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TIERS Information Technology Journal
Published by Universitas Pendidikan Nasional
ISSN : 27234533     EISSN : 27234541     DOI : 10.38043
Core Subject : Science,
Computer Science & IT
TIERS Information Technology Journal memuat artikel Hasil Penelitian dan Studi Kepustakaan dari cabang Teknologi Informasi dengan bidang Sistem Informasi, Artificial Intelligence, Internet of Things, Big Data, e-commerce, Financial Technology, Business Digital
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 10 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2023)" : 10 Documents clear
Implementation of Simple Additive Weighting For Scholarship Admission Selection Joko Kuswanto; M Nang Al Kodri; Trisilia Devana; Leni Pebriantika; Sulia Ningsih
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.v4i1.4022

Abstract

Various types of scholarships are given to students who have achievements both academic and non-academic achievements. Conditions that often occur in the process of awarding scholarships, the assessment is not always decided based on definite considerations and predetermined criteria. Therefore, a decision support system is needed that can assist the scholarship selection team in making effective and efficient decisions. The decision support system to be built applies the Simple Additive Weighting (SAW) method with criteria such as IPS, GPA, Parents' Income, Number of Dependents of parents and Achievement. With the new system, it is expected to help the selection team related to managing applicant data, selection and proposal of scholarship recipients can be done more easily and quickly. After calculation, the highest score of 90.5 was obtained on behalf of Candra K which deserves to be a priority and recommended in receiving scholarships.
Application of The K-Means Clustering Method To Search For Potential Tourists of Bendesa Hotel I Gede Karang Komala Putra; I Gede Wahyu Surya Dharma
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.v4i1.4297

Abstract

Hotels play a significant role in the growth of global tourism. With intense competition in the hotel industry, hotels are shifting their focus from solely providing superior services to identifying potential tourists. In a previous study, the J48 algorithm was employed to extract hotel transaction patterns, achieving an accuracy level of 71.6418% by considering gender and age characteristics[1]. In a separate study, foreign guest ratings by province were classified into three clusters. The study concluded that nearly 90% of provinces in Indonesia exhibit low levels of tourism, supported by the analysis of the number of tourists staying, as reported by the statistical center[2]. To identify potential tourists who can bring benefits to the hotel, hotel managers can utilize the k-means algorithm. In this study, a data mining process was conducted using data collected from tourists who stayed at the Bendesa Hotel. The process began with tourist segmentation using the K-means algorithm divided into clusters. Subsequently, the accuracy of the obtained data was calculated. This research employed room class as a reference value to discover tourist characteristics at the Bendesa Hotel. The results of applying the K-means model with 4 clusters indicated that the accuracy level for identifying potential tourists reached 84.4%.
Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data Muhammad Muharrom Al Haromainy; Dwi Arman Prasetya; 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.v4i1.4326

Abstract

Stock price data or similar time series data can be used to carry out forecasting processes using past data. The method that can be used is like a neural network, one type of neural network that is used is the Recurrent Neural Network. When using the Recurrent Neural Network (RNN) method, we need to determine the appropriate parameters in order to get the best forecasting results. It takes experience or . In this study, this problem can be solved using optimization algorithms, such as Genetic Algorithms. With genetic algorithms, neural networks can be trained to get the best objective function. So that after implementing the RNN which was optimized using the Genetic Algorithm on stock time series data, when the trial was carried out without optimization the Genetic Algorithm got an RMSE value of 0.108, after being combined using the genetic algorithm it got an RMSE value of 0.106.    
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.
Decision Support System For Selection of Prospective Members of BLM Polytechnic Caltex Riau Using The Weighted Product Method Vandi Rahman; Dini Hidayatul Qudsi
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.v4i1.4340

Abstract

The Caltex Riau Polytechnic pupil Legislative frame (BLM) is a pupil organization that includes out the capabilities of budgeting, regulation and supervision. the selection of prospective PCR BLM contributors is still conventional, such as selecting scholar documents one after the other which results in problems in organizing scholar documents. To help the manner of choosing BLM PCR members in figuring out the selected BLM PCR individuals, a choice-making gadget is wanted that may be used as an opportunity consideration between the selection outcomes acquired manually and the outcomes received from the gadget. in addition to being a device to help the pinnacle of BLM in making selections the usage of the Weighted Product approach. based totally at the effects of blackbox testing, it could be concluded that the BLM member selection system works in step with user needs. as well as the consequences of the usability testing, the test consequences obtained with a total percent of ninety two.35% (Strongly Agree). And the outcomes of checking out the accuracy of manual calculations with the system display that the accuracy stage is a hundred%. From the effects of this take a look at it became concluded that the gadget is acceptable to users in order that.
Recognition of Hijaiyah Letters with Punctuation Using Augmented Reality Nisa'ul Hafidhoh; Tri Lestariningsih; Ardian Prima Atmaja; Muhammad Syaeful Fajar; Ikhwan Baidlowi Sumafta; Dinar Nur Izzah
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.v4i1.4348

