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All Journal International Journal of Electrical and Computer Engineering Saintika Medika: Jurnal Ilmu Kesehatan dan Kedokteran Keluarga. TEKNIK INFORMATIKA Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Transformatika Science and Technology Indonesia Indonesian Journal of Artificial Intelligence and Data Mining Prosiding Seminar Nasional Teknoka BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Amerta Nutrition Jurnal Ilmu Manajemen dan Akuntansi Terapan Jurnal Didaktika Dwija Indria PREPOTIF : Jurnal Kesehatan Masyarakat Prima Ekonomika : Jurnal Ekonomi dan Bisnis Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Poltekita: Jurnal Pengabdian Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) SWARNA Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Dedikasi untuk Negeri Abdi Akommedia : Jurnal Pengabdian Masyarakat Journal of Evidence - Based Nursing and Public Health Proceeding Mulawarman International Conference on Tropical Public Health Integra: Journal of Integrated Mathematics and Computer Science
Dian Kurniasari
Mathematic Dept. Lampung of University
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Enhancing Weather Forecasting in Bandar Lampung: A Hybrid SARIMA-LSTM Approach Kurniasari, Dian; Salsabila, Anindya Dafa; Usman, Mustofa; Warsono, Warsono
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27188

Abstract

Indonesia’s tropical climate, marked by rainy and dry seasons, is increasingly affected by extreme weather events driven by climate change. Rising temperatures, shifting rainfall patterns, and sea-level rise have intensified health risks such as malaria, dengue hemorrhagic fever (DHF), and gastrointestinal infections. Accurate weather forecasting is essential for mitigating these challenges and informing risk management strategies. This study develops and evaluates a hybrid SARIMA-LSTM model for weather forecasting in Bandar Lampung, integrating time series analysis with deep learning to enhance predictive accuracy. SARIMA captures seasonal variations, while LSTM models nonlinear relationships, offering a robust approach to forecasting complex weather patterns. The SARIMA (6,1,0)(3,1,0)26 model was selected for its effective seasonal representation and combined with LSTM to leverage its capability in modelling nonlinear dependencies. Hyperparameter optimization using grid search further improved model performance. Two data partitioning approaches were tested: 70%-30% and 80%-20% splits for training and testing, respectively. The SARIMA-LSTM hybrid model demonstrated superior performance with the 80%-20% split, achieving MSE, RMSE, and MAPE values of 0.1174, 0.3426, and 0.0104%, respectively. The model accurately forecasted weather conditions over 21 weeks, aligning closely with observed trends and effectively capturing seasonal patterns. These findings underscore the model’s potential to support public health strategies, including disease outbreak mitigation for malaria and DHF, and enhance disaster preparedness in flood-prone areas.
CLASSIFICATION MODELS FOR ACADEMIC PERFORMANCE: A COMPARATIVE STUDY OF NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN ANALYZING UNIVERSITY OF LAMPUNG STUDENT GRADES Kurniasari, Dian; Hidayah, Rekti Nurul; Notiragayu, Notiragayu; Warsono, Warsono; Nisa, Rizki Khoirun
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2066

Abstract

At the university, students are provided with a comprehensive assessment of their academic achievements for each course completed at the end of every semester. This study aimed to compare the effectiveness of two classification methods, the Naïve Bayes and the Random Forest methods, in classifying student learning outcomes. The research process is segmented into various stages: data selection, data preparation, model building and testing, and model evaluation. The findings indicated that the Naïve Bayes and Random Forest approaches exhibited superior accuracy levels when employing data splitting strategies, in contrast to k-fold cross-validation. Based on the examination, the Random Forest approach demonstrated superiority in identifying the scores of University of Lampung students, achieving an accuracy percentage of 99.38%. Notably, both techniques showed a substantial performance improvement using Gradient Boosting. The Naïve Bayes method attained an accuracy rate of 99.89%, while the Random Forest method reached 99.45%. The results demonstrate that employing the Random Forest classification method consistently leads to superior performance in identifying and classifying student grades. Furthermore, using Gradient Boosting in the boosting process has demonstrated its efficacy in enhancing the classification methods' accuracy. These findings significantly contribute to the comprehension and advancement of evaluation systems for assessing student learning outcomes in the university environment.
Safe breath: A concept for air quality monitoring app using internet of things and early detection to support Tuberculosis elimination by 2030 Munandar, Ahmad Rizki; Rozak, Fatur; Simatupang, Agustino; Kurniasari, Dian
Journal of Evidence-based Nursing and Public Health Vol. 2 No. 1: (February) 2025
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jevnah.v2i1.2025.1710

