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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
Fristi Riandari
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Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty Judijanto, Loso; Riandari, Fristi
International Journal of Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i1.474

Abstract

This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries
Implementasi Metode Advanced Encryption Standard (AES 128 Bit) Untuk Mengamankan Data Keuangan Cristy, Niolinda; Riandari, Fristi
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 4 No. 2 (2021): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/jikomsi.v4i2.181

Abstract

Masalah keamanan data dan informasi merupakan salah satu aspek penting dari sebuah informasi computer. Salah satu contoh masalah keamanan data yaitu kemanan data uang SPP disekolah. Data uang SPP merupakan kumpulan data yang berifat sensitive bagi pihak sekolah. Data yang ada di dalamnya berupa rangkuman atau catatan pembayaran administrasi sekolah. Permasalahan yang terjadi pada data uang SPP yaitu masalah pencurian data dan informasi, hingga pencuri dapat memanipulasi data. Maka diperlukan sebuah Teknik untuk mengamankan data yang sering di sebut dengan kriptografi. Salah satu algoritma atau metode dalam kriptografi adalah Advanced Encryption Standard (AES). AES memiliki putaran kunci untuk proses enkripsi dan dekripsi. AES digunakan karena memberikan tingkat kemanan yang tinggi berdasarkan kunci rahasia yang kompleks sehingga dapat merahasiakan data yang akan diamankan. AES melakukan Teknik enkripsi-dekripsi pada data uang SPP sekolah agar tidak dapat dibaca, dicuri, dimanipulasi, dan dibocorkan oleh orang yang tidak bertanggung jawab. Teknik enkripsi membuat isi dari data berubah menjadi kode-kode tertentu yang tidak dapat dibaca isinya. Untuk itu, fungsi keberadaan kriptografi AES diperlukan sebagai cara untuk mengamankan isi dari data uang SPP pada sekolah SMK Harapan Bangsa tersebut agar aman dari pencurian data. Kata Kunci: AES Dekipsi Enkripsi Kriptgrafi Keamanan Data
Meta-Learning Algorithms for Resource-Constrained Intelligent IoT Devices Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid expansion of the Internet of Things (IoT) requires devices that can operate intelligently in dynamic environments despite severe hardware and energy constraints. Traditional machine learning models deployed on microcontroller-class IoT devices often struggle to adapt to new tasks, handle sensor noise, and maintain accuracy under changing environmental conditions. This research proposes a lightweight meta-learning framework specifically optimized for resource-constrained IoT platforms, combining gradient-based meta-learning techniques with model compression strategies such as quantization and pruning. The objective is to enable rapid few-shot adaptation, reduce computational overhead, and ensure robust performance in real-world IoT deployments. The study adopts a hardware-aware design approach, implementing the proposed model on ultra-low-power microcontrollers such as ARM Cortex-M series and ESP32. A two-phase training pipeline meta-training and on-device fine-tuning is used to evaluate adaptation speed, latency, memory footprint, accuracy, and energy consumption. Experimental results demonstrate that the lightweight meta-learning model adapts to new sensor-based tasks significantly faster than conventional supervised learning models while consuming substantially less energy. The model also shows improved resilience to environmental variations and sensor noise, outperforming baseline TinyML and standard meta-learning architectures under constrained conditions. Despite these promising results, the research identifies limitations related to computational cost, memory usage during adaptation, and the trade-off between model complexity and predictive accuracy. Nonetheless, the findings highlight the potential of meta-learning as a transformative approach for building intelligent, adaptive, and energy-efficient IoT systems. This study contributes to the advancement of TinyML and edge intelligence by providing a practical and scalable meta-learning solution tailored for ultra-low-power IoT devices.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Highly complex systems such as smart grids, autonomous transportation networks, and large-scale supply chains present significant challenges for optimization due to high dimensionality, nonlinear interactions, and pervasive uncertainty. Traditional deterministic models often fail under dynamic conditions, while many AI-based approaches lack robustness and stability when confronted with noisy or incomplete data. Addressing these issues, this study proposes a probabilistic decision model designed to enhance AI-driven optimization in uncertain and rapidly changing environments. The model integrates probabilistic graphical structures, Bayesian inference, and AI-based optimization techniques to quantify uncertainty and support adaptive decision-making. Experimental evaluations were conducted using a combination of synthetic datasets, simulation environments, and benchmark scenarios representative of real-world complex systems. Results show that the proposed model achieves significantly higher decision accuracy, improved stability under noisy conditions, and more efficient performance in high-dimensional settings compared with classical optimization, reinforcement learning, and standard probabilistic approaches. The model consistently reduces uncertainty and delivers robust, reliable solutions across a wide range of test conditions.The study presents a scalable, interpretable, and highly effective framework for uncertainty-aware optimization. Its strong performance and generalizability highlight its potential for deployment in critical real-world applications where reliability, safety, and adaptability are essential.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research proposes a novel Probabilistic Decision Model (PDM) designed to address the challenges of optimization in highly complex systems characterized by high-dimensional states, nonlinear interactions, and deep uncertainty. Traditional deterministic, heuristic, and deep learning-based methods often fail to provide reliable decisions under such conditions due to their limited scalability, lack of uncertainty quantification, or inability to guarantee constraint satisfaction. The proposed model integrates probabilistic constraints, expectation-based objective functions, and adaptive AI-driven scenario generation to deliver a robust and flexible optimization framework. A rigorous mathematical formulation is presented, including probability space definitions, risk measures, and feasible neighborhood rules. Validation through numerical simulations demonstrates that the model maintains high feasibility, reduces worst-case risks, and remains stable even under extreme uncertainty. Case studies in smart grid optimization, logistics routing, and manufacturing scheduling further highlight significant performance improvements over classical stochastic optimization, MDP/POMDP models, and deep reinforcement learning without probabilistic modeling. The results confirm the model’s strong scalability, enhanced uncertainty modeling, and practical relevance for real-world industrial environments. This research contributes a hybrid probabilistic-AI framework that advances the reliability, resilience, and intelligence of decision-making in modern complex systems, while opening pathways for future exploration in multi-agent coordination, automated parameter tuning, and real-time adaptive optimization.
Theoretical Advances in Hungarian Maximization Models for Multi-Site Human Resource Allocation Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study presents a theoretical and methodological advancement of the Hungarian maximization model for optimizing multi-site human resource allocation. Traditional Hungarian algorithms focus on single-site, cost-minimization assignments, limiting their applicability in modern workforce environments characterized by distributed operations and diverse employee attributes. To address these gaps, the study reformulates the classical objective function into a maximization framework and incorporates multi-site constraints, multi-criteria employee attributes, and workload balancing requirements. The enhanced model is evaluated through mathematical analysis and simulation-based case studies to assess its performance relative to baseline assignment and heuristic optimization methods. The results demonstrate that the proposed model achieves higher organizational productivity, reduces operational costs, improves staff distribution equity, and significantly accelerates computation time compared with existing approaches. Moreover, the model ensures more consistent alignment between employee capabilities and site-level demands, offering a more robust foundation for strategic workforce deployment. Comparisons with previous studies show that this research provides the first Hungarian-based maximization framework specifically tailored for multi-site HR allocation, overcoming key limitations related to scalability, fairness, and optimality. Overall, this study contributes a rigorous theoretical extension of the Hungarian method and offers practical implications for workforce scheduling, supply-chain staffing, healthcare deployment, and emergency response operations. The findings underscore the potential of deterministic optimization models to support intelligent and equitable human resource decision-making in increasingly complex organizational settings.
A Unified Mathematical Framework for NWC, MODI, and Stepping Stone as Foundational Models in Optimal Transport Theory Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1393.pp183-195

