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The Implementation of Geospatial Analysis on Hotel Occupancy Rate Nazuli, Muhammad Fachry; Panuntun, Satria Bagus; Maulana, Addin; Takdir; Pramana, Setia
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.797

Abstract

Introduction/Main Objectives: One of the main attributes of hotel selection and customer satisfaction is its location. Background Problems: Strategic location leads to higher demand for accommodation. Accommodation demand is reflected in hotel occupancy levels, which indicate the percentage of reserved rooms at a specific period. Novelty: This study aims to investigate the effect of spatial location on hotel occupancy rates by analyzing data collected in online hotel reservation applications. A study related to the effects of location and hotel occupancy has never been conducted in Indonesia. Research Methods: We use data from hotels located in the province of Yogyakarta, which contains 245 hotels spread over three regencies/cities, namely Yogyakarta City, Sleman Regency, and Bantul Regency. We conducted a spatial regression analysis, namely the Spatial Error Model (SEM), with a spatial weight matrix using a radius of 3.2 km. Finding/Results:  We found that spatial locations affect the occupancy rates of hotels based on the online hotel reservation application that we observed. These spatial locations include the distance from the hotel to the airport, the distance from the hotel to the bus stop, and the number of nearby restaurants, offices, and hotels.
Measuring ocean physical asset account using machine learning approaches Jane, Giani Jovita; Tasriah, Etjih; Pramana, Setia
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 13 No 3 (2024): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v13i3.563

Abstract

The blue economy concept has been adapted as a strategy in setting development programmes and public policies in managing Indonesia’s marine resources. As a supporting instrument, accurate field data is needed when compiling the ocean account. Meanwhile, the support of qualified resources is needed during the field data collection process. Research on mapping water areas using satellite technology and machine learning techniques in producing water maps, especially in coastal areas. The approach is suitable for arranging a physical asset account, which is a component of the ocean account framework. So far, no research has implemented these developments to produce ocean physical asset account. Therefore, this study will cover in arranging the account by utilising Sentinel-2 imagery and implementing Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost) machine learning methods, which according to previous studies are superior methods for mapping water areas. The modelling results show that there isan extensive change in coral, seagrass, and mixed ecosystem types (a combination of coral, seagrass, and macroalgae ecosystems) between 2020 and 2023.
Evaluation of Biclustering Imputation Methods for Glioblastoma Gene Expression Data Silalahi, Agatha; Titin Siswantining; Setia Pramana
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art7

Abstract

Glioblastoma is a highly aggressive primary brain tumor with a low survival rate. One of the main challenges in analyzing glioblastoma gene expression data is the presence of missing values, which can reduce biclustering accuracy and affect biological interpretation. This research compared six imputation methods k-nearest neighbors (KNN), mean imputation, singular value decomposition, nonnegative matrix factorization, soft impute, and autoencoderon the GSE4290 gene expression dataset with missing values ranging from 5% to 50%. An evaluation using root mean square error (RMSE), mean absolute error (MAE), and structural similarity index measure (SSIM) showed that soft impute provided the best performance at all levels of missing values, with RMSE of 0.0076, MAE of 0.0073, and perfect SSIM of 1.0000 at 50% missing values. Meanwhile, deep learning-based autoencoder experienced significant performance degradation at high missing values. These findings indicate that more complex models are not always superior, and regularization-based approaches like soft impute are more effective in preserving the biological structure of the data. The results of this research contribute to the optimization of imputation strategies to improve the accuracy of biclustering analysis in glioblastoma studies.
Socioeconomic impact of COVID-19 restrictions in Bali: A nighttime light analysis Putro, Dimas Hutomo; Pramana, Setia; Hendrawan, Daffa
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 14 No 1 (2025): April
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v14i1.220

