cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuhefizar
Contact Email
jurnal.resti@gmail.com
Phone
+628126777956
Journal Mail Official
ephi.lintau@gmail.com
Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.
Location
,
INDONESIA
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 26 Documents
 1   2   3 
Search results for , issue "Vol 7 No 6 (2023): December 2023" : 26 Documents clear
Biometrika Nirsentuh Berbasis Pengenalan Pembuluh Darah pada Telapak Tangan Menggunakan Wavelet dan Local Line Binary Pattern Jayanti Yusmah Sari; Suharsono Bantun
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.v7i6.4530

Abstract

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.
Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization Wikky Fawwaz Al Maki; Khaidir Mauladan; Indra Bayu Muktyas
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.v7i6.4839

Abstract

Various cultivars of date fruits distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally lack variability in date fruits, since various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit according to their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest-Neighbor (KNN) classifier. Considering the parameters applied to develop the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN.
The Examination of the User Engagement Scale (UES) in Small Medium Enterprise Social Media Usage: A Survey-Based Quantitative Study Rona Nisa Sofia Amriza; Khairun Nisa Meiah Ngafidin; Citra Wiguna
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.v7i6.4926

Abstract

Social networks have proven to be an essential marketing tool for the success of any product, service, or business. User participation affects the increase in revenue gain and creates long-term profit. The User Engagement Scale (UES) is one of the tools developed to measure user engagement and has been used in various digital domains. The UES intends to compute six dimensions of engagement: aesthetic appeal, perceived usability, focused attention, novelty, felt involvement, and endurability. This study investigates and verifies the three-factor structure of the UES. We used PCA to perform the analysis. The original data will be reanalyzed using UES, which consists of 220 valid responses. The result shows that the UES examination indicates good reliability in three factors. Factor 1 encompasses the feeling of involvement (FI), aesthetic appeal (AE), novelty (NO), and endurability (EN). Factor 2 aggregates the perceived usability (PU) elements. Factor 3 pertains to focused attention (FA) items. Our findings indicate that the User Engagement Scale is a valuable and suitable measurement tool for assessing user engagement in the context of social media within small and medium enterprises.
Analysis and Classification of Customer Churn Using Machine Learning Models Muhammad Maulana Sidiq Nurhidayat; Dyah Anggraini
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.v7i6.4933

Abstract

Analysis studies of customer loss (customer churn) have been used for years to increase profitability and build customer relationships with companies. Customer analysis using exploratory data analysis (EDA) to visualize data and the use of machine learning to classify customer churn are often used by past analysts. This study uses several machine learning models that can be used for customer churn classification, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). However, there is a class imbalance factor in the dataset, which is the biggest challenge that analysts usually face in achieving good results in the classification of machine learning models. The Synthetic Minority Oversampling Technique (SMOTE) method is a popular method applied to deal with class imbalances in datasets. The results of the analysis show that the classification of churn customers using the XGBoost algorithm has the best level of accuracy compared to other algorithms, with an accuracy value of 0.829424, and the oversampling method with SMOTE tends to reduce the accuracy value of each classification algorithm. The Permutation Feature Importance (PFI) technique of the XGBoost model gets the result that tenure, monthly contracts, and TV streaming are the features that affect customer churn the most.
Identifikasi Penyakit Tanaman Pisang Melalui Citra Daun Pisang Menggunakan Metode CNN Dengan Model ResNet50 dan VGG-19 Ilham Rahmana Syihad; Muhammad Rizal; Zamah Sari; Yufis Azhar
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.v7i6.5000

Abstract

Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the data set used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the data set used. The research objectives encompass data set processing, model architecture development, evaluation, and result reporting, all aimed at improving disease identification in banana plants. The ResNet50 model achieved impressive 94% accuracy, with 88% precision, 91% recall, and an F1 score of 89%, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing previous research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advances and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to improve disease identification accuracy.
ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming Enni Lindrawati; Ema Utami; Aiinul Yaqin
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.v7i6.5086

Abstract

Madurese is one of the regional languages ​​in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in information retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language; in several studies, the stemming method is often used, such as the modification of the Nazief and Adriani method, which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore, this research was developed by modifying Nazief and Adriani's algorithm for Madurese based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary that have received affixes. The accuracy of the algorithm is 89% with 890 words that match; the prefix has an accuracy of 93.81%; the suffix has an accuracy of 83.78%; and the confix has an accuracy of 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. Understemming is found in 104 words, and overstemming in 6 words. The time it takes to compile is 31.31 seconds.
Analysis of the Saintekmu Website Quality on User Satisfaction Using the Modified System Usability Scale and Webqual 4.0 Method Fitrah Juliansyah Fitrah; Abdul Fadlil; Rusydi Umar
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.v7i6.5116

