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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Masyarakat Informatika Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika Seminar Nasional Informatika (SEMNASIF) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Teknologi Indonesia Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Moneter : Jurnal Keuangan dan Perbankan Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
L. Budi Handoko
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Data Security Using Color Image Based on Beaufort Cipher, Column Transposition and Least Significant Bit (LSB) Handoko, Lekso Budi; Umam, Chaerul
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.7863

Abstract

One of cryptography algorithm which used is beaufort cipher. Beaufort cipher has simple encryption procedure, but this algorithm has good enough endurance to attack. Unauthorized people cannot break up decrypt without know matrix key used. This algorithm used to encrypt data in the form of text called plaintext. The result of this algorithm is string called ciphertext which difficult to understood that can causing suspicious by other people. Beaufort cipher encryption tested with avalanche effect algorithm with modified one, two, three and all key matrix which resulting maximum 31.25% with all key modification so another algorithm is needed to get more secure. Least Significant Bit (LSB) used to insert ciphertext created to form of image. LSB chosen because easy to use and simple, just alter one of last bit image with bit from message. LSB tested with RGB, CMYK, CMY and YUV color modes inserted 6142 characters resulting highest PSNR value 51.2546 on YUV color mode. Applying steganography technique has much advantage in imperceptibility, for example the image product very similar with original cover image so the difference can not differentiate image with human eye vision. Image that tested as much ten images, that consist of five 512 x 512 and five 16 x 16 image. While string message that used is 240, 480 and 960 character to test 512 x 512 image and 24, 48 and 88 character to test 16 x 16 image. The result of experiment measured with Mean Square Error (MSE) and Peak Signal Ratio (PSNR) which has minimum PSNR 51.2907 dB it means stego image that produced hood enough. Computation time calculation using tic toc in matlab resulting fastest value 0.041636 to encrypt 2000 character and the longest time is 4.10699 second to encrypt 6000 character and inserting to image. Amount of character and amount of multi algorithm can affecting computation time calculation.
File Cryptography Optimization Based on Vigenere Cipher and Advanced Encryption Standard (AES) Muslih, Muslih; Handoko, L. Budi; Rizqy, Aditya
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.7899

Abstract

The rapid The main problem in the misuse of data used in crime is the result of a lack of file security. This study proposes a data security method to protect document files using the Advanced Encryption Standard (AES) algorithm combined with the Vigenere Cipher. This research carried out 2 processes, namely the encryption process and the decryption process. The encryption process will be carried out by the AES algorithm and then encrypted again with the Vigenere Cipher algorithm. The experiments show that the proposed method can encrypt files properly, where there are changes in the value of the document file and the encrypted file cannot be opened and the description results do not cause changes to the original file. The results of this study are that the system is able to work properly so as to produce file encryption and decryption using the AES method combined with the Vigenere Cipher. In document files, the largest difference in encryption and decryption time is 8 seconds, while in image files the difference in encryption and decryption time is 17 seconds. This longest time difference is generated by large files.
Purwarupa Sistem Pemilihan Umum Elektronik dengan Pemanfaatan Protokol Ethereum pada Teknologi Blockchain Arianto, Eko; Umam, Chaerul; Handoko, L Budi
Jurnal Transformatika Vol. 19 No. 1 (2021): July 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i1.2746

Abstract

The development of information technology has penetrated various areas. This is driven by improved service, rapid increase in information needs and decision making. General elections that are held every time always leave problems about securities and speed of recapitulation. This is because the process is done in the traditional way. This research tries to apply blockchain technology to the e-Voting system security engineering process so that it creates a votes recapitulation process that is fast, accurate and accompanied by transparency values to maintain the reliability of the existing vote and maintain the confidentiality of the vote data being transacted. Transparency and confidentiality of voter data is a fundamental value in general elections or voting that must exist. Seeing this, blockchain technology deserves to be applied because the principles that needed can be met by applying this technology to the e-Voting system.
Analisa Forensik Kontainer Podman Terhadap Backdoor Metasploit Menggunakan Checkpointctl Sya'bani, Hafiidh Akbar; Umam, Chaerul; Handoko, L Budi
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8109