Abstract

This study investigates the socio-economic impacts of COVID-19 pandemic restrictions in Bali using nighttime light remote sensing as a proxy for socio-economic activity. The monthly NTL data from the Suomi-NPP VIIRS instrument, spanning from 2014 to 2021, are analyzed. This study focuses on changes in NTL trends before and after the restrictions, specifically the Large-Scale Social Restriction and Welfare Activity Restriction programs. To ensure that the NTL used in this study accurately measures human activity, we integrate the data with built-up area maps from the Global Human Settlement Layer. An ARIMA intervention model is employed to assess the impact of the restrictions on NTL, revealing a significant decrease in certain regions. Furthermore, we find a moderate correlation between NTL and Bali's quarterly GDP data. This study also highlights the potential of NTL remote sensing as a near-real-time proxy for socioeconomic change, allowing for the early evaluation of policy effectiveness. Keywords: nighttime light, COVID-19, proxy indicator, ARIMA intervention JEL Classification: C22; I18; R11
Aspect-based Sentiment Analysis and Topic Modelling of International Media on Indonesia Tourism Sector Recovery Rutba, Sita Aliya; Pramana, Setia
Indonesian Journal of Tourism and Leisure Vol 6, No 1 (2025)
Publisher : Lasigo Akademia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36256/ijtl.v6i1.502

Abstract

The international world's perception of a country is essential. In 2022, Indonesia attracted international media attention, which can form the nation’s image of Indonesia in the international public perception. Therefore, this study analyses international media, online news, and social media Twitter perceptions towards Indonesia. For the news, aspect-based sentiment analysis is carried out, and for Twitter opinion, sentiment classification using multiple classification algorithms, and topic modeling related to these sentiments are carried out. Furthermore, this research classifies news sentiment based on aspects of forming the country's image, such as tourism, exports, diplomacy, government policies, and people's behavior. It was obtained that aspects of people, policy, and tourism, besides being classified as “none” class, mostly classified in negative sentiment. While diplomacy and export are mostly positive sentiment. One limitation of this classifier is the insufficient number of cases in the training data, which led to relatively low accuracy and precision in this study.  On Twitter opinion, it was found that Twitter's positive sentiment about Indonesia is associated with tourism recovery. The topic modeling of positive tweets highlighted international interest in Indonesia's tourism. This study's findings can provide valuable insights for the government on boosting foreign tourism to support economic growth. Additionally, policymakers should focus on addressing issues that attract foreign media attention. By effectively managing these concerns, Indonesia’s branding can be enhanced, potentially leading to an increase in tourist arrivals.
Prediction of Main Transportation Modes using Passive Mobile Positioning Data (Passive MPD) Farhan, Muhammad; Suadaa, Lya Hulliyyatus; Sugiri; Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri; Pramana, Setia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6128

Abstract

Indicators of the main mode of transportation used by domestic tourists during tourism trips cannot yet be estimated using Passive MPD which is recorded based on the location of the BTS that captures the cellular activity of domestic tourists. Previous research on identifying transportation modes from Passive MPD has its own shortcomings because it only relies on speed and travel time features. Meanwhile, there is Active MPD which is recorded using active geo-positioning and real-time, where the research involves many features and has a data structure similar to Passive MPD. Therefore, this research aims to conduct a study of the implementation of the method used to identify modes of transportation in Active MPDs to Passive MPDs as an approach to predicting the main modes of transportation. As a result, the transportation mode identification method in the Active MPD can be implemented in the Passive MPD. The best accuracy of 83.56% was obtained by the LightGBM model using all features. However, the Multinomial Logistic Regression model, which only uses 10 selected features, is the most effective and efficient model with an accuracy of 76.43% and a much shorter execution time
Coastal Ecosystem Classification Using Satellite-Based Machine Learning Approaches Jane, Giani Jovita; Alifatri, La Ode; Tasriah, Etjih; Pramana, Setia
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30466

