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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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Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm Pohan, Immanuel Morries; Dwijayanti, Suci; Suprapto, Bhakti Yudho; Hikmarika, Hera; Hermawati, Hermawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5376

Abstract

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements.
Digital Image Encryption Using Logistic Map Muhammad Rizki; Erik Iman Heri Ujianto; Rianto Rianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5389

Abstract

This study focuses on the application of the logistic map algorithm in the Python programming language for digital image encryption and decryption. It investigates the impact of image type, image size, and logistic map parameter values on computational speed, memory usage, encryption, and decryption results. Three image sizes (300px 300px, 500px x 500px, and 1024px x 1024px) are considered in TIFF, JPG, and PNG formats. The digital image encryption and Decryption process utilizes the logistic map algorithm implemented in Python. Various parameter values are tested for each image type and size to analyze encryption and decryption outcomes. The findings indicate that the type of image does not affect memory usage, which remains consistent regardless of image type. However, image type significantly influences the decryption results and computation time. In particular, the TIFF image type exhibits the fastest computation time, with durations of 0.17188 seconds, 0.28125 seconds, and 1.10938 seconds for 300px x 300px, 500px x 500px, and 1024px x 1024px images, respectively. In addition, the encryption results vary depending on the type of image. The logistic map algorithm is unable to restore encryption results accurately for JPG images. Furthermore, research highlights that higher values of x, Mu and Chaos lead to narrower histogram values, resulting in improved encryption outcomes. This study contributes to the field by exploring the application of the logistic map algorithm in Python and analyzing the effects of image type, image size, and Logistic Map parameter values on computation time, memory usage, and digital image encryption and Decryption results. Prior research has not extensively addressed these aspects in relation to the Logistic Map algorithm in Python.
Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods Basuni, Nursela; Amril Mutoi Siregar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5401

Abstract

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.
A Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review Mikael Octavinus Chan; Setiadi Yazid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5403

Abstract

This research delves into the critical aspects of information security during the implementation stage of the Software Development Life Cycle (SDLC). Using a systematic review of the literature, the study synthesizes the findings of various digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework that addresses the unique security challenges of the implementation stage. This research contributes to the field by proposing a novel assurance model for software development vendors, focusing on improving information security measures during the implementation stage. The study's findings reveal 12 key steps organizations can adopt to mitigate security risks and improve information security measures during this critical phase. These steps provide actionable insights and strategies designed to support security protocols effectively. The paper concludes that by incorporating these steps, organizations can significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addresses immediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC.
Comparison of the RFM Model's Actual Value and Score Value for Clustering Samidi, Samidi; Suladi, Ronal Yulyanto; Kusumaningsih, Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5416

Abstract

Clustering algorithms and Recency-Frequency-Money (RFM) models are widely implemented in various sectors of e-commerce, banking, telecommunications and other industries to obtain customer segmentation. The RFM model will assess a line of data which includes the recency and frequency of data appearance, as well as the monetary value of a transaction made by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental approach, this research aims to find the best data set transformation model between actual RFM values and RFM scores. The method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator to obtain the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-Commerce application, where data was taken in the Wiradesa area, Central Java. The resulting data set consisted of 273,454 rows with 18 attributes from January 2022 to December 2022 by collecting historical data from shopping outlets to wholesalers. The analysis of the data set was carried out by transforming the data set using the RFM method into actual values and score values; then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time (time series) can be transformed into data in the RFM model where the actual value is better than the RFM score model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a series of data with a cluster label, thus forming supervised learning data.
Image Preprocessing Approaches Toward Better Learning Performance with CNN Tribuana, Dhimas; Hazriani; Arda, Abdul Latief
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5417

Abstract

Convolutional neural networks (CNNs) are at the forefront of computer vision, relying heavily on the quality of input data determined by the preprocessing method. An undue preprocessing approach will result in poor learning performance. This study critically examines the impact of advanced image pre-processing techniques on computational neural networks (CNNs) in facial recognition. Emphasizing the importance of data quality, we explore various pre-processing approaches, including noise reduction, histogram equalization, and image hashing. Our methodology involves feature visualization to improve facial feature discernment, training convergence analysis, and real-time model testing. The results demonstrate significant improvements in model performance with the preprocessed dataset: average accuracy, recall, precision, and F1 score enhancements of 4.17%, 3.45%, 3.45%, and 3.81%, respectively. Additionally, real-time testing shows a 21% performance increase and a 1.41% reduction in computing time. This study not only underscores the effectiveness of preprocessing in boosting CNN capabilities, but also opens avenues for future research in applying these methods to diverse image types and exploring various CNN architectures for comprehensive understanding.
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Dwiza Riana; Jufriadif Na'am; Saputri, Daniati Uki Eka Saputri; Sri Hadianti; Faruq Aziz; Suryadi Putra Liawatimena; Alya Shafra Hewiz; Dika Putri Metalica; Teguh Herwanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5420

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.
Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes Setiawan, Johan; Amalia, Dita; Prasetiawan, Iwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5445

Abstract

Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the various types of anemia is vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasizing Complete Blood Count (CBC) and demographic data. Data mining is key to building models that aid healthcare professionals in the diagnosis and treatment of anemia. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM), with its six phases, facilitates this endeavour. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner's tools, evaluating accuracy, mean recall, and mean precision. The J48 Decision Tree outperformed the others, highlighting the importance of algorithm choice in anemia classification models. Furthermore, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with a higher incidence among women. Recognizing gender disparities in the prevalence of anemia informs personalized healthcare decisions. Understanding demographic factors in specific types of anemia is crucial for effective care strategies.
MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture Ujilast, Novia Adelia; Firdausita, Nuris Sabila; Aditya, Christian Sri Kusuma; Azhar, Yufis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5457

Abstract

Alzheimer's disease is a neurodegenerative disorder or a condition characterized by degeneration and damage to the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes using the convolutional neural network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study used 6400 images that encompass four classes, namely mild demented, moderate demented, non-demented, and very mild demented. After conducting testing for both scenarios, the exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the exactness was 97.00%.
Visual Impaired Assistance for Object and Distance Detection Using Convolutional Neural Networks Parenreng, Jumadi Mabe; Andi Baso Kaswar; Ibnu Fikrie Syahputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5491

Abstract

Vision is a very valuable gift from God; Most aspects of human needs in the body are dominated by vision. Based on data from the World Health Organization (WHO) there are around 180 million people in the world experiencing visual impairment, while the prevalence of blindness in Indonesia reaches 3 million people (1.5% of Indonesia's population), so we designed a system in the form of a prototype that could detect objects around the user and convey data in the form of sound to the user. This research discusses the application of a machine learning model using the Convolutional Neural Network method to detect objects optimally. The objects that have been collected will be trained on machine learning and produce a model to be embedded in the system's main machine, namely the Raspberry PI 4B. The training of the machine learning model was carried out several times by changing the compositions of several layers until a model with optimal accuracy was obtained; however, the size of the resulting model was quite large, so the researchers carried out SSDMobileNetV2 transfer learning to obtain the optimal model. The optimal model was obtained with a model precision of 92% and a model size of 18 MB. Object detection tests carried out under 3 test conditions resulted in an average object detection accuracy of 84.3%, and distance detection tests carried out under 10 conditions resulted in an average distance detection error of 2.1 cm. The results show that the system was accurate and effective.

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