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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JURNAL AGRILAND Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Data Science: Journal of Computing and Applied Informatics JOURNAL OF SCIENCE AND SOCIAL RESEARCH CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Al-Ikhlas Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Journal of Research in Mathematics Trends and Technology Narra J Jurnal EduHealth Jurnal Agro Fabrica
Mahyuddin K. M Nasution
Faculty Of Computer Science And Information Technology, Universitas Sumatera Utara, Medan, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Veterinary

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A novel approach to optimizing customer profiles in relation to business metrics Elveny, Marischa; Nasution, Mahyuddin K. M.; Zarlis, Muhammad; Efendi, Syahril; Syah, Rahmad B. Y.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp440-450

Abstract

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.
Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems Syah, Rahmad B. Y.; Satria, Habib; Elveny, Marischa; K. M. Nasution, Mahyuddin
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5380

Abstract

In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors.
Automating Internet Distribution with Script-Driven Provisioning and Load Balancing Methods Albadri, Aldhi; Nasution, Mahyuddin K. M.; Sutarman, Sutarman
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3321

Abstract

The utilization of software-based automation technology in the internet network distribution process is currently relatively expensive, while conventional configuration methods cause inefficient use of time, cost, and energy. The time spent is about 5 minutes for each configuration process. The waiting time for a queue of 5 customers with 1 technician is 20 minutes. This problem can be solved by applying the concept of network automation using the Zero Touch Provisioning method, which can increase time efficiency to 5 seconds for each configuration process. Additionally, the use of Priority and Round-Robin algorithms is very helpful in overcoming queue management problems, allowing the server to work according to the desired process logic. The results showed an average wait time of 7.6 seconds with a quantum value of 10. This value was obtained in the process of 5 customer queues with 1 server.
Enhancing business analytics predictions with hybrid metaheuristic models: a multi-attribute optimization approach Syah, Rahmad B. Y.; Elveny, Marischa; Nasution, Mahyuddin K. M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1830-1839

Abstract

This approach aims to optimize business analytical predictions through multiattribute optimization using a hybrid metaheuristic model based on the modified particle swarm optimization (MPSO) and gravitational search optimization (GSO) algorithms. This research uses a variety of data, such as revenue, expenses, and customer behavior, to improve predictive modeling and achieve superior results. MPSO, an interparticle collaborative mechanism, efficiently explores the search space, whereas GSO models’ gravitational interactions between particles to solve optimization problems. The integration of these two algorithms can improve the performance of business analytical predictions by increasing model precision and accuracy, as well as speeding up the optimization process. Model validation test results, precision 95.60%, recall 96.35%, accuracy 96.69%, and F1 score 96.11%. This research contributes to the development of more sophisticated and effective business analysis techniques to face the challenges of an increasingly complex business world.
A novel MPK optimization framework for financial data analysis incorporating complexity and uncertainty management Syah, Rahmad Bayu; Elveny, Marischa; Ananda, Rana Fathinah; Nasution, Mahyuddin Khairuddin Matyuso
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9358

Abstract

In a competitive environment, the ability to scale quickly and successfully is a critical need. This research proposes a new framework using multi-objective complexity prediction model (MPK) for financial data analysis, including complexity and uncertainty management. This model integrates input, uncertainty, and output optimization functions (OOFs) (input optimization function (IOF), uncertainty optimization function (UOF), and OOF) to predict complex output values under dynamic business conditions. Model evaluation is carried out using performance metrics, namely mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R² score. The evaluation results show that this model has an MSE value of 20.112, an RMSE of 2.267, and an MAE of 2.351, reflecting a low prediction error rate and high accuracy. In addition, the R² value of 0.884259 indicates that this model is able to explain around 88.4% of the variability in the output data, indicating its ability to capture complex data patterns. Thus, the proposed MPK model is effective in predicting output values in complex business scenarios and can be applied for strategic decision-making under conditions of uncertainty.
Development of teaching materials based on learning video using movavi education set at Yayasan Perguruan Tunas Karya Batang Kuis Jaya, Ivan; Nasution, Mahyuddin K. M.
ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2021): ABDIMAS TALENTA : Jurnal Pengabdian Kepada Masyarakat
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.02 KB) | DOI: 10.32734/abdimastalenta.v6i1.5131

