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All Journal Jurnal Pendidikan Ekonomi (JUPE) Interest : Jurnal Ilmu Kesehatan Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Pendidikan Nonformal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jiko (Jurnal Informatika dan komputer) International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Kajian Pariwisata Jurnal Ekonomi Kependudukan dan Keluarga
Nooraeni, Rani
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Pekerja Anak dan Pendidikannya Di Masa Depan Hamdani, Febri; Nooraeni, Rani; Lumaksono, Adi
Jurnal Pendidikan Nonformal Vol 18, No 1 (2023): Maret 2023
Publisher : Fakultas Ilmu Pendidikan-Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um041v18i12023p24-35

Abstract

Child labour has an impact not only on health but also on education in the future. Being a child labour, the minimum consequence is the disruption of the child's time to go to school and in some cases, the child cannot go to school at all. This study uses data from the Indonesia Life Family Survey (IFLS) where the individuals studied are the same for each IFLS period. The results show that there is a difference between those who have worked as child workers in the past, who have a lower level of education than those who were not child laborers. The difference can be seen 14 years later. This is reinforced by the results of statistical tests with Logistic Regression where there is a strong relationship between child labour and the level of final education. Those who are child workers have a greater chance of only graduating from elementary and junior high school compared to those who are not child laborers.
COMPARATIVE ACCURACIES USING MACHINE LEARNING MODELS FOR MAPPING OF SUGARCANE PLANTATION BASED ON SENTINEL-2A IMAGERY IN KEDIRI AREA, EAST JAVA Pulungan, Ridson Alfarizal; Nooraeni, Rani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 21, No 1 (2024)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3840

Abstract

Data collection in smallholder sugarcane plantations is still very sensitive to the subjectivity of informants and data collectors. In the meantime, the problem with data collection on sugarcane plantation companies is a low response rate. This situation can reduce the precision of the estimates that are produced. Consequently, the goal of this research is to recognize sugarcane fields using the machine learning models on Sentinel-2A satellite imagery in Kediri Area that covering Kediri Regency and Kediri Municipality, East Java. Along with developing machine learning algorithms, this research will evaluate how well LightGBM performs when compared to other algorithms, including CART, SVM, Random Forest, and XGBoost. Each model employed hyperparameter tuning with random search and stratified 10-fold cross validation to avoid overfitting. The process of labelling satellite imagery using images from Google Street View, then predictor variables used are NDVI, NDWI, NDBI, EVI, and elevation. The most accurate classification model obtained was LightGBM, with a 98% accuracy and a cohen’s kappa of 97.7%. The estimated area of sugarcane plantations in the Kediri Regency and Kediri Municipality in September 2022 is 18,897.6 ha and 571.87 ha. 
Development of a Hybrid Fuzzy Geographically Weighted K-Prototype Clustering and Genetic Algorithm for Enhanced Spatial Analysis: Application to Rural Development Mapping Santoso, Agung Budi; Candra, Arya Candra; Nooraeni, Rani
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.789

Abstract

Introduction/Main Objectives: Clustering methods are crucial for geodemographic analysis (GDA) as they enable a more accurate and distinct characterization of a region. This process facilitates the creation of socio-economic policies and contributes to the overall advancement of the region. Background Problems: The fuzzy geographically weighted clustering (FGWC) method, which is a GDA technique, primarily handles numerical data and is prone to being stuck in local optima. Novelty: This study proposed two novel clustering methodologies: fuzzy geographically weighted k-prototypes (FKP-GW) and its hybrid clustering model, which combines genetic algorithm-based optimization (GA-FKP-GW). Research Methods: This research conduct simulation study comparing two of the proposed clustering method. For the empirical application, this study applied clustering technique using the official Village Potential Survey of Temanggung, Indonesia. Finding/Results: The evaluation results of experiments conducted on simulated data and study cases indicate that the proposed method yields distinct clustering results compared to the previous method while being comparably efficient. The empirical application identifies four distinct groups from the clustered villages, each displaying unique characteristics. The results of our research have the potential to benefit the development of the GDA method and assist the local government in formulating more effective development policies.
Forecasting Shallot Prices in Indonesia Using News-Based Sentiment Indicators Salsabila, Atikah; Nooraeni, Rani
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1422

Abstract

The volatile price changes of shallots are a challenge in controlling their prices. The fluctuation in the price of shallots is always reported in the media because it affects people's lives. The news is released online via the internet and has beneficial information so it can be utilized. This study aims to provide a comparative analysis of forecasting models for shallot prices in Indonesia, evaluating the impact of using the most effective sentiment indicators derived from four lexicon-based methods. Data were collected by scraping method on three news portals and one food price information source website during the period from 2020 to 2023. The correlation and causality analysis was conducted to determine the relationship between food prices and sentiment indicators that was obtained using four sentiment analysis methods. The selected sentiment indicators for each day were used as an additional variable in forecasting using ARIMA, SARIMA, and BSTS models. The results showed that the use of news sentiment could reduce RMSE, MAPE, and MAE in forecasting shallot food prices.  
DETECTING URBAN SLUMS IN DKI JAKARTA: A KOTAKU DATA APPROACH WITH ENSEMBLE METHODS MS, Muhammad Muawwad; Nooraeni, Rani; Prasetya, Ananda Galuh Intan
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/barekengvol18iss3pp1649-1664

