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All Journal Jiko (Jurnal Informatika dan komputer) bit-Tech TIERS Information Technology Journal
Dwi Arman Prasetya
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT

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The Effect of Information Quality and Service Quality on User Satisfaction of the Government of Kabupaten Malang Dwi Arman Prasetya; Anggraini Puspita Sari; Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin
TIERS Information Technology Journal Vol. 4 No. 1 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i1.4328

Abstract

Currently, the government has implemented performance digitization through information systems that are published through official channels owned by the government, one of which is the government of Kabupaten Malang. The objective of this research was to assess or gauge the measurement and test variables and indicators that affect the quality of the Kabupaten Malang government website with the link www.malangkab.go.id/mlg The problem is, not many governments have launched applications paying attention to the factors that influence user satisfaction so that the government has not been able to prioritize repairs and optimize website performance to meet constituent needs that continue to grow in the digital era. This research employs a survey to identify the causal elements that impact the factors contributing to user satisfaction on the website. The causal factors include website service quality, information quality, and usability quality in user satisfaction. Respondents used in this study were website operators for regional apparatus in Kabupaten Malang, consisting of 81 respondents who met the requirements. In obtaining valid and reliable data, multiple linear regression and hypothesis testing were carried out. There are 4 multiple linear regressions that are carried out, namely, multicollinearity test, autocorrelation test, heteroscedasticity test, and normality test. The results of the influence of service quality, and information quality on user satisfaction through usability quality are 5 models that have a significant influence, that is Service Quality to Usability Quality, Information Quality to Usability Quality, Service Quality to User Satisfaction, Information Quality to User Satisfaction, and Usability Quality to User Satisfaction.
Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets Mohammad Idhom; Dwi Arman Prasetya; Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Anggraini Puspita Sari
TIERS Information Technology Journal Vol. 4 No. 1 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i1.4380

Abstract

This research focuses on accurately classifying pneumonia in children under the age of 5 using X-ray images, considering the challenge of an imbalanced dataset. A modified VGG-16 CNN architecture is evaluated for pneumonia classification in Chest X-Ray Images. The study compares testing results with and without data augmentation techniques and explores the potential application of the model in an Android-based machine learning system for pneumonia diagnosis assistance. Using a dataset of 5,856 Chest X-Ray images categorized as normal or pneumonia, obtained from Kaggle, the research conducts two test scenarios: one without data augmentation and another with data augmentation techniques. The modified VGG-16 CNN algorithm's performance is evaluated using the accuracy metric. The results highlight the effectiveness of data augmentation in improving pneumonia classification accuracy. The augmented tests outperform the non-augmented ones, achieving an impressive 92% accuracy, indicating a significant 15% improvement over the non-augmented scenario. This improvement underscores the efficacy of data augmentation techniques in enhancing the CNN's ability to accurately classify pneumonia, particularly when faced with an imbalanced dataset. Furthermore, the research explores the potential integration of the trained model into an Android-based machine learning system for pneumonia diagnosis assistance. This integration would enable doctors to analyze X-ray images and identify potential pneumonia cases in patients. The integration of advanced machine learning systems in healthcare holds promise for improving patient care and the accuracy of pneumonia diagnoses. In summary, this research contributes to the accurate classification of pneumonia in children under 5 years old using X-ray images. It emphasizes the efficacy of data augmentation techniques in enhancing classification accuracy and explores the practical application of an Android-based machine learning system for pneumonia diagnosis assistance. These findings underscore the importance of advanced machine learning systems in healthcare and their potential to improve pneumonia diagnosis accuracy and enhance patient care.
Heckman Probit Two-Step Regression Approach for Analyzing Open Unemployment Factors in West Java Province Holly Patrycia; Dwi Arman Prasetya; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3096

Abstract

Open unemployment remains a major socio-economic challenge in Indonesia, with West Java recording the highest national rate in August 2024 at 6.75%. This study investigates the determinants of open unemployment using the Heckman Probit Two-Step model, an approach rarely applied in Indonesian labor market research. Unlike conventional regression methods, this model corrects for sample selection bias by simultaneously estimating labor force participation and unemployment status. Data are drawn from the 2024 Survei Angkatan Kerja Nasional (SAKERNAS) conducted by Badan Pusat Statistik (BPS), covering working-age individuals in West Java Province. The first stage models labor force entry, while the second stage incorporates the Inverse Mills Ratio (IMR) to adjust for selection effects. Results show that the IMR coefficient (–0.3100, p = 0.0412) is statistically significant, confirming the necessity of the two-step correction. The explanatory power of the model is substantial, with Pseudo-R² values of 0.385 for labor force participation and 0.381 for open unemployment. Marginal effects indicate that being married reduces unemployment probability by 5.50%, each additional year of age decreases it by 2.79%, whereas a longer job search increases it by 3.35%. Training experience lowers unemployment risk, while disabilities and larger household size increase vulnerability. Methodologically, the study demonstrates the advantages of Heckprobit in producing unbiased estimates compared to descriptive or conventional probit approaches previously used in Indonesia. Nonetheless, the cross-sectional design and focus on a single province limit generalizability. Findings provide valuable evidence for policymakers to design targeted, inclusive employment strategies aligned with regional development goals
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Nova Auliyatul Maulidiyyah; Trimono Trimono; Aviolla Terza Damaliana; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
Co-Authors Anggraini Puspita Sari Aviolla Terza Damaliana Holly Patrycia Kartika Maulida Hindrayani Mohammad Idhom Nova Auliyatul Maulidiyyah Prismahardi Aji Riyantoko Tresna Maulana Fahrudin Trimono, Trimono