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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) INOVTEK Polbeng - Seri Informatika
Novi Rukhviyanti
STMIK IM Bandung
Computer Science & IT Control & Systems Engineering Education

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PERANCANGAN SISTEM MONITORING AKTIVITAS SALES LAPANGAN BERBASIS WEB MENGGUNAKAN METODE WATERFALL DAN ALGORITMA K-MEANS Virki Fardian Nur Rohman; Novi Rukhviyanti
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7473

Abstract

Penelitian ini merancang dan membangun SIMVIS (Visual and Sales Monitoring System), sebuah platform pemantauan penjualan lapangan berbasis web yang dikembangkan menggunakan arsitektur MERN Stack (MongoDB, Express.js, React.js, Node.js) dengan metode Waterfall sebagai kerangka pengembangan sistemnya. SIMVIS mengintegrasikan validasi kunjungan berlapis berdasarkan koordinat GPS dan bukti foto digital, navigasi lapangan real-time menggunakan Leaflet.js dan OSRM, manajemen tugas terstruktur dengan Role-Based Access Control tiga tingkat (Manager, Officer, AR/Sales), dan pengelompokan kinerja menggunakan algoritma K-Means dengan feature vector tiga dimensi yang terdiri dari Total Task, Total Reward, dan Total Prospect, menghasilkan kategori Top Performer, Medium, dan Need Improvement. Hasil pengujian menunjukkan Black Box Testing pada 15 skenario fungsional mencapai tingkat keberhasilan 100%, sistem mampu melayani 10 pengguna secara bersamaan dengan waktu respons 120–350 milidetik, dan User Acceptance Testing terhadap 15 responden menghasilkan skor rata-rata 4,49 dari skala 5,00 dalam kategori Sangat Baik. SIMVIS terbukti menjadi platform pertama yang mengintegrasikan kelima komponen tersebut, yaitu validasi GPS-foto, K-Means tiga dimensi, navigasi real-time, RBAC tiga tingkat, dan visualisasi scatter plot interaktif secara bersamaan dalam satu ekosistem web yang kohesif
Forward Chaining Expert System for Optimizing Marketing Strategies in Social Commerce Platforms Andhika Rudiansyah; Novi Rukhviyanti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34382

Abstract

The complexity of digital performance indicators in social commerce environments poses significant challenges for small and medium enterprises (SMEs) in formulating coherent and actionable marketing strategies. This study develops and evaluates a forward chaining based expert system to support structured, data driven, and interpretable marketing decision-making. A design science research methodology was employed, encompassing problem identification, artifact development, and evaluation. Knowledge was elicited through literature synthesis, expert consultation, and empirical observation, and subsequently formalized into IF–THEN production rules within a structured knowledge base. The system applies a forward chaining inference mechanism to process key indicators, including followers, engagement rate, promotion frequency, and conversion rate, in order to generate prioritized strategic recommendations. Evaluation was conducted using scenario-based testing and expert validation to assess accuracy, consistency, and contextual appropriateness. The results demonstrate complete alignment between system outputs and expert judgment across all evaluation scenarios, indicating high reliability and logical consistency of the rule-based reasoning process. The system also produces context-sensitive and interpretable recommendations aligned with varying levels of business performance. This study contributes by advancing rule-based decision support systems in social commerce and providing an explainable and practically applicable tool to enhance marketing decision quality among SMEs.
Website-Based Baduy Tourism Information System Using The Software Development Life Cycle Method Putri Adinda; Devita Eviliana; Novi Rukhviyanti
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/v8vtvt27

Abstract

Baduy cultural tourism has great potential, but limited information and a manual ticket booking system result in long queues and inadequate tourism services. This study aims to develop a Web-based Baduy Tour Ticket Booking Information System using the Software Development Life Cycle method for tourist ticket reservations using the waterfall method approach. System development includes needs analysis, system design, implementation, testing, and maintenance. The assessment of the system is carried out through functional testing and black-box testing methods to ensure the legality and confidentiality of the application. The development of this system utilises Laravel as its full-stack framework, which includes Javascript, PHP, blade and CSS with Bootstrap. The test results showed that the system was able to save reservation time by up to 60% compared to the manual method, and from the 50 users surveyed, the customer satisfaction rate was 85%. Thus, the development of the Tourism Information System provides a more effective and efficient solution to help Buduy's cultural tourism.
Minimarket Sales Optimization: Implementation of  FP-Growth dan MongoDB  With Python Nia kurniati; Novi Rukhviyanti
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2qh79f26

Abstract

This study applies an integrated FP-Growth algorithm with MongoDB and Python to analyze 150,000 minimarket transaction records over a one-year period. The dataset includes transaction numbers, product names, quantities sold, transaction dates, purchase prices, and selling prices. The parameters of a minimum support of 0.001, a confidence of 0.01, and a lift above 1.0 are used to ensure relevant association rules. The analysis indicates that the discovered product association patterns can increase operational efficiency by up to 15%, particularly in instant food and ready-to-drink beverage categories. These data-driven strategies also boost sales volume by 12.3% and reduce dead stock by 8.7%. Beras MCS 5KG stands out as the most profitable product, with a margin of IDR 1,066,724,400. The main strength of this study lies in the integration of FP-Growth with MongoDB, enabling large-scale real-time analysis without generating candidate itemsets. This approach enhances data processing efficiency, allowing minimarkets to optimise inventory and promotional strategies more accurately.
Co-Authors Andhika Rudiansyah Devita Eviliana Nia kurniati Putri Adinda Virki Fardian Nur Rohman