Abstract

This research introduces a unified mathematical framework connecting three classical transportation problem methods Northwest Corner Rule (NWC), Modified Distribution Method (MODI), and the Stepping Stone Method to the modern theory of Optimal Transport (OT). Despite their long-standing use in operations research, these classical algorithms have traditionally been treated as heuristic procedures without a formal theoretical link to the rigorous Monge Kantorovich formulation. This study demonstrates that each method corresponds directly to fundamental geometric and dual structures of the transportation polytope: NWC generates an initial extreme-point solution, MODI computes dual potentials analogous to Kantorovich potentials, and Stepping Stone identifies improvement cycles consistent with movements along polytope edges. Using formal definitions, algebraic mappings, and geometric interpretation, the research establishes a coherent connection between classical OR algorithms and OT duality theory. The results show that these methods are not isolated heuristics, but structured approximations of optimal transport processes. The unified framework improves theoretical understanding, simplifies instructional explanations, and offers methodological insights that may support future algorithmic enhancements. Limitations include scalability challenges and reduced applicability to complex continuous OT settings. Overall, this research contributes a foundational unification that bridges classical transportation algorithms with contemporary optimal transport theory, advancing both theoretical rigor and practical comprehension.
Ekplorasi Timeline : Waktu Respon Pesan Terbaik WhatSapp Group “Gurauan kita STMIK Amik” Susandri susandri; Sarjon Defit; Fristi Riandari; Bosker Sinaga
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1149

