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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Data Science: Journal of Computing and Applied Informatics Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science
Opim Salim Sitompul
Universitas Sumatera Utara
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Goal Programming Method in Optimizing Course Student Admission, Operational Costs and Profits Muhammad Khahfi Zuhanda; Saib Suwilo; Opim Salim Sitompul; Mardingsih Mardingsih
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6072

Abstract

In today's business competition, educational institutions or courses require optimizing profits. However, this is not easy because, in its implementation, there are many priority objective functions that must be fulfilled. In this case, the course institution has different class programs, namely literacy, numeracy, and math olympiad programs, with various operational costs per program type and course fees per program type. This problem requires setting priorities because of the limited number of revenues, operating costs, and profit targets. In this paper, the researcher uses Winter's Exponential Smoothing forecasting model to determine the number of students, operating costs, and profits in the following year. Then the researcher analyzes the planning with the goal programming method to minimize the deviation of the multi-objective programming. This research shows that the number of student admissions who can meet market demand for January 2021 decreased by 5.41%, in February decreased by 3.20%, in March decreased by 1.29%, April remained constant, in May increased by 1.44%, June increased by 2.16%, July increased 2.82%, August increased 2.78%, September increased 3.47%, October increased 3.22%, November an increase of 3.33%, and for December an increase of 2.69%. The total operational cost that does not exceed the target limit is Rp. 30,475,0000 for one year. The total profit has reached the target to be achieved, which is Rp. 322,150,000 for one year.
Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds Afriandy Sharif; Opim Salim Sitompul; Erna Budhiarti Nababan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8566

Abstract

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features.
Co-Authors Afriandy Sharif Alexander F.K. Sibero Andrew Handoko Dedy Arisandi Dennis Dennis Elviawaty Muisa Zamzami Erna Budhiarti Nababan Goklas Tomu Simbolon Hartono Hartono Jonas Franky R Panggabean Lubis, Arif Ridho Mahyuddin K. M Nasution Mardingsih Mardingsih Muhammad Khahfi Zuhanda Rahmad Nurhadi Yusra Romi Fadillah Rahmat Saib Suwilo Sarah Purnamawati Sawaluddin Nasution Seniman Tulus Tulus Yundari, Yundari Yuni Cancer