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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) 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 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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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.
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.
PENERAPAN VIRGIN COCONUT OIL UNTUK MENGURANGI PRURITUS PADA PASIEN HEMODIALISIS : Literature Review Dinita, Fera Alfina; Qotrunnada, Hasna Fadhilah; Purwanti, Okti Sri; Kurniasari, Dian
PREPOTIF : JURNAL KESEHATAN MASYARAKAT Vol. 9 No. 2 (2025): AGUSTUS 2025
Publisher : Universitas Pahlawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/prepotif.v9i2.46971

Abstract

Pruritus merupakan salah satu komplikasi yang sering dialami oleh pasien dengan penyakit gagal ginjal kronis (PGK) yang menjalani hemodialisis. Kondisi ini berdampak signifikan terhadap kenyamanan dan kualitas hidup pasien. Penanganan pruritus umumnya dilakukan melalui terapi farmakologis dan nonfarmakologis. Namun, penggunaan obat-obatan jangka panjang dapat menimbulkan efek samping yang cukup serius. Oleh karena itu, dibutuhkan alternatif terapi yang lebih aman dan mudah diaplikasikan. Virgin coconut oil (VCO) sebagai emolien alami muncul sebagai salah satu pilihan terapi komplementer yang relatif aman dan mudah digunakan oleh pasien.Literature review ini bertujuan untuk mengetahui efektivitas VCO dalam mengurangi pruritus pada pasien hemodialisis. Proses penelusuran literatur dilakukan melalui database Scopus, ResearchGate, dan Google Scholar dengan rentang pencarian lima tahun terakhir. Kata kunci yang digunakan meliputi “virgin coconut oil”, “pruritus”, dan “hemodialysis”. Dari hasil pencarian, terdapat sepuluh jurnal yang memenuhi kriteria inklusi dan dianalisis lebih lanjut.Hasil analisis menunjukkan bahwa VCO efektif dalam menurunkan tingkat pruritus pada pasien hemodialisis. Selain itu, VCO juga diketahui dapat memperbaiki kelembapan kulit dan mengurangi inflamasi lokal, yang umumnya menjadi faktor penyerta munculnya rasa gatal pada pasien. Beberapa penelitian juga menunjukkan bahwa efektivitas VCO dalam menurunkan skor pruritus lebih unggul dibandingkan dengan minyak zaitun, yang juga dikenal sebagai emolien alami.Berdasarkan hasil tinjauan literatur ini, dapat disimpulkan bahwa VCO merupakan pilihan terapi komplementer yang efektif dan aman untuk mengurangi pruritus pada pasien PGK yang menjalani hemodialisis. Selain memberikan manfaat fisik, penggunaan VCO juga berpotensi meningkatkan kenyamanan dan kualitas hidup pasien secara keseluruhan.
Workshop Pelatihan Pemahaman Konsep Aritmatika Sosial untuk Siswa SMKS Nurul Huda Pringsewu Kurniasari, Dian; Asmiati; Widiarti; Sawitri, Riza
ABDI AKOMMEDIA : JURNAL PENGABDIAN MASYARAKAT Vol. 3 No. 3 (2025)
Publisher : ABDI AKOMMEDIA : JURNAL PENGABDIAN MASYARAKAT

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

Abstract

Aritmatika sosial salah satu aspek penting dalam numerasi dasar dan memiliki manfaat praktis yang relevan dengan dunia kerja dan kehidupan sehari-hari. Rendahnya pemahaman siswa SMKS Nurul Huda terhadap konsep matematika yang kontekstual, khususnya pada materi aritmatika sosial, sehingga diperlukan pelatihan pemahaman konsep aritmatika sosial. Kegiatan ini bertujuan untuk memberikan pemahaman konsep aritmatika sosial secara aplikatif serta memotivasi siswa untuk belajar secara mandiri dan berkelanjutan. Workshop ini dilakukan dalam bentuk ceramah interaktif, diskusi, dan simulasi perhitungan kontekstual agar siswa lebih mudah memahami konsep matematika yang diajarkan. Keberhasilan kegiatan workshop dievaluasi melalui pre-test dan post-test. Berdasarkan nilai pre-test dan post-test, diperoleh rata-rata selisih perbedaan nilai post-test dan pre-test sebesar 33,08. Pengujian selisih beda dua rata-rata dengan menggunakan uji t memberikan hasil bahwa secara statistik nilai post-test lebih besar dibandingkan dengan nilai pre-test. Hasil ini menunjukkan bahwa para peserta memahami apa yang disampaikan oleh narasumber dan membuka wawasan mereka tentang artimatika sosial dalam kehidupan sehari-hari.
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%.
Co-Authors Aang Nuryaman Ahmad Faisol Antonio, Yeftanus Asmiati Asomah, Nur Eli Budi Susetyo Candriasih, Putu Desi, Nur Husnina Dinita, Fera Alfina Elfaki, Faiz A.M Fahmi Hafid 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 Lumbanraja, Favorisen R Matsuri, Matsuri Maulana, Andi Muh Monoarfa, Yustiyanty Mukhlisin, Zaenal Munandar, Ahmad Rizki Mustofa Usman Napitupulu, Joni Nisa, Fauzizah Nasyirotun Nisa, Rizki Khoirun Nitro Galenso 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 Tiwow, Marlin Tri Sumiyanti Utami, Bernadhita Herindri Samodera Wamiliana Wamiliana Wamiliana Warsono Warsono Warsono Widiarti Wijianto Wijianto