Abstract

Container systems are type of virtualization technology with isolated environment. The isolated environment in container system does not make cyber attacks impossible to occur. In this research, containers in which a cyber incident occurred were forensically tested on the container's memory to obtain digital evidence. The forensic process is carried out using standards from NIST framework with the stages of collection, examination, analysis and reporting. The forensic process begins by performing a checkpoint on the container to obtain information from the container's memory. In Podman the checkpoint process is carried out on one of the containers and will produce a file in .tar.gz form, where this file contains the information contained in the container. After the checkpoint process is complete, forensics is then carried out by reading the checkpoint file using a tool called checkpointctl. Forensic results showed that the container was running a malicious program in the form of a backdoor with a PHP extension.
Kriptografi Teks Berbasis Algoritma Substitusi Vigenere Cipher 8 Bit Karima, Nida Aulia; Aisyah, Ade Nurul; Silla, Hercio Venceslau; Handoko, Lekso Budi; Sani, Ramadhan Rakhmat
Jurnal Masyarakat Informatika Vol 15, No 1 (2024): May 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.1.60836

Abstract

Vigenere Cipher merupakan salah satu algoritma kriptografi klasik dalam dunia kriptografi. Penelitian ini berfokus pada penggunaan metode Vigenere Cipher dan implementasinya dalam mengamankan sebuah teks pesan berbentuk ASCII. Penelitian ini menggunakan empat metode pengujian yaitu, Avalanche Effect, Character Error Rate (CER), Bit Error Rate (BER), dan Entropi. Hasil pengujian mendapatkan bahwa nilai Avalanche Effect yang dihasilkan rata-rata berada pada angka 50% ke atas, artinya diperoleh nilai Avalanche Effect yang baik. Selain itu, CER dan BER yang dihasilkan bernilai 0, artinya tidak terjadi kesalahan selama proses enkripsi. Nilai Entropi yang dihasilkan juga meningkat seiring dengan panjang plaintext yang digunakan dan juga dipengaruhi penggunaan ASCII 256 berupa huruf, angka, dan simbol.
A Super Encryption Approach for Enhancing Digital Security using Column Transposition - Hill Cipher for 3D Image Protection Handoko, Lekso Budi; Umam, Chaerul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1984

Abstract

Image encryption is an indispensable technique in the realm of information security, serving as a pivotal mechanism to safeguard visual data against unauthorized access and potential breaches. This study scrutinizes the effectiveness of merging columnar transposition with the Hill Cipher methodologies, unveiling specific metrics from a curated set of sample images. Notably, employing column transposition with the key "JAYA" and the Hill Cipher with the key "UDINUSSMG," the encrypted images underwent rigorous evaluation. 'Lena.png' demonstrated an MSE of 513.32 with a PSNR of 7.89 dB, while 'Peppers.png' and 'Baboon.png' recorded MSE values of 466.67 and 423.92, respectively, with corresponding PSNR figures of 7.12 dB and 7.31 dB. Across all samples, a consistent BER of 50.00% indicated uniform error propagation, while entropy values settled uniformly at 7.9999, highlighting consistent data complexity. While the findings underscore a consistent error rate and complexity, there's a compelling need for further refinement to enhance image quality and security. Moreover, the study proposes future research avenues exploring a three-layer super encryption paradigm, amalgamating columnar transposition, Hill Cipher, and other robust algorithms. This approach aims to fortify encryption methodologies against evolving threats and challenges in data protection, offering heightened resilience and efficacy in safeguarding sensitive information.
Analisis Performa Model Random Forest dan CatBoost dengan Teknik SMOTE dalam Prediksi Risiko Diabetes Irfannandhy, Rony; Handoko, Lekso Budi; Ariyanto, Noval
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27990