Abstract

Slums are one of the problems that often occur in urban areas, especially in developing countries. Slum settlements cause various social, economic, and environmental problems, including social injustice, infrastructure inefficiency, and a decrease in the population's quality of life. The PUPR Ministry representing the Indonesian government is trying to overcome slum settlements in Indonesia by creating the Cities Without Slums (KOTAKU) program. The KOTAKU program provides relevant and detailed data on slum settlements in Indonesia. Challenges arise when analyzing and utilizing KOTAKU data to identify slum indicators and map slums broadly. The method used in detecting slums using KOTAKU data is still conventional. Machine learning can be used to model data and classify or predict data by applying the Ensemble Method. This modeling will look for patterns or structures from the data that has been provided so that the detection results become more objective. This study aims to model slum indicators from KOTAKU data and detect urban slum settlements in DKI Jakarta. Modeling is done using the Random Forest algorithm. Data sourced from the KOTAKU program website established by the Ministry of PUPR RI. The results of the study show that the indicators that contribute most to the modeling of urban slum indicators in DKI Jakarta are the availability of safe access to drinking water and not fulfilling needs for drinking water. The slum indicator model without additions has good performance after going through the parameter tuning process with parameters ntree = 500 and mtry = 6. In contrast, the slum indicator model with additions has good performance if it does not go through a parameter tuning process or retains its initial parameters namely ntree = 500 and mtry = 4.
Identifying Provinces With RMNCH-IC Disparities Between Urban - Rural Residences In Indonesia Satria, Manca; Nooraeni, Rani
Interest : Jurnal Ilmu Kesehatan INTEREST: Jurnal Ilmu Kesehatan Volume 12 Number 1 Year 2023
Publisher : Poltekkes Kemenkes Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37341/interest.v12i1.485

Abstract

Background: Intervention coverage in reproductive, maternal, newborn, and child health (RMNCH-IC) is still unequal between urban and rural residences. This inequality is considered to also occur in Indonesia. The Composite Coverage Index (CCI) measures RMNCH-IC. However, CCI measurements at provincial levels according to residences are not yet available in Indonesia due to the limited sample size at some CCI indicators. Therefore, provinces with a large RMNCH-IC inequality, or disparity, between residences have not been identified. Thus, this study aims to measure CCI as a whole at provincial levels according to residences in Indonesia through the estimation of CCI indicators using MRP, to be used to identify provinces with CCI disparities between residences. Methods: Small Area Estimation (SAE), especially Multilevel Regression and Poststratification (MRP) models, can be used to estimate parameters for each province according to residences with limited samples. The secondary data used in this study come from the latest survey, the 2017 Indonesian Health Demographic Survey (IDHS). Results: Based on the value of the CCI dimension, urban residences have better dimensions of maternal and newborn health, while rural residences have better dimensions of reproductive and child health. There are 5 provinces with RMNCH-IC disparities between residences in Indonesia. Conclusion: Efforts to reduce CCI inequalities are still needed for each residence in their respective dimension, especially for provinces with RMNCH-IC disparities. Further research is needed to explain the determinants of the large disparities between the five provinces.
Prediction of Tsunamis in Indonesia Using an Optimized Neural Network with SMOTE Siregar, Aisyah Anjani Putri; Sudianto, Fauzan Bayu Hera; Nooraeni, Rani
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4263

Abstract

Tsunamis have the potential to have a large impact on the environment, therefore early detection and preparation for tsunami need to be carried out to reduce the impact of casualties and losses incurred. This research aims to predict tsunami events due to large earthquakes in Indonesia as a form of early detection. The optimized neural network method is used in research to classify tsunami events in Indonesia in 2000-2023 for large earthquakes with strength more than 5 magnitudes. The research results show that the neural network structure formed consists of an input layer, a hidden layer, and an output layer. The results of the evaluation of the neural network model with SMOTE obtained an accuracy value of 99.43%, precision of 96.31%, and an F1 score of 97.86%, which means the resulting model is good. Therefore, an optimized neural networks can be applied as a warning system in various regions to detect potential tsunami events in the future.
Pan-Sharpening Analysis for Improved Detection Accuracy and Estimation of Coffee Plantation Land Area (Case Study: South OKU Regency, South Sumatra Province) Anasrul, Anasrul; Nooraeni, Rani
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 2 (2025): April 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i2.424-436

