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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 417 Documents
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APPLICATION OF PROFILE MATCHING ALGORITHM IN SELECTION OF THE BEST EMPLOYEES IN PROPERTY COMPANY Laeli Nurchasanah; Annisa Cintakami Firdaus; Desti Fitriati
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i2.150

Abstract

Giving awards to employees who have advantages and good work performance is one way to increase positive competitiveness among employees in a company. This study aims to find the advantages of each employee to find out which employees excel. Through achievements in the world of work, it can be a benchmark for finding the best employees who deserve awards. Analysis of the data used in this study is sourced from data on sales of property companies for the last three months. This study uses the Profile Matching Method to determine the best employees in property companies. This research was conducted by comparing one employee with another employee candidate based on predetermined criteria. The results of this study are in the form of rankings that show the order of the best employees who are entitled to an award from the company.
THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA Anton Sunardi; Sienny Rusli; Christina Juliane
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i2.157

Abstract

Of the many academic data, data in the form of an achievement index needs to be used in-depth so that it does not become a display of numbers and information only. This achievement index evaluation data reflects the educational process students and teaching staff carries out in an educational process. This study aims to measure the accuracy of data mining processing based on differences in test data by analyzing the C4.5 algorithm using RapidMiner as a data processing tool and determining the decisions students can make and academic institutions in developing study strategies and educational curricula to be maximized. The data processing is carried out by classifying the student achievement index data at a private university using data analysis test equipment. The data source comes from kaggle.com, which consists of 1687 data that have been processed and processed. The conclusion from the results of this study is that the amount of data turns out to have a significant influence on the accuracy value of the C4.5 algorithm, where an accuracy rate of 91.69% is obtained from the test results of 1687 data with four main attributes, namely IPK1, IPK2, IPK3, IPK4 and correctly or not as a label.
ANALYSIS OF PREFERRED FREIGHT FORWARDING SERVICE USING ANALYTICAL HIERARCHY PROCESS METHOD Surya Kelana Saputra; Anggi Oktaviani; Dahlia Sarkawi; Deny Novianti
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.161

Abstract

Buyers and sellers who use online transactions for shipping can use two face-to-face shipping methods or delivery services. Everyone needs fast and safe shipping to ensure goods are transported at the right time and place. The interest in dispatch administrations is progressively required. The ongoing portability constraints are compelling many people to purchase online instead of shopping centers. The Analytical Hierarchy Process technique was created in the mid-1970s by Dr. Thomas L. Saaty, a mathematician from the University of Pittsburgh. The Analytical Hierarchy Process is designed to rationally capture people's perceptions closely related to specific problems through procedures designed to arrive at a preference scale among various sets of alternatives. This choice model decays a complex multi-rules issue into a solitary various, leveled structure. From the study results, it can be concluded that J&T has the highest score of 0.345 (34.5%), then JNE with a score of 0.339 (33.9%), SICEPAT with a score of 0.316 (31.6%), so that the most desirable Expedition Service based on data processed from 103 respondents is J&T.
DECISION SUPPORT SYSTEM FOR TOKO ANDA SUPPLIER SELECTION WITH THE SIMPLE ADDITIVE WEIGHTING (SAW) METHOD Ricki Ardiansyah; Maha Rani; Revi Gusriva; Elmi Rahmawati
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.163

Abstract

Choosing a supplier or supplier is an important factor for running a business because suppliers can affect the availability of goods, quality, and profits from the business. However, the selection of suppliers is a complicated problem in business because of the many alternatives and criteria that are the determining factors in supplier selection and the difficulty of choosing the right supplier with objective criteria in a short time. To assist in the selection of suppliers in Toko Anda, an SPK is built that can help provide the best supplier recommendations from several alternatives based on the selection criteria provided by the shop owner so that the selection of suppliers in Toko Anda can be done quickly using the system and produces an objective supplier choice. In carrying out the decision-making process in this SPK, the method that will be used is SAW. In the calculation process, the SAW method performs a weighted summation of the alternatives against all the criteria that are the reference in determining suppliers. The criteria that determine this decision support system are quality, price, completeness, packaging, warranty, delivery time, service. based on the calculation process using the saw method, the supplier ranking results from several existing alternatives are obtained. The supplier ranking results from this decision support system can be used by Toko Anda owner as a reference in determining the supplier. From the results of the calculation process with SAW, the ranking of Supplier F with the highest value is 22, then Supplier E and Supplier B with a value of 21.4, then Supplier D with a value of 21, then Supplier A with a value of 20.6 and finally Supplier C with a value of 20.4. From this research on making SPK, the SAW method can provide a ranking of suppliers in Toko Anda with an objective and short time.
CLASSIFICATION OF THE POOR IN SUMATERA AND JAVA ISLAND USING NAIVE BAYES ALGORITHM AND NAIVE BAYES ALGORITHM BASED ON PSO Palupi, Endang Sri
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.164

