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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 40 Documents
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Search results for , issue "Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
K-Means and Fuzzy C-Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment darmayanti, irma; Mustofa, Dinar; Hidayati, Nurul; Saputri, Inka
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4552

Abstract

Packaged food and beverages often pose a risk of increasing diabetes when consumed regularly. This study aims to classify these products based on their nutritional content listed on the labels, with a focus on identifying diabetes risk. The methods employed include K-Means and Fuzzy C-Means, K-Means is used to determine initial center of cluster, while Fuzzy C-Means enhances the clustering by assigning probabilistic memberships to each data point. These methods are applied to products sold in stores in Banyumas Regency, Central Java, Indonesia. This research is the first to combine these two methods in the context of product clustering based on nutritional labels. The results indicate that packaged food and beverage products can be classified into high-risk and low-risk clusters for diabetes. Consequently, this study provides important guidance for consumers in choosing healthier.
Bridging the Gap: Integrating Organizational Change Management with IT Project Delivery Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4450

Abstract

In today's rapidly evolving technological landscape, the successful implementation of IT projects is increasingly contingent upon effective organizational change management (OCM). This research paper explores the intersection of OCM and IT project delivery, proposing a comprehensive framework that integrates these two critical domains. Through a review of existing literature and analysis of case studies, we identify key challenges and best practices for synchronizing OCM strategies with IT project management processes. Our findings reveal that the alignment of OCM with IT project delivery not only enhances project success rates but also promotes sustainable organizational transformation. This integrated approach ensures that technological advancements are supported by a well-prepared workforce, thereby minimizing resistance and maximizing adoption. The paper concludes with practical recommendations for practitioners aiming to bridge the gap between OCM and IT project delivery, ultimately fostering a more agile and resilient organizational environment.
Evaluation of Usability Based on User Satisfaction with the UI/UX Design of the E-Repository Prototype Sabandar, Vederico Pitsalitz; Sintaro, Sanriomi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4292

Abstract

The user interface (UI) design of software in the early stages of development is crucial to ensure the future success of the product and is key to creating an effective user experience (UX). This study aims to produce a UI design for software, namely an e-repository, and conduct a usability evaluation based on user satisfaction with the UX of the design. The resulting e-repository design is a prototype, where the research process implemented the design thinking method consisting of five main phases: empathize, define, ideate, prototype, and test. The evaluation is conducted in the test phase, where the evaluation uses the usability testing method by assigning a number of task scenarios, totaling 8 tasks, to respondents. Furthermore, to measure user satisfaction levels, a questionnaire based on the System Usability Scale (SUS) is used, consisting of 10 statements with both positive and negative orientations. The research results indicate that the UI design has addressed the issues and met the needs of users. Meanwhile, the analysis results from the usability evaluation show that the Adjective Ratings are categorized as Good. Additionally, in terms of Acceptability Ranges, the prototype is deemed Acceptable by users, with a Grade Scale obtained being B, where the evaluation results indicate that the scores obtained are above the average SUS score standard.
Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes Ramadhan, Muhammad Akbar; Nusyura, Fauzan; Rahmanti, Farah Zakiyah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3444

Abstract

One way to see the signs of disease in grapevines is a change in leaf color. Ordinary people detect diseases in grapevines only based on subjective vision. On this basis, we need a system that can help the layman to be able to detect diseases in grapevines based on the color of the leaves using the Naïve Bayes algorithm classification method. This algorithm uses simple calculations, so the process is carried out faster. In this study, testing was carried out using the Naive Bayes classification model with 800 training data and 160 validation data. The accuracy results obtained are 90% using the color historgram scenario on channel RGB interval 16 and GLCM with features of dissimilarity, correlation, homogeneity, contrast pixel spacing 5. 90% accuracy is also obtained in the color histogram scenario on channel HSV with interval 16 and GLCM with features of dissimilarity, correlation, homogeneity at pixel spacing of 5. Thus, it can be concluded that the Naive Bayes classification model can gain application in identifying diseases in grapevines through leaf color analysis.
Application of SMART Method and Dashboard Visualization for Student Code of Conduct Violations Devega, Mariza; Darmayunata, Yuvi; yuhelmi, Yuhelmi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4593

Abstract

In order to handle student discipline infractions at school, this project intends to design a decision support system (DSS) based on the Simple Multi-Attribute Rating Technique (SMART) technique integrated with a graphical dashboard. A variety of visual tools, such as scatter plots, heatmaps, pie charts, bar charts, and line charts, are used to evaluate and display violation data. This integration's primary goals are to make monitoring and analysis faster and more efficient and to support decision-making with regard to student infractions. The findings demonstrate how the SMART approach and visualization dashboard can be used to manage violation data more effectively, provide a better knowledge, and speed up reactions to infractions. This technique makes it easier for schools to spot trends in infractions, choose the best course of action for corrective measures, and enhance overall student discipline. It is anticipated that this system will enable discipline management in a learning environment in an efficient manner.
Analysis To Predict The Quality Of Toddler Growth By Implementing The KNN And Naïve Bayes Methods Reza, Elfira Yolanda; Widyaningsih, Tri Wahyu
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4121