Abstract

The use of remote sensing technology in monitoring coffee plantations is becoming increasingly important considering the vital role of coffee in the economy as an export product that increases state revenue. However, challenges remain, especially regarding the low resolution of satellite imagery which hinders accurate and efficient monitoring of coffee fields. This study aims to improve the accuracy of coffee plantation land analysis in South OKU Regency, South Sumatra Province, by using a pan-sharpening method consisting of IHS, Brovey, and Gram-Schmidt and assisted by a composite index. Satellite image sampling data from Landsat-8 was carried out at 1800 points divided into six classes. The results of the study show that the characteristics of coffee plantation land have NDVI, EVI, and ARVI values that tend to be lower, but the NDBI and NDWI values tend to be higher than the non-coffee plantation and forest classes. This study also compares the data from the pan-sharpening method using machine learning and deep learning methods to get the best classification model. The results showed that the SVM model machine learning method on the pan-sharpening brovey data gave the best results with an ACCURACY value of 83.49 and an F1-score of 83.59 percent. Keywords: Coffee Plantations, Deep Learning, Machine Learning, Pan-sharpening, Remote Sensing.
Pendeteksian Hubungan Rating Hotel Terhadap Okupansi Hotel Pasca Pandemi Covid-19 Yustiani, Dwi; Nooraeni, Rani; Lumaksono, Adi
Jurnal Ilmiah Ilmu Pariwisata Vol 5 No 1 (2023): Jurnal Kajian Pariwisata
Publisher : LPPM STP ARS Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jiip.v5i1.1078

Abstract

After the Covid-19 pandemic, Bali tourism began to revive, marked by an increase in hotel occupancy. Digitalization is playing its part in improving tourism in general. This study aims to look at hotel occupancy profiles in the Sarbagita and Non Sarbagita areas and to see how high digital hotel ratings are in the two areas. This study also detects a relationship between hotel ratings and hotel occupancy in the two regions. The data used in this study are VHTS data for 2019 and 2022 sourced from the Central Bureau of Statistics, as well as hotel rating data by utilizing big data. with web scraping technique. This study used descriptive statistics and inferential statistics using the Chi Square test. The research results obtained were changes in hotel occupancy patterns in the Sarbagita area which tended to increase in December after the Covid-19 pandemic. The Chi-Square test shows that there is a relationship between hotel ratings and hotel occupancy for both the Sarbagita and non-Sarbagita areas.
I'm Married too Young: How To Pursue Legal Marriage? Yusnissa, Hertina; Nooraeni, Rani; Lestariningsih, Eni
Jurnal Ekonomi Kependudukan dan Keluarga Vol. 1, No. 1
Publisher : UI Scholars Hub

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

Abstract

One of the social problems in West Nusa Tenggara Province, Indonesia is high rates of under-registered child marriage. Marriage registration is an obligation in marriage legislation in Indonesia and also an issue of human rights. Studies on marriage registration are very limited in Indonesia. Therefore, this study aims to examine the critical determinants that affect the provision of having a marriage certificate in case of child marriage in West Nusa Tenggara, Indonesia. The study used the 2022 Indonesia National Socio-Economic Survey (SUSENAS) data. The analysis was done using descriptive statistics and binary logistic regression. The results indicate that the variables included in the analysis are statistically significant. The odds ratio indicates that women are more likely to register their marriage than men. It is more likely that people who lived in the urban areas, people who married after 16 years, people who had been educated in senior high school and above, people who were employed, and people who accessed the internet/social media were more likely to register their marriage. People who had not joined Bank Accounts, who came from rich families, and who had social insurance from the government were in line with those who did not register their marriage (odd ratio=1). The finding in this study concluded that gender, age at marriage, place of residence, wealth index, education, working status, joint bank account, media exposure, and receiving social insurance from the government are the leading determinants of registered child marriage in West Nusa Tenggara, Indonesia. Furthermore, there is a need to educate the importance of registering a marriage to society more intensively and the policy makers should campaign awareness of existing marriage certificates.
Co-Authors A, Yolanda Rizkie Adi Lumaksono Agung Budi Santoso Al Baihaqi, Rakai Anasrul, Anasrul Astriani, Vina Briliyanto, Muhamad Bagus Adji Candra, Arya Candra Dewi, Aprilia Lutviana Firmansyah, Adrian Fitri, Muti Nurjannah Fitri, Nurhidayatul Hakim, Syifa Rahmawati Hamdani, Febri Helmiah, Najia Hirma, Emalia Septiani Khoirun Nisa', Intan Maulida Lestariningsih, Eni Mahendra, Fiqih Tri MS, Muhammad Muawwad Munia, Nufaisa Muqiit WS, Andriansyah Nulkarim, Aldi Rochman Prasetya, Ananda Galuh Intan Pratama, Firza Refo Adi Pulungan, Ridson Alfarizal Putra, Amran Pratama Rizki, M. Alfa Salsabila, Atikah Saputri, Aprillia Anis Saragih, Meilani Thereza Br. Sari, Esa Anindika Satria, Manca Shariati, Ilhamsyah Ali Siregar, Aisyah Anjani Putri Sofyan, Sabiq Sudianto, Fauzan Bayu Hera Susanti, Amalia Taufiqurrahman, Febi Yusnissa, Hertina Yustiani, Dwi Zekha F, Noval Irgi