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All Journal International Journal of Electrical and Computer Engineering IPTEK The Journal for Technology and Science JSI: Jurnal Sistem Informasi (E-Journal) JURNAL E-DUMATH Jurnal Transformatika Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Science and Technology Indonesia Jurnal Komputasi Indonesian Journal of Artificial Intelligence and Data Mining Prosiding Seminar Nasional Teknoka BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Matematika UNAND Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Siger Matematika Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Dedikasi untuk Negeri SWARNA: Jurnal Pengabdian Kepada Masyarakat Integra: Journal of Integrated Mathematics and Computer Science
Dian Kurniasari
Faculty Of Mathematics And Natural Science, Universitas Lampung, Bandar Lampung, 35141, Indonesia
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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.
A Hybrid ARIMA–GRU Model for Forecasting Palm Oil Prices at PT Sawit Sumbermas Sarana in Central Kalimantan Kurniasari, Dian; Shella, Tiara Pramay; Usman, Mustofa; Warsono
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 1 (2025): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252112

Abstract

The palm oil industry plays a strategic role in Indonesia's economic landscape. As one of the world’s largest producers, Indonesia holds substantial potential in marketing both crude palm oil (CPO) and palm kernel oil on domestic and international fronts. Palm oil prices consistently correlate with CPO prices, given that the pricing of palm oil is benchmarked against CPO, resulting in market fluctuations. Forecasting future palm oil prices becomes an essential measure in response to this volatility. The ARIMA (AutoRegressive Integrated Moving Average) model has been widely recognized as a reliable method for time series forecasting. Despite its strengths, ARIMA faces challenges in identifying the non-linear components that are often present in real-world data. The Gated Recurrent Unit (GRU) model, which incorporates an update gate and a reset gate, offers an alternative that effectively captures complex non-linear patterns. A hybrid model integrating ARIMA and GRU has therefore been developed with the aim of improving predictive accuracy. This hybrid approach includes two stages: the ARIMA model for initial predictions and a GRU model that processes the residuals from the ARIMA output. In this study, the ARIMA-GRU hybrid model demonstrated strong performance, yielding a Mean Squared Error (MSE) of 868.4690, a Root Mean Squared Error (RMSE) of 29.4698, a Mean Absolute Percentage Error (MAPE) of 0.0117, and an overall accuracy of 99.9824%.
Integrating VAR and CNN Models for Accurate Forecasting of Money Supply in Indonesia Warsono; Sulandra, Ardelia Maharani; Kurniasari, Dian; Usman, Mustofa; Susetyo, Budi
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252230

Abstract

Economic forecasting serves as a fundamental element in supporting decision-making processes across multiple sectors. One of the main areas of interest in this field is the estimation of the money supply within an economy. The Vector Autoregressive (VAR) model is a commonly applied method for forecasting; however, it often encounters limitations when processing data with nonlinear patterns. Convolutional Neural Networks (CNNs) offer an alternative approach, particularly effective in identifying nonlinear structures that are not adequately captured by VAR models. A hybrid VAR-CNN model is therefore proposed, combining the respective strengths of both techniques to improve the accuracy of predictions. This research applies to the hybrid VAR-CNN model to forecast economic variables for the period from July 2022 to June 2023. The model consists of two main components: the first utilizes forecasted values generated by the VAR model, while the second processes the residuals from the VAR output using a CNN. With 80% of the data allocated for training and 20% for testing, the hybrid VAR-CNN model demonstrates improved performance over alternative forecasting methods. Evaluation based on Mean Absolute Percentage Error (MAPE), supremum (D) values, and p-values confirms the effectiveness of this hybrid approach.
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.
Understanding Consumer Sentiments: A TextBlob-Based Sentiment Analysis Study Kurniasari, Dian; Hdiana, Yazid Zinedine; Lumbanraja, Favorisen R.; Warsono, Warsono; Hadi, Normi Abdul
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 3 (2025): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252340

