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All Journal Jurnal Gaussian Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Matematika UNAND Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
K. Hasan, Isran
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Mathematics Physics Other

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Utilizing the Welch-Powell Algorithm and the IDO (Incident Degree Ordering) Algorithm in Traffic Light Settings Latif, Sintia Abdul; Nurwan; K. Hasan, Isran; Achmad, Novianita; Wungguli, Djihad; Nashar, La Ode
Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Vol. 21 No. 1 (2024): Sainmatika : Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/sainmatika.v21i1.9630

Abstract

The road junction needs some help with the timing of traffic lights. One method for optimizing crossroads traffic light settings is using a graph approach that applies a vertex coloring algorithm. The Welch-Powell and IDO (Incident Degree Ordering) algorithms are used to solve this problem. This case study covers two crossroads, namely: the crossroads of Prof. Dr. H.B. Jassin, Jenderal Sudirman Street, and the crossroads of Prof. Dr. H.B. Jassin, Palma, Sarini Abdullah Street. The result showed that the Welch-Powell and IDO algorithms used for vertex coloring produced XG=3 chromatic numbers for Prof. Dr. H.B Jassin, Jenderal Sudirman Street, and XG=4 for Prof. Dr. H.B Jassin, Palma, and Sarini Abdullah Street. New data shows that green-light efficiency increases by 23.85% and red-light efficiency decreases by 19.26% for crossroads of three, and new data at crossroads of four shows that data in the field is more effective than new data.
PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP Alamri, Fahima; Ningsih, Setia; Djakaria, Ismail; Wungguli, Djihad; K. Hasan, Isran
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.314-321

Abstract

The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.
ANALISIS PERPINDAHAN PENGGUNAAN APLIKASI TRANSPORTASI ONLINE MENGGUNAKAN RANTAI MARKOV K. Nasib, Salmun; Nurwan, Nurwan; Aryasandi, I Wayan Can; K. Hasan, Isran; Asriadi, Asriadi
Jurnal Matematika UNAND Vol. 13 No. 1 (2024)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.13.1.26-40.2024

Abstract

The purpose of this study is to find out the opportunities for switching to the use of online transportation applications and predict the future use of online transportation applications by Gorontalo State University students using the Markov chain. The data used in this study are primary data obtained through questionnaires. The results of the prediction of the proportion for future market share show that the proportion of users of the Maxim transportation application is 82.89%, Grab is 7.75%, Gojek is 5.06% and InDriver is 4.48%.
Analisis Sentimen Pengguna X (Twitter) Terhadap Kebijakan Tapera Di Indonesia Menggunakan Metode CNN Dan BERT Putri Inombi, Syindikha; Rahmawaty Isa, Dewi; Asriadi; K. Nasib, Salmun; K. Hasan, Isran; Nurmardia Abdussamad, Siti
Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 6 No. 2 (2025): Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3483/trigonometri.v6i2.12267

Abstract

The government’s Housing Savings Program (TAPERA) has sparked various public reactions, particularly on social media platform X (Twitter). This study aims to analyze user sentiment toward the TAPERA policy using the Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) methods. The dataset was collected using a crawling technique on X (Twitter), comprising a total of 1,790 tweets. These data were processed through preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The CNN and BERT models were then trained and tested to classify sentiments as positive or negative. The findings indicate that the BERT model outperformed CNN, achieving a higher accuracy of 86% compared to CNN’s 85%, along with superior recall, precision, and F1-score values. These results suggest that the BERT method is more effective in comprehensively understanding sentiment context.
Co-Authors Alamri, Fahima Aryasandi, I Wayan Can Asriadi Djihad Wungguli Ismail Djakaria K. Nasib, Salmun La Ode Nashar Latif, Sintia Abdul Ningsih, Setia Nurmardia Abdussamad, Siti Nurwan Nurwan, Nurwan Putri Inombi, Syindikha Rahmawaty Isa, Dewi Sembiring, Rinawati