Abstract

The COVID-19 pandemic that has recently hit various countries including Indonesia has resulted in major changes in various fields, including in the development of the education sector. The teaching and learning process has turned from face-to-face into an online method. However, there are several obstacles experienced by schools that implement an online learning system, one of them was the ability of teachers who do not understand various learning application platforms. In addition, the material provided by the teacher is not maximally acceptable to students because most teachers provide learning material from the pages of textbooks or teacher writings (scans, photos, or presentation files). For this reason, it is necessary to have variations in the provision of teaching materials to students by making interesting and creative learning videos using the Movavi Education Set. With learning videos, students can do lessons at home, repeat it, and can ask the teacher some points from it if they don't understand. By using Movavi Education Set, teachers are also free to be creative in making learning videos that can be shared through commonly used communication applications such as e-mail, WhatsApp, line, google classroom and other applications.
Mathematical Philosophy Nasution, Mahyuddin K.M.
Journal of Research in Mathematics Trends and Technology Vol. 2 No. 2 (2020): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v2i2.4678

Abstract

Mathematics and philosophy are two words with different meanings and the same thing. With various historical evidence, mathematics as the basis of science is not part of or born from philosophy. In the same position in knowledge, mathematics confirm the answers to intimate problem in philosophy. Often there is confusion in philosophy because of conflicting concepts with one another. Mathematics without philosophy does not move swiftly, because without the meanings that are sometimes driven by philosophy. Logically, truth is not well developed in evidence except when mathematics and philosophy get long. It is to provide an understanding of the need for a foundation of truth thought, which generally reveals in the comprehension of mathematics, namely in meta-mathematics and philosophy.
ANALISIS PENGELOMPOKAN KARAKTERISTIK SISWA MENGGUNAKAN METODE K-MEANS DALAM PERSPEKTIF FILSAFAT SAINS KOMPUTER Sipayung, Sardo Pardingotan; Nasution, Mahyuddin K. M.
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.5153

Abstract

Abstract: The development of information technology in education is changing the way students construct and access knowledge, but differences in academic ability, motivation, discipline, and digital literacy often lead to learning disparities. This study grouped student characteristics using K Means Clustering and reviewed them from the perspective of computer science philosophy: ontology, epistemology, axiology, logic, and induction. The data from 120 students included academic scores, learning motivation, discipline, and digital literacy. After normalization, the number of clusters was determined using the Elbow and Silhouette methods, and the quality of the clusters was evaluated using the Davies–Bouldin Index. The findings indicate an optimal number of three clusters, with a Silhouette value of 0.466 and a DBI of 0.733, indicating fairly good and stable clustering. The three clusters describe: 1) highly motivated students with high digital literacy; 2) disciplined students with good academic performance but moderate digital skills; 3) low-motivated students with low digital literacy who require a personalized and empathetic learning approach. Ontologically, data is not just numbers, but the manifestation of students' digital existence in the modern learning space. Epistemologically, knowledge is formed inductively from students' interactions with technology and data. Axiologically, the clustering results support fairness in digital learning with an approach tailored to student characteristics. The dimensions of logic and induction show the clustering process as a scientific pattern of thinking from observation to meaningful rational generalization. The findings support a balance between algorithmic rationality and human values in digital education. Keyword: K-Means Clustering; Philosophy of Computer Science; Ontology, Epistemology; Axiology; Student Characteristics; Digital Learning. Abstrak: Perkembangan teknologi informasi di pendidikan mengubah cara siswa membangun dan mengakses pengetahuan, tetapi perbedaan kemampuan akademik, motivasi, kedisiplinan, dan literasi digital sering menimbulkan ketimpangan pembelajaran. Penelitian ini mengelompokkan karakteristik siswa dengan K Means Clustering dan meninjaunya melalui perspektif filsafat sains komputer: ontologi, epistemologi, aksiologi, logika, dan induksi. Data 120 siswa meliputi nilai akademik, motivasi belajar, kedisiplinan, dan literasi digital. Setelah normalisasi, jumlah klaster ditentukan lewat metode Elbow dan Silhouette, lalu kualitas klaster dievaluasi dengan Davies–Bouldin Index. Temuan menunjukkan jumlah klaster optimal tiga, dengan nilai Silhouette 0,466 dan DBI 0,733, mengindikasikan pengelompokan yang cukup baik dan stabil. Tiga klaster menggambarkan: 1) siswa bermotivasi dan berliterasi digital tinggi; 2) siswa disiplin dan berprestasi akademik baik, namun kemampuan digital sedang; 3) siswa bermotivasi dan literasi digital rendah yang memerlukan pendekatan pembelajaran personal dan empatik. Secara ontologis, data tidak sekadar angka, melainkan wujud eksistensi digital siswa dalam ruang belajar modern. Epistemologis, pengetahuan terbentuk secara induktif dari interaksi siswa dengan teknologi dan data. Aksiologis, hasil klasterisasi mendukung keadilan pembelajaran digital dengan pendekatan sesuai karakteristik siswa. Dimensi logika dan induksi menunjukkan proses klasterisasi sebagai pola berpikir ilmiah dari observasi menuju generalisasi rasional bermakna. Temuan mendukung keseimbangan antara rasionalitas algoritmik dan nilai kemanusiaan dalam pendidikan digital. Kata kunci: K-Means Clustering; Filsafat Sains Komputer; Ontologi, Epistemologi; Aksiologi; Karakteristik Siswa; Pembelajaran Digital.
FROM ONTOLOGY TO INFERENCE: A COMPUTATIONAL PHILOSOPHICAL FRAMEWORK OF IoT-ML FOR READING THE MATURITY AND VOLUME OF PALM OIL Siregar, Ratu Mutiara; Prayogi, Andi; Nasution, Mahyuddin KM
Jurnal Agro Fabrica Vol. 7 No. 2 (2025): Desember 2025
Publisher : Institut Teknologi Sawit Indonesia (ITSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47199/jaf.v7i2.403