Abstract

In particular, stunting and being under the Red Line (BGM) are significant issues for society and the healthcare system. This research utilizes machine learning, particularly the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms, for classifying the health of children experiencing stunting or BGM. The training data used comes from the Indonesian Posyandu website, serving as the foundation for classifying new data. This research not only identifies patterns in the data through KNN but also compares the prediction results between KNN and Naïve Bayes in assessing the probability of stunting or BGM in children. This issue reflects nutritional deficiencies and has the potential to cause developmental delays and long-term health impacts. This approach allows for the comparison of predictive outcomes, enhancing the accuracy of children's health assessments. By using the RapidMiner application, the accuracy result for KNN is 70.62% and for Naïve Bayes is 99.47%, providing a deeper understanding of the effectiveness of each algorithm in addressing child health challenges. The aim of this research is to classify new toddler data using the KNN and Naïve Bayes methods, implemented in the form of a Visual Basic application. It is hoped that this will help monitor children's health more effectively and be more easily accessible to interested parties.
Implementasi Principal Component Analysis (PCA) dan Gap Statistic untuk Clustering Kanker Payudara pada Algoritma K-Means Afifa, Ridha; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Indriani, Fatma; Muliadi, Muliadi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4015

Abstract

Breast cancer is one of the most common causes of death worldwide. Data mining can be utilized to detect breast cancer, where information is extracted from data to provide valuable insights. Clustering of breast cancer is conducted to assist medical professionals in grouping the characteristics of each cancer type. However, multicollinearity in breast cancer data can impact clustering results. To address this issue, dimensionality reduction through Principal Component Analysis (PCA) is employed. PCA can effectively handle multicollinearity issues and enhance computational efficiency. Additionally, the K-Means method has limitations in determining the optimal number of clusters. Therefore, the Gap Statistic method is employed to find the optimal K value suitable for breast cancer data. This study compares the evaluation results of the K-Means clustering model, the combined PCA-KMeans clustering model, and the combined PCA-GapStatistic-KMeans clustering model. The findings indicate that the evaluation results for the K-Means model with PCA dimensionality reduction and optimal Gap Statistic K are superior to the K-Means model without dimensionality reduction. The Gap Statistic suggests 2 clusters as the optimal number, with an evaluation result of 1.195513.
The Best Tourism Recommendation Intelligent System Model: Weighted Product and K-Means Methods Kanafi, Kanafi; Fitriana, Mira
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4514

Abstract

Magelang Regency, with a wealth of tourist destinations with a variety of things, such as Borobudur Temple and Nepal Van Java, has extraordinary tourism potential. With this diversity, it is a challenge for tourists to determine the best tourist objects that suit their preferences. This study aims to develop an intelligent system for recommending the selection of the best tourist attractions in Magelang Regency by integrating the Weighted Product and K-Means Clustering Methods. The system is designed to provide accurate recommendations based on tourist criteria such as location, facilities, tickets, and security, as well as group attractions based on their level of potential. The Weighted Product method is used to determine the best tourist attractions, while K-Means Clustering groups tourist destinations into high, medium, and low potential categories. In this study, several stages were carried out: literature study, data collection, system design, data analysis, implementation, and system testing to produce an effective and efficient recommendation system for tourists in Magelang Regency. The results of this research obtained the best tourism, namely at Borobudur Temple.
Rainfall Prediction in Tegal Regency using ETSFormer Tridinatha, Zenitha Eunike; Hartomo, Kristoko Dwi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4422

Abstract

Weather is the atmospheric condition at a specific location and time that is variable and constantly changing. Many factors influence weather conditions, one of which is rainfall. Rainfall is a climatic parameter characterized by high variability due to climate anomalies. These anomalies make rainfall prediction very challenging. Specific factors can cause uneven distribution patterns of rainfall between different regions. The exact amount of rainfall that will occur cannot be determined precisely, but predictions or estimations can be made for future rainfall amounts. This study predicts rainfall in Tegal Regency using ETSFormer. Its aim is to provide useful information about future rainfall patterns for the community, especially in Tegal Regency, to facilitate daily activities. The results show that the ETSFormer model effectively predicts rainfall, achieving optimal results with an 8:2 data composition using univariate analysis, yielding the best MSE evaluation metric of 0.002925439039245248 and MAE of 0.036676984280347824.
Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression Azzahra, Aura; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4155

Abstract

PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.

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