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All Journal Jurnal Matematika UNAND Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
K. Nasib, Salmun
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Mathematics Physics

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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%.
Analysis Analisis Sistem Antrian dengan Model M/M/C dalam Meningkatkan Efektivitas Kinerja Sistem Ibrahim, Novita; K. Nasib, Salmun; Nuha, Agusyarif Rezka; Katili , Muh Rifai; Nurwan Nurwan; Wungguli , Djihad
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 3 No. 2 (2025): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v3i2.431

Abstract

Study This aim For analyze system queue at the Population and Registration Service Civil ( Disdukcapil) Bone Bolango Regency as well as apply the M/M/C (Multi Channel Single Phase ) queuing model optimizing performance system and upgrade effectiveness service to public . Arrival data visitors and time service collected for 5 days through observation . Analysis results show system applied queue moment This is the model (M/M/4 ): (FIFO/∞/∞) with level arrival visitors Poisson distribution , time service distribute Exponential , 4 counters service , discipline first-come first-served (FCFS) queues , as well source arrival and capacity queue No limited . Size performance the system in existing conditions shows time wait for the average visitor in system amounting to 26.4 minutes and time Wait in queue amounting to 44.4 minutes . For optimizing performance , research recommend application of the model (M/M/7 ) : (FIFO/∞/∞) with add amount counter service into 7 counters . In this model , level utility system (ρ) is below 50 % ie about 41%, which is considered effective Because enter in range level utility low (5%-10%). Application of the queuing model with 7 counters projected can shorten time wait for the average visitor in system to 18.42 minutes and time Wait in queue to 18 minutes . Findings This expected can increase effectiveness service and satisfaction public to service Disdukcapil Bone Bolango Regency.
ANALISIS SENTIMEN TWITTER TERHADAP NYAMUK WOLBACHIA MENGGUNAKAN METODE LSTM DENGAN PENDEKATAN NLTK Lakoro, Tiara; K. Nasib, Salmun; Imansyah Yahya, Nisky; S. Panigoro, Hasan; 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.v6i2.12266

Abstract

Dengue Hemorrhagic Fever (DHF) is one of the major health issues in Indonesia. One of the preventive measures is the Wolbachia mosquito program. However, the implementation of this program has sparked various reactions from the public, which can be observed through social media, particularly Twitter. This study aims to analyze public sentiment towards Wolbachia mosquitoes using the Long Short-Term Memory (LSTM) method and the Natural Language Toolkit (NLTK) approach. Data was collected through a crawling process from Twitter using keywords related to "Wolbachia mosquitoes." Preprocessing was then carried out using NLTK, including tokenization, stopword removal, and stemming. The data was manually labeled into positive, negative, and neutral sentiment categories. The LSTM model was used for sentiment classification with the best parameters, including 100 neurons, a learning rate of 0.001, a sigmoid activation function, a batch size of 32, and 7 epochs. The results indicate that the LSTM model used was able to classify sentiment with an accuracy of 95%, precision of 94%, recall of 97%, and an F1-score of 95%. This demonstrates that the LSTM method with the NLTK approach is effective in analyzing public sentiment towards
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 Agusyarif Rezka Nuha Aryasandi, I Wayan Can Asriadi Djihad Wungguli Hasan S. Panigoro Ibrahim, Novita K. Hasan, Isran Lakoro, Tiara Muhammad Rifai Katili NISKY IMANSYAH YAHYA Nurmardia Abdussamad, Siti NURWAN NURWAN Nurwan, Nurwan Putri Inombi, Syindikha Rahmawaty Isa, Dewi Sembiring, Rinawati