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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 84 Documents
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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.
Java and Bali Shoreline Change Detection Based on Structural Similarity Index Measurement of Multispectral Images I Gede Wahyu Surya Dharma; I Gede Karang Komala Putra
TIERS Information Technology Journal Vol. 4 No. 2 (2023): IN PRESS
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.4468

Abstract

The abstract effectively delineates the pertinent issues addressed in the research, presenting a clear exposition of the challenges associated with coastline monitoring in Indonesia. The methodology is well-defined, incorporating the utilization of Landsat images, Structural Similarity Index Measurement (SSIM), and the application of Hidden Markov Random Field for segmentation. Moreover, the influence of Indonesia's equatorial positioning on cloud cover and the subsequent application of morphological operations are appropriately highlighted. However, it is crucial to provide explicit details regarding the research findings. Specifically, the abstract should elucidate the specific outcomes or results obtained from the conducted experiments or analyses. This addition would enhance the clarity and scientific robustness of the abstract, ensuring that it accurately reflects the research objectives and their corresponding achievements. Inclusion of quantitative data or statistical analyses would be particularly valuable in this regard. This would not only bolster the abstract but also furnish a more comprehensive overview of the study's empirical contributions.
Analysis and Design of PT KDA Langling Cooperative System Through Web-based Savings and Loan Transformation Technology Ade Agung; Widja Yanto; Dafit Afianto
TIERS Information Technology Journal Vol. 4 No. 2 (2023): IN PRESS
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.4582

Abstract

PT KDA Langling Cooperative, as an economic enterprise dedicated to the interests of villagers and employees, is located in Langling Village RT 15, PT KDA Langling Sinarmas Housing. In order to manage savings and loans, the current system faces significant obstacles because it has not used computer technology. Recording is still done manually in books, causing delays in data collection and the potential for loss or damage to data in the archive. This research aims to analyze the current system, with a focus on fixing these problems. The solution is to design a web-based savings and loan information system that will optimize the efficiency and accuracy of data. The method that will be used by the author is literature review, data collection and using the waterfall model with the stages of the process of analyzing needs, design, implementation, testing and maintenance, using the PHP programming language and MySQL DBMS, UML and Usecase diagram. So that this research produces an integrated system that provides convenience for users in savings and loan activities and presents web-based financial information.
Resampling Techniques in Rainfall Classification of Banjarbaru using Decision Tree Method Selvi Annisa; Yeni Rahkmawati
TIERS Information Technology Journal Vol. 4 No. 2 (2023): IN PRESS
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.5069

Abstract

Continuous heavy rains, such as in 2021, can cause flood emergencies in various areas of Banjarbaru. Therefore, classification modeling is needed to predict rainfall classes based on climate parameters. The problem faced in the classification case is the unbalanced class distribution. Class imbalance occurs when the minority class is much smaller than the majority class. This research aims to compare three resampling techniques in handling imbalanced rainfall data in Banjarbaru using the Decision Tree model. The comparison methods used were sensitivity, specificity, and G-Mean values. In this research, the method used is a decision tree model with Random undersampling, Random Oversampling, and SMOTE. The result shows that the best model is the Decision tree model with the Random Undersampling technique because it provides the highest G-Mean value and sensitivity and specificity values above 70%. Based on this model, the variables that can separate the Rainy and Cloudy classes are Minimum temperature, Maximum temperature, and Sunshine duration, with the best separator being Maximum Temperature.
Clustering Time Series Using Dynamic Time Warping Distance in Provinces in Indonesia Based on Rice Prices Yeni Rahkmawati; Selvi Annisa
TIERS Information Technology Journal Vol. 4 No. 2 (2023): IN PRESS
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.5081

Abstract

Rice is a food commodity that is a basic need for Indonesian people. Since the end of 2022, average rice prices in Indonesia have been increasing, breaking the record for the highest price from August to October 2023. The price of rice in each province in Indonesia is different. This can happen because rice center provinces will distribute their rice production to other regions to meet rice needs. The grouping of provinces in Indonesia based on rice prices over time is an interesting thing to research. The analysis method used to group similar objects into groups for time series data is called clustering time series. The distance that can be used to measure the closeness of two-time series is the Dynamic Time Warping (DTW) distance. The clustering analysis used is the single, complete, average, Ward, and median linkage method. The results of the analysis show that time series clustering in provinces in Indonesia based on rice prices is best using median linkage hierarchical clustering. The median linkage method has a cophenetic correlation coefficient value of 0.899064, meaning that clustering using the DTW distance with the median difference is very good. The resulting clusters contained 3 clusters which had different characteristics between the clusters. There are 2 clusters that can be of concern to the government, because there are clusters that have rice prices that have always been high in the last period and there are provincial clusters that have rice prices that are very diverse or can be said to be unstable.

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