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All Journal Jurnal Mantik Penusa MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Journal of Innovation Information Technology and Application (JINITA) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing International Journal of Basic and Applied Science Jurnal Mandiri IT Jurnal Nasional Teknologi Komputer LEBAH Journal of Computer Science and Research International Journal of Enterprise Modelling Indonesian Journal of Education And Computer Science
Fristi Riandari
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Tackling uncertainty in vehicle routing: Advancements in time windows and stochastic demands optimization Fristi Riandari; Demita Sihotang; Hamed Huckle Schubert
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.611 KB) | DOI: 10.35335/emod.v16i2.60

Abstract

This research focuses ons addresses vehicle routing uncertainty in time windows and stochastic needs. The project intends to increase vehicle routing efficiency, adaptability, and robustness by developing optimization approaches. Traffic congestion, unanticipated events, and changing client expectations can greatly impact truck routing solutions. Traditional methods presume fixed time frames and deterministic needs, resulting in suboptimal or infeasible paths. This paper presents a mathematical model that includes time window uncertainty and stochastic needs into the vehicle routing issue to address these restrictions. The formulation incorporates arrival times, delivery amounts, and route decisions to minimize transportation costs and ensure timely deliveries and resource efficiency. Advanced algorithms and solvers tackle the optimization challenge. Integer programming, flow conservation constraints, and temporal window constraints are used to identify optimal or near-optimal solutions to uncertainty and dynamic changes. Numerical examples and case studies demonstrate the approach's efficacy. Numerical examples demonstrate the mathematical formulation, while the case study shows the practical consequences and benefits for a dynamic delivery service organization. The research shows that the proposed approach can handle temporal window uncertainties and stochastic demands. These innovations can optimize vehicle routing, reduce transportation costs, boost customer happiness, and increase resource utilization. Addressing time window uncertainty and stochastic demands advances vehicle routing. The proposed approach helps logistics and transportation industries overcome dynamic and uncertain operating environments, boosting operational efficiency and competitiveness.
Robust mathematical model for supply chain optimization: A comprehensive study Lise Pujiastuti; Mochamad Wahyudi; Barreto Jose da Conceição; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i2.137

Abstract

This research provides a comprehensive review of existing literature and research on supply chain optimization, aiming to capture the advances made in the field and identify emerging perspectives. Supply chain optimization plays a vital role in improving operational efficiency, reducing costs, and enhancing customer satisfaction. By analyzing a wide range of studies, this review examines various approaches, models, and techniques used in supply chain optimization, including mathematical programming, stochastic programming, simulation, and metaheuristic algorithms. The review also encompasses key aspects such as demand forecasting, inventory management, production planning, transportation, and distribution network design. Furthermore, the study investigates recent trends, such as incorporating sustainability considerations, addressing uncertainties and risks, and utilizing real-time data and decision support systems. By identifying the gaps and limitations in the existing research, this review sets the stage for future investigations and provides valuable insights for researchers and practitioners seeking to advance supply chain optimization efforts. The findings of this review contribute to enhancing the understanding of supply chain optimization and provide a roadmap for future research directions in this dynamic and critical field
Sistem Pendukung Keputusan Pemberian Uang Pertanggungan Terhadap Claimer Asuransi Kesehatan Menggunakan Metode SAW Dedi Setiawan Halawa; Fristi Riandari
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 4, No 5 (2021): Oktober 2021
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v4i5.3429

Abstract

DSS can provide interactive tools that allow decision makers to perform various analyzes of the available models. There are several methods used in DSS and one of them is the Simple Additive Weighting (SAW) method. The criteria used in determining the sum insured are length of stay, accidents and medical expenses. The application system can only provide recommendations for the provision of sum assured for health insurance claims at PT. Jasindo Health Care. The formulation of the research problem is to apply the Simple Additive Weighting (SAW) method in determining the award of sum insured to health insurance claimers using the PHP programming language, database, mysql. The calculation results show that Alternative A1 has the highest V value, namely V1 = 100, so alternative A1 is the most entitled to receive the sum insured against health insurance claimers at Jasindo Health Care.
Perancangan Aplikasi Prediksi Jumlah Pendaftar Siswa Baru Dengan Metode Regresi Linier (Studi Kasus: SMA RK Deli Murni Bandar Baru) Novita Ria Lase; Fristi Riandari
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 3, No 3 (2020): Desember 2020
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v3i3.2520

