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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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The Application of The Manhattan Method to Human Face Recognition Sunardi; Abdul Fadlil; Novi Tristanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4265

Abstract

In face recognition, the input image used will be converted into a simple image, which will then be analyzed. The analysis was carried out by calculating the distance of data similarity. In the process of measuring data similarity distances, they often experience problems implementing complex algorithm formulas. This research will solve this problem by implementing the Manhattan method as a method of measuring data similarity distances. In this study, it is hoped that the Manhattan method can be used properly in the process of matching test images and training images by calculating the proximity distance between the two variables. The distance sought is the shortest distance; the smaller the distance obtained, the higher the level of data compatibility. The image used in this study was converted into grayscale to facilitate the facial recognition process by thresholding, namely the process of converting a grayscale image into a binary image. The binary image of the test data is compared with the binary image of the training data. The image used in this study is in the Joint Photographic Experts Group (JPEG) format. Testing was carried out with 20 respondents, with each having two training images and two test images. The research was conducted by conducting experiments as many as 20 times. Facial recognition research using the Manhattan method obtains an accuracy of 70%. The image lighting used as the dataset influenced the accuracy results obtained in this study. Based on the results of this study, it can be concluded that the Manhattan method is not good for use in facial recognition research with poor lighting.
Information Technology Governance Awareness: A Proposed Formula for Assessment Uky Yudatama; Dwi Ekasari Harmadji
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4310

Abstract

This article aims to provide a proposed formula that can be used to measure the level of success in the practice of Information Technology Governance. To obtain this formulation, in-depth surveys and interviews involving several experts are needed. The calculation results show that organization G has an awareness value of 93 (good) with a maturity value of 3.13. On the other hand, organization E has an awareness value of 70 (medium) with a maturity value of 2.60. This proposed formula can be used as an alternative way to determine the level of success of an organization in the practice of Information Technology Governance by knowing the level of awareness. So far, to determine the level of success in implementing IT Governance practices in an organization, the method used is to calculate the maturity level that refers to COBIT best practices, which only focus on objects but do not focus on subjects (stakeholders) in the organization.
Best Employee Decision Using Multi Attribute Utility Theory Method Sunardi; Rusydi Umar; Dewi Sahara
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4318

Abstract

Selection of the best employee is a form of appreciation that can be shown by the company for the achievements of its employees. This appreciation can motivate employees to be more enthusiastic in improving their performance at work. Appropriate evaluation and decision-making methods need to be taken so that the best employee selection process runs objectively, transparently, and in accordance with established standards. This study aimed to select the best employee candidates at PT Kerry Express Indonesia using the multi attribute utility theory (MAUT) method. The criteria for the selection process as follows: attendance (weight = 2), output obtained (weight = 3), discipline (weight = 3), and reporting (weight= 2). The employees in this study were 30 respondents from 150 populations. The assessment was carried out for three months from January to April 2022. The calculations were carried out using the Microsoft Access tool. The results of calculations using the MAUT method show that the highest rank among all candidates has a score of 7.75 while the lowest rank had a score of 3.25. It can be concluded that the MAUT method can be used to select the best employees at PT. Kerry Express Indonesia effectively and efficiently.
Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3 Ahmad Darman Huri; Rizal Arya Suseno; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4357

Abstract

Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied.
Naïve Bayes-Support Vector Machine Combined BERT to Classified Big Five Personality on Twitter Billy Anthony Christian Martani; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4378

Abstract

Twitter is one of the most popular social media used to interact online. Through Twitter, a person's personality can be determined based on that person's thoughts, feelings, and behavior patterns. A person has five main personalities likes Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. This study will make five personality predictions using the Naïve Bayes method – Support Vector Machine, Synthetic Minority Over Sampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder from Transformers Representations (BERT). A questionnaire was distributed to people who used Twitter to collect and become a dataset in this research. The dataset obtained will be processed into SMOTE to balance the data. Linguistic Inquiry Word Count is used as a linguistic feature and BERT will be used as a semantic approach. The Naïve Bayes method is used to perform the weighting and the Support Vector Machine is used to classify Big Five Personalities. To help improve accuracy, the Optuna Hyperparameter Tuning method will be added to the Naïve Bayes Support Vector Machine model. This study has an accuracy of 87.82% from the results of combining SMOTE, BERT, LIWC, and Tuning where the accuracy increases from the baseline.
Comparison of Grid Search and Evolutionary Parameter Optimization with Neural Networks on JCI Stock Price Movements during the Covid 19 Wresti; Gunawan; Purwanto; Catur Supriyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4402