Abstract

WhatsApp merupakan salah satu aplikasi pesan instan yang banyak di gunakan saat ini. WhatsApp memungkinkan pengguna membuat grup. Sering pesan pada grup tidak terbaca dan terabaikan oleh anggota grup. Perlu dilakukan analisa waktu yang tepat sebuah pesan direspon anggota grup dengan cepat sehingga informasi dapat disampaikan dengan baik pada semua anggota. Penelitian ini melakukan explorasi WhatSapp Group “Gurauan kita STMIK Amik” untuk menentukan waktu terbaik menyampaikan pesan dengan metode timeline serta menganalisis anggota yg berjumlah 32 orang, emoji dan sentimen. Pada Analisis sentimen dari 1095 total pesan, sentimen positif 35.53% dan sentimen negatif 64.47%. Respon emoji dari anggota sebanyak 46% menggunakan pesan emoji diatas 50% dan 34% anggota menggunakan emoji dibawah 50% sedangkan 18 % anggota tidak pernah menggunakan emoji. Dalam penelitian ini dari proses timeline dapat disimpulkan waktu terbaik untuk mengirimkan pesan pada hari selasa dan jum’at pada jam 10, 13 sampai 15 siang dan jam 20 pada malam hari.
Forecasting the Number of Students in Multiple Linear Regressions Fristi Riandari; Hengki Tamando Sihotang; Husain Husain
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1348

Abstract

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results.
Sistem Pakar untuk Identifikasi Kandungan Formalin dan Boraks pada Makanan dengan Menggunakan Metode Certainty Factor Hengki Tamando Sihotang; Fristi Riandari; Pilisman Buulolo; Husain Husain
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1364

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui identifikasi kandungan zat pengawet berbahaya boraks dan formalin pada makanan. Metode yang digunakan untuk mengidentifikasi kandungan zat berbahaya pada makanan dengan menggunakan Certainty Factor dengan teknik pemberian bobot pada setiap premis (gejala) hingga memperoleh persentase keyakinan untuk mengidentifikasi makanan yang mengandung formalin dan boraks. Hasil penelitian ini adalah Kandungan boraks pada makanan, dari 4 sampel makanan (100%) yaitu 4 sampel atau seluruh sampel tidak mengandung boraks dengan persentase sebesar 100%. Kandungan formalin pada makanan, dari 4 sampel makanan (100%) yaitu ada 2 sampel makanan positif mengandung formalin dengan persentase sebesar 50% dan ada 2 makanan negative mengandung formalin dengan persentase sebesar 50%. Dari hasil pemeriksaan menggunakan spektrofoto meter UV-VIS kadar formalin yang terendah terdapat pada sampel (Ikan Segar) dengan nilai 0,6631 mg/l. Kadar formalin yang tertinggi terdapat pada sampel C (Mi Bakso) dengan nilai 1,7140 mg/l.
Co-Authors Ade Rizka Adjie Bintang Pamungkas Afifa, Rizky Maulidya Afrisawat, Afrisawat 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 Dahayu Annisa Nathania Dalimunthe, Yulia Agustina Danvy Nadhira Dedi Setiawan Halawa Demita Sihotang Dewi Novika Simanjuntak Dimas Ribowo Esera Gulo Ezra Natasya.S Ginting, Ramadhanu Hamed Huckle Schubert Hasugian , Paska Marto Hasugian, Penda Sudarto Hengki Tamando Sihotang Husain Husain Ibnu Rizky Indri Sulistianingsih John Foster Marpaung Jonhariono Sihotang Jonson Manurung Judijanto, Loso Kharisma Wiati Gusti 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 Panjaitan, Firta Sari Petti Indrayati Sijabat Pilisman Buulolo R. Mahdalena Simanjorang Ramadhanu Ginting 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 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 Vinsensia, Desi Virdyra Tasril Wan Wimar Yahya Wildan Alrasyid Yuda Perwira Yudistira Alif Raditya Yunus Simare-mare