Abstract

This study employs advanced sentiment analysis techniques to enhance the understanding of drug reviews, with a specific focus on TextBlob-based sentiment classification. As the accessibility of health products through pharmacies and online platforms continues to increase, individuals with limited health literacy are increasingly relying on user-generated feedback to inform their decision-making. By utilizing the TextBlob labelling method, this research categorizes user sentiments into positive, neutral, or negative, addressing the limitations inherent in traditional sentiment analysis approaches. The analysis is supported by an innovative model known as BERT, which effectively captures the emotional expression within textual data. The results indicate that the proposed approach consistently achieves an accuracy of 98% across training, validation, and testing phases, highlighting its strong performance in sentiment classification. This accomplishment underscores TextBlob’s ability to consistently and reliably assess user sentiment, thereby enriching the understanding of consumer perspectives in the pharmaceutical industry. The findings highlight the importance of effective sentiment analysis methods in healthcare, offering valuable insights for both consumers and stakeholders. Moreover, this study provides a foundation for future investigations focused on improving sentiment analysis methods across varied datasets, which will enhance the precision and applicability of classification results in different scenarios.
APLIKASI STATISTIKA DESKRIPTIF PADA DATA HASIL PENGABDIAN KEPADA MASYARAKAT: STUDI KASUS DATA HASIL PELATIHAN DISAIN MEDIA PEMBELAJARAN DAN PENGOLAHAN NILAI DENGAN MS WORD BAGI GURU SLTP DI KOTA BANDAR LAMPUNG Dian Kurniasari; Fitri ani; Warsono Warsono
Jurnal Dedikasi untuk Negeri Vol 1, No 1 (2022): Jurnal Dedikasi untuk Negeri (JDN)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (596.686 KB) | DOI: 10.36269/jdn.v1i1.876

Abstract

Abstrak Statistika deskriptif sebagai alat bantu dalam analisis data dan penelitian telah banyak digunakan secara intensif. Analisis statistika deskriptif dalam analisis pelatihan desain media pembelejaran dan pengolahan nilai dengan MS Word merupakan bentuk pengabdian kepada masyarakat oleh institusi Universitas Lampung pada umumnya dan Jurusan Matematika pada khususnya yang bertujuan untuk meningkatkan pengetahuan dan keterampilan guru dalam menggunakan komputer sebagai alat penunjang aktivitas/pekerjaan mereka di sekolah, khususnya dalam mengolah nilai siswa dan menjadikan transparansi sebagai media pembelajaran dalam menghadapi era globalisasi. Berdasarkan pretest dan posttest kegiatan pelatihan ini memberikan hasil yang 'Sangat Baik'. Artinya terjadi peningkatan hasil yang memuaskan dilihat dari hasil ujian akhir (Posttest) peserta pelatihan yang mengalami peningkatan secara signifikan sebesar 95% dari kondisi awal (pretest).   Kata kunci: Statistika Deskriptif, Analisis Data Pelatihan, Desain Media Pembelajaran, Pengolahan Nilai Abstract Descriptive statistics as a tool in data analysis and research has been used intensively. Descriptive statistical analysis in the analysis of learning media design training and value processing with MS Word is a form of community service by the University of Lampung institutions in general and the Department of Mathematics in particular, which aims to improve the knowledge and skills of teachers in using computers as a tool to support their activities/work in schools, especially in processing student values and making transparency as a medium of learning in the face of globalization. This training activity gave 'Very Good' results based on the pretest and posttest. Those means an increase in satisfactory results seen from the trainees' final exam (Posttest), which has increased significantly by 95% from the initial condition (pretest). Keywords: Descriptive Statistics, Data Trainning Anaysis,  Learning Media Design, Value Processing 
Hybrid Autoregressive Integrated Moving Average - Long Short-Term Memory (ARIMA-LSTM) Model for Forecasting BRI Bank Stock Prices Dian Kurniasari; Silvi Fitriani; Warsono Warsono; Mustofa Usman
Jurnal Matematika UNAND Vol. 15 No. 3 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.3.274-290.2026

Abstract

Investors highly favour the BRI stock as one of the most popular investment options. The ARIMA method is a suitable approach for predicting stock prices. ARIMA is flawed because it assumes linearity. An alternative approach for predicting stock prices is using Long Short-Term Memory (LSTM) models. LSTM has advantages over ARIMA in handling both linear and nonlinear data. This study utilizes the ARIMA-LSTM hybrid approach to forecast the stock price of BRI. Utilizing a hybrid approach that involves dividing the data 90 percent of the data used for training The hybrid model, which uses 80 percent of the data for training and 20 percent for testing, does not achieve the same degree of accuracy as the model that uses 10 percent of the data for testing.
Classifying Electronic Health Records with Long Short-Term Memory and Gated Recurrent Unit Networks Dian Kurniasari; Zida Bunga Sobara; Riza Sawitri
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 2 (2026): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263262

