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All Journal Jurnal Mahasiswa Fakultas Teknologi Informasi Journal of Information Technology and Computer Science (JOINTECS) SMARTICS Journal J I M P - Jurnal Informatika Merdeka Pasuruan RESEARCH : Computer, Information System & Technology Management Kurawal - Jurnal Teknologi, Informasi dan Industri Rainstek : Jurnal Terapan Sains dan Teknologi BIMASAKTI Informasi interaktif : jurnal informatika dan teknologi informasi
Danang Aditya Nugraha
Universitas PGRI Kanjuruhan Malang
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PENERAPAN DATA MINING UNTUK MENGKLASIFIKASI PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Amelia Jesika; Alexius Endy Budianto; Danang Aditya Nugraha
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.12826

Abstract

The Family Hope Program (PKH) is a governmental initiative in Indonesia designed to decrease poverty and improve the welfare of families. However, the process of identifying eligible families frequently encounters difficulties. To address this, the study applies data mining techniques with the Support Vector Machine (SVM) method to classify prospective PKH recipients in Bangka Leleng Village. The research utilizes 1,039 data samples of recipients from 2019 to 2023, based on five key attributes: age, income, number of dependents, occupation, and home ownership status. Data processing was conducted using Python in the Google Colab environment. The research workflow involved data collection, preprocessing, splitting data for training and testing, analysis, and evaluation using a Confusion Matrix. The test results indicated that the SVM method is highly effective in classifying PKH recipients, achieving an accuracy rate of up to 96%. This optimal accuracy was obtained by employing the RBF kernel, which demonstrated superior performance compared to other kernels. It is anticipated that this research will provide a more efficient and transparent method for determining aid recipients, leading to a more precise distribution of assistance.
ANALISIS PERBANDINGAN PERFORMA GALOIS COUNTER MODE (GCM) PADA AES DAN CAMELLIA UNTUK ENKRIPSI DAN DEKRIPSI DATA Usman Hidayatulloh Sidiq; Akhmad Zaini; Danang Aditya Nugraha
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 11 No 2 (2026): Jurnal Informasi Interaktif
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

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Abstract

Digital data security has become a critical necessity in tandem with the increasing threats to information confidentiality and integrity in the modern era. The Advanced Encryption Standard (AES) and Camellia are to internationally standardized block cipher algorithms with equivalent security levels but distinct internal structures. The Substitution-Permutation Network (SPN) in AES and the Feistel Network in Camellia result in different performance characteristics. The lack of comprehensive research comparing both algorithms in Galois Counter Mode (GCM) using multithreading and hardware acceleration parameters serves as the background for this study. This research aims to compare the performance of AES and Camellia in GCM based on parameters including key size, file size, mode of operation, multithreading, and hardware acceleration. The implementation was conducted using the Java programming language and the BouncyCastle library. The result indicate that AES is superior, exhibiting a 30% higher processing rate. Multithreading efficiency is optimal at 2 threads but decreases at 8 threads due to thread management overhead. Hardware acceleration yields varied impacts, such as accelerating encryption time and slowing down the decryption time for AES. Overall, AES demonstrates superior performance, whereas Camellia remains competitive under specific conditions.
ANALISIS PERFORMA ALGORITMA XGBOOST PADA KLASIFIKASI KANKER PARU-PARU Maria Ere; Danang Aditya Nugraha; Heri Santoso
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13159

Abstract

This study aims to analyse the performance of the Extreme Gradient Boosting (XGBoost) algorithm in lung cancer classification. This test used a dataset obtained from Kaggle, comprising 220,632 data points and 23 attributes. The research methods included exploration, data preprocessing such as cleaning, selection, and transformation, as well as dividing the data for training and testing five times (70%:30%, 75%:25%, 80%:20%, 85%:15%, 90%:10%). Model evaluation metrics such as accuracy, precision, recall, and F1-score were used in conjunction with the confusion matrix. Based on the results of XGBoost model testing with varying parameter settings, it demonstrates good performance even when the proportion of training and testing data is altered, achieving the highest accuracy of 95.76%, precision of 96%, recall of 100%, and F1-score of 98%.
Co-Authors Adam Abdullah Ahmad Zaelani Akhmad Zaini Amak Yunus Amelia Jesika Budianto, Alexius Endy Damianus Daton Witak Ferdi Ari Hendrawan Fery Agung Prastyo Fransisco L. Z. Meko Galang Paksi Permana Heri Santoso Kadwi Suharsono Maria Ere Mohammad Ahsan Mukhlisin Efendi Nemesius Sunarjo Novita Karima Nurfatika Sari Plasidius Y.M Bate Rachel Trivica Amelia Rizka Laela Saputri Ramadhanti Sartika Wiguna, Anggri Sely Prastikasari Sulistyanto, Muhammad Priyono Tri Syahminan . Usman Hidayatulloh Sidiq Wahid Asrori Wahyudi Harianto Wahyudi Harianto Windy Syarifudin