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All Journal Data Science: Journal of Computing and Applied Informatics JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences
Mohammad Andri Budiman
Universitas Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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On Factoring The RSA Modulus Using Tabu Search Ade Candra; Mohammad Andri Budiman; Dian Rachmawati
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.763 KB) | DOI: 10.32734/jocai.v1.i1-65

Abstract

It is intuitively clear that the security of RSA cryptosystem depends on the hardness of factoring a very large integer into its two prime factors. Numerous studies about integer factorization in the field of number theory have been carried out, and as a result, lots of exact factorization algorithms, such as Fermat’s factorization algorithm, quadratic sieve method, and Pollard’s rho algorithm have been found. The factorization problem is in the class of NP (non-deterministic polynomial time). Tabu search is a metaheuristic in the field of artificial intelligence which is often used to solve NP and NP-hard problems; the result of this method is expected to be close-to-optimal (suboptimal). This study aims to factorize the RSA modulus into its two prime factors using tabu search by conducting experiments in Python programming language and to compare its time performance with an exact factorization algorithm, i.e. Pollard’s algorithm. The primality test is done with Lehmann’s algorithm.
Implementation and comparison of Berry-Ravindran and Zhu- Takaoka exact string matching algorithms in Indonesian-Batak Toba dictionary Efelin Siburian; Mohammad Andri Budiman; Jos Timanta
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.032 KB) | DOI: 10.32734/jocai.v1.i1-67

Abstract

Indonesia has a variety of local languages, which is the Batak Toba language. This time, there are still some Batak Toba people who do not know speak Batak Toba language fluently. Nowadays, desktop based dictionary is one of reference that very efficiently used to learn a language and also to increase vocabulary. In making the dictionary application, string matching can be implemented for word-searching process. String matching have some algorithm, which is Berry – Ravindran algorithm and Zhu-Takaoka algorithm and will be implemented on the dictionary application. Zhu-Takaoka algorithm and Berry – Ravindran algorithm have two phases, which are the preprocessing phase and the searching phase. Preprocessing phase is a process to make the shifting values according to in pattern that input by user. To know the shifting value with Zhu-Takaoka algorithm, it’s need Zhu-Takaoka Bad Character (Ztbc) and Boyer-Moore Good Suffix (Bmgs). Then, Ztbc will be compared to Bmgs to get the maximum value of them that will be set as shifting value. While Berry-Ravindran algorithm, to know the shifting value is needed Berry-Ravindran Bad Character, which the two characters right of the text at the position m + 1 and m+ 2, is needed to determine the shifting value, where m is length of the pattern.
Using random search and brute force algorithm in factoring the RSA modulus Mohammad Andri Budiman; Dian Rachmawati
Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1163.568 KB) | DOI: 10.32734/jocai.v2.i1-91

Abstract

Abstract. The security of the RSA cryptosystem is directly proportional to the size of its modulus, n. The modulus n is a multiplication of two very large prime numbers, notated as p and q. Since modulus n is public, a cryptanalyst can use factorization algorithms such as Euler’s and Pollard’s algorithms to derive the private keys, p and q. Brute force is an algorithm that searches a solution to a problem by generating all the possible candidate solutions and testing those candidates one by one in order to get the most relevant solution. Random search is a numerical optimization algorithm that starts its search by generating one candidate solution randomly and iteratively compares it with other random candidate solution in order to get the most suitable solution. This work aims to compare the performance of brute force algorithm and random search in factoring the RSA modulus into its two prime factors by experimental means in Python programming language. The primality test is done by Fermat algorithm and the sieve of Eratosthenes.
Computing the Value of Pi in the Manner of Lambda Function with R Statistical Programming Language Muhammad Reza Fahlevi; Mohammad Andri Budiman
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 1 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i1-5556

Abstract

The value of π is one of the famous mathematical constant, not only to mathematicians, but also to physicists and to engineers. Numerous algorithm is used to compute what is the value of π, but most of programmer do not use lambda function and neglect the aesthetic of their script. This study aims to compute the value π by write it first as infinite series using Riemann sum, and then the computing of it is conducted in R programming language. We involved the role of anonymous function or known as lambda function to make the R code is more beautiful, artistic, and elegant.
PERBANDINGAN ALGORITMA MESSAGE DIGEST-5 (MD5) DAN GOSUDARSTVENNYI STANDARD (GOST) PADA HASHING FILE DOKUMEN Marthin Benedict; Mohammad Andri Budiman; Dian Rachmawati
JTIK (Jurnal Teknik Informatika Kaputama) Vol 1, No 1 (2017)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.69 KB)

