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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) Aisyah Journal of Informatics and Electrical Engineering INOVTEK Polbeng - Seri Informatika
Renny Sari Dewi
Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Aplikasi Estimasi Usaha dan Biaya Pengembangan Software Menggunakan Metode Function Points dan Use Case Points Berbasis Android Juliana Kristi; Renny Sari Dewi
JRSI (Jurnal Rekayasa Sistem dan Industri) Vol 8 No 01 (2021): Jurnal Rekayasa Sistem & Industri - Juni 2021
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v8i1.427

Abstract

Estimasi usaha dan biaya perangkat lunak perlu dilakukan dengan hati-hati dan terukur secara jelas sehingga keberhasilan proyek pengembangan perangkat lunak dapat tercapai. Terdapat 2 metode yang sudah lazim digunakan untuk mengestimasi usaha dan biaya pengembangan perangkat lunak, diantaranya adalah Function Points (FP) dan Use Case Points. Tahapan penelitian yang dilakukan ada 4, yaitu studi literatur, analisis kebutuhan, perancangan, dan implementasi (pengembangan aplikasi hingga ujicoba). Analisis dan perancangan aplikasi menggunakan tools Signavio. Sedangkan pengembangan aplikasinya penulis menggunakan open source rapid application development (RAD) yaitu Kodular. Tahap ujicoba dilakukan dengan cara melakukan verifikasi terhadap perhitungan manual yang proyeknya sudah selesai dikerjakan. Hasil dari penelitian ini adalah terciptanya aplikasi berbasis Android yang dapat difungsikan dalam mempercepat para pelaku bisnis di bidang pengembangan perangkat lunak dalam melakukan proses estimasi harga produk. Penelitian ini memiliki batasan yaitu belum dilakukan pengujian validitas terhadap proyek pengembangan lunak yang masih dalam tahap perencanaan.
PERANCANGAN USER INTERFACE (UI) & USER EXPERIENCE (UX) APLIKASI PENCARI KOST ABC DI KOTA XYZ MENGGUNAKAN METODE DESIGN THINKING Mohammad Adam Prasetyo; Muhammad Choirul Rozikin; Renny Sari Dewi
Aisyah Journal Of Informatics and Electrical Engineering Vol 3 No 1 (2021): Aisyah Journal Of Informatics and Electrical Engineering
Publisher : Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30604/jti.v3i1.59

Abstract

Boarding house can also be called a temporary lodging house. What is meant is the house that is used to stay for 1 day or more. The presence of boarding houses that exist today is of great benefit to the community, especially for people who are migrating. In this day and age where technology is growing rapidly, especially in information technology, which triggers many applications that are created with different uses. The ABC application is an application that can help find boarding. This application makes it easy for people who want to find boarding houses in the city of XYZ and its surroundings. This application is also equipped with information about the boarding house that will be searched for, for example: address, price, and other facilities in detail. In the design solution, User Interface (UI) & User Experience (UX) Design was chosen by using the Design Thinking method as a problem solution for boarding seekers so that it was easier to find detailed information about prices, addresses, and other facilities using only a smartphone. This User Interface (UI) & User Experience (UX) design serves to communicate the available system features so that users can understand and be able to use the system properly, and have satisfaction and comfort when using the application.
Design and Build Android-Based Applications of Software Development Projects Effort and Cost Estimation Juliana Kristi; Renny Sari Dewi
Jurnal Rekayasa Sistem & Industri Vol 8 No 01 (2021): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v8i1.427

Abstract

Prediction of software business and costs needs to be done carefully and clearly measured so that thesuccess of a software development project can be achieved. There are 2 methods that are commonly usedto estimate software development efforts and costs, such Function Points (FP) and Use Case Points. Therewere 4 stages of the research carried out: literature study, needs analysis, design, and implementation(application development to testing). Analysis and application design using the Signavio tools. Meanwhile,the author uses open source rapid application development (RAD), called Kodular. The trial phase iscarried out by verifying manual calculations whose projects have been completed. The result of thisresearch is the creation of an Android-based application that can be used to accelerate business players inthe software development sector in the process of estimating product prices. This research has limitation isnot tested the validity of the soft development project which is still in the planning stage.
Analysis of Cryptocurrency Investment Patterns Using Machine Learning Farrel Amri Naufal Sandio; Renny Sari Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7ny29y07

Abstract

The rapid growth of cryptocurrency, particularly Bitcoin, has introduced high-return investment opportunities accompanied by extreme price volatility, posing challenges for accurate forecasting. Previous studies have applied various machine learning models for Bitcoin price prediction; however, limited attention has been given to how different training data horizons affect model performance and generalization. This study addresses this gap by comparing three machine learning algorithms: Linear Regression (LR), XGBoost, and Long Short-Term Memory (LSTM). The analysis examines different training periods, with a primary focus on a 3-year training scenario. Historical Bitcoin data (1-minute intervals) from Kaggle was aggregated into daily observations and processed using strict chronological splitting (80:20) without data leakage. Feature engineering was applied using lag-based variables, moving averages, and volatility indicators, while LSTM utilized sequence windowing with 30–60 time steps. Empirical results from the 3-year training scenario show that LR and XGBoost achieve strong predictive performance (R² = 0.9757 and 0.9667), whilst LSTM performs moderately (R² = 0.72) with higher prediction errors. Additional exploratory experiments on shorter training horizons (e.g., 6 months) indicate a decline in performance across models, reflected in unstable generalization and negative R² values on test data, suggesting overfitting. However, directional accuracy remains above 55% in the primary scenario. These findings suggest that model performance is sensitive to the length and stability of historical data. While simpler models such as linear regression and tree-based methods demonstrate consistent performance in the evaluated setting, conclusions regarding model superiority should be interpreted within the scope of the experiment.
Co-Authors Farrel Amri Naufal Sandio Juliana Kristi Mohammad Adam Prasetyo Muhammad Choirul Rozikin