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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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House Prices Segmentation Using Gaussian Mixture Model-Based Clustering Muhammad Hafidh Raditya; Indwiarti; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4459

Abstract

House is a place for humans to live and the main necessity for humans. For years, the need for houses is increasing and varied so it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilizes the log-likelihood function to optimize the GMM parameters. The result of this research is housed in DKI Jakarta and can be segmented into 3 different clusters. The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for high-profile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite good because it’s close to a value of 1.
Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints Keszya Wabang; Oky Dwi Nurhayati; Farikhin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4498

Abstract

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.
Implementation of Maggot Cage Temperature and Humidity Control Using ESP8266 Based On the Internet of Things Luluk Suryani; Ery Murniyasih; Marcelinus Petrus Saptono; Raditya Faisal Waliulu; Imam Trianggoro Saputro; Sony Rumalutur; Wennie Mandela
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4502

Abstract

Black Soldier Fly (BSF) is a fly that can produce a maggot or larvae that are useful for human life, like a decomposer waste in the form of composting, animal feed, animal oil production, source of chitin, and for the economic incomes of the society. This study aims to develop a device that can be used to control the maggot cage temperature and humidity using the ESP8266 microcontroller based on the Internet of Things (IoT). The benefit of this study is the utilization of nozzle-based water spraying that can be used to maintain the maggot cage temperature and humidity to improve the quality of maggot cultivation results. In this study, the sensor used to read the temperature and humidity on the maggot cage is DHT11, then used a water spraying method to handle the temperature and humidity controlled by using the ESP8266 and online based on the Blynk IoT platform. This study result shows that the device built in this study can maintain the maggot cage temperature between 28 to 30oC and humidity over 60%.
Implementation of Open-Source ERP-Based Fleet Management System on SMEs Transportation Service Provider Muhammad Faisal Ibrahim; Yogantara Setya Dharmawan; Ngatini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4538

Abstract

Enterprises Resource Planning (ERP) systems can be analogized as the backbone of the information system in a company. Many large-scale companies have adopted ERP systems to increase efficiency in the company's business processes. This research departs from the issue of Small and Medium Enterprises (SMEs) that are required to use ERP to compete in the global market but cannot implement an ERP system at a high cost. However, SMEs have financial limitations in adopting high-cost ERP systems. On the other hand, many open-source ERP systems can currently be used for free but with a limited number of modules. This study focuses on implementing an open-source ERP system using Odoo software version 15 on SMEs Transportation Services Providers. The goal is to develop an open-source ERP-based fleet management system for SMEs. The system developed successfully met the company's managerial expectations. All processes previously carried out manually have been carried out using the developed system. All cross-sectional data is stored in the company's master data and can be integrated to support the decision-making process, and company archives have been well documented. Based on the results of User Acceptance Testing (UAT), 98% of the system has met the needs of SMEs. It can be concluded that the implementation of an open-source ERP-based fleet management system is very helpful in managing the business processes of SMEs Transportation Services Providers more effectively and efficiently.
Fire Detection on Video Using ViBe Algorithm and LBP-TOP Kurniawan Nur Ramadhani; Febryanti Sthevanie; Gamma Kosala; Ketut Sudyatmika Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4164

Abstract

In this research, we built a system to detect fire using the ViBe (Visual Background Extractor) algorithm to extract dynamic targets. The ViBe algorithm is better at detecting moving target objects such as flame combustion. In this research we combined the ViBe algorithm with three frame differencing to gain better results on movement object. The HSI color space model was applied after the movement object was obtained. We used Local Binary Pattern-Three Orthogonal Planes to obtain the feature extraction to be classified with Support Vector Machine. Our result has shown that the proposed system were able to detect the fire using the LBP-TOP and ViBe algorithm methods with an average accuracy rate of 88.10%, and the best accuracy was 90.37%. The parameters used to achieve this accuracy in the feature extraction process were T=120, Radius=2, and frame gap=15, then the threshold value parameter for three-frame difference parameter was 25.
K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region Mawaddah Harahap; Arief Wahyu Dwi Ramadhanu Zamili; Muhammad Arie Arvansyah; Erwin Fransiscus Saragih; Selwa Rajen; Amir Mahmud Husein
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4199

Abstract

Cocoa agricultural production in Indonesia is currently very low while demand continues to increase every year, so it is very important to build a model that can categorize cocoa farming data. The main objective of this research is to analyze agricultural data using data mining techniques that specifically use the K-Means Clustering algorithm, and Gaussian Mixture Models. In this research, we used quantitative research because it measure number-based data. The results of cocoa production so far still depend on land area, then the number of cocoa trees has a significant effect on the amount of production so it is very important for the government and researchers to develop technologies that can increase cocoa production yields where the demand for cocoa is currently very high in demand worldwide because it can classify the cocoa quality from good quality to poor quality. Based on testing the K-Means Clustering and Gaussian Mixture Model algorithms on data on cocoa production in four provinces, namely North Sumatra, West Sumatra, Lampung and Aceh which were optimized by the Silhouette method, it produced cluster values ​​of 2, 3 and 4. second with a value of 59.8%.
Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Iis Rodiah; Medria Kusuma Dewi Hardhienata; Agus Buono; Karlisa Priandana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4201

Abstract

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.
Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques Moh. Badris Sholeh Rahmatullah; Aulia Ligar Salma Hanani; Akmal Muhammad Naim; Zamah Sari; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4213

Abstract

Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes.
The Role of Genetics in Domestic Research on Forestry Issues: A Text Mining Analysis Titis Hutama Syah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4228

Abstract

As a megadiverse country, Indonesia has plentiful genetic resources. The interest of domestic researchers in it and its relation to forestry scope is the focus of this paper. The objective is to determine the genetics aspects represented in forestry scholarly articles. Text mining analysis is carried out for the abstract articles, followed by topic modeling and trend analysis. Python libraries were used to conduct this research. Garuda website was the main source of the data collection. Natural language Toolkits (NLTK) were used to retrieve article information from Garuda. Sci-kit learn (SKLearn) of Latent Dirichlet Allocation module was used for topic modeling analysis, and pyLDAVis was used to represent it. SKLearn was also used for trending analysis. After article text retrieval, three topic clusters were found: forest diversity, products, and land use. The topics were scattered in 1966 abstract articles that were found during data retrieval. Article growth showed the quadratic pattern known after regression analysis. The trend showed the rapid growth of topics and scholars' interest, but the number of articles was low compared to the total articles on the Garuda portal.
Learning Management System Acceptance Analysis Using Hedonic Motivation System Adoption Model Victor Alva Andrian Rehy; Johan J.C. Tambotoh
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4233

Abstract

Online learning using LMS (Learning Management System) results in demotivation for Lecturers and Students. This study aims to explore the relationship between the contentment of using LMS with the behavioural intentions and user focus while using the LMS. The present study employed the user's perception of using LMS with HMSAM (Hedonic Motivation System Adoption Model) as the theoretical basis. The quantitative research method employed a questionnaire as a data collection method. The collected data were analysed statistically using the PLS-SEM method with SmartPLS 3.2.9 application. The results of the study showed that of the 10 (ten) hypotheses, 9 (nine) were accepted, and 1 (one) was rejected. In particular, the hypothesis indicating excitement affects behavioural intentions using the LMS shows a t-statistic value of 1.887 (t-statistics < t-value) hence being rejected. This study also provides recommendations for LMS development based on usability, curiosity, excitement, and control factors.

Page 61 of 105 | Total Record : 1046

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