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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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Analisis Struktur Terbaik Neural Network dengan Algoritma Backpropagation dalam Memprediksi Indeks Kandungan Sulfida (SO2) di Ibu Kota Jakarta Kurniasari, Dian; Rafdiono, M Naufal Ammar; Warsono, Warsono
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 2 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i2.76166

Abstract

Polusi udara merupakan masalah lingkungan yang umum terjadi di kota-kota besar di tanah air, tidak terkecuali Ibu Kota Jakarta. Tingginya jumlah penduduk di Jakarta menyebabkan konsentrasi polusi udara semakin tinggi karena peningkatan jumlah kendaraan. Kondisi ini diperburuk dengan banyaknya limbah yang dihasilkan oleh pabrik dari berbagai industri. Sulfida (SO2) merupakan salah satu polutan dengan konsentrasi tertinggi di Jakarta dengan total beban emisi sebesar 19.7 kton. Oleh karena itu, prediksi indeks kandungan SO2, merupakan isu penelitian yang penting karena zat SO2 dapat berdampak terhadap berbagai faktor, seperti lingkungan, pertanian, dan kesehatan. Tujuan dari penelitian ini adalah menemukan model terbaik dalam melakukan prediksi SO2 di Jakarta menggunakan Artificial Neural Network (ANN). Jenis algoritma ANN yang digunakan adalah backpropagation. Lebih lanjut, model prediksi dibangun dan dibandingkan berdasarkan tiga fungsi aktivasi dan skema pembagian data yang berbeda untuk memperoleh struktur atau arsitektur model ideal dengan tujuan mengoptimalkan hasil prediksi. Model dievaluasi menggunakan nilai Mean   Absolute Percentage Error (MAPE) terkecil. Hasil penelitian menunjukkan bahwa model terbaik untuk prediksi indeks kandungan SO2 adalah model yang menerapkan fungsi aktivasi Tanh dengan skema pembagian data 90% data pelatihan dan 10% data pengujian. Model tersebut memperoleh nilai MAPE sebesar 15.87412%, dan akurasi sebesar 84.12588%. Hal ini mengindikasikan bahwa model memiliki tingkat akurasi yang cukup baik dalam memprediksi indeks kandungan SO2 di Ibu Kota Jakarta dan dapat dijadikan sebagai pendekatan alternatif dalam meningkatkan efektivitas pengendalian SO2.
Comparative analysis of deep Siamese models for medical reports text similarity Kurniasari, Dian; Usman, Mustofa; Warsono, Warsono; Lumbanraja, Favorisen Rosyking
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6969-6980

Abstract

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
Application of the Artificial Neural Network Algorithm to Predict the Realization of the Duty Tax on the Name of Motor Vehicles in Lampung Province Kurniasari, Dian; Ramadhani, Putri Salsabila; Wamiliana, Wamiliana; Warsono, Warsono
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29456

Abstract

Regional taxes, specifically the Motor Vehicle Name Return Tax (BBNKB), provide the primary source of revenue for regions from the several forms of taxes. The BBNKB tax is crucial in funding government and regional development due to its significant annual growth, encompassing four-wheeled and two-wheeled vehicles. Furthermore, the BBNKB tax catalyzes regional economic expansion and significantly contributes to the government's income. Hence, predicting and forecasting the BBNKB Tax in Lampung Province is necessary to monitor future tax rate fluctuations. That will enable the government to devise innovative tax payment systems and establish tax revenue targets. This study utilizes the Artificial Neural Network (ANN) methodology, using many approaches for distributing training and testing data to forecast. In addition, we utilize hyper-tuning on several factors to obtain the most favourable configurations. The ideal model achieved has a training data allocation of 80% and a testing data allocation of 20%. It was trained for 50 epochs and used a batch size of 16. The model has exceptional predictability, attaining an accuracy rating of 96.51%. Additionally, it showcases a low Root Mean Square Error (RMSE) of 0.246 and a minimal Mean Absolute Percentage Error (MAPE) score of 3.48%. Therefore, it is appropriate to predict the next two-year term. As a result, the forecast for the amount of tax collected from motor vehicle name returns in Lampung has fluctuated.
On the Characteristic Function of the Four-Parameter Generalized Beta of the Second Kind (GB2) Distribution and Its Approximation to the Singh-Maddala, Dagum, and Fisk Distributions Warsono; Kurniasari, Dian
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.1.201-211

Abstract

Researchers have thoroughly investigated generalized distributions due to their inherent flexibility, which allows them to include several well-known distributions as special cases. Among these, the four parameter Generalized Beta of the Second Kind (GB2) distribution stands out as one of the most versatile frameworks in probability theory. Despite its broad applications, the GB2 distribution’s characteristic function, a critical tool in probability and statistical analysis, lacks a closed-form solution in the existing literature. This study pursues two primary objectives: first, to derive the characteristic function and the kth moment of the GB2 distribution, and second, to demonstrate how the GB2 distribution can serve as a close approximation to the Singh-Maddala, Dagum, and Fisk distributions using its characteristic function and kth moment. These derivations and approximations rely on gamma and beta functions, supplemented by the Maclaurin series expansion.
Modeling Vector Error Correction with Exogeneous (VECMX) Variable for Analyzing Nonstationary Variable Energy Used and Gross Domestic Product (GDP) Usman, Mustofa; Wamiliana; Russel, Edwin; Kurniasari, Dian; Widiarti; Elfaki, Faiz A.M
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.1.283-293

Abstract

Analysis of energy used, GDP and population has been carried out in many countries and has become a topic of interest for many researchers and governments. This is because energy used is an important factor for society and industry in a country. In this study, the modeling of the relationship between energy used, GDP and population as an exogenous variable for the cases of Indonesia from 1967-2023 will be discussed. The energy used and GDP data are nonstationary with order one, I(1), and there is cointegration between energy used and GDP. Therefore, the model which will be used is the Vector Error Correction Model with Exogenous variable (VECMX) with population as the exogenous variable. From the results of analysis, the best model is VECMX(3,1) with cointegration rank R=1. Based on this model, the pattern of the relationship among the three variables, Granger-causality between energy used and GDP, exogenous impact on energy used and GDP, and forecasting for the next 10 years will be discussed.
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.
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.
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.
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