Abstract

The poverty rate in Indonesia is still quite high, due to the large population and uneven development and economic center. With a large population and an archipelagic country that stretches from west to east, it is not easy for the government to level the economy in order to reduce poverty in Indonesia. This study was conducted to classify the poverty rate in districts on the island of Sumatra and Java using Nave Bayes and Nave Bayes based on Particle Swarm Optimization. Thus, it is hoped that the central government and local governments can monitor the implementation of programs in order to reduce poverty rates, especially in districts with high poverty rates. Based on research conducted on the classification of the poor in districts on the island of Sumatra and Java with confusion matrix testing and validation validation techniques using the Naïve Bayes algorithm, the accuracy rate is 59.75% and AUC 0.768 is included in a good classification. While the results of the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization produces an accuracy rate of 82.93% and AUC of 0.849 is included in a good classification. From the results of this study, it can be said that Al-Qur'an Naïve Bayes is a good technique for classification in data mining, and for maximum results using Particle Swarm Optimization.
ON-SR UII: AN ONLINE SELF-REGULATED LEARNING WEB APPLICATION TO ASSIST INDEPENDENT COLLEGE LEARNERS Ahmad R. Pratama; Puji Rahayu; Andri Setiyadi; M. Fachry Azhar; M. Fajri Ashshiddiq
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i4.165

Abstract

Self-regulated Learning (SRL) is a learning method that strongly emphasizes the importance of self-learning skills. Unfortunately, many existing educational technologies employed by colleges and universities continue to place a premium on technical support for the learning process within the classroom that does not provide the same level of support for SRL. This study aims to close this gap by developing the ON-SR UII, a new SRL platform that can assist college students in their quest to become independent learners. Using the design thinking approach, ON-SR UII is developed as a responsive web app that can be accessed by college students through the Internet anywhere at any time at their own pace using any computing device of varying screen sizes. This article describes how ON-SR UII was designed before its Prototype was developed, deployed, and evaluated by stakeholders for functionality, usability, and responsiveness. The encouraging results indicate that ON-SR UII has the potential to be widely implemented, allowing for the measurement of its implications in future research.
IOT-BASED HOME AUTOMATION USING NODEMCU ESP8266 Paul K.A Windesi; Mingsep Rante Sampebua; Remuz MB Kmurawak
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i4.166

Abstract

Home automation is an automation technology that manages circuits and electronic equipment in homes, offices, and others. Home automation is a form of Internet of Things (IoT) development that allows communication and control through devices connected to the internet. This study aims to design a Home Automation prototype on lighting devices such as lamps, light sensors to activate lights, and several lights controlled using mobile devices. The research method uses the prototype method, where system development is focused on the results of input from customers who will be evaluated for software development. The stages in this research begin with analyzing device requirements, literature study, system design, hardware design, user interface design testing, and arriving at the results. This research output will be made in the form of a prototype, where all components will be placed based on the layout described in the design. This system can help users control the equipment in the house from anywhere and anytime, including using light sensors to provide input to turn the lights on or off.
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using BERT Julianto, Muhammad Fahmi; Malau, Yesni; Hidayat, Wahyutama Fitri
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.169

Abstract

News about the war between Russia and Ukraine can not be denied affecting various aspects of life worldwide. It affects the writings of every world citizen on various social media platforms, one of which is Twitter. Sentiment analysis is a process of identifying and making sentiment categories computationally. The sentiment analysis process is also intended to make computers understand the meaning of human sentences by processing algorithms. This research uses the deep learning method of the BERT (Bidirectional Encoder Representation Form Transform) model language to analyze the sentiments in the tweets written about the wars between Russia and Ukraine by Twitter social media users. The sentiment will be divided into positive, neutral, and hostile. The hyperparameters in this study used ten epochs, with a learning rate of 2e-5 and a batch size of 16. The test used in sentiment analysis was the BERTbase Multilingual-cased-model model, and the accuracy was 97%. Suggestions for further research are the need for a more balanced dataset between positive, neutral, and negative sentiments. They reward the dataset before training so that better results are expected.
DECISION SUPPORT SYSTEM FOR WI-FI MODEM SELECTION USING MULTIPLE ATTRIBUTE DECISION MAKING WITH TOPSIS Nur Sucahyo; Ahmad Fitriansyah; Yogasetya Suhanda
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i4.172

Abstract

The modem is a device that can connect mobile phones, PCs, or laptops to the internet network. The modem popular today is MiFi or Wi-Fi Modem, a portable modem that makes it easier for users to carry the device anywhere. Many Wi-Fi modems are circulating in the market with various brands with different specifications and capabilities. For this reason, people must be observant in choosing the right Wi-Fi Modem for their needs. Selection of the right Wi-Fi Modem will maximize the internet network's performance to facilitate internet use activities. This study aims to develop a decision support system by implementing the Multiple Attribute Decision Making (MADM) approach using the TOPSIS method for selecting a Wi-Fi Modem to facilitate the selection of the best Wi-Fi Modem according to user needs. Based on black-box testing, it shows that the system built can run well. In addition, the calculation results of the TOPSIS method generated by the system with manual calculations produce the same value.
PERFORMANCE COMPARISON OF MUSHROOM TYPE CLASSIFICATION BASED ON MULTI-SCENARIO DATASET USING DECISION TREE C4.5 AND C5.0 Citra Mirna Wati; Abd. Charis Fauzan; Harliana Harliana
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.173

Abstract

Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.

Page 33 of 42 | Total Record : 417

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