Abstract

Sebagai negara kepulauan yang kaya akan sumber daya alam, Indonesia memiliki potensi ekonomi kelautan yang besar. Untuk mempertahankan potensi ekonomi ini dalam jangka panjang, ekonomi biru diperlukan sebagai konsep dalam menetapkan program pembangunan dan kebijakan publik. Salah satu cara untuk mengimplementasikan konsep tersebut adalah dengan menyusun neraca laut, yang kerangka kerjanya mengimplementasikan konsep ekonomi biru dalam bentuk neraca lingkungan. Neraca laut dapat dianggap mendukung pembentukan kebijakan dan program nasional suatu negara. Oleh karena itu, data spasial yang akurat yang mencerminkan kondisi terkini sangat penting untuk menyusun neraca ini. Namun, pengumpulan data tersebut dapat memakan biaya dan sumber daya yang besar, sehingga menjadi tantangan untuk memastikan ketersediaan informasi yang terkini dan akurat. Dalam konteks ini, sumber data alternatif dapat memberikan solusi yang layak. Penelitian sebelumnya telah berhasil membuktikan bahwa pemodelan pembelajaran mesin juga citra satelit Sentinel-1 dan Sentinel-2 mampu memetakan wilayah pesisir, seperti wilayah pasang surut dan bentik. Oleh karena itu, penelitian ini mencoba mengklasifikasikan ekosistem pesisir Taman Nasional Karimunjawa dengan memanfaatkan citra Sentinel-1 dan Sentinel-2 dan membandingkan hasil klasifikasi dari tiga metode pembelajaran mesin, yaitu Random Forest (RF), Support Vector Classification (SVC), dan Extreme Gradient Boosting (XGBoost), dan menganalisis perubahan ekosistem antara tahun 2020 dan 2023. Hasilnya menunjukkan bahwa RF memberikan hasil terbaik dalam melakukan klasifikasi untuk daerah bentik yang mencapai 0,77 dan 0,78 dalam skor F1 dan Koefisien Korelasi Matthew (MCC), sedangkan model SVC berhasil mencapai 0,83 dalam skor F1 dan MCC memberikan hasil terbaik untuk daerah pasang surut. Selanjutnya, luas terumbu karang dan padang lamun menurun masing-masing sebesar 6,524 km 2 dan 1,39 km 2 . Sedangkan, luas mangrove, kawasan terbangun, dan hutan menunjukkan sedikit perubahan.
INCORPORATING COMPLEX SURVEY DESIGN FOR ANALYSING THE DETERMINANT OF WOMEN IN REPRODUCTIVE AGE PARTICIPATION IN FAMILY PLANNING PROGRAM IN INDONESIA Astuti, Erni Tri; Rahani, Rini; Pramana, Setia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1401-1410

Abstract

Data generated from complex survey are often treated as un-weighted simple random samples by analyst. This is unfortunate because everyone has different probability to be selected as sample in each stage of the complex survey design. Fail taking it into account will have serious impact in parameter and variance estimation. This paper aims to examining relationship between participation in family planning program and socio demographic status of women in reproductive age in Indonesia used data from latest Indonesian’s Demographic and Health Survey (IDHS). IDHS employs a multi stage stratified sampling design, thus there are a number of weights included in public-use IDHS datasets to account for this complex sample design. We found that the complex design features of the IHDS increased the variance estimates of the estimated parameters in the logistic regression models by about 1.325 – 1.88 times, compared to a simple random sampling. Therefore, using variance estimated from un-weighted simple random samples would lead to wrong conclusion of the significance parameter suggested by the model. The result also found that all of socio demographics variables used as predictors are significant. Thus, women with moderate education, unemployment, exposed by media, living in rural community and wealthy, have spouse that have moderate education and have a job tend to participate in family planning program.
Genetic Cluster Analysis of Insulin Resistance Using KNN Imputation and FABIA-CCA Biclustering Soemarso, Ditoprasetyo Rusharsono; Siswantining, Titin; Pramana, Setia
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art10

Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disorder primarily driven by insulin resistance, involving complex genetic regulation. Understanding the molecular mechanisms underlying insulin resistance is crucial for identifying therapeutic targets. This study compared the performance of two biclustering algorithms, factor analysis for bicluster acquisition (FABIA) and the Cheng and Church algorithm (CCA), in analyzing gene expression data associated with insulin resistance. Using the GSE19420 dataset, simulated missing values were introduced to evaluate the robustness of both methods. Results showed that CCA consistently achieved lower mean squared error (MSE) in reconstructing gene expression patterns, suggesting higher accuracy in capturing co-expression structures. Nevertheless, FABIA effectively detected sparse, biologically relevant clusters. Notably, key genes such as MYO5B, DLG2, AXIN2, and PTK7 were identified within the biclusters, supporting their involvement in insulin signaling and metabolic regulation. These findings underscore the need to select biclustering methods that align with specific analytical goals and offer insights into gene networks involved in insulin resistance.
Analisis Sentimen dan Pemodelan Topik Opini Publik Terkait Data Badan Pusat Statistik Tahun 2024 Rahman, Dimas Haafizh; Alistin, Zharifah Dhiya Ayu; Pramana, Setia
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2365

