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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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Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter Rusydi Umar; Sunardi; Muhammad Nur Ardhiansyah Nuriyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4931

Abstract

On Twitter, users can post tweets, videos, and images. It can, however, also be disruptive and difficult. To categorize the material and improve searchability, hashtags are crucial. This study focuses on examining the opinions of Twitter users who participate in trending topics. The algorithms K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used for sentiment analysis. The data set comprises tweet information on popular topics that was collected using the Twitter API and saved in Excel format. SVM and K-NN are used for data preparation, weighting, and sentiment analysis. With 105 data points, the study provides insight into user sentiment. SVM identified 99% of positive responses and 1% of negative responses with an accuracy of 80%. KNN successfully identified 90% of the positive responses and 10% of the negative responses, with an accuracy rate of 71.4%. According to the results, SVM performs better when analyzing the sentiment of hashtag users on Twitter.
A Security Architecture for Mobile Computing-Based IoT Farina Mutia; Eugene Ario Suradilaga; Raymond Giovadius
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4950

Abstract

The Internet of Things (IoT) is a complex technology with various applications that have become a vital part of our daily life. The number of connected devices and the Internet of Things continues to increase. In terms of bandwidth, service availability, security controls, cyberattacks, and privacy problems, transporting the huge data created by these IoT devices to the cloud offers concern and challenges comprising intermittent connection, service unavailability, data loss at rest, in use, and in motion, unhardened device and server, unpatched device and server, and exploitation vulnerabilities. Mobile computing (MC) is a strategic solution to tackle these difficulties by offering flexible data processing and storage to end users, increase security controls and with customized IoT devices for varied geographic locations. This paper provides a complete description of the components of the IoT architecture. After that, it elaborates on similar security threats and potential cyberattacks in the context of mobile computing-based IoT and suggests solutions. In conclusion, we provide a secure mobile computing architecture design for IoT applications.
Development Steps of Avionics and Flight Control System of Flight Vehicle Herma Yudhi Irwanto; Purnomo Yusgiantoro; Zainal Abidin Sahabuddin; Romie Oktovianus Bura; Aris Sarjito; Oka Sudiana; Faisa Lailiyul Mutho' Affifah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4953

Abstract

The success of a research is highly dependent on the method adopted, especially research related to dangerous and expensive matters which will certainly require special handling in the development or maintenance steps. One of them is research related to space technology such as aviation and rocketry technology, which is very dependent on the design model of the flying vehicle and, in general, will always use simulation to ensure that the entire system being built is carried out safely and can be implemented properly according to plan. In the development of the prototype flying vehicle, especially the development of the avionics and flight control system, the vehicle will go through sequential simulation steps from Software in the Loop Simulation (SILS), Hardware in the Loop Simulation (HILS), and Ready-to-Fly System (RTFS). In this paper, the simulation steps will be described with the intention of facilitating integration and testing of each sub-system being developed, testing the control strategy applied or eliminating bugs if something goes wrong. In the end, with a series of flying vehicle simulations, it can be developed quickly and cost-effectively, including saving human resources.
Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm Nanang Lestio Wibowo; Moch Arief Soeleman; Ahmad Zainul Fanani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4997

Abstract

Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, is easy to use, and is fast in clustering data. The K-means method groups the data based on k distances and randomly determines the initial centroid as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method to determine the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the sum of intragroup distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli, and cancer, in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and dataset is measured by providing a ranking proving that the proposed method on the iris, wine, and cancer datasets gets the first rank, and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first, which provides evidence that the proposed method can improve the clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method.
Utilization of Mobile Network Infrastructure to Prevent Financial Mobile Application Account Takeover Aldiansah Prayogi; Rizal Fathoni Aji
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5025

Abstract

The Covid-19 pandemic has kept almost everyone at home and forced them to perform online activities using their mobile gadgets. Penetration of the Internet and mobile use is increased as lockdowns or restrictions on meeting face to face are getting used to. This has become a new market for cyber criminals to carry out their actions, such as spreading Social Engineering, sending Phishing, doing Account Takeover, and ending in theft of money in Financial Mobile Applications. Application protection with OTP SMS and Magic Link SMS still has vulnerabilities, with several examples of cases that have occurred. For this reason, this problem was raised to find a solution using the Mobile Network Infrastructure. The method used is to compare the congruence between the phone numbers registered in the application and the phone numbers used. Every time a user signs in or signs up, the Financial Mobile Application will perform Mobile Network Verification to cellular operators via API. Verification is carried out by utilizing the header enrichment in the background of the application process that was installed on the user's smartphone or tablet to the Mobile Network Verification Server. The Financial Mobile Applications can then determine whether the user is using a valid or invalid telephone number. Therefore, the target account cannot be taken over because the cyber criminal's mobile device does not have the phone number attached to the victim’s mobile device. This proof is carried out with four test case scenarios with the sign-up and sign-in processes on the same phone number and different phone numbers between devices and applications. It is hoped that this kind of protection model can reduce losses experienced by users of Financial Mobile Applications due to Account Takeover.
Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification Achmad Naila Muna Ramadhani; Galuh Wilujeng Saraswati; Rama Tri Agung; Heru Agus Santoso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5028