Abstract

Background: Tuberculosis (TB) remains a significant global health challenge, particularly in countries with poor air quality and high population density. Delayed diagnosis and environmental factors, such as air pollution, contribute to the high prevalence and mortality rates associated with this disease despite advancements in treatment and prevention. A review of the literature highlights a significant association between long-term exposure to air pollutants, such as delicate particulate matter ( ) and an increased risk of TB. Internet of Things (IoT) technology, which integrates real-time environmental sensors with analytical algorithms, offers the potential to support TB prevention through data-driven and modern technological approaches. This study aims to design a conceptual framework based on IoT technology to enhance early TB detection through air quality monitoring. Methods: A literature review was conducted from 2020 to 2025, focusing on designing the Safe Breath conceptual framework. Relevant articles were retrieved from databases including PubMed, ScienceDirect, and Google Scholar, filtered by inclusion criteria and full-text availability. Data were synthesized to explore the relationship between air quality and TB incidence. Findings Poor air quality is closely linked to TB risk, making environmental monitoring essential in disease control. IoT technology can collect real-time data through air quality sensors, monitoring environmental risk factors continuously. The Safe Breath application concept integrates air sensors with early detection features to improve TB screening accuracy while encouraging community participation in disease prevention efforts. Conclusion: The proposed Safe Breath application combines IoT technology with air quality monitoring and early detection systems, improving screening accuracy and proactive TB control through a community-based approach. Novelty/Originality of this article: This study presents a novel approach by integrating IoT technology and environmental monitoring for TB control. The combined use of air sensors and early detection tools offers a scalable, data-driven solution for global TB prevention.
Implementation of Random Forest Method for Customer Churn Classification Kurniasari, Dian; Humairosi, Lutfia; Warsono; Notiragayu
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.202

Abstract

Annually, the banking sector consistently undergoes substantial expansion, as demonstrated by the escalating quantity of banks. Nevertheless, this expansion has led to escalating rivalry among banks as they strive to offer superior service to consumers, ultimately impacting customer migration across organizations. Customer churn, or attrition, substantially influences a company's financial performance. Hence, it is crucial to discern the conduct of clients who can discontinue their association with the organization. Precise identification is essential to gather the necessary information for the organization to retain clients and decrease churn rates. An effective strategy for addressing this issue is categorizing client behaviour using historical data. The study utilized the Random Forest approach, employing a 90% training data and 10% testing data ratio. The hyperparameter tuning findings indicate that the optimal parameter combination for constructing a Random Forest model is 400 n_estimators and 40 max_depth. The Synthetic Minority Over-Sampling Technique (SMOTE) mitigates data during categorization. The evaluation of the model demonstrates its exceptional performance in classifying imbalanced data, achieving an accuracy of 90.83%, precision of 89.29%, recall of 92.07%, and  f1-score of 90.66%.
Pelatihan Pembuatan Infografis Desa dalam Rangka Mendukung Program Desa Cantik Widiarti; Kurniasari, Dian; Wamiliana; Asmiati
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.129

Abstract

The Desa Cantik program is one of the Central Statistics Agency (BPS) programs to realize sectoral statistical development at the village level in a sustainable and comprehensive manner. BPS Tanggamus Regency is one of the BPS that participates in developing Desa Cantik. In 2024, BPS Tanggamus will develop 4 villages spread across Tanggamus Regency. The four villages are Kampung Baru, Kagungan, Purwodadi and Banding Agung. The purpose of this development is to increase the capacity of the village or the ease in identifying data needs and potential owned by the village in order to eradicate poverty and increase statistical literacy in the village. In line with the responsibility carried out by BPS regarding this Desa Cantik program, the development is also a challenge for the staff of the Mathematics Department FMIPA Unila to participate in transferring knowledge and skills, especially related to Statistical Techniques. Through this program, human resources in the village are trained to process village monographic data and present it in the form of infographics with the help of the Tableu and Canva applications. The results of this training activity showed that 71% of participants had actively participated in preparing infographics.
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 Kurniasari, Dian; ani, Fitri; Warsono, Warsono
Jurnal Dedikasi untuk Negeri Vol 1, No 1 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat UML

