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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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Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning Kurniati, Florentina Tatrin; Purnomo, Hindriyanto Dwi; Sembiring, Irwan; Iriani, Ade
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5597

Abstract

Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation
YOLO-based Small-scaled Model for On-Shelf Availability in Retail Fudholi, Dhomas Hatta; Kurniawardhani, Arrie; Andaru, Gabriel Imam; Alhanafi, Ahmad Azzam; Najmudin, Nabil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5600

Abstract

The availability of the shelf (OSA) in the retail industry plays a very crucial role in continuous sales. Unavailability of products can make a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advances in recent decades, artificial intelligence has begun to be applied to support OSA, particularly by using object detection technology. In this research, we develop a small-scale object detection model based on four versions of the You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of postpractical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scale model implementation also offers flexibility in implementation. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance.
Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding Amrul Faruq; Merinda Lestandy; Adhi Nugraha; Abdurrahim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5641

Abstract

Depression, a prevalent mental condition worldwide, exerts a substantial influence on various aspects of human cognition, emotions, and behavior. The alarming increase in deaths attributable to depression in recent years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to express their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It is suitable for tasks dependent on input order or for addressing data uncertainties. The Reddit dataset, which contains text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%.
Integration Waterfall and Scrum Methodology in The Development of SIMARGA Web Application Muay, Nikson Theys; Sediyono, Eko; Tambotoh, Johan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5652

Abstract

This research explores the integration of waterfall and scrum methodologies in the development of the SIMARGA web application. Integration aims to maximize the strengths of each methodology, with Waterfall contributing to planning, analysis, and initial system structure, while Scrum supports the creation of product backlog and implementation of Scrum events. The combination results in a structured and methodical product management process, simplifying task lists, and improving efficiency. The achievement of good efficiency and effectiveness in task execution is facilitated by leveraging both methodologies, reducing waste. Progress in product development and team productivity is measured through daily meetings, evaluations, and sprint reviews. Emphasis is placed on fostering strong relationships among the Scrum team, customers, and stakeholders, promoting effective communication and collaboration. However, challenges are identified in team commitment to daily meetings, which are potentially influenced by their involvement in additional business activities. Future efforts should focus on improving technological resources and maintaining software to achieve the product goals initially outlined. The effectiveness of this product development initiative can be measured using metrics such as views and user data, particularly in the Kabupaten Pegunungan Bintang.
LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index Yunus, M.; Soleh, Agus M; Saefuddin, Asep; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5656

Abstract

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modeling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have a large variance and overfitting in the prediction results. The modeling solution uses LASSO and GLASSO. The binary categorical response data use binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. On the basis of the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables and the correlation between them is relatively high. The Indonesian government recommendation, if it is to increase the status of VDI in Indonesia, especially in the north Maluku province, is to increase the number of electricity users, food and beverage stores, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centers, because they still need to be developed compared to other villages, and more than 50% of the villages are underdeveloped. If the Village SDGs are formulated by increasing the VDI status, it will support the achievement of the SDGs goals nationally.
An Analysis of Meteorological Data in Sumatra and Nearby using Agglomerative Clustering Handhayani, Teny; Lewenusa, Irvan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5663

Abstract

Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatra is also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerative clustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11. The clusters are then analyzed to find their unique pattern. The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The results can be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities shows that there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling.
Enhancing Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification Octavian, Octavian; Badruzzaman, Ahmad; Muhammand Yusuf Ridho; Trisedya, Bayu Distiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5669

Abstract

Deep learning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant disease classification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble prediction technique using a weighted soft-voting method. Instead of assigning a generalized weight to each CNN model, our approach emphasizes giving weights to each label's prediction within every individual model. We employed three respected CNN architectures for our experiments: DenseNet201, InceptionV3, and Xception focus on classifying various diseases that affect grapes. By harnessing transfer learning coupled with end-to-end fine-tuning, we achieved a streamlined and efficient training process. In particular, the f1-score for each grape disease class was used as a parameter for weight determination and as a metric for the final evaluation. In our study, the newly proposed method was tested across various datasets and ensemble scenarios, demonstrating its effectiveness by not only outperforming the conventional soft-voting and prevalent weighted soft-voting methods, which achieved best scores of 95.68% and 95.81% respectively, but also by achieving a remarkable accuracy of 96.56%. The efficacy of this method is enhanced when the ensemble models exhibit distinct characteristics; the more varied the model characteristics, the more enhanced the ensemble results.
Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives Ondra Eka Putra; Randy Permana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5683

Abstract

Syariah Financing Saving And Loan Cooperatives (KSPPS) is an Islamic financial institution aimed at people who are on the lower middle scale to lift the economy of small communities through microfinancing programs. Problems that often occur in member recommendations to get KSPPS financing are often not on target. In addition, The amount of member financing is often problematic due to a lack of analysis, resulting in poor financing instalments. This research aims to present an analysis model for clustering and classification using hybrid data mining algorithms. This research method is using hybrid data mining Algorithms, namely K-Medoids, Naïve Bayes, and k-Nearest Neighbors (k-NN). This study uses the historical dataset of the last two years on KSPPS BMT Dadok Tunggul Hitam as a total of 70 data samples. The analysis parameters consist of income, business, residence Status, financing application, billing history, and balance amount. The best analysis Model will be obtained by comparing the results between Naïve Bayes with K-Medoids, and K-Nearest Neighbor (k-NN) with K-Medoids. The results of this research showed the best performance is using the hybrid Naïve Bayes data mining model with K-Medoids which has an accuracy of 90.91% for data split 70:30, while performance with K-fold cross-validation shows an accuracy of 93.49% using this algorithm. Overall, the results of this study can provide an effective analysis model to determine the status of the loan.
Evaluating the Bibit App: The HEART Framework Approach in UX Design Setya Perdana, Dandi; Lintang Yuniar Banowosari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5714

Abstract

With the rise of investment applications like Bibit in Indonesia, evaluating user experience is crucial to improve user engagement and satisfaction. This study aims to assess the user experience of the Bibit app using the HEART framework to identify areas for improvement. A descriptive survey was conducted using the Slovin method to determine a sample size of approximately 100 people. The study used a questionnaire distributed through Google Forms that measures the five aspects of the HEART framework on a Likert scale. Data were analyzed for validity and reliability using IBM SPSS and stored in Google Spreadsheets. In general, the Bibit app demonstrated high satisfaction, engagement, adoption, retention, and task success among its users, with most categories scoring above the 80% target. However, specific areas, particularly Happiness 2, Adoption 1, Retention 1, and Task Success 3, require further development attention. The Bibit app provides a generally satisfying user experience, with significant potential for improvement in navigation tooltips, investment product development, and app speed and efficiency.
Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability Widi Nurcahyo, Gunadi; Akbari Wafridh; Yuhandri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5507

Abstract

Anomalies and data unavailability are significant challenges in conducting surveys, affecting the validity, reliability, and accuracy of analysis results. Various methods address these issues, including the Backpropagation Neural Network (BPNN) for data prediction. However, BPNN can get stuck in local minima, resulting in suboptimal error values. To enhance BPNN's effectiveness, this study integrates Genetic Algorithm (GA) optimization, forming the BPGA method. GA is effective in finding optimal parameter solutions and improving prediction accuracy. This research uses data from the 2022 National Socio-Economic Survey (Susenas) in Solok District to compare the prediction performance of BPNN, Multiple Imputation (MI), and BPGA methods. The comparison involves training the models with a subset of the data and testing their predictions on a separate subset. The BPGA method demonstrates superior accuracy, with the lowest mean squared error (MSE) and highest average accuracy, outperforming both BPNN and MI methods.

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