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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmu Komputer dan Informasi Jurnal Sains dan Seni ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi Madaniya Proceeding ISETH (International Summit on Science, Technology, and Humanity) Sewagati International Journal of Computing Science and Applied Mathematics-IJCSAM
Fithriasari, Kartika
Departemen Statistika Institut Teknologi Sepuluh Nopember
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Workshop Implementasi Statistika dalam Penelitian Tindakan Kelas di SMKN 5 Surabaya Adatul Mukarromah; Kartika Fithriasari; Nur Iriawan; Irhamah; Heri Kuswanto; Wiwiek Setya Winahju
Sewagati Vol 6 No 6 (2022)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.981 KB) | DOI: 10.12962/j26139960.v6i6.434

Abstract

Salah satu kompetensi yang harus dimiliki oleh guru berdasarkan Peraturan Menteri Pendidikan Nasional Republik Indonesia Nomor 16 Tahun 2007 Tentang Standar Kualifikasi Akademik dan Kompetensi Guru adalah melakukan penelitian dan pengembangan. Metode Statistika memegang peranan penting dalam penyusunan dan pelaksanaan suatu penelitian. Pada umumnya guru belum mempunyai dasar Statistika yang kuat. Penulisan karya ilmiah juga hal yang penting dalam pencapaian kompetensi bagi guru. Oleh karena itu, pengabdian ini bertujuan untuk memberikan workshop metode statistika ke guru di SMKN 5 Surabaya sehingga dapat memberikan hasil Penelitian Tindakan Kelas (PTK) yang lebih baik setelah mengimplementasikan ilmu statistika didalamnya. Materi yang diberikan antara lain Pendahuluan tentang Statistika dan Penelitian Tindakan Keguruan, Sekilas Penulisan Ilmiah dan Penyusunan Dokumen Hasil Penelitian Tindakan Keguruan, Statistika Deskriptif dan Estimasi Parameter, Pengujian Hipotesis, Korelasi, Tabulasi Silang dan Analisis Regresi. Setelah workshop, dilakukan pendampingan terhadap penulisan artikel ilmiah hasil PTK yang disusun oleh guru, baik dari segi pengolahan dan analisis data maupun penyajian dalam artikel berupa jurnal maupun seminar nasional/ internasional. Selain itu modul workshop tentang metode statistika untuk PTK dapat dimanfaatkan oleh peserta pengabdian untuk mengembangkan kemampuan mengolah dan menganalisis data.
Impact of SST Anomalies on Coral Reefs Damage Based on Copula Analysis Pratnya Paramitha Oktaviana; Kartika Fithriasari
Jurnal Varian Vol 6 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i2.2324

Abstract

The condition of coral reefs in Indonesia is alarming. One of the influenting factors of coral reefs damage is extreme climate change. The aim of this study is to determine the relationship of climate change, that is Sea Surface Temperature (SST) anomaly index, and coral reefs damage in West, Central and East Region of Indonesia. The method used in this study is Copula analysis. Copula is one of the statistical methods used to determine the relationship of two or more variables, in which case the distribution can be normal or not. First, data is transformed into Uniform [0,1] domain. Then, Copula parameter is estimated to get significance parameter. Lastly, the best Copula that has the highest log likelihood value is selected to represent the relationship of data. The result indicates that percentage of coral reefs damage in West and Central Region has relationship with SST Nino 4, while coral reefs damage in East Region does not have relationship with any of SST Nino anomalies. In West Region, the best Copula represents the relationship is Gaussian Copula (parameter = -0.32); it concludes that the higher the value of SST Nino 4, the lower the percentage of coral reefs damage and otherwise. While in Central Indonesia, Frank Copula (parameter = -4.89) is selected; it does not have tail dependency so that the SST Nino 4 and the percentage of coral reefs in damage condition in Central Region has low correlation.
Strategi Pelayanan dan Pemasaran melalui Pelatihan Data Analytics di Bank X A. Tuti Rumiati; Ismaini Zain; Kartika Fithriasari; Wibawati Wibawati; Setiawan Setiawan; Vita Ratnasari; Dedy Dwi Prastyo; Erma Oktania Permatasari; Adatul Mukarohmah; Shofi Andari; Husna Miratin Nuroini; Veniola Forestryani; Rhifda Zukhrufi
Madaniya Vol. 5 No. 2 (2024)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/27214834.801

