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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 4 Documents
 1 
Search results for , issue "Vol. 5 No. 1 (2025)" : 4 Documents clear
OPTIMISASI ALGORITMA K-MEANS DENGAN METODE REDUKSI DIMENSI UNTUK PENGELOMPOKAN BIG DATA DALAM ARSITEKTUR CLOUD COMPUTING Putra, Bayu Anugerah; Mukhtar, Harun; Br Bangun, Elsi Titasari; Gusnanda, Alris; Maisyarah, Adila; Kurniawan, Muhammad Irgi; Pradipa, Raditya; Ali, Zurrahman Muhammad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.7616

Abstract

In the era of big data, data clustering becomes a major challenge due to the complexity and huge volume of data. The K-means algorithm is one of the clustering techniques that is often used due to its simplicity. However, K-means faces difficulties in handling high-dimensional and large-volume data. This study proposes an optimization of the K-means algorithm using the Principal Component Analysis (PCA) dimensionality reduction method to improve the efficiency and accuracy of big data clustering in cloud computing architecture. The KDD Cup 1999 dataset is used to test this method. The dataset undergoes pre-processing and dimensionality reduction using PCA, then K-means clustering is applied. The clustering results are evaluated using the Silhouette Score and Davies-Bouldin Index. The implementation is carried out in the Google Colab environment to utilize cloud computing resources. The results show that dimensionality reduction using PCA significantly reduces computational complexity and improves clustering quality. This method is effective in clustering big data, making it an efficient solution for data clustering in cloud computing architecture.
TINJAUAN LITERATUR: PEMANFAATAN KECERDASAN BUATAN DALAM PEMANTAUAN KUALITAS UDARA MELALUI INOVASI GOOGLE PROJECT AIR VIEW Widhi, Eko Prasetio; Firlana Umi Azzakiy; Mar’ah Rofidah Abidah Khosyatullah
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8347

Abstract

Polusi udara merupakan salah satu masalah lingkungan yang berdampak signifikan terhadap kesehatan masyarakat dan ekosistem. Seiring perkembangan teknologi, kecerdasan buatan (AI) telah menjadi alat penting dalam meningkatkan efektivitas dan efisiensi pemantauan kualitas udara. Artikel ini menyajikan tinjauan literatur tentang pemanfaatan AI dalam pemantauan kualitas udara, dengan fokus pada inovasi Google Project Air View. Teknologi ini menggunakan kendaraan Google Street View yang dilengkapi sensor canggih untuk menghasilkan data kualitas udara secara real-time dengan resolusi tinggi. Melalui analisis data besar dan algoritma pembelajaran mesin, sistem ini mampu memetakan konsentrasi polutan seperti karbon dioksida (CO₂), nitrogen dioksida (NO₂), dan partikel halus (PM2.5) secara lebih akurat dibandingkan metode tradisional berbasis sensor statis. Artikel ini juga membahas keunggulan teknologi AI, termasuk integrasi dengan IoT, penerapan UAV, edge computing, dan model prediktif berbasis data besar, serta dampaknya dalam mendukung kebijakan publik dan perencanaan kota berkelanjutan. Meskipun terdapat tantangan dalam implementasi teknologi ini, seperti kebutuhan akan infrastruktur yang kompleks dan validasi data, potensi AI untuk mengatasi tantangan polusi udara tetap besar. Artikel ini menyimpulkan bahwa pengembangan lebih lanjut pada sistem berbasis AI dapat memberikan manfaat signifikan bagi pengelolaan kualitas udara global dan mendukung pembangunan yang lebih ramah lingkungan.
SUPPORT VECTOR MACHINE ALGORITHM FOR EARLY DETECTION SYSTEM FOR MENTAL EMOTIONAL DISORDERS IN ADOLESCENTS Muthya Cahyani Putriabhimata; Ida Widaningrum; Dyah Mustikasari
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8351

Abstract

A mental-emotional disorder is a condition characterized by emotional fluctuations that, if left untreated, might progress into an abnormal state. In Indonesia, the treatment of mental problems is infrequently conducted due to a scarcity of psychiatric personnel and the high expenses associated with comprehensive mental health therapy and treatment. An early detection system for mental-emotional illnesses in teenagers was developed by implementing the Support Vector Machine (SVM) algorithm as a solution to this issue. The Support Vector Machine algorithm is a very accurate classification approach. This study utilizes data that is categorized into two distinct groups: anxiety and depression. The data is partitioned in an 80:20 ratio, with 80% allocated for training data and 20% for test data. The research findings indicate that the testing accuracy levels yielded a value of 85%. The value is derived using the RBF kernel with a gamma value of 0.1 and a C value 10. The Support Vector Machine model is implemented within the Graphical User Interface (GUI). The user experience questionnaire was assessed on the Graphical User Interface, resulting in a user experience score within the "good" category.
OPTIMASI KNN DENGAN PSO UNTUK KLASIFIKASI KASUS HUKUM DI AUSTRALIA MENGGUNAKAN N-GRAM Karan; M Alidin; Rafi Fadilla
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8644

Abstract

This study aims to improve the accuracy of legal case classification in Australia by integrating the K-Nearest Neighbors (KNN) algorithm optimized using Particle Swarm Optimization (PSO) and N-Gram-based text representation. The dataset consists of 15,263 legal documents collected from the Federal Court of Australia (FCA) with an 80:20 data split for training and testing. The classification process is carried out by applying TF-IDF weighting and a combination of N-Gram (unigrams, bigrams, trigrams) to enrich the data representation. The PSO optimization results show an optimal K value of 9, with a testing accuracy reaching 96%. The evaluation of the model performance shows a precision value of 0.95, a recall of 0.96, and an F1-Score of 0.94. These results indicate that the combination of KNN, PSO, and N-Gram is able to significantly improve the performance of legal document classification, especially in the Cited case category. However, the weakness of the model in the Not Cited category indicates the need to develop a method to handle data imbalance in order to improve model generalization.

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