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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JST ( Jurnal Sains Terapan ) INOVTEK Polbeng - Seri Informatika ILKOM Jurnal Ilmiah JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Sistem Informasi dan Informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS Jurnal Algoritma JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang

Triyani Arita Fitri

STMIK Amik Riau

Author-ID : 535687

Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Hotel Employee Acceptance Assessment System With Android-Based Moora Method Karpen Karpen; Widi Paramita; Triyani Arita Fitri; Koko Harianto
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 1 (2020): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Hotel is a company engaged in the field of services, which requires employees who have competencies in accordance with their fields. At present the existing employees are deemed to lack competency in accordance with their field of work. In order for employees to have competence according to their field of work, the hotel must carry out an acceptance assessment that requires certain criteria, such as education, foreign language acquisition, discipline, experience and others. One of the appropriate decision making methods for evaluating acceptance is the MOORA (Multi Objective Optimization on the basis of Ratio Analysis) method which is a multi-objective system that optimizes two or more conflicting criteria. The MOORA method has a degree of flexibility and ease of understanding in separating the subjective parts of an evaluation process into criteria, by determining the weights and attributes as decision making, the form of decision matrices and ranking. The criteria used in this study are height, experience, discipline, interviews, written tests, foreign languages, friendly, computer, software and medical checkups. To facilitate the assessment of acceptance, this system is applied based on Android, so that in addition to the hotel, prospective employees can also find out the results of the assessment. Based on the implementation of the system application, the use of the MOORA method for the hotel employee acceptance evaluation system can produce the right decision system, in accordance with the predetermined ranking, criteria and weight of the assessment

Neural Network Method in Text Message Categorization of Online Discussion Erlin; Johan; Triyani Arita Fitri; Agustin; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

This paper presents research in neural network approach for text messages categorization of collaborative learning skill in an online discussion. Although a neural network is a popular method for text categorization in the research area of machine learning, unfortunately, the use of neural network in educational settings is rare. Usually, text categorization by neural network is employed to categorize news articles, emails, product reviews, and web pages. In an online discussion, text categorization that is used to classify the message sent by the student into a certain category is often manual, requiring skilled human specialists. However, human categorization is not an effective way for a number of reasons; time- consuming, labor-intensive, lack of consistency in a category, and costly. Therefore, this paper proposes a neural network approach to code the message automatically. Results show that neural networks achieving useful classification on eight categories of collaborative learning skills in an online discussion as measured based on precision, recall, and balanced F-measure.

Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media Bunga Nanti Pikir; M. Khairul Anam; Hadi Asnal; Rahmaddeni; Triyani Arita Fitri; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 1 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the NaÃ¯ve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.

Prediction of Student Study Duration Using Multiple Linear Regression Method Fitri, Triyani Arita; Rahmawati; Lusiana; Rini Yanti
JAIA - Journal of Artificial Intelligence and Applications Vol. 3 No. 2 (2023): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v3i2.1054

Data mining is a process of extracting valuable and meaningful information from large or complex data sets. In the field of education, data mining can be used to predict the length of study of students by identifying factors that affect the length of study of students. This research aims to predict the length of study of students and to find out the most influential variables in completing the length of study. The method used in this research is the Multiple Linear Regression method. Training data as much as 292 data is taken from data on graduates from 2016 - 2018. While the testing data is taken from the active student data class of 2018 as much as 148 data. The model formed will be evaluated to determine the accuracy and RMSE values. The results showed that the Multiple Linear Regression method succeeded in carrying out the prediction process optimally with a percentage accuracy value of 85%, and an RMSE value of 0.76, which means that the error rate of this model is very low. Based on the resulting coefficient value, the SKS variable is the most influential variable in the length of study of students.

Co-Authors Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Ahmad Zamsuri, Ahmad Aldi Saputra Anam, M Khairul Boby Hasbul Fikri Bunga Nanti Pikir Cesmawati Cesmawati Cesmawati, Cesmawati Dewi Sari Wahyuni Edwar Ali Efrizoni, Lusiana Erleni - Erleni -, Erleni Erlin Erlin - Erlin -, Erlin Fatdha, Eiva Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Hadi Asnal, Hadi Hamdani Hamdani - Hamdani . Hanafiah, Anggi Hanafiah, Anggi Handoko Hazira, Nadila Herwin Herwin, Herwin Ismanizan, Ryan Johan Junadhi Junadhi Junadhi, Junadhi Karpen Kharisma Rahayu Koko Harianto Koko Harianto Lusiana Lusiana Efrizoni Lusiana Lusiana M Naofal Arsyad M. Mashudi Muhammad Bambang Firdaus Muhammad Jamaris Nu'man, Nu'man Nurhadi Nurhuda, Agus Tri Nurjayadi Nurjayadi Parlindungan Kudadiri Perty Putriani Saragih Praba Isna Tasya Rahmaddeni Rahmaddeni Rahmawati Rahmiati Rahmiati Rahmiati Rinaldi Yuslami Rini Yanti Rini Yanti, Rini Rio Ferdiansyah Rita Novita Safitri, Dea Susandri, Susandri SUSANTI Susanti Susanti Susanti, Susanti SUSI ERLINDA Syam, Salmaini Safitri T. Sy. Eiva Fatdha Teguh Sujana Torkis Nasution Widi Paramita Wirta Agustin Yuslami, Rinaldi Yusuf Nuron Zuriatul Khairi

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