Abstract

The problem of the SMA RK Deli Murni Bandar Baru school is to predict how many facilities that need to be provided for new students such as chairs, tables and others. This study discusses the prediction of the number of new student registrants at SMA RK Deli Murni Bandar Baru based on the amount of tuition fees using a simple linear regression method. From a commercial point of view, the use of data mining can be used to handle the explosion of data volumes, using computational techniques can be used to produce information needed which is an asset that can increase the competitiveness of an institution. Prediction is almost the same as classification and estimation, except that in the prediction the value of the results will be in the future. This system can be used to predict the number of applicants in the following year to help the school. The advantage is that this simple linear regression method is very simple so that it is easy to calculate and use. Saves the time needed to solve problems, especially those that are very complex.
Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Analisis Keranjang Belanja Pada Transaksi Penjualan Pada PT Madu Kembang Joyo Nasib Ratna Sari Purba; Fristi Riandari
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 4, No 1 (2021): Februari 2021
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v4i1.2745

Abstract

In the sale of goods (products), companies often experience problems because of the irregular level of consumer spending. Determination of product layout is done to make it easier for consumers to find honey products so as not to disappoint consumers in finding the location of which products are suitable to be combined with other products that are often in demand by consumers, so that consumers can save time. Based on the problems faced by the company, data mining analysis tools are needed. Currently, the utilization of data that is owned is not fully maximized, it is limited to making reports. The problem of research is the accumulation of unused transaction data, the difficulty of placing products according to consumer needs. The absence of an effective product sales strategy. The application is built using the PHP programming language with the MySQL database. The data used for shopping cart analysis on the sales transaction of Joyo Flower Honey is 1 month transaction data. The combination of items with the highest support x confidence value will be used as a combination to determine the placement of the suitable item to be connected between the two products that consumers are most interested in. In addition, the combination of these items can be used by management to position the product on the shelf which will make it easier for consumers to find the product they need.
Implementasi Metode K-Means Clustering Dalam Pengelompokan Bibit Tanaman Kopi Arabika Benny Ginting; Fristi Riandari
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 3, No 2 (2020): OKTOBER 2020
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v3i2.2381

Abstract

The diversity of coffee seed material which in the end makes it difficult for the Department of Agriculture and Plantation in classifying recommended seeds to be distributed and planted in coffee planting centers in their working areas, especially in Sarimunthe Village, Kec. Munte Karo District. Data mining is used to extract valuable information from a dataset and then present it in a format that is easy for humans to understand in order to make a decision. In this study, data processing of Arabica coffee seeds consisting of 30 items, in the Karo District Agriculture sector, in preparing the seeds to be distributed to the public, the assessment is divided into 3 phases, namely coffee seeds that do not produce (Phase 0-1 Year), immature (Phase 0-1 Year). Phase 1-2 Years) and produce (Phase 2 Years and above). The results of the calculation of the K-Means algorithm which have been grouped into clusters, it can be concluded that the recommended coffee seeds (C1) consist of 10 items, the coffee seeds that are not recommended (C2) consist of 7 types of coffee seeds and unfit coffee seeds. (C3) consists of 13 types of coffee seeds. .
Sistem Pakar Mendiagnosa Penyakit Keputihan Pada Wanita Dengan Metode Teorema Bayes Christina Simanjuntak; Fristi Riandari
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 4, No 2 (2021): April 2021
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v4i2.2847

Abstract

Leucorrhoea is a discharge other than blood from the vaginal canal, if abnormal vaginal discharge is left untreated, as a result the infection can spread, enter the uterus, fallopian tubes, and can infect the ovaries. The application of an expert system in the health sector will greatly assist in survival someone. Expert systems can help diagnose a type of disease based on their own symptoms quickly and precisely. An expert system that is designed as a tool to diagnose the type of leucorrhoea, especially in women who experience vaginal discharge, but often they feel able to recognize themselves that they are suffering from vaginal discharge without feeling the need to go to a doctor for a more detailed examination, and only treat themselves with medication. Free-sale vaginal discharge medication. This expert system will display a selection of symptoms that can be selected by the user, then get the final result with the Bayes theorem method by providing a diagnosis result in the form of a probability value for the emergence of each type of disease and solution. Expert system applications are built on a web basis using HTML, PHP, CSS using the mysql database.
Data-driven corporate growth: A dynamic financial modelling framework for strategic agility Sihotang, Hengki Tamando; Vinsensia, Desi; Riandari, Fristi; Chandra, Suherman
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.485

