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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Kesehatan Masyarakat Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Artificial Intelligence Research Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan Buletin Ilmiah Litbang Perdagangan EKO-REGIONAL JURNAL PENGEMBANGAN EKONOMI WILAYAH Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan Jurnal Varian Jurnal Statistika dan Aplikasinya Jurnal Kependudukan Indonesia STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Ekonomi dan Pembangunan Indonesia Economic and Finance in Indonesia Buletin Ilmiah Litbang Perdagangan Jurnal Ekonomi Kesehatan Indonesia Indonesian Journal of Statistics and Its Applications
Siskarossa Ika Oktora
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Analysis of Underdeveloped Regency Using Logistic Threshold Regression Model Salsabila, Annisa Nur; Oktora, Siskarossa Ika
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3570

Abstract

Regional development inequality causes some regions to lag behind other regions. An underdevelopedregency is a regency where territories and people are less developed than other regions nationally. Thegovernment has set a Human Development Index (HDI) target of 62.2 to 62.7 to accelerate the development of underdeveloped regency and prevent the regions from lagging. This study aims to evaluatethe HDI target and obtain the HDI value that reduces the risk of underdeveloped regency and acquiresvariables that affect underdeveloped regency’s status. The logistic threshold regression model is usedin this study with HDI as the threshold variable, 22 indicators for determining underdeveloped regencyas explanatory variables, and the underdeveloped regency’s status as the response variable. Thresholdregression can handle non-linear relationships between response and explanatory variables, includingvarious types of threshold models such as step, segmented, hinge, stegmented, and upper hinge. By applying a hinge threshold regression model using the R package ’chngpt,’ this study addresses non-linearrelationships and categorical responses. The results showed a threshold effect with a threshold value of62.9, indicating that the HDI target can reduce the region’s risk of being left behind.
DETERMINAN TRANSAKSI NONTUNAI DI INDONESIA DENGAN PENDEKATAN ERROR CORRECTION MECHANISM (ECM) MODEL Zulfa Nur Fajri Ramadhani; Siskarossa Ika Oktora; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.190

Abstract

Consumption is an activity that must be done by everyone. In order to consume something, a transaction is needed to get the goods or services desired. One kind of transaction that is used by many people nowadays is non-cash transaction. Since Bank Indonesia established Gerakan Nasional Non Tunai (GNNT) in August 2014, the value of non-cash transactions exceeds the value of cash transactions. It happenned because people prefer non-cash to cash transaction which is easier, safer, more practical, and more economical. Besides, an increase in non-cash transactions can also be influenced by other factors. Therefore, a study is conducted to analyze the determinants of non-cash transactions from the macro side by using Error Correction Mechanism (ECM). The data used in this study are secondary data from Bank Indonesia and Badan Pusat Statistik with monthly period from January 2010 until December 2017. The results showed that in the long run, private savings and BI rate have positive effect on non-cash transactions. In the short run, private savings and money supply have positive effect on non-cash transactions. While inflation does not affect non-cash transactions, both in the short and long run.
ANALISIS KURVA ROC PADA MODEL LOGIT DALAM PEMODELAN DETERMINAN LANSIA BEKERJA DI KAWASAN TIMUR INDONESIA Muhammad Rizqi Fachrian Nur; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.524

Abstract

Binary logistic regression is used for probability modeling or to predict binary response variables (Success / Failure) from one or more explanatory variables that are continuous or categorical. In carrying out this analysis, there are several ways to test the suitability of the resulting model, and one of them is the area under the ROC curve. The application of the analysis method in this study is the determinant of the elderly population to work. The population of the elderly in Indonesia is increasing every year. Many views that the elderly depend on other residents, especially in terms of the economy. However, if seen from the percentage of elderly working in Indonesia, it is increasing, including the elderly in KTI. The purpose of this study is to determine the characteristics of the elderly in KTI, know the factors that influence the decision of the elderly population to work in KTI and find out the tendency of variables that affect the decision of the elderly to work in KTI. The data used are raw data from Badan Pusat Statistik (BPS) was Survei Sosial Ekonomi Nasional (Susenas) Kor March 2018. This study using descriptive analysis methods and binary logistic regression. The results are that the variables that significantly influence the decisions of the elderly to work are residence, gender, age, education, family status, marital status, health complaints, and health insurance. Elderly who has characteristics residing in rural, male sex, classified as young elderly (60-69 years old), has the highest level of elementary school education, has the status of KRT in his family, is married, has no complaints health, and not having health insurance will have a greater tendency to decide to work.
ANALISIS SPASIAL KETERTINGGALAN DAERAH DI INDONESIA TAHUN 2018 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Tata Pacu Maulidina; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.690