Abstract

Chili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not addressed promptly, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and effective. Common chili plant diseases include chili leaf yellowing disease, chili leaf curling disease, cercospora leaf spots, and magnesium deficiency with symptoms that can be observed through the shape and color of the leaves. This research aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model performance using the Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001, produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential.
Analysis and Mitigation of Religion Bias in Indonesian Natural Language Processing Datasets Muhammad Arief Fauzan; Ari Saptawijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5035

Abstract

Previous studies have shown the existence of misrepresentation regarding various religious identities in Indonesian media. Misrepresentations of other marginalized identities in natural language processing (NLP) datasets have been recorded to inflict harm against such marginalized identities in cases such as automated content moderation, and as such must be mitigated. In this paper, we analyze, for the first time, several Indonesian NLP datasets to see whether they contain unwanted bias and the effects of debiasing on them. We find that two of the three data sets analyzed in this study contain unwanted bias, whose effects trickle down to downstream performance in the form of allocation and representation harm. The results of debiasing at the dataset level, as a response to the biases previously discovered, are consistently positive for the respective dataset. However, depending on the data set and embedding used to train the model, they vary greatly at the downstream performance level. In particular, the same debiasing technique can decrease bias on a combination of datasets and embedding, yet increase bias on another, particularly in the case of representation harm.
Systematic Mapping Study: Research Opportunities on Capacity Planning Yuggo Afrianto; Rendy Munadi; Setyorini; Arief Goeritno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5037

Abstract

The central idea of the research is to improve the efficiency and sustainability of data centers by implementing accurate capacity planning, which will also improve their performance and availability. Various literature reviews have been conducted to understand the current status of capacity planning implementation in different domains and perspectives. However, a more organized and systematic approach is required to map research and implementation results in the relevant areas of capacity planning that have the potential for further development. The present study aims to fill this gap by conducting a systematic mapping study that combines both quantitative and qualitative methodologies. The quantitative approach involved the collection of literature and the classification of topics using the Latent Dirichlet Allocation (LDA) method. On the contrary, the qualitative approach used content analysis to identify future research directions based on keyword trends and topics. The PRISMA framework was followed to guide the search for relevant studies in electronic research literature databases. The mapping results revealed 15 topics, with topics 8, 10, 11, and 15 showing significant potential for further research and exhibiting increasing trends. The identified topics encompass capacity planning, energy and resource management, computing and technology, data analysis and statistics, engineering, and industry, all crucial for businesses and industries to operate efficiently and sustainably. This study provides a comprehensive overview of the state of capacity planning implementation and highlights areas that require further investigation.
Evaluating Player Experience for Fear Modeling of 2D East Java Horror Game Alas Tilas Herman Thuan To Saurik; Harits Ar Rosyid; Aji Prasetya Wibawa; Esther Irawati Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5043

Abstract

Developing a 2D horror game and evaluating the reliability of the player experience are two things that are interrelated and equally important. Developers must ensure that the game can provide a satisfying and reliable gaming experience for their players. This study aims to evaluate the reliability of the player's experience in the game entitled Alas Tilas, East Java. This study used the User Experience Questionnaire (UEQ) in Indonesian as a survey approach method, which was given to 30 teenagers who at least played horror games once. UEQ may provide feedback to developers on the attractiveness, clarity, efficiency, accuracy, stimulation, and novelty aspects of the game. From the results of the UEQ, a reliability test will be carried out using the Cronbach Alpha technique. The results of the descriptive analysis show that these variables are Attractiveness (mean, 0.933), Clarity (mean, 1.808), Efficiency (mean, 1.508), Accuracy (mean, 0.217), Stimulation (mean, 0.667) and Novelty (mean, 0.242). Attractiveness, clarity, and efficiency averaged positive results. The average aspects of accuracy, stimulation, and novelty of the game get neutral results. The results of the reliability test conducted on UEQ data obtained a Cronbach alpha value > 0.6 which indicates that the research data used to test the player experience are considered reliable so that they can be used to provide input for future development of the Alas Tilas game. To increase the average score, the researcher provides recommendations for improvement, namely, adjusting the accuracy and novelty aspects of the horror scenario game entitled Alas Tilas East Java. Therefore, it is expected to improve the quality of the game.
Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network Fatkhurokhman Fauzi; Dewi Ratnasari Wijaya; Tiani Wahyu Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5052

Abstract

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.

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