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Text security by using a combination of the vigenere cipher and the rubik's cube method of size 4×4×4 Safitri, Raudhatun; Prasetyo, Puguh Wahyu; Wijayanti, Dian Eka; Arifin, Samsul; Setyawan, Fariz; Repka, Joe
Al-Jabar: Jurnal Pendidikan Matematika Vol 14 No 2 (2023): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v14i2.14276

Abstract

Background: In the current era of technology, information security is increasingly important. The growth of technology leads to a higher level of threat to the security of data and information dissemination, and cryptography is a valuable protective tool.Aim: The primary objective of this research is to enhance text security through the fusion of the Vigenere cipher and the Rubik's cube algorithm. By leveraging this novel approach, we aim to fortify the confidentiality of textual data against potential eavesdroppers and adversaries. To demonstrate the practicality of this method, we perform a simulation using the Python programming language within the Google Colab environment. Method: This study employs a qualitative research methodology supplemented by empirical simulation. The combination of the Vigenere Cipher and the Rubik's Cube algorithm in a 4×4×4 configuration is implemented to encrypt and decrypt text. The simulation is executed using the Google Colab platform, enabling a practical illustration of the encryption process.Result: The results of our research indicate the feasibility of generating ciphertext through the amalgamation of the Vigenere Cipher and the Rubik's Cube algorithm in the specified 4×4×4 configuration. The simulation conducted in Google Colab serves as concrete evidence of the effectiveness and practicality of this combined encryption method.Conclusion: In conclusion, this research offers a compelling approach to bolstering text security in the modern era of information technology. By combining the Vigenere Cipher with the Rubik's Cube algorithm in a 4×4×4 configuration, we have demonstrated the potential to significantly enhance the confidentiality of sensitive textual data. The empirical simulation conducted in Google Colab reaffirms the practicality and viability of this innovative encryption technique, highlighting its potential as a valuable tool in the realm of information security.
OUTPUT VISUALIZATION FROM RESULT OF DISCRETE EVENT SYSTEM SIMULATION WITH ‘simmer’ R PACKAGE Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Arifin, Samsul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.269 KB) | DOI: 10.30598/barekengvol17iss1pp0581-0592

Abstract

This study aims to describe the various capabilities of the simmer package on R, especially in running a discrete event simulation model of a circular system, then develop a DES simulation model building technique, which is effective and can represent real systems well, and explore the simulation output on this simmer, both in statistical summary form and parameter estimation. The method used in this research is the literature study with descriptive and exploratory approaches. Model development is more effective when it is carried out starting from simple models, to more complex forms step by step, and describing the system using a flow chart. Replication for simulations is easy to perform so as to get standard error values ​​for model parameter estimators. The stages in developing a discrete event simulation model with a simmer, start with compiling a simple flowchart to a more complex form, and replication is carried out. The simmer output in the form of a data frame makes it very easy to process the output further. The simple R API on Simmer will also make it easier to simulate.
Stunting Risk Factor Analysis Using Seemingly Unrelated Regression (SUR) Integrated with Machine Learning Wiyanti, Wiwik; Zenklinov, Amanatullah Pandu; Seleki, Jacob Stevy; Ramadhani, Sausan; Jimy, Valensius; Arifin, Samsul
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9630

Abstract

Stunting is a disorder in children caused by chronic nutritional problems. Stunting is usually characterized by a child’s failure to grow and develop optimally. In Indonesia, the government is focusing on addressing of stunting case, as it aims to develop the superior of human resources. In Tangerang regency is currently holding a “Gebrak Tegas” program to address the problem of stunting. The causes of stunting required scientific study to help the government understand the scientific factors that causing stunting. Therefore, this study aims to analyze the factors causing stunting the children in the Tangerang regency. The data analysis method in this study use the Seemingly Unrelated Regression (SUR), which is integrated with a machine learning, namely Random Forest. The data used in this study were obtained from primary data through questionnaires. The subjects of this study were parents of stunting and non-stunting children who were at “Posyandu” under of “Kelapa dua” and “Binong” health centers. Sampling method in this study is purposive-random sampling. The results of the data analysis showed that the five variables from the factors measured had the most significant influence, namely nutritional, socio-economi, and pregnancy and childbirth history factors. Five variables that influence children stunting are animal protein, which has the highest probability of 87.5% when the children consumes protein once or twice a week. The children consume vitamin A twice has a 97% probability. The source of income for the parents, whether from the private or self-employment, has a probability of over 90%. Furthermore, the consumption of iron-boosting tablets by mother during pregnancy and the amount of income from the parents have a probability of 84%.
ANALISIS FAKTOR JARINGAN KOLABORASI PENELITI MENGGUNAKAN METODE ANALISIS BIBLIOMETRIK Dwi Anto, Septian; Elita Putri, Rizky; Winarto Selamet, Muhammad; Widyakusuma, Nita; Arifin, Samsul
Pedagogik : Jurnal Pendidikan Guru Sekolah Dasar Vol. 13 No. 1 (2025): PEDAGOGIK : Jurnal Pendidikan Guru Sekolah Dasar
Publisher : Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/pedagogik.v13i1.10631