Abstract

Development inequality in Indonesia has led the developed and underdeveloped regions. Regional backwardness caused by high inequality must be handled properly to prevent negative impacts on national stability. But in fact, the handling of underdeveloped regions is only effective in Western Indonesia, while in Eastern Indonesia tends to be not optimal. This study aims to explore regional backwardness in Indonesia and examines the factors that influence it. Based on data, underdeveloped regions tend to cluster in eastern Indonesia, and the independent variables have large variations between regions. This indicates dependence and spatial heterogeneity. Therefore, this study applies spatial analysis using the Geographically Weighted Logistic Regression (GWLR) method. GWLR shows better performance in modeling the regional backwardness in Indonesia compared to its global model (binary logistic regression). This study provides a local model for each district/city that can be used by local governments to implement more effective policies based on factors that do have significant effects on regional backwardness.
Determinants of Male Adolescents Smoking Behavior in Indonesia using Negative Binomial Regression Angel Zushelma Hartono; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p182-194

Abstract

Adolescent smoking habits have become the Ministry of Health's major program associated with tobacco consumption. In 2016, the prevalence of adolescent smoking aged 10-18 years reached 8.8% and were rate increasingly against the Strategic Planning Ministry of Health 2015-2019 target to lower adolescent smoking prevalence to 5.4%. Male adolescents consuming cigarettes are higher than females. Whereas, high consumption of cigarettes in men will increase the risk of impotence and decrease reproductive health quality to affect future generations' quality. This study aims to determine the general picture of smoking behavior in Indonesia's male adolescent in 2018 and any variables that affect the number of cigarettes consumed. The analytical method used is Poisson Regression and Negative Binomial Regression. The data source used is raw data Riskesdas 2018 with the unit of analysis are male adolescent smokers aged 10-18 years. Research indicates that most male adolescents are light smokers. Heavy smokers were dominated by older age, living in a rural area, poorly educated, employed, lived with a household head who was a smoker, and had low education. Age, location of residence, education level, working status, smoking status, and household head education level significantly affect male adolescents' smoking behavior.
Data-Driven Insights Into Underdeveloped Regencies: SHAP-Based Explainable Artificial Intelligence Approach Oktora, Siskarossa Ika; Matualage, Dariani; Notodiputro, Khairil Anwar; Sartono, Bagus
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1399

Abstract

Classification analysis in high-dimensional data presents significant challenges, particularly due to the presence of complex non-linear patterns that traditional methods, such as logistic regression, fail to capture effectively. This limitation is often reflected in relatively low model accuracy. One approach to addressing this issue is through machine learning-based classification methods, such as Random Forest and Support Vector Machine (SVM). While these models generally achieve higher accuracy than logistic regression, their black-box nature limits interpretability, making it difficult to explain their classification decisions. As machine learning models continue to advance, interpretability has become a crucial concern, especially in data-driven decision-making. Post-hoc explainable artificial intelligence (XAI) techniques offer a viable solution to enhance model transparency. This study applies SHAP to machine learning models to gain insights into the underdevelopment status of regencies in Indonesia. The results indicate that SVM outperforms both logistic regression and Random Forest. SHAP values estimated from SVM, using various permuted variable subsets, exhibit stability. Clustering analysis identifies five optimal clusters of underdeveloped regencies. Based on average SHAP values, underdevelopment alleviation strategies should focus on social factors (Cluster 1), infrastructure (Cluster 2), accessibility (Cluster 3), and a combination of infrastructure, accessibility, education, and healthcare (Cluster 4), while Cluster 5 requires improvements in accessibility and economic conditions.
Analisis Ketertinggalan Desa di Provinsi Papua dan Papua Barat Menggunakan Association Rule Mining Primanda, Etsa; Oktora, Siskarossa Ika
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.2302