Abstract

Electronic Health Records (EHR) are a complete digital store of medical information which contains valuable information about a person’s health status. These data are an important tool for health care professionals to decide the management of the patient, i.e. the need for hospitalization (inpatient care) or outpatient treatment. The importance of EHR data underscores the need for hospitals to act quickly and develop appropriate follow-up for patients. The aim of this study is to assess and compare the performance of two deep learning models, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) for accurate classification of EHR data to predict whether a patient is hospitalized (inpatient or outpatient) based on laboratory test results. This study aims to compare these models to identify the best way to classify patient treatment needs. The data set used in this study is EHR data from a private hospital in Indonesia which contains haematocrit, haemoglobin, erythrocytes, leukocytes, thrombocytes, MCH, MCHC, MCV, age, gender, and source (inpatient or outpatient). The data was pre-processed by labelling, variable selection and handling missing and duplicate values. The data set is split for training and testing the models, LSTM and GRU. Performance evaluation is based on accuracy, training time and other relevant metrics to evaluate both the classification precision and computational efficiency of the models. The classification accuracies of the two models were close to each other with LSTM being 75.57% and GRU being 75.34%. However, GRU showed better training efficiency, executing faster than LSTM model. In the cases where the training time efficiency is the priority, the GRU model is the better choice for its faster speed. However, LSTM performs slightly better in terms of accuracy, especially in classifying outpatient cases, making it more appropriate for applications where accuracy is of high importance.
Robusta London Coffee Price Forecasting Analysis Using Recurrent Neural Network – Long Short Term Memory (RNN – LSTM) Ferzy Tryanda Nosa; Dian Kurniasari; Amanto Amanto; Warsono Warsono
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5482

Abstract

Coffee price forecasting has a significant role in preventing price fluctuations at a time. Therefore, a method is needed that can be used to forecast the price of coffee. This study discusses the analysis of coffee price forecasting using the Recurrent Neural Network – Long Short-Term Memory (RNN – LSTM) method. This study will be determined the best LSTM model that aims to get the results of forecasting the price of London robusta coffee with the smallest  Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. Using the LSTM model with units of 128 and dropouts of 0.1, forecasting the price of London robusta coffee has an RMSE value of 1,303 and MAPE of 3.53%. Therefore, the LSTM model can indicate the cost of London robusta coffee with an accuracy rate of 96.47%. 
Co-Authors . Wamiliana Aang Nuryaman Ahmad Faisol Amanto Amanto Amanto Amanto Amelia Fallizia Putri Anis Mahfud Al’afi Anisa Fitriyani Asmiati Asmiati Asmiati Budi Susetyo Dalfa Habibah Nurul Aini Dian Ekawati Dian Kurniasari Dian Kurniasari Dolly Yudhistira, Dolly Elfaki, Faiz A.M Evita Fitriasari Fatkur Rokhman Fitri ani Hadi, Normi Abdul Hdiana, Yazid Zinedine Hidayah, Rekti Nurul Humairosi, Lutfia Indah Suciati Jevri Setia Nugraha Kartika Candra Buana Kurnia Muludi Kurniawati, Virda Lumbanraja, Favorisen R Muhammad Taufiq Sofrizal Mukhlisin, Zaenal Mustofa Usman Mustofa Usman Mustofa Usman Nisa, Rizki Khoirun Noferdis Setiawan Notiragayu Notiragayu Notiragayu Notiragayu Notiragayu Notiragayu Notiragayu, Notiragayu Nur Alifiah Rafdiono, M Naufal Ammar Rahman Cahyadi, Rahman Ramadhani, Putri Salsabila Ranti Vidia Mahyunis Riza Sawitri Russel, Edwin Salsabila, Anindya Dafa Shella, Tiara Pramay Silvi Fitriani Siti Laelatul Chasanah Sulandra, Ardelia Maharani Sulistian Oskavina Utami, Bernadhita Herindri Samodera Wamiliana Wamiliana Wamiliana Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Wenty Okzarima Widiarti Widiarti Widiarti Yeftanus Antonio Zida Bunga Sobara