Abstract

Learning hijaiyah letters is an initial step to reading the Al-Qur'an. Because the Al-Qur'an is written in Arabic using hijaiyah letters with special punctuation. Currently, learning hijaiyah letters still uses simple media in the form of books, posters, display boards, etc. so it is less interesting. The rapid development of technology allows mobile devices to become smartphones that can be used as learning media. Therefore, mobile devices can be used for learning hijaiyah letters to make them more attractive. One technology that can be utilized is Augmented Reality which can combine the virtual world with the real world in the form of 3D through applications accessed on mobile devices. This research developed the introduction of hijaiyah letters equipped with punctuation and pronunciation using marker based augmented reality. The development of mobile application applies the Mobile Application Development Lifecycle (MADLC) method. The development of augmented reality applications utilizing Blender, Vuforia and Unity 3D Game Engine. The results of the Black box testing show that all functional requirements have been met and are running well.
Implementation of LightGBM and Random Forest in Potential Customer Classification Laura Sari; Annisa Romadloni; Rostika Lityaningrum; Hety Dwi Hastuti
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.v4i1.4355

Abstract

Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%. Meanwhile, for the two bagging methods, Random Forest produced the highest accuracy compared to Extra Trees, namely 99.03%. The research uses the LGBM and Random Forest methods to classify potential customers. The results of this study indicate that in imbalance data the LightGBM method has better accuracy than the Random Forest, which is 85.49%, when the Random Forest is unable to produce a model. The SMOTE method used in this study affects the accuracy of the random forest but does not affect the accuracy of LightGBM. Over all the Accuracy, Recall, Specificity, and Precision values, Random Forest produces a good value compared to LightGBM on balanced data. Meanwhile, LightGBM is able to handle unbalanced data.
Implementation of Decision Support in Mutual Fund Investment Selection using MOORA Soetam Rizky Wicaksono
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.v4i1.4369

Abstract

This research focuses on the application of the Multi-Objective Optimization method based on Ratio Analysis (MOORA) as part of the Decision Support System (DSS) in the selection of mutual fund investments in Indonesia. The purpose of this study is to help novice investors who often find it difficult to choose mutual funds due to lack of knowledge. Considering that the number of investors continues to increase, especially during and after the pandemic, this research becomes relevant and important. The MOORA method was chosen because of its ease and flexibility in handling various criteria compared to other Multiple Criteria Decision Making (MCDM) methods. The five criteria taken as calculation material are return, risk, cost, liquidity, and reputation of the investment manager. The results showed that the MOORA method is effective in providing objective and data-driven investment recommendations. Considering various relevant criteria and weights, MOORA can provide mutual fund ratings that match investors' preferences and risk tolerance. Thus, this research successfully achieved its goal of assisting novice investors in choosing mutual funds. These results suggest that MOORA can be an important part of DSS in the context of mutual fund investing.
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.
Decision Support System for Extreme Poverty BLT Recipients Combining the ROC and WASPAS Methods Derry Asari Nuryadi; Mochzen Gito Resmi; Chandra Dewi Lestari
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.v4i1.4477

Abstract

The Covid-19 pandemic has begun to be under control and the basis for the Village Fund BLT distribution has been adjusted. BLT Dana Desa aims to increase the income of extremely poor families in the village. In determining and determining prospective Beneficiary Families (KPM), each village will be guided by the data for the Acceleration of Extreme Poverty Elimination (PPKE) provided by the central government through the local government which will later be verified by the village government. Therefore, a Decision Support System is needed to find out who really deserves assistance, so that the allocation can be right on target according to predetermined criteria. In this study the results of this study show that the proposed model can be used well in conducting the selection process for laboratory assistant admissions. In this research, the use of ROC is able to provide appropriate criteria weights based on the level of importance of the criteria from the decision maker. Meanwhile, the use of the WASPAS method is able to produce decisions in the form of the best alternatives that can be used to help decision makers. From the calculation process that has been done, it can be concluded that Sunardi got the highest score, namely 0.7276 and Muksin got the lowest score, namely 0.5491. The existence of this system can make it easier for the government, especially villages, to identify beneficiaries and minimize errors in selecting beneficiaries.

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