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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT
Nurhindarto, Aris
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Computer-Aided Diagnosis (CAD) of Stroke in The Brain CT-Scan Images Using Integration of Grey Level Co-Occurrence Matrix (GLCM) Texture Feature Extraction And K-Nearest-Neighbour (KNN) Classification Casidi, Casidi; Syukur, Abdul; Soeleman, M. Arief; Nurhindarto, Aris
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.646

Abstract

This study presents an advanced and efficient computer-aided diagnosis (CAD) system for stroke detection using brain CT images, integrating Grey Level Co-Occurrence Matrix (GLCM) feature extraction and K-Nearest Neighbour (KNN) classification. The objective is to enhance stroke detection accuracy and efficiency in clinical settings. A dataset of 400 brain CT images, divided into 300 for training and 100 for testing with equal normal and stroke classes, was used to evaluate performance. The GLCM texture features significantly differentiated between normal and stroke images. The optimized KNN model demonstrated high performance, achieving 99% classification accuracy, 100% sensitivity, 98% specificity, 97% precision, a 99% F1 score, 100% positive predictive value, and 98% negative predictive value. The average computation time per image was 3.2 seconds, indicating feasibility for real-time application. In conclusion, the GLCM-KNN integrated CAD system proves to be an accurate and efficient method for stroke diagnosis on brain CT scans, offering a potential solution for early stroke detection in resource-limited healthcare facilities.
Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management Caturkusuma, Resha Meiranadi; Alzami, Farrikh; Nurhindarto, Aris; Sulistiyono, MY Teguh; Irawan, Candra; Kusumawati, Yupie
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14310

Abstract

In the digital era, ensuring customer satisfaction with IT services is crucial for business success. However, the complexity of IT infrastructure makes it difficult to manage services, requiring companies to focus on improving efficiency and reducing operational costs. One of the strategies used is Information Technology Service Management (ITSM), the main component of which is incident management, which aims to minimize service disruptions. While various studies on ITSM exist, research focused on Machine Learning models for predicting incident resolution times is relatively limited. This research aims to develop an incident resolution duration prediction model using a Random Forest Regressor-based regression approach. The dataset used is an event log from the ServiceNow system containing data on 24,918 incidents. The model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics, where the model achieved a MAE of 14.33 hours, RMSE of 69.8 hours, and R2 of 0.98. These results show that the model can provide accurate predictions and support better decision-making in IT incident handling. Time-related features, such as sys_update_month and closed_month, proved to be the most influential factors in predicting incident resolution duration.
Clustering and Profiling Analysis for FIFA Football Player using K-Means Azzami, Salman Yuris Adila; Hadi, Heru Pramono; Alzami, Farrikh; Irawan, Candra; Nurhindarto, Aris; Sulistyono, MY Teguh
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.7897

Abstract

The selection of football players is a complex process involving talent evaluation based on various performance indicators, combining objective measures with subjective assessments by coaches and scouts. This research aims to improve the football player selection process using the K-Means clustering method based on the attributes of transfer price, performance, body specifications, position, and player ability. The dataset used consists of 17.947 players taken from the FIFA 19 edition of the soFIFA.com platform, which includes complete information such as transfer price, performance, body specifications, position, and player ability. The data was processed using principal component analysis (PCA) to reduce the dimensions, followed by the Elbow Method to determine the optimal number of clusters. The clustering results show the distribution of players based on their on-field roles, such as center back, goalkeeper, striker, and left wing back. The profiling of players from each cluster is identified based on position, body type, dominant foot usage, transfer price, and rating. This research provides useful insights for coaches and scouts in selecting players that suit specific roles in the team using better analysis. The findings also highlight the importance of player clustering for data-driven decision-making, which can optimize team composition and overall performance.
Clustering IT Incidents Using K-Means: Improving Incident Response Time in Service Management Anggraeni, Rini; Alzami, Farrikh; Nurhindarto, Aris; Budi, Setyo; Megantara, Rama Aria; Rizqa, Ifan; Muslih, Muslih
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14822

Abstract

Incident management is one of the critical processes in Information Technology service management that aims to manage disruptions and minimize the impact of unexpected incidents on business services. This study applies the K-Means algorithm to cluster IT service incidents, aiming to enhance company operational efficiency. Utilizing a dataset from the UCI Machine Learning Repository comprising 141,712 events related to 24,918 incidents, this research analyzes incident patterns and characteristics for optimized handling. The data was analyzed through a series of preprocessing stages, and the elbow and silhouette methods were used to determine the optimal number of clusters. From the results, it was successfully grouped into 4 (four) clusters with a distortion score value of 964264294.569 and 0.52 silhouette score based on incident characteristics, such as urgency, priority, and number of reassignments. From this, the clustering results show that the K-Means algorithm effectively identifies incidents that require further handling, such as those with high urgency and priority, as well as helping the company focus resources to resolve incidents that have the most impact on the business sector. This research provides a data-driven solution to improve incident management and Service Level Agreement (SLA) fulfillment, while offering a framework for more effective and efficient IT incident analysis and resource allocation.
Co-Authors Abdul Syukur Alzami, Farrikh Azzami, Salman Yuris Adila Budi, Setyo Candra Irawan Casidi, Casidi Caturkusuma, Resha Meiranadi Hadi, Heru Pramono Ifan Rizqa Kusumawati, Yupie Megantara, Rama Aria Muslih Muslih MY. Teguh Sulistyono Rini Anggraeni Soeleman, M. Arief Sulistiyono, MY Teguh