Abstract

Diabetes mellitus (DM) is increasing in prevalence globally and is becoming a serious health problem. Early detection reduces long-term complications. The purpose of our research is to evaluate and compare the effectiveness of Random Forest (RF) and CatBoost models with SMOTE technique in predicting DM risk based on test data processed to produce comparative analysis performance of both models in the form of precission, recall, F1-Score and accuracy. Our research type is quantitative using methods that include EDA, transformation, dividing test and training data, implementation of RF and CatBoost methods with SMOTE and evaluation of model performance. The dataset from the platform (Kaggle) includes 768 individual health data consisting of eight independent variables of pregnancy, glucose, blood pressure, skin thickness, insulin, Body Mass Index (BMI), DM history, age as well as one target (outcome) variable of DM status. The SMOTE analysis technique was applied to balance the class distribution and improve the representation of the minority class, making the prediction model more accurate and stable. The findings of the SMOTE-RF model were 82% accuracy and SMOTE CatBoost 81% accuracy. Based on the feature importances analysis, the main variables affecting DM risk prediction of both models are glucose, BMI and age. Glucose variable is the main DM risk indicator used for prediction to be more efficient. The practical implication of improved machine learning early detection has the potential to support doctors' decision making more accurately to prevent more serious complications in diabetes mellitus.
Model Hybrid Random Forest dan Information Gain untuk Meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11216

Abstract

Evolusi malware atau perangkat lunak berbahaya semakin meningkatkan kekhawatiran, menyerang tidak hanya komputer tetapi juga perangkat lain seperti smartphone. Malware kini tidak hanya berbentuk monomorfik, tetapi telah berkembang menjadi bentuk polimorfik, metamorfik, hingga oligomorfik. Dengan perkembangan massif ini, perangkat lunak antivirus konvensional tidak akan mampu mengatasinya dengan baik. Hal ini disebabkan oleh kemampuan malware untuk menyebarkan dirinya dengan pola sidik jari dan perilaku yang berbeda. Oleh karena itu, diperlukan antivirus cerdas berbasis machine learning yang mampu mendeteksi malware berdasarkan perilaku bukan sidik jari. Penelitian ini berfokus pada implementasi model machine learning dalam deteksi malware dengan menggunakan algoritma ensemble dan seleksi fitur untuk mencapai kinerja yang baik. Algoritma ensemble yang digunakan adalah Random Forest, dievaluasi dan dibandingkan dengan k-Nearest Neighbor dan Decision Tree sebagai state-of-the-art. Untuk meningkatkan kinerja klasifikasi dalam hal kecepatan proses, metode seleksi fitur yang diterapkan adalah Information Gain dengan 22 fitur. Hasil tertinggi dicapai dengan menggunakan algoritma Random Forest dan metode seleksi fitur Information Gain, mencapai skor 99.0% untuk akurasi dan F1-Score. Dengan mengurangi jumlah fitur, kecepatan pemrosesan dapat ditingkatkan hingga hampir 5 kali lipat.
Model Hybrid Random Forest dan Information Gain untuk meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The evolution of malware, or malicious software, has raised increasing concerns, targeting not only computers but also other devices like smartphones. Malware is no longer just monomorphic but has evolved into polymorphic, metamorphic, and oligomorphic forms. With this massive development, conventional antivirus software is becoming less effective at countering it. This is due to malware's ability to propagate itself using different fingerprint and behavioral patterns. Therefore, an intelligent machine learning-based antivirus is needed, capable of detecting malware based on behavior rather than fingerprints. This research focuses on the implementation of a machine learning model for malware detection using ensemble algorithms and feature selection to achieve optimal performance. The ensemble algorithm used is Random Forest, evaluated and compared with k-Nearest Neighbor and Decision Tree as state-of-the-art methods. To enhance classification performance in terms of processing speed, the feature selection method applied is Information Gain, with 22 features. The highest results were achieved using the Random Forest algorithm and Information Gain feature selection method, reaching a score of 99.0% for accuracy and F1-Score. By reducing the number of features, processing speed can be increased by almost fivefold.
Optimasi Analisis Sentimen Lowongan Kerja di Twitter Dengan XGBoost-Vader dan Evaluasi SMOTE Borderline Ja'far, Luthfi; Handoko, L. Budi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 4 (2025): JPTI - April 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.741

