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All Journal Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Indonesia Symposium on Computing KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications Journal of Research in Mathematics Trends and Technology Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun MATHunesa: Jurnal Ilmiah Matematika Jurnal Siger Matematika Jurnal Matematika Integratif BACA: Jurnal Dokumentasi dan Informasi Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications
Aang Nuryaman
Universitas Lampung
Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Optimizing Multi-Layer Perceptron Performance in Sentiment Classification through Neural Network Feature Extraction Alam, Muhammad Fikri; Nuryaman, Aang; Khotimah, Purnomo Husnul; Parlina, Anne; Sihombing, Andre
BACA: Jurnal Dokumentasi dan Informasi Vol. 46 No. 1 (2025): BACA: Jurnal Dokumentasi dan Informasi (Juni)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/baca.2025.8240

Abstract

There are some problems with using the Multi-Layer Perceptron (MLP) model for complex tasks because it can be hard to understand hierarchical relationships and tends to overfit data with a lot of dimensions. This research proposes an enhanced MLP model for sentiment classification by integrating feature extraction layers from advanced neural networks, specifically the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM). These layers aim to improve the model's representation capabilities by capturing more nuanced features. To evaluate the performance improvements of this augmented MLP model, metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve for Receiver Operating Characteristics (ROC-AUC) were employed. A key metric focus is the delta value, representing changes in the ROC-AUC, to assess the significance of these enhancements. The integration of CNN as a feature extraction layer yielded optimal ROC-AUC results, achieving values of 93.30% and 93.00%, which reflect an improvement of 0.51% and 4.46% over the baseline model. These findings indicate that adding feature extraction layers significantly enhances MLP performance in sentiment classification tasks. Future research may explore the potential of using alternative neural networks as feature extractors to continue advancing MLP capabilities in complex NLP applications.
IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA Kurniasari, Dian; Kurniawati, Virda; Nuryaman, Aang; Usman, Mustofa; Nisa, Rizki Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1919-1930

Abstract

Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.
Comparison of Naïve Bayes and Random Forest Models in Predicting Undergraduate Study Duration Classification at the University of Lampung Hestina P., Shelvira; Widiarti; Nuryaman, Aang; Usman, Mustofa
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241317

Abstract

This study aims to compare the performance of the Naïve Bayes and Random Forest classification algorithms in predicting the study duration of undergraduate students in the Mathematics Study Program at the University of Lampung. The dataset consists of 537 graduation records from 2020–2024. The research steps include data preprocessing, data partitioning (train-test split and k-fold cross validation), model building, and evaluation using a confusion matrix. The results show that the Random Forest algorithm achieved the highest accuracy of 94.44%, outperforming Naïve Bayes which reached a maximum accuracy of 92.59%. These findings suggest that Random Forest is more effective for classifying student study durations. These findings suggest that Random Forest is more effective for classifying student study durations.
Pelatihan LaTeX Menggunakan Overleaf untuk Meningkatkan Kemampuan Penulisan Karya Ilmiah bagi Dosen di Pringsewu Fitriani, Fitriani; Faisol, Ahmad; Nuryaman, Aang; Kurniasari, Dian; Utami, Bernadhita Herindri Samodera
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 5 No. 3 (2024)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v5i3.184

Abstract

Keunggulan LaTeX telah menjadikannya sebagai standar dalam penulisan karya ilmiah. Saat ini, sebagian dosen belum dapat menggunakan LaTeX sehingga diperlukan pelatihan penggunaan Latex bagi Dosen di Pringsewu Lampung. Kegiatan ini bertujuan untuk meningkatkan kemampuan penulisan karya ilmiah bagi para Dosen. Kegiatan ini dilaksanakan di Institut Bakti Nusantara (IBN) Pringsewu dengan metode ceramah interaktif dan praktik langsung menggunakan Overleaf. Keuntungan menggunakan overleaf adalah peserta tidak perlu mengunduh aplikasi untuk menjalankan LaTeX. Pada sesi terakhir, beberapa peserta mempresentasikan hasil kerjanya. Selanjutnya, peserta diberikan kuesioner mengenai tingkat pemahaman peserta terhadap materi yang diberikan. Kegiatan ini diikuti oleh 34 dosen di Pringsewu. Berdasarkan hasil kuisioner, sebanyak 76,47% peserta memahami materi dengan sangat baik, 20,59% memahami dengan baik dan 2,94% cukup memahami materi yang diberikan. Selain itu, sebanyak 97,06% peserta tertarik menulis artikel menggunakan LaTeX.
Simulasi Jumlah Klaim Agregasi Berdistribusi Poisson Dengan Besar Klaim Berdistribusi Gamma dan Rayleigh Rudi Ruswandi; Aang Nuryaman; Subian Saidi
Limits: Journal of Mathematics and Its Applications Vol. 17 No. 2 (2020): Limits: Journal of Mathematics and Its Applications Volume 17 Nomor 2 Edisi De
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A claim is a transfer of risk from the insured to the guarantor. Claims that occur individually are called individual claims, whereas collections of individual claims are called aggregation claims in a single period of vehicle insurance. Aggregation claims consist of a pattern of the number and amount (nominal value) of individual claims, so that the model of aggregation claims is formed from each distribution of the number and amount of claims. The distribution of claims is based on the probability density function and the cumulative density function. One method that can be used to obtain a claim aggregation model is to use convolution, which is by combining the distribution of the number of claims and the distribution of the amount of claims so that the expected value can be obtained to predict the value of pure premiums. In this paper, aggregation claim modeling will be carried out with the number of claims distributed Poisson and the amount of claims distributed Gamma. As comparison, we compare it with claim amount distributed Rayleigh. By using VaR (value at risk) and MSE (Mean Square Error) indicators, the results of the analysis show that the Rayleigh distribution is better used for distributing data that has extreme values.
Co-Authors . Wamiliana Agus Yodi Gunawan Ahidiyah, Siti AHMAD FAISOL Ahmad Faisol Ahmad Rizki Wiranto Alam, Muhammad Fikri Anne Parlina Ardiyanti, Lily Asmiati Asmiati Asmiati Asmiati Asmiati Asmiati Atika Faradilla Bagus Hayatul Jihad Bagus Hayatul Jihad Citra Puspa Tria Dalfa Habibah Nurul Aini Danuari Fitrio, Beni Dewi Rakhmatia Nur Dian Kurniasari Dian Kurniasari Dina Eka Nurvazly Eri Setiawan Eri Setiawan Eri Setiawan Fachri Faisal Faradilla, Atika Fathudin Fathudin Fatimatuzzahra Fatimatuzzahra Fitriani Fitriani Fitriani, Debi Nur Hestina P., Shelvira Ismawati Ismawati Khotimah, Purnomo Husnul Kuntjoro Adji Sidarto Kurniawati, Virda La Zakaria La Zakaria Mardani, Maya Mega Putri Muhammad Ihsan Abyan Muhammad Ihsan Abyan Mulia Astuti Mustofa Usman Mustofa Usman Mustofa Usman Nabilla Yolanda Paramitha Nisa, Rizki Khoirun Notiragayu Notiragayu Nur, Dewi Rakhmatia Ranti Vidia Mahyunis Raratia ulsa Alfian Rudi Ruswandi Rudi Ruswandi Saidi, Subian Saiful Rohman Sihombing, Andre Soleh Fajar Junjunan Soleh Fajar Junjunan Sutrisno, Agus Swita, Baki Utami, Bernadhita Herindri Samodera Warsono Warsono Warsono Warsono Widarti, Widiarti Widiarti Widiarti Widiarti Yogi Wibisono Budhi Zakaria, La