Abstract

ABSTRAK Pada hakikatnya, pembangunan dimaksudkan untuk mengupayakan kondisi kehidupan yang lebih layak. Namun, data Indeks Pembangunan Desa 2018 menunjukkan persentase desa tertinggal di Pulau Papua paling banyak dibandingkan pulau lainnya. Terlebih lagi, penelitian mengenai karakteristik utama ketertinggalan desa di wilayah tersebut belum dilakukan secara komprehensif. Oleh sebab itu, penelitian ini bertujuan untuk mengetahui gambaran umum desa tertinggal, serta menganalisis karakteristik utama ketertinggalan desa di Provinsi Papua dan Papua Barat. Sumber data yang digunakan adalah data indikator Indeks Pembangunan Desa 2018 yang diperoleh dari Subdirektorat Statistik Ketahanan Wilayah Badan Pusat Statistik (BPS) berdasarkan Pendataan Potensi Desa 2018. Metode analisis yang digunakan adalah analisis deskriptif menggunakan diagram batang dan peta tematik, serta teknik data mining menggunakan association rule mining. Hasil analisis menunjukkan Kabupaten Tolikara, Provinsi Papua dan Kabupaten Pegunungan Arfak, Provinsi Papua Barat memiliki persentase desa tertinggal yang tertinggi. Sebagian besar desa tertinggal di Provinsi Papua dan Papua Barat berada di wilayah dengan topografi dataran tinggi dan pegunungan. Hasil association rule mining menunjukkan karakteristik utama ketertinggalan desa sebagian besar kabupaten di Provinsi Papua adalah pelayanan kesehatan, sarana transportasi, dan infrastruktur ekonomi. Sementara itu, karakteristik utama ketertinggalan desa sebagian besar kabupaten di Provinsi Papua Barat adalah pelayanan kesehatan. ABSTRACT The goal of development is to seek more decent living conditions. However, the Village Development Index 2018 data shows that the percentage of rural underdevelopment on Papua Island is the highest compared to other islands. Moreover, researchers have yet to conduct comprehensive research in the region on the main characteristics of rural underdevelopment. Therefore, this study aims to observe the general description of rural underdevelopment and analyze the main characteristics of rural underdevelopment in Papua and West Papua Provinces. The data source used is the Village Development Index 2018 indicator data from the Regional Resilience Statistics Sub-Directorate of Badan Pusat Statistik (BPS) based on the Village Potential Data 2018. The analytical methods used are descriptive analysis using bar charts, thematic maps, and data mining techniques, namely association rule mining. The results show that the percentages of rural underdevelopment in Tolikara Regency, Papua Province, and Arfak Mountains Regency, West Papua Province, are higher among other regions. Areas characterized by highlands and mountainous terrain in Papua and West Papua Provinces concentrate most of the rural underdevelopment. Then, the results of association rule mining show that the main characteristics of rural underdevelopment in most districts in Papua Province are health services, transportation facilities, and economic infrastructure. Meanwhile, the main characteristic of rural underdevelopment in most districts in West Papua Province is health services.
OUTLIERS HANDLING ON SEASONAL ARIMA INTERVENTION MODEL (CASE: IMPACT OF MOST FAVORED NATION POLICY ON INDONESIAN HOT ROLLED COIL/PLATE) Maksum, Fadhila Annisa; Oktora, Siskarossa Ika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0603-0614

Abstract

Intervention analysis measures the impact of various external events or interventions capable of changing data patterns. This research aims to determine the outliers handling on the seasonal ARIMA intervention model using the Box-Jenkins method. The pre-intervention model formed contains seasonal and step functions, which does not fulfill the white noise of the final intervention model. Therefore, the outliers need to be detected the model meets the white noise assumption. The intervention model and outlier detection in this study are conducted to capture the impact of a tariff-setting policy of 5 and 15 percent, called the first and second intervention, on the volume of Hot Rolled Coil/Plate (HRC/P) imports. When the outlier is detected, the next step is to examine and adjust its effect on the model by adding the effect of the outlier in the model. Using the seasonal ARIMA intervention model, the results showed that the first and second interventions significantly reduced the volume of HRC/P imports. A limitation of this research is that this model cannot include other independent variables in the modeling.
CONSTRUCTION OF BLUE ECONOMY DEVELOPMENT INDEX AT THE PROVINCIAL LEVEL IN INDONESIA USING EXPLORATORY FACTOR ANALYSIS Sari, Dewi Novita; Oktora, Siskarossa Ika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp603-616

