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All Journal INTECOMS: Journal of Information Technology and Computer Science International Journal Software Engineering and Computer Science (IJSECS)
Muhamad Zaeni Nadip
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Computer Science & IT

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Penerapan Kriptografi Aes Pada Fres-Caesas: Perlindungan Pesan Teks Dan Fail Dokumen Dadang Mulyana Iskandar; Muhamad Zaeni Nadip; Nandy Dinilhaq; Anggit Purnama
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 3 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i3.9426

Abstract

Solusi keamanan data telah memimpin kemajuan teknis. Kriptografi dan steganografi adalah dua dari beberapa bidang ilmu yang telah mengembangkan teknik ini untuk melindungi data sensitif. Keamanan data memerlukan seperangkat alat, bukan solusi tunggal. Tujuan utama penelitian ini adalah menggunakan algoritma Advanced Encryption Standard (AES) dan kriptografi untuk mengamankan percakapan teks, fail dokumen, beserta isinya. AES adalah metode kriptografi yang efektif. Data dapat dienkripsi dan didekripsi menggunakan blok ciphertext simetris. Pengguna dapat mengenkripsi SMS, menyimpannya dalam dokumen terenkripsi, mengenkripsi konten dokumen, dan mengompresnya, sesuai dengan analisis data. Untuk menjamin perlindungan data melalui keamanan dan enkripsi, hasil enkripsi fail dokumen disembunyikan dalam sebuah gambar. Kata kunci : Kriptografi, Advanced Encryption Standard (AES), Pesan Teks, Isi Fail Dokumen, Steganografi.
Automatic Purchase Order Classification Using SVM in POS System at Skus Mart Sri Lestari; Muhamad Zaeni Nadip; Yuma Akbar; Aditya Zakaria Hidayat; Raisah Fajri Aula
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4564

Abstract

In retail business processes, decision-making regarding Purchase Order PO submissions often remains manual and subjective, creating risks that impede procurement efficiency. The study develops an automatic classification model to predict PO approval status using Support Vector Machine SVM algorithm integrated within Point of Sale POS systems. Historical purchase transaction data was obtained from SKUS Mart POS database containing 133 entries, including attributes such as item quantity, purchase price, previous stock levels, and total purchase amounts. The research applies CRISP-DM methodology, encompassing business understanding, data exploration, preprocessing normalization using StandardScaler, model training, evaluation, and deployment phases. The model was trained using linear kernel and validated through holdout technique with 80:20 ratio for training and testing. Test results demonstrate that the SVM model achieves 76.69% accuracy, 82.21% precision, 76.69% recall, and 78.51% F1-score. The model was implemented in a web-based POS system CodeIgniter 3 combined with Python scripts to generate automatic classifications displayed directly in the user interface. Although the model demonstrates adequate performance, the study has not compared its effectiveness against other machine learning algorithms such as Random Forest or K-Nearest Neighbor. These findings establish initial groundwork for machine learning integration to support decision automation in procurement systems.
Co-Authors Aditya Zakaria Hidayat Akbar, Yuma Anggit Purnama Dadang Iskandar Mulyana` Nandy Dinilhaq Raisah Fajri Aula Sri Lestari