Abstract

The timing of harvesting in oil palm plantations necessitates objective and rapid ripeness assessment, coupled with an estimation of extractable oil volume. This paper presents a philosophical-computational framework with an end-to-end architecture integrating Internet of Things (IoT) sensors and machine learning (ML) for the classification of fresh fruit bunch (FFB) ripeness levels and oil volume regression. The approach rests on explicit ontological and epistemological foundations, operationalizes latent targets through standardized field protocols, and implements reproducible ML practices. We delineate a multimodal pipeline (RGB imagery + environmental sensors + weight), a late fusion modeling strategy (CNN embeddings + tabular features), and an evaluation design that emphasizes cross-block generalization, model explainability, and drift monitoring. Performance targets include an F1-macro ≥ 0.88 for ripeness classification and a Mean Absolute Error (MAE) ≤ 4 ml/kg for oil volume regression on out-of-block data. Discussions also encompass the ethics and axiology of transparency, data governance, and economic impacts, along with future directions such as federated learning and portable hyperspectral integration.
Co-Authors Al Khowarizmi Albadri, Aldhi Alexander F.K. Sibero Amelia Oktarini Siregar Aritonang, Evawany Y. Azmi, Zulfian - Betty Yel, Mesra Efendi, Syahril Elveny, Marischa Elviawaty Muisa Zamzami Erna Budhiarti Nababan Habib Satria Harahap, Latifah Hanum Herman Mawengkang Heru Santosa Imelda, Fatwa Ivan Jaya Jaya, Ivan Lubis, Arif Ridho Lumbanraja, Sarma N. Marischa Elveny, Marischa Muhammad Ichwan Muhammad Zarlis Muhammad Zarlis, Muhammad Nainggolan, Pauzi Ibrahim Opim Salim Sitompul Prayogi, Andi Rahmad Syah Rahman Aulia, Rahman Rambe, Basyit Mubarroq Rana Fathinah Ananda Ratu Mutiara Siregar Sawaluddin Sawaluddin, Sawaluddin Siahaan, Andre MP. Sipayung, Sardo Pardingotan Sri Melvani Hardi Sutarman Sutarman Syah, Rahmad B. Y. Syah, Rahmad Bayu Syamsafitri Syamsafitri Tobing, Immanuel DL. Verdi Yasin Y. Syah, Rahmad B. Zarlis, M Zarlis, M Zulfian Azmi