Abstract

Perkembangan komunikasi digital telah menjadikan Twitter sebagai platform utama dalam rekrutmen di Indonesia. Namun, analisis sentimen pada platform ini masih jarang diterapkan secara optimal, padahal dapat memberikan wawasan penting bagi pencari kerja dan perekrut dalam memahami persepsi publik terhadap lowongan kerja. Penelitian ini mengembangkan model analisis sentimen menggunakan XGBoost dan VADER untuk mengklasifikasikan postingan lowongan kerja berbahasa Indonesia ke dalam tiga kategori: positif, negatif, dan netral. Dataset terdiri dari 2.181 postingan, dengan rincian 1.711 netral, 414 positif, dan 56 negatif. Untuk menangani ketidakseimbangan data, diterapkan Synthetic Minority Over-sampling Technique (SMOTE) Borderline, yaitu teknik penyeimbangan data yang secara selektif menghasilkan sampel sintetis pada batas keputusan. Namun, teknik ini tidak meningkatkan akurasi model secara signifikan. Sebelum tuning, akurasi model konsisten di 99,95% hingga 100%, sementara setelah tuning, akurasi awalnya sedikit lebih rendah tetapi kemudian stabil di 100%. Evaluasi menggunakan classification report, confusion matrix, dan Stratified K-Fold Cross Validation menunjukkan bahwa model tetap stabil dan mampu menggeneralisasi data dengan baik tanpa indikasi overfitting. Dibandingkan pendekatan sebelumnya, penelitian ini menunjukkan bahwa kombinasi XGBoost dan VADER tanpa balancing data tambahan tetap mampu memberikan analisis sentimen yang lebih akurat dan stabil untuk platform lowongan kerja di Indonesia. Hasil ini berkontribusi dalam pengembangan model analisis sentimen berbasis machine learning yang lebih sesuai dengan karakteristik bahasa Indonesia, serta membuka peluang penelitian lebih lanjut dalam analisis opini publik di media sosial.
Co-Authors ., Muslih Abdus Salam, Abdus Abdussalam Abdussalam Abu Salam Abu Salam Acun Kardianawati Ade Surya Ramadhan Adelia Syifa Anindita Aisyah, Ade Nurul Aisyatul Karima Aisyatul Karima Ajib Susanto Al zami, Farrikh Alzami, Farrikh Andi Danang Krismawan Ardytha Luthfiarta Ari Saputro Ari Saputro, Ari ARIANTO, EKO Ariya Pramana Putra Ariyanto, Noval Budi Harjo Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Firman Wahyudi, Firman Ghulam Maulana Rizqi Guruh Fajar Shidik Hafiidh Akbar Sya'bani Hanif Setia Nusantara Hanny Haryanto Hasan Aminda Syafrudin Hendy Kurniawan Herfiani, Kheisya Talitha Irfannandhy, Rony Irwan, Rhedy Isinkaye, Folasade Olubusola Izza Khaerani Ja'far, Luthfi Junta Zeniarja Karima, Nida Aulia Khafiizh Hastuti Khafiizh Hastuti Lucky Arif Rahman Hakim Maulana Ikhsan Megantara, Rama Aria Mira Nabila Mira Nabila Muhammad Jamhari Muslih Muslih Muslih Muslih Nurhindarto, Aris Ocky Saputra, Filmada Oki Setiono Pulung Nurtantio Andono Raihan Yusuf Rama Aria Megantara Ramadhan Rakhmat Sani Reza Pahlevi, Mohammad Rizky Rizqy, Aditya Rofiani, Rofiani Saputra, Filmada Ocky Saputri, Pungky Nabella Sarker, Md. Kamruzzaman Sendi Novianto Silla, Hercio Venceslau Soeleman, M Arief Sya'bani, Hafiidh Akbar Umi Rosyidah Valentino Aldo Wellia Shinta Sari Wildanil Ghozi