  Authentication, Digital Signature, Encryption
Pelatihan Transformasi Digital Arsip di Desa Darmaji Menggunakan Google Drive Apriana Irawati; Sofiansyah Fadli; Sunardi Sunardi; Muhammad Masjun Efendi; Edi Haryanto
Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat Vol. 2 No. 3 (2024): Juli: Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/bumi.v2i3.134

Abstract

Darmaji Village is a village located in Kopang District, Central Lombok Regency, where the village office has a central role in managing administrative affairs and community services. However, archive management in Darmaji Village still uses conventional methods that are vulnerable to the risk of damage and loss of documents. As a solution, the pengabdi took the initiative to conduct digital archive transformation training using Google Drive to help village officials manage archives more efficiently. The methods used in this service training include observation, interviews, socialization, and training. The results of this activity showed a significant increase in the ability of the participants, where the pretest results showed 88.9% of the 9 participants could not operate Google Drive as a digital archive, while the posttest results showed 100% of the participants were able to operate Google Drive properly. With the application of this technology, it is expected that archive management in Darmaji Village will become more effective, efficient, and secure, and will be able to encourage the implementation of digital technology in various aspects of village administration
PEARLVISION AI: AN AUTOMATED PEARL QUALITY GRADING SYSTEM BASED ON MORPHOLOGICAL FEATURES AND ENSEMBLE LEARNING Karim, Muh. Nasirudin; Muhammad Masjun Efendi; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.472

Abstract

Conventional pearl quality assessment remains heavily reliant on manual visual inspection, which is subjective and inconsistent. This study develops PearlVision AI, an automated system for grading Lombok pearls using morphological feature extraction and ensemble learning. The dataset comprises 361 South Sea pearl images (Pinctada maxima) labeled into three commercial grades: A (n=120), AA (n=120), and AAA (n=120). The proposed pipeline integrates hybrid segmentation (Hough Circle Transform + Convex Hull) for robust object isolation, extraction of four geometric descriptors (circularity, eccentricity, area, perimeter), and comparative evaluation of four classification algorithms: Random Forest, Gradient Boosting, K-Nearest Neighbor, and SVM (RBF). Results demonstrate that Random Forest achieved optimal performance with a test accuracy of 97.22% and a 5-fold cross-validation score of 91.68%, consistently maintaining precision, recall, and F1-score >0.95 across all grade classes. Feature importance analysis revealed that size-related features (area and perimeter) contributed more significantly to class discrimination than shape-based metrics (circularity), reflecting the natural correlation between pearl diameter and commercial value in this dataset. With an inference time of <0.5 seconds per image, PearlVision AI offers an objective, efficient, and reproducible solution for reducing manual grading bias and enhancing quality control consistency in the pearl industry
Co-Authors Ahmad Subki Apriana Irawati Edi Haryanto Erfan Wahyudi Imran, Bahtiar Karim, Muh. Nasirudin Moh Subli Muhammad Rijal Alfian Sofiansyah Fadli sunardi sunardi