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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer)
Ziegel, Dennis Jusuf
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Computer Science & IT Decision Sciences, Operations Research & Management

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ANALISIS GERAKAN MATA UNTUK DETEKSI ALZHEIMER: STUDI KOMPARATIF LIMA METODE UTAMA Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1598

Abstract

In the digital era, eye-tracking technology has emerged as a valuable non-invasive tool for assessing neurological and cognitive functions. This review explores five key methods for evaluating Alzheimer’s Disease (AD) using eye-tracking: fixation and saccade analysis, pupil size measurement, task-specific eye-tracking, reading task analysis, and novelty preference scores. Fixation and saccade metrics reveal significant disruptions in visual scanning and information processing in AD patients, characterized by longer fixation durations and reduced saccade frequency. Pupil size measurements indicate diminished cognitive load and emotional responsiveness. Task-specific eye-tracking, including tasks such as image description, shows difficulties in maintaining focus and interpreting visual stimuli. Reading task analysis highlights increased fixation durations and backward saccades, reflecting challenges in text comprehension and information retention. Novelty preference scores suggest reduced interest in new stimuli, correlating with cognitive decline. These findings underscore the potential of eye-tracking metrics for early detection and monitoring of AD, though variability in eye movement patterns and additional factors like sleep disorders emphasize the need for comprehensive diagnostic approaches.
Comparison of Classification Algorithm in Predicting Stroke Disease Hutabarat, Fenna Kemala; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2714

Abstract

ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Stroke
COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION Indra, Evta; Suwanto, Jacky; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2848

Abstract

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study.
Laptop Price Prediction with Machine Learning Using Regression Algorithm Siburian, Astri Dahlia; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850

Abstract

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.
THE USE OF DATA AUGMENTATION AND EXPLORATORY DATA ANALYSIS IN ENHANCING IMAGE FEATURES ON APPLE LEAF DISEASE DATASET Rifaldo, Rifaldo; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Rahmad, Julfikar; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3438

Abstract

Apples are an essential commodity produced in Batu City, Malang. In 2017, Batu City, Malang, produced 19.1 tons of apples, while in 2018, Batu City, Malang, produced 15.9 tons of apples. It can be concluded that the decline in the number of apple harvests in Batu City, Malang. With the influence of technology in agriculture, the influence of technology can be used to detect diseases on leaves to overcome the decrease in the number of harvests. With the Image Augmentation method used in this study, the existing dataset can have 6x more features. So that the healthy category, which previously had 516 image features, now has 3096 image features, the scab category, which previously had 592 image features, now has 3552 image features and the rust category, which previously had 622 image features, now has 3732 image features. With a dataset with 3000 image features, the model to be made can have a higher accuracy value. The model can be said to be sturdy/sturdy/good, or the model to be made can carry out the classification process with a good level of accuracy.
Co-Authors Hutabarat, Fenna Kemala Indra, Evta Rahmad, Julfikar Rifaldo, Rifaldo Ruben Ruben, Ruben Siburian, Astri Dahlia Sinurat, Stiven Hamonangan Sitompul, Daniel Ryan Hamonangan Situmorang, Andreas Suwanto, Jacky