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Rizka Hafsari
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INDONESIA
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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PENGGUNAAN FRAMEWORK COBIT 5 PADA AUDIT SISTEM INFORMASI KEUANGAN (SIKU) DI PT. X Makmur, Muhammad Makmur Hasan; Jaya, Joy Nashar Utama
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.6888

Abstract

Pada era globalisasi, perusahaan memanfaatkan teknologi informasi untuk mencapai visi dan misi merka. Di Indonesia, pertumbuhan teknologi informasi memberikan kemudahan dalam merancang dan menganalisis data untuk menghasilkan informasi yang relevan. Penggunaan sistem informasi keuangan (SIKU) di perusahaan telah diterapkan di PT. X yang bergerak pada bidang distributor alat berat. Evaluasi dan audit berkala diperlukan untuk memastikan kinerja optimal perusahaa. Melalui evaluasi ini, perusahaan dapat memperbaiki aplikasi, sumber daya manusia, dan tata Kelola perusahaan untuk meningkatkan efektivitas dan efisiensi. Pada penelitian ini berfokus pada domain deliver, service, and support (DSS). Hasil yang diperoleh menunjukkan bahwa tingkat kematangan rata-rata pada domain DSS adalah 3,67, dan dibulatkan menjadi 4 mengindikasikan proses yang bersifat Predictable Process. Analisis kesenjangan menunjukkan adanya kesenjangan rata-rata sebesar 1,4 antara tingkat kematangan saat ini dengan target yang diinginkan yaitu 5. Aplikasi SIKU telah memenuhi proses yang diukur karena telah berada di kategori predictable process namun perlu ditingkatkan lagi untuk memenuhi target yang diinginkan yakni optimizing process.
DEVELOPMENT OF ZAKAT INFORMATION SYSTEM FOR THE ABC ZAKAT INFAQ SHADAQAH CHARITY BASED ON WEBSITE Adi Imantoyo; Arnolis; Helvin. S
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7451

Abstract

Information System is a collection of interconnected elements that work together to gather, process, store, and disseminate information that is beneficial to users. The method used in implementing this project is the sequential linear method, which has several stages, including analysis, design, coding, and testing. . The result of this research Zakat information system based on a website for the ABC Zakat, Infaq, and Shadaqah Amil Zakat Institution using the Laravel framework that utilizes the PHP programming language and is connected to the MySQL database. This system is valuable in enhancing the efficiency, transparency of Zakat management, and distributing funds more effectively within the ABC Zakat, Infaq, and Shadaqah Amil Zakat Institution (LAZIS) ABC.
ANALISIS KESUBURAN PERTANIAN MELALUI IRIGASI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING Mukhtar, Harun; Syafutri, Trimaiyuza Maulina; Rahman, Rayhan Aulia; Putra, Afyuadri; Hafsari, Rizka
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7599

Abstract

Indonesia is an agricultural country where the majority of its population makes a living from agriculture. The agricultural sector is a very important sector for economic development in an agricultural country like Indonesia. Poor irrigation facilities greatly affect the results of the agricultural sector. Crop quality is based on many factors such as the characteristics of the irrigation process, including the amount of air and irrigation time. Overwatering irrigation can cause air wastage, soil freezing disease, yellowing of plant leaves, wilting of plant leaves, and many other problems. K-Means clustering is a method used to group data into one or more groups or clusters. The advantages of the K-Means algorithm are that it is easy and simple to implement, scalability, speed in convergence, and the ability to adapt to sparse data. K-Means to group agricultural land based on soil fertility and rainfall data, found that this grouping can help in more efficient irrigation planning. The clustering results show that agricultural land can be divided into three main clusters based on soil fertility and irrigation. Soil fertility is formed into three clusters based on the level of soil fertility using the Kmeans algorithm which can also be effective in helping in the Indonesian agricultural sector. By adding technological elements, the results provided will of course be even better.
ALGORITMA K-MEANS UNTUK PENGELOMPOKAN PERILAKU CUSTOMER Mukhtar, Harun; Dwi Pramaditya, Ilham; Saputra Weisdiyanto, Wahyu; Hardian_Putra, Saddam; Trimuawasih, Diana; Auralia Rilda, Azzahra
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7615

Abstract

In the rapidly evolving digital era, understanding customer purchasing behavior is crucial for marketing strategies and business development. This study uses the K-means clustering algorithm to analyze and segment customer purchasing behavior. This algorithm effectively partitions data into groups based on similar characteristics. The aim of this study is to identify purchasing behavior patterns using attributes such as purchase frequency, expenditure amount, and product types. By segmenting customers into homogeneous groups, companies can design more effective marketing strategies and better personalization. The results show that the K-means clustering method successfully segments customers based on similar behavior patterns, which can be used for market segmentation and strategy development. The application of this algorithm in purchasing behavior analysis is expected to provide deep insights and support better business decision-making, offering a competitive advantage for companies.
TEKNIK MACHINE LEARNING UNTUK ANALISA KLASIFIKASI KUALITAS UDARA: A REVIEW Alfian, Haris; Wahyuni, Sri; Revalino, Aqil; Mirano, M. Fitter; Rahmayana, Elsa; Mukhtar, Harun
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7617

Abstract

Air quality has a significant impact on human health and the environment, making its monitoring and classification extremely important. This review explores the application of machine learning techniques in analyzing and classifying air quality. Various methods such as decision trees, support vector machines, neural networks, and ensemble learning are evaluated to assess their effectiveness in processing complex and multidimensional air sensor data. This study also discusses challenges in data collection and preprocessing, selection of relevant features, and interpretation of classification results. Furthermore, this review identifies recent trends and future research opportunities in the use of machine learning to improve the accuracy and efficiency of air quality monitoring systems. The analysis results show that machine learning techniques have great potential to enhance our understanding of air quality dynamics and support better decision-making in environmental management
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.
MODEL KLASIFIKASI JARAK MANHATTAN PADA PENGENALAN CITRA SISTEM BAHASA ISYARAT BAHASA INDONESIA Tory, Alfa Rado Andre Yusa Saka; Pradana, Afu Ichsan; Maulindar, Joni
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.v5i2.9466

Abstract

This study aims to design and implement an image recognition system for Sistem Isyarat Bahasa Indonesia (SIBI) by applying the Manhattan distance classification method. Sign language serves as a vital means of visual communication for individuals with hearing impairments and disabilities. However, public understanding of this language remains limited, often leading to ineffective communication between hearing and non-hearing communities. Therefore, an assistive system capable of accurately recognizing sign language is highly needed. The Manhattan method was selected due to its simplicity and efficiency in calculating distances between data points. The dataset used in this study was obtained from the Kaggle website, consisting of 130 training images and 130 testing images, each representing 26 alphabet letters in the SIBI system. All images underwent initial preprocessing using Jupyter Notebook, including resizing, background removal, and conversion to grayscale to facilitate feature extraction. The grayscale images were then transformed into histograms and normalized to maintain a consistent value scale. The classification process was carried out by computing the Manhattan distance between the test and training image histograms. The system was developed using MATLAB R2015a, featuring a user interface that displays classification results directly. The test results showed that out of 130 test images, 104 were accurately recognized, achieving an accuracy rate of 80%. These findings indicate that the Manhattan method is effective for use in image-based sign language recognition systems. The developed system is expected to serve as an inclusive and educational tool to enhance communication between the hearing-impaired community and the general public. Further development may involve integrating additional methods and expanding the dataset.

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