Abstract

This study aims to determine the effect of covid 19 on the movement of the JCI Stock Price by testing various combinations of the input variables of closed price stock data on the JCI. The analysis is carried out to find the best RMSE value from the combination of these input variables using the neural network method. The best RMSE results are compared using the optimization of grid search and evolutionary parameters. The data used in this study was taken from the Yahoo.finance.com page on the JCI Historical Data, during the covid pandemic, from 12/11/2019 to 12/30/2021. The data obtained are 509 records. The input variable used is the closing price data (closed price) as a target. The preprocessing data used are data cleansing, filtering, and windowing until seven days before. The results obtained an RMSE value of 0.104 five days before Close t (P=5), training cycle 9000. Momentum 0.9 and learning rate 0.2 is then optimized using the grid search parameter to produce RMSE 0.101, training cycle 100. Learning rate 1 and momentum 0.1 are then compared with evolutionary parameters, which make RMSE 0.103 at learning rate 0.029, momentum 0.68, and training cycle 86. Based on this research, optimizing grid search parameters produces better RMSE than evolutionary parameter optimization. This small RMSE result shows that investors are still safe to invest.
UI/UX Analysis and Design Development of Less-ON Digital Startup Prototype by Using Lean UX Rio Andika Malik; Marta Riri Frimadani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4454

Abstract

The growth of startups in Indonesia continues to experience upward growth. Behind the growth that continues to move up, there is a success rate statistic which is a contradiction behind its development. The startup statistics show that about 90% of startups fail. As many as 75% of unicorn startups believe that a good UI/UX design can increase startup valuations and additional investors' funds. User Interface (UI) and User Experience (UX) are closely related because UX results from UI interactions. Less-On is a provider of private tutoring service providers who serve as an intermediary bridge between teachers and students. This research will be carried out by integrating the processes in the Lean UX method into every process that exists at the stages of software engineering development. The results obtained from this study are a final prototype validated in terms of criticism and suggestions through a questionnaire as a form of Less-On branding. Positive UX and better usability are significant for further development of the prototype private tutor booking application, which plays a vital role in acceptance, satisfaction and efficiency in using this Less-ON application. The UI has good usability for users, with a SUS scoring earn 85.53, which is above average and acceptable.
Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation Teri Ade Putra; Pradani Ayu Widya Purnama; Riandana Afira; Yesri Elva
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4469

Abstract

PNPM Mandiri is an organization engaged in financing small and medium enterprises in the community. The problem that always occurs is an error in determining the loan status resulting in bad credit. This study aims to present a classification analysis model for determining loan status at PNPM Mandiri. The classification analysis model was built using the Perceptron algorithm artificial neural network. The analysis model will later be optimized using the Person Correlation (PC) method to measure the accuracy of the variables used. The research dataset is based on historical data from the last 2 years as many as 67 data samples. The analysis variables consist of Business Type (X1), Loan Amount (X2), Collateral (X3), Income (X4), and Expenses (X5). The results of the analysis show that the model built can provide optimal classification results. These results can be seen based on the results of variable measurements using the PC method indicating that variable X2 has no significant relationship. With the results of these measurements, the performance of the artificial neural network presents maximum results in determining loan status. Overall, the results of this study can provide an effective analytical model as well as an alternative solution for determining loan status.
DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method Reza Rendian Septiawan; Bambang Hadi Prakoso; Isman Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4470

Abstract

Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.
A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting Hazriani Hazriani; Yuyun; Mashur Razak
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4478

Abstract

Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing (SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE.

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