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Rizka Hafsari
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Journal of Software Engineering and Information System (SEIS)
Published by Universitas Muhammadiyah Riau
ISSN : -     EISSN : 28090950     DOI : https://doi.org/10.37859/seis.v3i1
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Journal of Software Engineering and Information System (SEIS) is a peer-reviewed journal published twice a year (January and August) by the Department of Information System - Faculty of Computer Science, Universitas Muhammadiyah Riau. The scope of the journal is: Artificial Intelligent Business Intelligence and Knowledge Management Data Mining E-Bussiness IT Governance Enterprise System System Design Information Design & Development Database System Expert System Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 61 Documents
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ANALISIS DAN VISUALISASI DATA SAMSUNG SALES MENGGUNAKAN EXPLORATORY DATA ANALYSIS PADA TABLEAU Putra, Yahya Nugraha; Wahyu, Ofel Idhan; Yovita, Kristian; Utomo, Pradita Eko Prasetyo
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9538

Abstract

The development of 5G technology has a significant impact on the mobile device industry, but its adoption is uneven in many regions. The study was conducted to analyze Samsung’s 5G device sales trends globally and examine the relationship between network infrastructure, consumer preferences and device sales performance. The method used is Exploratory Data Analysis (EDA) with the help of interactive visualization through Tableau. Secondary data is obtained from Kaggle and covers the period 2019–2024, with variables such as number of units sold, network coverage, 5G average speed, and preference score. Results show that about 65% of sales come from high preference models, and since 2021 5G devices have mastered more than 70% of the global market. In addition, the Galaxy S Series model recorded preference score above 85%, showing that consumer perception is highly influential on sales performance. Visualization in the form of dashboards supports strategic understanding of markets based on regions, products, and time. This EDA-based visualization is able to provide deep insight for policymakers and manufacturers in strategizing 5G market penetration strategies more effectively and sustainably
PEMODELAN RFM & K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN DALAM PENJUALAN ONLINE Lukas, Ivander; Finanta Okmayura; Aidha Tita Irani; Ernia Juliastuti; Muhammad Amirulhaq; Rizky Ardiansyah; Sherly Fillia
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9556

Abstract

The exponential growth of e-commerce platforms necessitates sophisticated customer analytics to maintain competitive advantage and optimize revenue streams. This study addresses the critical challenge of understanding heterogeneous customer purchasing behaviors in online retail environments through advanced data mining techniques. The research implements RFM (Recency, Frequency, Monetary) modeling integrated with K-Means clustering algorithm to achieve comprehensive customer segmentation for strategic marketing optimization. A quantitative-exploratory methodology was employed, utilizing a comprehensive online sales dataset comprising over 40,000 transactional records. The analytical framework involved systematic data preprocessing using Python libraries (Pandas, NumPy), followed by RFM parameter calculation and standardization through StandardScaler normalization. K-Means clustering was subsequently applied with optimal cluster determination via Elbow Method validation, yielding three distinct customer segments. Visualization and interpretation were conducted using Tableau, Matplotlib, and Seaborn for comprehensive segment characterization. Results demonstrate successful identification of strategically significant customer clusters: high-value loyal customers, moderate-engagement prospects, and potential churn-risk segments, each exhibiting distinctive RFM behavioral patterns. The segmentation framework enables targeted marketing strategy formulation, personalized customer retention programs, and optimized resource allocation. This research contributes valuable insights for e-commerce practitioners seeking data-driven approaches to enhance customer relationship management and sustain long-term business profitability in competitive online marketplaces.
PEMODELAN MACHINE LEARNING DENGAN ALGORITMA RANDOM FOREST DALAM MEMPREDIKSI RISIKO STROKE Arman, Doni; Indayana, Nurul Sakhila; Okmayura, Finanta; Anjani, Suci Putri; Dayani, Fitri Nur; Farhan, Muhammad; Faturrahman, Ariya
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9590