Abstract

Indonesia, as an archipelagic country, holds marine resources of significant economic value in improving the welfare of its people. However, the community's use of marine resources does not pay attention to sustainability. The government then uses the Blue Economy concept to maximize the economic value obtained while maintaining the sustainability of the marine ecosystem through national policies and plans. In realizing blue economy development, enabler factors, such as technology and government governance, have an important role. This research aims to construct a Blue Economy Development Index (IPEB) at the provincial level in Indonesia in 2021, including enabler factors for blue economy development. The analytical method used is Exploratory Factor Analysis. The results show that the distribution of the minimum values for the indicators that make up the IPEB is found in the provinces of the Eastern Region of Indonesia. In contrast, the distribution of the maximum values of the indicators is found in the provinces of the Western Region of Indonesia. The province with the highest IPEB score is South Sulawesi, while the lowest is Central Sulawesi. The limitation of this study is the data derived from the Village Potential Survey (Potensi Desa) data collection, so several variables are not yet available in annual time. The results of this study are important in improving the ability to monitor implementation and assist in decision-making in increasing blue economic development, especially at the provincial level.
STRATEGY FOR ELIMINATING NEGLECTED TROPICAL DISEASES THROUGH INDIVIDUAL AND AREA ASPECTS USING THE HIERARCHICAL LOGISTIC REGRESSION METHOD Oktora, Siskarossa Ika; Matualage, Dariani; Amalia Pasaribu, Asysta; Fitriyani Sahamony, Nur; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2495-2506

Abstract

Filariasis is one of the Neglected Tropical Diseases (NTDs) that is often associated with poverty and marginalized community groups. Papua is the province with the highest number of chronic filariasis cases and has the largest number of endemic districts/municipalities compared to other provinces in Indonesia. Papua is also the province with the highest poverty rate in Indonesia. To support the government's filariasis elimination program, this study aims to determine variables that influence the incidence of filariasis in Papua at the individual and area levels. This study uses 2018 Indonesia Basic Health Research data from the Ministry of Health and regional data from BPS-Statistics Indonesia. The results using Hierarchical Binary Logistic Regression concluded that defecation behavior in latrines, prevention behavior against mosquito bites, participation in mass preventive drug administration, number of poor people, and number of health workers have a significant effect on the incidence of filariasis. In contrast, the variables age, gender, type of work, and level of education do not have a significant effect.
Co-Authors Aghnia Ussyarovi Aloysius Bela Boro Amalia Pasaribu, Asysta Anang Kurnia Andrian Dwi Putra Angel Zushelma Hartono Bagus Sartono Butar Butar, Daniel Daffa Dhiaulhaq, Hafsha Dewi Novita Sari Diva Amadea Ermawan, Geri Yesa Ernawati Pasaribu Fitriyani Sahamony, Nur Geraldi Putra P Balebu Geri Yesa Ermawan Ibnu Santoso Ika Yuni Wulansari Indonesian Journal of Statistics and Its Applications IJSA Ira Helsa Triana Leonita Amara Husna Metanda Maksum, Fadhila Annisa Maria Valentina Makung Matualage, Dariani Metanda, Leonita Amara Husna Muhammad Rizqi Fachrian Nur Nanda Bagus Rahmawan Nashif Satria Ramadhan Ni Luh Evindia Andini Nora Muhtasib Nursaliyawati, Adella Siti Primanda, Etsa Ristanto Hadi Salsabila, Annisa Nur Sari, Rika Dwi Puspita Siti Muchlisoh Subriandi, Askar Tata Pacu Maulidina Valencia Shabrina Putri Wulansari, Ika Yuni Yafi’, Muhammad Almas Yohana Rosaripatria Zulfa Nur Fajri Ramadhani