Abstract

Dokumen elektronik memiliki sifat terbuka, artinya isi dokumen dapat dibaca dan diubah dengan mudah olehpihak-pihak yang tidak berhak. Hal tersebut menyebabkan integritas dokumen menjadi tidak terjamin. Integritasdokumen elektronik dapat dijamin dengan menggunakan teknik kriptografi, salah satunya hash function. Adabanyak algoritma yang dapat digunakan untuk hashing file atau dokumen, dua algoritma diantaranya adalahalgoritma Message Digest-5 (MD5) dan algoritma Gosudarstvennyi Standard (GOST). Secara garis besar,kedua algoritma mengambil panjang isi file atau pesan dalam bit lalu dibagi menjadi blok-blok bit setelah itupada setiap blok akan dilakukan operasi matematika sehingga menghasilkan 128 bit nilai hash pada MessageDigest-5 (MD5) dan 256 bit nilai hash pada Gosudarstvennyi Standard (GOST). Setelah itu, nilai hash diubahdalam bentuk heksadesimal sehingga Message Digest-5 (MD5) akan menghasilkan 32 karakter heksadesimalnilai hash dan 64 karakter heksadesimal nilai hash pada Gosudarstvennyi Standard (GOST).
K-Cube Consensus Clustering with Centroid Improvement and Variance-Based Metrics on High-Dimensional Data Efori Bu'ulolo; Poltak Sihombing; Sutarman Sutarman; Mohammad Andri Budiman
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1209

Abstract

High-dimensional and multidimensional cube data structures (K-Cube) are posing a significant challenge for conventional clustering algorithms due to the effect of dimensionality, uniform feature weight assumptions, and loss of hierarchical information. Therefore, this study aimed to propose K-Cube Consensus Clustering framework, which integrates Variance-Based Centroid Refinement, Weighted Distance Metrics, and consensus voting mechanism to overcome the challenges of high-dimensional cube data. The proposed method systematically clustered all dimensions and sub-dimensions of cube data, refined centroid by emphasizing more stable low-variance attributes, and applied adaptive distance weighting based on variance-derived feature weights integrated into the distance metric to improve cluster assignment. The final clusters were obtained through majority voting of the clustering results for each dimension. Unlike existing consensus clustering methods that operate on flat data representations or combine independent clustering results, the proposed framework explicitly exploits the hierarchical structure of multidimensional cube data by clustering dimensions and sub-dimensions prior to consensus integration. Moreover, variance-based centroid refinement and weighted distance metrics are jointly embedded within each cube dimension rather than applied as isolated enhancements. This hierarchy-aware design preserves cube semantics while simultaneously improving centroid stability and distance adaptivity, resulting in a distinct and scalable clustering framework for complex high-dimensional cube data. The framework processes cube dimensions independently with iterative convergence control, enabling scalable application to large-scale cube data. The results of synthetic and real-world high-dimensional datasets, including cube data with approximately 2.2 million instances, showed that the proposed method consistently outperformed K-Means, K-Medoids, and Hamiltonian formulations. The method produced lower SSE such as 3,179,328 on Arcene and 1,422.21 on Lung Cancer, higher Silhouette Score of approximately 0.5718 and 0.4905 for consensus results, better cluster stability of 0.9947, and faster convergence. These results confirmed the effectiveness of K-Cube Consensus Clustering in producing stable and meaningful clusters in large-scale high-dimensional data applications.
Pengembangan Otomasi Inventaris Farmasi Rumah Sakit Gigi dan Mulut dengan Metode Convolutional Neural Network Khairul Abdi; Amalia; Mohammad Andri Budiman
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9832

Abstract

Pengelolaan inventaris farmasi di Rumah Sakit Gigi dan Mulut memerlukan proses yang cepat, teliti, dan terdokumentasi karena ketersediaan obat serta bahan kedokteran gigi berpengaruh langsung terhadap kelancaran pelayanan klinis. Penelitian ini mengembangkan prototipe otomasi inventaris berbasis citra menggunakan model deteksi objek YOLO11s yang dibangun di atas prinsip Convolutional Neural Network. Dataset terdiri atas 2.562 citra, 11 kelas produk, dan 5.486 anotasi bounding box yang dibagi menjadi data latih, validasi, dan uji dengan rasio 70:15:15. Tahapan penelitian meliputi akuisisi citra, anotasi, quality control dataset, praproses, augmentasi, pelatihan, evaluasi, serta perancangan integrasi hasil deteksi ke rekap inventaris. Pada data uji terbatas, model menghasilkan precision 99,37%, recall 98,86%, mAP@0.5 99,41%, dan mAP@0.5:0.95 90,03%. Hasil ini menunjukkan bahwa YOLO11s berpotensi membantu pengenalan jenis produk, lokalisasi objek, dan perhitungan jumlah item pada skenario inventaris farmasi yang tercakup dalam dataset. Namun, klaim kinerja masih perlu divalidasi lebih lanjut pada jumlah kelas yang lebih besar dan kondisi rak farmasi nyata yang lebih beragam.
Co-Authors Ade Candra Amalia Dian Rachmawati Efelin Siburian Efori Bu'ulolo Jos Timanta Khairul Abdi Marthin Benedict Muhammad Reza Fahlevi Poltak Sihombing Sutarman Sutarman