Abstract

Stroke is one of the diseases that significantly affects health and economy, becoming the second most common cause of death in the world after coronary heart disease. Based on data from the World Health Organization (WHO), stroke is ranked second as the leading cause of death in the world after ischemic heart disease. In 2019, stroke was responsible for around 11% of total global deaths. One important way to reduce the death rate from stroke is to make prevention efforts through early prediction. Machine learning methods, especially Random Forest, are used in this study to predict the risk of stroke. The data used comes from a public dataset that includes age, gender, blood pressure, blood sugar, smoking status, and other medical history. The research process includes data pre-processing stages (data cleaning, outlier handling, and category coding), model training using the Random Forest algorithm, and model evaluation using a confusion matrix to evaluate accuracy, precision, recall, and F1 score. The evaluation results show an accuracy value of 97.55%, which indicates very good predictive performance so that this model has very good predictive performance.
A SYSTEMS ENGINEERING APPROACH TO SUSTAINABILITY DECISION SUPPORT SYSTEM BASED ON HESI Syaukani, Muhammad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9593

Abstract

Climate change and global sustainability demands are prompting universities to integrate sustainable principles into all academic and non-academic activities. This research led to the design and implementation of a Sustainability Decision Support System (SDSS) at Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia (ITBA-DCC), using the System Engineering Process (SEP) and the Higher Education Sustainability Initiative (HESI) framework. The SDSS uses Multi-Criteria Decision Making (MCDM) and interactive data visualization to assess sustainability across four dimensions: environmental, social, economic, and governance. Initial pilot testing showed an overall sustainability index of 74/100, with the highest score in the environmental dimension (82/100) and the lowest in governance (68/100). User Acceptance Testing (UAT) reported a 91% satisfaction rate among staff, lecturers, and campus leaders. Although the system relies on annual data and some manual inputs, the SDSS shows potential for adoption by similar institutions and supports Sustainable Development Goals (SDGs) in higher education.
EKSPLORASI SENTIMEN PENGGUNA X TERHADAP ISU KESEHATAN MENTAL BERBASIS MACHINE LEARNING Rifaldi, Dianda; Famuji, Tri Stiyo; Fanani, Galih Pramuja Inngam; Ramadhan, Fauzan Purma; Mulyadi, Iriene Putri; Saputra, Vanji
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9594

Abstract

Mental health has become an increasingly relevant topic in the digital era, particularly on social media platforms such as X, which serve as public spaces for expressing opinions and sharing personal experiences. This study aims to analyze public sentiment toward mental health topics on Twitter using the Multinomial Naive Bayes algorithm. Data were collected from tweets containing mental health-related keywords and processed through text cleaning and feature extraction using the TF-IDF method. The classification results showed that the model achieved an accuracy of 71%, with stronger performance in identifying negative sentiment compared to positive sentiment. A WordCloud visualization also revealed the frequent appearance of terms such as “mental,” “health,” “self,” and “disorder,” reflecting the main focus of online discussions. These findings indicate that machine learning-based sentiment analysis is effective in capturing public perceptions of mental health issues on social media. This research is expected to contribute to the development of digital communication strategies and real-time monitoring of psychosocial issues in online spaces.
OPTIMASI JARINGAN DENGAN VIRTUAL LAN (VLAN) UNTUK MENINGKATKAN EFISIENSI DATA TRANSFER Amisyah, Husnul; Fitriah
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9602

Abstract

Optimal network management is crucial in the digital era, where efficient data exchange is essential, especially in environments with numerous devices such as educational institutions and offices. The aim of this study is to evaluate the impact of implementing Virtual Local Area Networks VLAN on data transmission efficiency in computer networks. This research adopts an experimental quantitative approach through the simulation of two scenarios: networks without Virtual Local Area Networks VLAN and networks with VLAN, utilizing analytical tools such as Ping, iPerf, and Wireshark. The results show that implementing Virtual Local Area Networks VLAN can reduce average latency from 6.5 ms to 3.2 ms and increase throughput from 35 Mbps to 48 Mbps. Additionally, Virtual Local Area Networks VLAN significantly reduce broadcast traffic and packet collisions while enhancing data security through logical segmentation between departments. Therefore, Virtual Local Area Networks VLAN implementation greatly improves overall data communication efficiency and network performance.
STRATEGI PENERAPAN WIRELESS MESH NETWORK UNTUK PENGINGKATAN DAN KEANDALAN JARINGAN NIRKABEL Juniarti, Tiara Sela Juniarti; Fitriah
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9604

