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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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Implementation of Word Recommendation System Using Hybrid Method for Speed Typing Website Melinda; Maulana Imam Muttaqin; Yudha Nurdin; Al Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4518

Abstract

Typing is one of the most frequently done activities in society therefore a medium is necessary to help train typing words that are often mistyped. Methods used in this research are the Content-Based Filtering Algorithm to gather the words that have a similar pattern to the words that are often mistyped based on the user's previous typing records and the Collaborative Filtering Algorithm that uses other users typing pattern to recommend the words. The result of this study shows the Collaborative Filtering Algorithm was able to gather words that are hard to type by the user with an accuracy of 49.2%, dan the Collaborative Filtering able to predict the score on how difficult for the user to type a word with the result of Root Mean Square Error (RMSE) value of 0.82 and with the Root Mean Square Percentage Error (RMSPE) value of 30% from the actual value, and a website which is the combination of the two algorithms with the result of 28% of the total word that is recommended was indeed difficult to type by the user with the typing speed of 103 WPM, and 72.3% for the user that has a typing speed of 39 WPM.
Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data　 Mochamad Wahyudi; Solikhun Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4526

Abstract

The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results.
Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16 Bella Dwi Mardiana; Wahyu Budi Utomo; Ulfah Nur Oktaviana; Galih Wasis Wicaksono; Agus Eko Minarno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4550

Abstract

Herbal leaves are a type that is often used by people in the health sector. The problem faced is the lack of knowledge about the types of herbal leaves and the difficulty of distinguishing the types of herbal leaves for ordinary people who do not understand plants. If any type of plant is used, it will have a negative impact on health. Automatic classification with the help of technology will reduce the risk of misidentification of herbal leaf types. To make identification, a precise and accurate herbal leaf detection process is needed. This research aims to facilitate the classification model of herbal leaf images with a higher accuracy value than previous research. Therefore, the proposed method in this classification process is one of the Transfer Learning methods, namely Convolutional Neural Network (CNN) with a pretrained VGG16 model. This research uses a dataset of herbal leaves with a total of 10 classes: Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri and Sirih. The performance of the results of the proposed classification method on the test dataset using Classification Report shows an increase in the results of the previous research accuracy value from 82% to 97%. This research also applies Image Data Generator in the augmentation process which aims to improve the image of herbal leaves, reduce overfitting, and improve accuracy.
Pengembangan Aplikasi Mobile untuk Penyelesaian Vehicle Routing Problem Benni Agung Nugroho; Abidatul Izzah; Kunti Eliyen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4552

Abstract

The vehicle routing problem (VRP) is a combinatorial optimization problem faced by transportation services related to pick up or delivery, such as industrial raw materials distribution, tour and travel, or travel routing problems in general. VRP is an NP-hard problem where the higher the dimensions of the problem will have a higher computational complexity. Without realizing it, VRP problem are often encountered every day. Therefore, it will be very useful if VRP solver is implemented in mobile application media. So, the aim of this work is developing a mobile application to get the shortest path and minimal cost in VRP problem. It is integrated by both Mapbox API and Google Maps API to get a real distance for modeling problem. The result show that the developed application can run well in all possibility condition.
Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering Kusuma Ayu Laksitowening; Made Diva Prasetya; Dawam Dwi Jatmiko Suwawi; Anisa Herdiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4655

Abstract

Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained.
Implementing Agile Scrum Methodology in The Development of SICITRA Mobile Application Oktavia Citra Resmi Rachmawati; Deyana Kusuma Wardani; Wifda Muna Fatihia; Arna Fariza; Hestiasari Rante
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4688

Abstract

Software development management system is important in application development. A proper software development management system will create a team that can adapt to system requirements and changes during application development. Various software development management systems are developed and widely implemented in software development, one of which is Agile Scrum. This study aims to implement well-documented Scrum for end-to-end application development, including the development of servers and mobile applications that we develop. We developed a bus application called SICITRA, with the main feature of being able to help passengers share their travel information with those closest to them. Scrum is used because it has agility which can make application development faster and more organized, and there is a close relationship between everyone involved in the project. The results of this study are that by using well-documented Scrum, we can make it easier to track progress, become a guide during system development, become history and evaluate Scrum implementation during development.
Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers Abdullah Abdullah; Agus Harjoko; Othman Mahmod
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4693

Abstract

Fruit classification is an important task in many agriculture industry. The fruit classification system can be used to identify the types and prices of fruit. Manual classification of fruit is not efficient for large amount of fruits. The advancement of information technology has made possible fruit classification be done by a machine. This research aims to propose a fruit classification methodology based on shape and color. To reduce the effect of lighting variability a color normalization is carried out prior to feature extraction. The color features used in this research are mean and standard deviation. The shape features are area, perimeter, and compactness. The classification of an unknown fruit is carried out using the nearest mean classifier. The method developed in this research is tested using 12 classes of fruits where each class is represented by a number of samples. The experimental results show that the method proposed in this research provides an accuracy of 95.83% for two samples per class and 100% for three samples per class. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classifiers and results obtained showed that the technique was able to provide good accuracy.
Software as a Service-based Integrated Interactive Online Course System Daniel Soesanto; Marcellinus Ferdinand Suciadi; Liliana Liliana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4724

Abstract

Several online learning platforms use recorded videos as a medium for delivering their material. In addition, several applications, such as Zoom, Google Meet, and WhatsApp, can help communicate interactively during this learning process. However, because the application is still separate from the existing online learning platform, users must switch applications and make the necessary data settings. Another obstacle experienced is that not all online learning service providers can have the infrastructure to use online learning systems, especially if the providers are individuals. This research uses a simple sequential method and aims to build an integrated online course system equipped with an interactive learning management system that many online courses, in general, can use. The main features are submissions of new courses by teachers, ordering courses by students, classes can be held using live video streams, the interaction between teachers and students in real-time with live chat, and a learning management system that includes sending and receiving assignments and quizzes. The test results show that as many as 73.2% of respondents gave the highest score for the built system.
Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network Umar Aditiawarman; Dimas Erlangga; Teddy Mantoro; Lutfil Khakim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4437

Abstract

Facial recognition is a part of Computer Vision that is used to get facial coordinates from an image. Many algorithms have been developed to support Facial Detection such as Cascade Face Detection using Haar-Like features and AdaBoost to classify its Cascade and Convolutional Neural Network (CNN). Face recognition in this study uses the Deep Convolutional Neural Network (DCNN) method, and the output of this method is the measurement value of the face. In the model training process, Triplet Loss from Triplet Network Deep Metric Learning is used to get good face grouping results. The value of this face measurement will then be measured using the Euclidean distance calculation to determine the similarity of the input face from the dataset. This Research is using 6 images of Government officers in Indonesia to determine the accuracy of the model when there is a new picture of these officers inputted into the training machine. The result provides a 94% accuracy level with a variety of face positions and levels of brightness.
Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm Amir Saleh; Yusuf Roni Laia; Fransiskus Gowasa; Victor Daniel Sihombing
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4441

Abstract

One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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