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 
Robusta London Coffee Price Forecasting Analysis Using Recurrent Neural Network – Long Short Term Memory (RNN – LSTM) Nosa, Ferzy Tryanda; Kurniasari, Dian; 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%. 
PERFORMANCE OF THE ACCURACY OF FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH AND ANN METHODS Kurniasari, Dian; Mukhlisin, Zaenal; Wamiliana, Wamiliana; Warsono, Warsono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0931-0944

Abstract

The Consumer Price Index (CPI) is the most widely used indicator of the inflation rate. Then, the value of CPI in the future must be known to be the basis of the government's making appropriate and accurate policies. The CPI data used in this study was taken from the Central Statistics Agency (BPS) from January 2006 - to December 2021. The CPI data used has a data pattern containing symptoms of heteroskedasticity. To overcome the symptoms of heteroskedasticity, the author uses the GARCH and ANN methods to determine the value of CPI in the future. The GARCH method can overcome the symptoms of heteroskedasticity in the time series forecasting process, while ANN is an effective method in time series forecasting because of its high level of accuracy. In this study, mape error calculation results were obtained with the ARIMA model (4,2,2)~GARCH(1.1) of 3.19% or with an accuracy of 96.81%, and ANN using two hidden layers of 1.24% or with an accuracy of 98.76%. Thus, the results of this study show that the ANN method is the best method of forecasting Consumer Price Index (CPI) data.
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.
The Correlation Between Educational Attainment and Duration of Breast-feeding of Women Fertil in Rural Banggai Regency S Otoluwa, Anang; Monoarfa, Yustiyanty; Kurniasari, Dian; Forsberg, Neil
Mulawarman International Conference on Tropical Public Health Vol. 1 No. 1 (2025): The 3rd MICTOPH
Publisher : Faculty of Public Health Mulawarman University, Indonesia

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

Abstract

Background : The Government of Indonesia has recently initiated a national effort to reduce incidence of child stunting. One of the important effort to reduce stunting is breastfeeding practice. Objective : To evaluate the correlation between the level of education and duration of breast feeding on women fertile in Banggai Regency. Research Methods/ Implementation Methods : A sample of 454 women in children bearing from twenty villages contained with Banggai Regency were selected to participate in this study. Variables included duration of breastfeeding, mothers education level, mothers ages. Data was analysed using binary logistic regression. Results : The data indicate that large proportions (near 45%) of women completed only elementary school. Approximately 30% of the women had completed high school. Most women breast-fed for over six months. Less than 15% of the women in all villages breast-fed for less than 6 months. There was a significant positive correlation between educational attainment and duration of breast-feeding (P<.0455). Conclusion/Lesson Learned : Educational attainment has positive correlation to the duration of breastfeeding.
Co-Authors Aang Nuryaman AHMAD FAISOL Amanto Amanto Andi Muh. Maulana Antonio, Yeftanus Asmiati Asomah, Nur Eli Budi Susetyo Candriasih, Putu Desi, Nur Husnina Dinita, Fera Alfina Elfaki, Faiz A.M Fahmi Hafid Favorisen R. Lumbanraja Fitri Ani, Fitri Fitriani Fitriani Forsberg, Neil Galonso, Nitro Hadi, Normi Abdul Hdiana, Yazid Zinedine Hidayah, Rekti Nurul Humairosi, Lutfia Jenny Indrastoeti Siti Poerwanti, Jenny Indrastoeti Siti Kalaha, Evelin Kurniawati, Virda Matsuri, Matsuri Monoarfa, Yustiyanty Mukhlisin, Zaenal Munandar, Ahmad Rizki Mustofa Usman Napitupulu, Joni Nisa, Fauzizah Nasyirotun Nisa, Rizki Khoirun Nitro Galenso Nosa, Ferzy Tryanda Notiragayu Notiragayu, Notiragayu Nugroho, Prihasantyo Siswo Nuraini, Maydia Egi Okti Sri Purwanti Okzarima, Wenty Putri, Amelia Fallizia Qotrunnada, Hasna Fadhilah Rafdiono, M Naufal Ammar Ramadhani, Putri Salsabila Romdhoni, Muhammad Fadhol Rozak, Fatur Russel, Edwin S Otoluwa, Anang Salsabila, Anindya Dafa Sari Rahmadhani Sawitri, Riza Setiawan, Noferdis Shella, Tiara Pramay Simatupang, Agustino Su'admaji, Arif Suciati, Indah Sulandra, Ardelia Maharani Sumiyanti, Tri Tiwow, Marlin Utami, Bernadhita Herindri Samodera Wamiliana Wamiliana Wamiliana Warsono Warsono Warsono Widiarti Wijianto Wijianto