Abstract

The role of BPS has become increasingly crucial with the rising demand and sources of data over time. The quality of BPS data is evaluated through the Data Needs Survey (SKD). The 2024 SKD indicates that 98.16% of consumers are satisfied with the quality of BPS data. However, this evaluation only involved data consumers from BPS PST, and there remains a time gap between the implementation and dissemination of the survey results. Social media platform X, which is popular in Indonesia, allows its users to express their opinions through tweets. This research is conducted to understand public sentiment, identify the best classification model, and discover topics discussed by the public regarding BPS data based on tweets from the X platform in 2024. The tweets were taken through labeling and preprocessing before applying Machine Learning methods to classify public sentiment. The Support Vector Machine (SVM) method, with a weighted average of 0.68, performed best compared to Naïve Bayes, Rocchio Classification, and K-NN in modeling public opinion sentiment. The implementation of LSA and LDA discovered topics consisting of public opinions and issues related to BPS data such as poverty rate manipulation and BPS data as a credible source.
Co-Authors ., Yunofri Achmad Fauzi Bagus Firmansyah Addin Maulana Aditama, Farhan Satria Aini Izzati, Fitri Alifatri, La Ode Alistin, Zharifah Dhiya Ayu Amnur, Muh. Alfian Ana Lailatul Fitriyani Ana Lailatul Fitriyani Anang Kurnia Arie Wahyu Wijayanto Arif Handoyo Marsuhandi Ariya Jalaksana, Faruq Arkandana, M. Tharif Astrinariswari Rahmadian Prasetyo Astuti, Erni Tri Bintang Yuliani Manalu, Jernita Busaina, Ladisa Bustaman, Usman Charvia Ismi Zahrani Cholifa Fitri Annisa Dandy Adetiar Al Rizki Dede Yoga Paramartha Dede Yoga Paramartha Deli, Nensi Fitria Dewi Krismawati Dewi Krismawati Dhiar Niken Larasati Diory Paulus Pamanik Erni Tri Astuti Erwin Tanur Fadila Utami, Nurul Fajar Fathur Rachman Fajar Fatur Rachman Farakh Khoirotun Nasida Farhan Y. Hidayat Fitriyani, Ana Lailatul Fitriyyah, Nur Retno Geri Yesa Ermawan Gilang Hidayat, Muhammad Hady Suryono Hanafi, Zulfaning Tyas Hardiyanta, I Komang Y. Hendrawan, Daffa Hidayat, Farhan Y. Hizir Sofyan Hulliyyatus Suadaa, Lya I Komang Y. Hardiyanta I Nyoman Setiawan Imam Habib Pamungkas Jane, Giani Jovita Khairani, Fitri Krisela Fabrianne, Elisse Krismawati, Dewi Ladisa Busaina Larasati, Dhiar Niken Linta Ifada Linta Ifada Maftukhatul Qomariyah Virati Magfirah, Deanty Fatihatul Mariel, Wahyu Calvin Frans Maulana Faris Median Ramadhan, Alif Muhammad Farhan Muhammad Gazali, La Ode Muhammad Nur Aidi Muhammad Tharif Arkandana Mumtaz Siregar, Amir Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri Nasiya Alifah Utami Nastiar, Gery Nazuli, Muhammad Fachry Nensi Fitria Deli Nisa Rahayu Ananda Suwendra, Made Nora Dzulvawan Nurmalasari, Mieke Nurtia Nurtia Nurwijayanti Oktari, Rina S. Panuntun, Satria Bagus Paramartha, Dede Yoga Putro, Dimas Hutomo Rahman, Dimas Haafizh Rahmaniar, Masna Novita Rifqi Ramadhan Rina S. Oktari Rini Rahani Rutba, Sita Aliya Safrizal Rahman Safrizal Rahman, Safrizal Salim Satriajati Salwa Rizqina Putri Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Silalahi, Agatha Simanjuntak, Tigor Nirman Siswantining, Titin SITI MARIYAH Siti Mariyah Soemarso, Ditoprasetyo Rusharsono Suadaa, Lya Hulliyyatus Sugiri Suhendra Widi Prayoga Takdir Tasriah, Etjih Thosan Girisona Suganda Thosan Girisona Suganda Tigor Nirman Simanjuntak Usman Bustaman Usman Bustaman Utami, Nandya Rezky Wahyu Calvin Frans Mariel Wirata Raja Panjaitan, Eurorea Wiwin Srimulyani Yeni Rimadeni Yuniarti Yuniarti Yuniarti Yuniarti Yuniarto, Budi Zen, Rizqi Annisa