Abstract

Today, websites are a major means of finding or providing information. The Saintekmu website was created to offer top-notch service to students. One way to ensure that the website's services are appropriate and that information technology is being used to its fullest potential is to evaluate the level of service provided and improve its quality. This study aims to compare the results of two methods - the System Usability Scale (SUS) and Webqual - used to determine the quality and expectations of website users. The study distributed questionnaires online using Google Forms and had a sample size of 20 students. The data collected was analyzed using the SPSS program. The results of the SUS method indicated that the website acceptability range was in the marginal category, with a score of 69.9 and a classification rating of OK. The Webqual method yielded an R square of 0.948, indicating that the website's usability, quality, and interaction variables had a significant effect on user satisfaction. All WebQual 4.0 dimensions had a positive and significant effect on user satisfaction, both partially and simultaneously. This study provides Muhammadiyah Saintek University with reference material to evaluate its website in the future.
A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Dodon Turianto Nugrahadi; Rudy Herteno; Mohammad Reza Faisal; Nursyifa Azizah; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
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.v7i6.5126

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested 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 characteristics. 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 future studies in developing new methods to enhance identification accuracy.
Android Application for Tomato Leaf Disease Prediction Based on MobileNet Fine-tuning Mutia Fadhilla; Des Suryani
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.v7i6.5132

Abstract

Tomato is one of the most well-known and widely cultivated plants in the world. The result of tomato production is affected by the conditions of the plants when they are grown. It may decrease due to leaf plant disease caused by climate change, pollinator decrease, microbial pets, or parasites. To prevent this, an image-based application is needed to identify tomato plant disease based on visually unique patterns or marks seen on leaves. In this paper, we proposed a CNN fine-tuned model based on MobileNet architectures to identify tomato leaf disease for mobile applications. Based on the results tested by K-fold cross-validation, the best accuracy achieved by the proposed model is 97.1%. Additionally, the best average precision, recall and F1 Score are 99.8%, 99.8%, and 99.5%, respectively. The model with the best results is also implemented into Android-based mobile applications.
Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Narandha Arya Ranggianto; Diana Purwitasari; Chastine Fatichah; Rizka Wakhidatus Sholikah
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.v7i6.5170

Abstract

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.

Page 1 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 8 No 1 (2024): February 2024 Vol 7 No 6 (2023): December 2023 Vol 7 No 5 (2023): October 2023 Vol 7 No 4 (2023): August 2023 Vol 7 No 3 (2023): Juni 2023 Vol 7 No 2 (2023): April 2023 Vol 7 No 1 (2023): February 2023 Vol 6 No 6 (2022): Desember 2022 Vol 6 No 5 (2022): Oktober 2022 Vol 6 No 4 (2022): Agustus 2022 Vol 6 No 3 (2022): Juni 2022 Vol 6 No 2 (2022): April 2022 Vol 6 No 1 (2022): Februari 2022 Vol 5 No 6 (2021): Desember 2021 Vol 5 No 5 (2021): Oktober2021 Vol 5 No 4 (2021): Agustus 2021 Vol 5 No 3 (2021): Juni 2021 Vol 5 No 2 (2021): April 2021 Vol 5 No 1 (2021): Februari 2021 Vol 4 No 6 (2020): Desember 2020 Vol 4 No 5 (2020): Oktober 2020 Vol 4 No 4 (2020): Agustus 2020 Vol 4 No 3 (2020): Juni 2020 Vol 4 No 2 (2020): April 2020 Vol 4 No 1 (2020): Februari 2020 Vol 3 No 3 (2019): Desember 2019 Vol 3 No 2 (2019): Agustus 2019 Vol 3 No 1 (2019): April 2019 Vol 2 No 3 (2018): Desember 2018 Vol 2 No 2 (2018): Agustus 2018 Vol 2 No 1 (2018): April 2018 Vol 1 No 3 (2017): Desember 2017 Vol 1 No 2 (2017): Agustus 2017 Vol 1 No 1 (2017): April 2017 More Issue