Abstract

The need for reliable communication networks that cover a wide area is becoming increasingly important, especially in areas with limited conventional network infrastructure. One promising solution to overcome this challenge is the implementation of Wireless Mesh Network (WMN), which offers flexibility, scalability, and redundancy through direct connectivity between nodes. This study aims to design and evaluate a Wireless Mesh Network (WMN) implementation strategy to improve the reliability and performance of wireless networks. The methods used include mesh topology analysis, adaptive routing protocol selection, and network performance simulation using throughput, delay, and packet delivery ratio parameters. The preliminary results show that the implementation of Wireless Mesh Network (WMN) with proactive routing protocols such as Optimized Link State Routing (OLSR) and a hybrid approach provides an increase in throughput of up to 35% and reduces the average delay by 20% compared to traditional infrastructure-based networks. In addition, network reliability is increased through self-healing capabilities, namely the ability of the network to reset the communication path when there is a disruption to one of the nodes. These findings indicate that the right Wireless Mesh Network (WMN) implementation strategy can significantly improve the efficiency and reliability of wireless networks, especially in environments with physical or geographical limitations. This study recommends the use of adaptive mesh topology to support continuous connectivity and more resilient network management.
PERANCANGAN SISTEM INFORMASI AKADEMIK BERBASIS WEB PADA MTSN 5 MUARO JAMBI Ramadhan, Fauzan Purma; Dianda Rifaldi; Iriene Putri Mulyadi; Vanji Saputra
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9611

Abstract

The manual management of academic data has the potential to cause various obstacles, such as delays in grade distribution, data duplication, and input errors. This study aims to design a web-based academic information system at MTsN 5 Muaro Jambi as a solution to these problems. The system was developed using the Waterfall method with the PHP programming language and a MySQL database. The database design was carried out in a structured manner to support the integrity and efficiency of academic information management. The novelty of this study lies in the integration of grade management, attendance, and schedule features into a single web-based platform, which has never been implemented before at MTsN 5 Muaro Jambi. The system was designed to involve three main actors: the admin as data manager, teachers as data managers, and students as recipients of online academic information. The implementation results showed that the system was able to improve recording accuracy, accelerate information distribution, and support academic data transparency. Testing using the blackbox testing method proved that the system's main functions ran as needed, while user acceptance trials involving teachers, students, and administrative staff showed the system was easy to use and useful in supporting the academic process.
KLASIFIKASI MAKANAN BERDASARKAN NILAI GIZI MENGGUNAKAN ALGORITMA RANDOM FOREST DAN TEKNIK SMOTE Br Bangun, Elsi Titasari; Bayu Anugerah Putra; Aryanto
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9725

Abstract

Classifying food based on nutritional content is essential for developing personalized dietary recommendation systems and promoting healthier eating habits. This study aims to construct a food classification model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the dataset. The dataset includes various nutritional attributes such as calories, protein, fat, carbohydrates, fiber, sugar, sodium, and cholesterol, along with additional information such as food category and meal time. After preprocessing, the data were split into training and testing sets, with SMOTE applied to the training data to improve class representation. The model was trained using Random Forest and evaluated using accuracy, precision, recall, and F1-score. The results show that the model achieved an accuracy of 83.35% and an average F1-score above 0.80, with the best performance observed in majority classes. The confusion matrix analysis indicates that most predictions were accurate, although misclassifications occurred among classes with overlapping nutritional values. Protein, calories, and carbohydrates were identified as the most influential features in the classification process. These results show that combining Random Forest and SMOTE works well for creating food classification systems using nutritional data and could be useful in apps for diet recommendations and managing nutrition.
PERANCANGAN SISTEM INFORMASI PEMESANAN TIKET TRAVEL BERBASIS WEB DI PT YOSSY MANDIRI Mulyana, Wide; Izaky Arif Rahman; Meli Aulia; Muhammad Fadly; Nabil Andra Putra; Sintia Hadisty
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9894

Abstract

This research aims to design a web-based ticket booking information system that can enhance the efficiency and quality of services at PT. Yossy Mandiri. This system is designed to facilitate customers in making ticket reservations online and assist the admin in managing booking data, departure schedules, and customer information. The system development method used is the waterfall method, which includes requirements analysis, system design, implementation, testing, and maintenance. The result of this research is a web-based travel ticket booking information system that can improve efficiency and service quality at PT. Yossy Mandiri. This system is expected to be a solution for the company in enhancing customer service and optimizing internal business processes.

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