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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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Perbandingan Metode Teorema Bayes dan Dempster Shafer Menentukan Status Gunung Berapi Ginting, Ramadhanu; Riandari, Fristi; Afrisawati, Afrisawati; Sulistianingsih, indri
Jurnal Nasional Teknologi Komputer Vol 5 No 3 (2025): Juli 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i3.274

Abstract

Penelitian ini membahas perbandingan dua metode dalam sistem pakar yang digunakan untuk menganalisis status aktivitas gunung berapi. Gunung berapi merupakan struktur geologis yang terbentuk akibat pergerakan magma dari dalam bumi ke permukaan melalui erupsi, yang biasanya disertai pelepasan lava, gas, dan material vulkanik lainnya. Sistem pakar dalam konteks ini bertujuan mentransformasikan pengetahuan seorang ahli ke dalam bentuk algoritma komputasional yang mampu melakukan diagnosis secara otomatis. Dua metode yang dianalisis dalam penelitian ini adalah Teorema Bayes dan Dempster Shafer. Tujuan utama dari studi ini adalah mengevaluasi efektivitas kedua metode dalam menentukan status gunung berapi dan mengidentifikasi metode yang paling akurat untuk diimplementasikan dalam aplikasi sistem pakar. Hasil analisis menunjukkan bahwa metode Dempster Shafer memiliki tingkat akurasi sebesar 98%, lebih tinggi dibandingkan dengan metode Teorema Bayes dalam konteks data gejala yang diuji. Temuan ini menunjukkan bahwa Dempster Shafer lebih unggul dalam klasifikasi status gunung berapi pada kasus ini, serta memberikan kontribusi terhadap pengembangan sistem pakar yang andal dalam mendukung pengambilan keputusan di bidang geologi dan mitigasi bencana alam
Distribution cost optimization: Comparison of NWC, MODI, and Stepping Stone methods in transportation problems Riandari, Fristi; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.688

Abstract

Solving transportation problems is essential in minimizing distribution costs in logistics and supply chains. Three classical methods North West Corner (NWC), Modified Distribution Method (MODI), and Stepping Stone are frequently used, but few studies offer a comprehensive comparison. This study fills this gap by evaluating their performance using simulated data representing real-world distribution scenarios. This study applies a structured comparative framework to analyze NWC (a cost-agnostic initial allocation technique), MODI (a dual-variable-based optimization approach), and Stepping Stone (a closed-loop path evaluation method). Each method was tested on a simulated cost matrix using Python. Evaluation metrics included total distribution cost, number of iterations, and computation time. The NWC method yielded a feasible but suboptimal solution with a cost of 540 units. Optimization using MODI reduced the cost to 425, while Stepping Stone further minimized it to 410 after three iterations. MODI showed greater computational efficiency, while Stepping Stone offered visual traceability of cost reductions. This study contributes methodologically by combining heuristic and iterative optimization techniques in one analytical framework. Practically, it provides decision-makers with insights into selecting appropriate solution methods based on trade-offs between simplicity, efficiency, and cost minimization.
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)

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

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.
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 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