Abstract

Penelitian ini bertujuan untuk menganalisis jaringan kolaborasi peneliti dengan menggunakan metode analisis bibliometrik. Data jaringan yang divisualisasikan dalam bentuk grafis menggunakan VOSviewer menunjukkan hubungan antara peneliti berdasarkan faktor kolaborasi. Analisis ini mengidentifikasi peneliti kunci, pola kolaborasi, serta klaster atau kelompok berdasarkan faktor keterkaitan dalam penelitian. Hasil penelitian ini memberikan gambaran tentang struktur kolaborasi dalam komunitas akademik serta potensi kolaborasi antar peneliti. Data dikumpulkan dari database Scopus dengan menggunakan kata kunci “analisis AND faktor". Kemudian penulis menggunakan software VOSviewer untuk menganalisis dan memvisualisasikan database yang diperoleh. Hasil penelitian menunjukkan bahwa hasil analisis sitasi menunjukkan jumlah kutipan per tahun dari 2020 - 2024 adalah 2.562 data.
Trends, Contributions and Prospects: Bibliometric Analysis of ANOVA Research in 2022-2023 Sutrisno, Utis; Wulandari, Yulia; Usep; Arifin, Samsul; Roni; Manurung, Monica Mayeni; Faisal, Muhamad
Indonesian Journal of Applied Mathematics and Statistics Vol. 1 No. 1 (2024): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v1i1.7

Abstract

This study aims to analyze the development and contribution of research on the topic of ANOVA (Analysis of Variance) using the bibliometric analysis method. ANOVA is a statistical method used to compare the means of three or more groups. Through bibliometric analysis, we explore articles published in journals related to ANOVA within a certain time span, namely 2022-2023. The method of bibliometric analysis involves collecting bibliographic data from relevant sources and analyzing characteristics such as frequency of publication, notable authors, and most frequently cited journals. This study uses a bibliometric analysis method that retrieves 1,911 metadata from Scopus. The results of the bibliometric analysis revealed an increase in the number of publications about ANOVA during the time span studied, namely 2022-2023. These findings indicate that ANOVA remains a relevant and interesting topic for researchers in various disciplines. In addition, there is a wide variety of research topics related to ANOVA, including the use of ANOVA in various contexts, such as laboratory experiments, clinical research, and analysis of survey data. By analyzing the contribution of research on the topic of ANOVA, this study provides valuable insights for us. Moreover, the researcher also discussed prospects for future research on this topic, including the development of new analytical methods and the wider application of ANOVA in various scientific and practical contexts.
Clustering Analysis: A Note on Methodologies and Trends Raditha, Alya Maura; Arifin, Samsul
Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v2i2.23

Abstract

This study conducts a bibliometric analysis of clustering techniques in scientific research using VOSviewer and Gen-AI-based Consensus.app. The dataset was collected from Scopus and the Web of Science using predefined queries to filter articles published in 2024 and 2025. VOSviewer was utilized to visualize co-authorship networks, keyword co-occurrence, citation relationships, bibliographic coupling, and co-citation patterns, revealing key research clusters and influential studies. Additionally, Consensus.app was employed to generate AI-driven insights, summarizing key themes and emerging trends in clustering methodologies. The results indicate that clustering research is highly collaborative, with strong institutional networks and interdisciplinary applications. Machine learning, data mining, and network analysis emerge as dominant themes, with key publications shaping methodological advancements. The co-citation network highlights foundational studies that have influenced the field. By combining traditional bibliometric techniques and AI-based analysis, this study offers a comprehensive perspective on clustering research, identifying knowledge gaps and potential future directions. These findings provide valuable insights for researchers seeking to explore emerging topics, collaborate effectively, and contribute to the development of clustering methodologies. However, this study is limited to publications indexed in Scopus and Web of Science within the years 2024–2025, which may not fully capture longer-term developments. Future research could expand the scope to other databases and timeframes for a broader perspective.
Self-Protection Equipment Detection System in Heavy Weight Workshop of Politeknik Negeri Jakarta Using Artificial Intel-ligence Muhammad Rezakusuma; Abdul Azis Abdillah; Dewi Yanti Liliana; Ega Edistria; Samsul Arifin; Zahran Muzakki
Recent in Engineering Science and Technology Vol. 1 No. 1 (2023): RiESTech Vol. 1 No. 1 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.4

Abstract

The creating process, how it works and the performance of the detection system using Artificial Intelligence. The development of this innovation contributes to the Heavy Equipment Workshop of the Jakarta State Polytechnic to detect the early potential for work accidents. The methods are device tuning, inputs, training models, performance, trials and outputs. The creating process and how the detection system works using Artificial Intelligence each has 3 steps and accuracy using 3 cameras, namely the internal webcam (1MP), the JETE external webcam (720P) and the Samsung Galaxy A22 mobile phone camera (13MP). The process of making this innovation has 3 steps, namely data input, export, file grouping. There are 3 steps to work, namely open the file, run and output. The result of the accuracy of the internal webcam is very low, the JETE external webcam is better than the internal webcam and the mobile phone camera is better than the JETE external webcam.
Machine Predictive Maintenance by Using Support Vector Machines Idrus Assagaf; Agus Sukandi; Abdul Azis Abdillah; Samsul Arifin; Jonri Lomi Ga
Recent in Engineering Science and Technology Vol. 1 No. 1 (2023): RiESTech Vol. 1 No. 1 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.6

Abstract

Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses.  Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.