Abstract

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application
Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks Riandari, Fristi; Afrisawati, Afrisawati; Afifa, Rizky Maulidya; Syahputra, Rian; Ginting, Ramadhanu
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.499

Abstract

This research introduces a dynamic optimization algorithm designed to enhance blockchain network resilience against distributed attacks such as Distributed Denial of Service (DDoS), Sybil, and eclipse attacks. The primary objective is to develop a real-time, adaptive control strategy that minimizes network performance degradation while dynamically responding to evolving threats. The research design integrates multi-objective optimization, game theory, and reinforcement learning to formulate a defense strategy that adapts to adversarial conditions. The methodology is based on a modified state-space model, where the blockchain's performance is represented by a system of dynamic equations influenced by both control actions (defensive measures) and attack vectors. The optimization problem is formulated to minimize a cost function that balances network resilience and resource usage. A numerical example is presented to validate the model, demonstrating the algorithm’s effectiveness in maintaining network performance under attack by adjusting defense mechanisms in real-time. The main results indicate that the proposed method significantly reduces the impact of distributed attacks while ensuring efficient resource allocation. In conclusion, this research offers a novel framework for enhancing blockchain security, with implications for real-world applications in decentralized systems, financial services, and critical infrastructure. Future work will address the scalability of the algorithm and explore more advanced reinforcement learning techniques to handle more complex and unpredictable attack patterns.
Increasing cybersecurity awareness among teenagers through digital education and simulation Riandari, Fristi; Tasril, Virdyra; Ritonga, Rama Prameswara
Lebah Vol. 18 No. 1 (2024): September: Pengabdian
Publisher : IHSA Institute

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

Abstract

This community service aims to explore the role of digital education and simulation tools in enhancing cybersecurity awareness among teenagers. The review examines various educational methods, including e-learning platforms, gamification, and phishing simulations, to evaluate their effectiveness in increasing cybersecurity knowledge and skills. The methodology involves analyzing relevant studies from academic databases, combining both qualitative and quantitative research. Key findings suggest that interactive digital education and simulations significantly improve teenagers' ability to recognize and address online threats. However, challenges remain regarding long-term retention and engagement. The review highlights the growing importance of these tools in educating teenagers, emphasizing the need for their integration into educational settings. Trends include the use of gamification and simulations, while gaps in research, such as long-term effectiveness and cultural influences, remain. Recommendations for future initiatives include AI-driven simulations and incorporating cybersecurity education into social media platforms for broader reach.
Co-Authors Adjie Bintang Pamungkas Afifa, Rizky Maulidya Afrisawat, Afrisawat Afrisawati afrisawati, Afrisawati Agustina Simangunsong Aisyah Alesha Arjon Samuel Sitio Baik Sepwanri Sinaga Barreto Jose da Conceição Benny Ginting Bosker Sinaga Chandra, Suherman Christina Simanjuntak Cristy, Niolinda Dalimunthe, Yulia Agustina Danvy Nadhira Dedi Setiawan Halawa Demita Sihotang Dewi Novika Simanjuntak Dimas Ribowo Esera Gulo Ginting, Ramadhanu Hamed Huckle Schubert Hasugian , Paska Marto Hasugian, Penda Sudarto Hengki Tamando Sihotang Husain Husain Ibnu Rizky John Foster Marpaung Judijanto, Loso Lamhot Situmorang Lise Pujiastuti Marsoit, Patrisia Teresa Mochamad Wahyudi Muhammad Rafli Muhammad Syaif Nasib Ratna Sari Purba Nellysa Putri Denila Nasution Novita Ria Lase Nurul Hayani Petti Indrayati Sijabat Pilisman Buulolo R. Mahdalena Simanjorang Ramayuni Marpaung Rehentiara Siringoringo Rian Syahputra Rita Zahara Tarigan Ritha Zahara Tarigan Ritonga, Rama Prameswara Rohit Gautama Roimasro Sibagariang Safitri, Habibi Ramdani Sari Murni Sarjon Defit Sethu Ramen Sihotang , Jonhariono Sim, Lee Choi Simangunsong, Agustina Sinaga, Baik Sepwanri Siringoringo, Rimmar Siti Ilya Suwella Situmorang, Lamhot Song , Jiang Lou Subowo Subowo Subowo Subowo Sulistianingsih, Indri Susandri, Susandri Tarisa Tarigan Tasril, Virdyra Temasokhi Ndruru Teresia Herniamwati Zebua Vina Winda Sari Vinsensia, Desi Wan Wimar Yahya Weno Syechu Yuda Perwira Yunus Simare-mare