Abstract

Pelatihan Data Analytics yang diadakan oleh salah satu bank terbesar di Indonesia, yaitu Bank X, merupakan sebuah inisiatif untuk meningkatkan kapasitas karyawan dalam memanfaatkan data nasabah guna menentukan strategi pelayanan dan pemasaran kedepan. Pelatihan ini memberikan pemahaman komprehensif tentang data analytics, keterampilan analisis terhadap data nasabah baik oleh bagian riset dan pengembangan maupun oleh bagian marketing untuk memahami potensi pasar, dan kebutuhan nasabah yang dapat digunakan untuk memperbaiki strategi pemasaran. Pelatihan dilakukan oleh Statistics Service Center (SSC) Institut Teknologi Sepuluh Nopember selama dua hari. Materi pelatihan dirancang sesuai dengan kebutuhan Bank X, meliputi: 1) Pembahasan profiling nasabah bank, segmentasi nasabah, target market serta pembahasan faktor-faktor yang mempengaruhi loyalitas nasabah; 2) Metode statistika untuk profiling nasabah, segmentasi pasar dan penentuan target pasar; 3) Studi kasus dan aplikasi praktis. Selain penyampaian materi pelatihan, pendekatan pelatihan melibatkan serangkaian sesi interaktif dan praktek analisis dari studi kasus dan data aktual dari bank yang dipandu oleh fasilitator. Pembelajaran dirancang untuk memfasilitasi peserta memperoleh pemahaman praktis menggunakan metode statistika, diikuti dengan penerapan langsung keterampilan analisis menggunakan perangkat lunak SPSS. Pendekatan ini diharapkan dapat menghubungkan teori dengan praktik dalam konteks yang relevan bagi peserta. Dari kegiatan yang telah dilaksanakan, tercatat peningkatan kompetensi peserta, yang tercermin dari perbedaan yang sangat signifikan hasil pre-test dan post-test serta feedback positif terhadap materi dan metode penyampaian. Manfaat kegiatan ini meliputi peningkatan kemampuan analisis data yang lebih baik dan penguasaan alat-alat analitik yang dapat diterapkan dalam berbagai fungsi bisnis di bank. Kesimpulan dari pelatihan ini menekankan pentingnya pembelajaran berkelanjutan dalam data Analytics sebagai sarana untuk meningkatkan kualitas pengambilan keputusan dan strategi bisnis di sektor perbankan. Keterlibatan karyawan dalam pelatihan ini mencerminkan komitmen bank terhadap pengembangan sumber daya manusia yang berorientasi pada inovasi dan pemanfaatan teknologi.
Per capita expenditure prediction using model stacking based on satellite imagery Kuswanto, Heri; Rouhan, Asva Abadila; Qori’atunnadyah, Marita; Hia, Supriadi; Fithriasari, Kartika; Widhianingsih, Tintrim Dwi Ary
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1220-1231

Abstract

One of the indicators for measuring poverty is per capita expenditure. However, collecting timely and reliable per capita expenditure data is quite challenging and expensive, as it requires collecting detailed household data directly. One way to deal with this issue is to use satellite image data processed by machine learning methods. This research proposes a method to predict the per capita expenditure of regencies or cities in Indonesia based on satellite imagery using machine learning techniques, such as k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). The predictions are stacked to predict per capita expenditure using least absolute shrinkage and selection operator (LASSO) regression as the meta-learner. The model is trained on Google-Earth-based satellite imagery of Java Island, Indonesia, which provides more update field conditions compared to data collected from Statistics Indonesia (BPS). The research found that the stacked model outperforms the individual methods. However, the R2 criterion of the stacked method is comparable to that of RF, which is slightly higher than the others.
MODELLING OF POVERTY PERCENTAGE IN EAST JAVA PROVINCE WITH SEMIPARAMETRIC REGRESSION APPROACH Syahzaqi, Idrus; A., Salman Alfarizi P.; Fithriasari, Kartika
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/barekengvol17iss2pp0727-0734

Abstract

Poverty is an economic problem faced by all countries in the world, including Indonesia. Poverty is seen as the inability of a person from an economic standpoint to meet basic food and non-food needs as measured from the expenditure side. East Java Province is used as the object of research because this province has the highest economic growth in Java Island after DKI Jakarta province in the last 5 years. However, East Java is also included in the province with the highest number of poor people on the island of Java. Several independent variables that are thought to influence the percentage of poverty in East Java are the Open Unemployment Rate (TPT), Life Expectancy Rate (AHH), Average Years of Schooling (RLS), Population Density, and GRDP Rate. Sources of research data come from the East Java BPS website and East Java Open Data. Data analysis was performed using a semiparametric regression approach. The results of the analysis obtained good performance values, namely the MSE value of 12,2156 and the R2 value of 98,71%.
The Comparison of Classical and Bayesian Bivariate Binary Logistic Regression Prediction for Unbalanced Response (Case Study: Customers of Antivirus Software 'X' Company) Susila, Muktar Redy; Kuswanto, Heri; Fithriasari, Kartika
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2394

Abstract

The purpose of this study was to compare the performance of classical bivariate binary logistic regression and Bayesian bivariate binary logistic regression. The sizes of sample used in research were small and large sample. The size of the small sample was 200 and the large sample was 10000 samples. Parameter estimation method that often used in logistic regression modeling is maximum likelihood which is called the classical approach. However, using a maximum likelihood parameter estimation has several weaknesses. When the number of sample is small and the dependent variable is unbalanced, bias parameters are frequently obtained. Nevertheless, when the sample size is too large, it has propensity to reject H0. As the solution, the use of Bayesian approach to overcome the small sample size problem and unbalanced dependent variable is suggested. The case study carried out in this research was customer loyalty of 'X' Company. This study used two dependent variables, i.e. Customer Defections and Contract Answer. Initial information on the number of consumers who defected and not defected was unbalanced, likewise for the Contract Answers. Based on the comparison of classical and Bayesian bivariate binary logistic regression prediction, Bayesian method was evidenced to yield better performance compared to classical method.
Handling Imbalance Data in Classification Model with Nominal Predictors Kartika Fithriasari; Iswari Hariastuti; Kinanthi Sukma Wening
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 6 No. 1 (2020)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Decision tree, one of classification method, can be done to find out the factors that predict something with interpretable result. However, a small and unbalanced percentage will make the classification only lead to the majority class. Therefore, handling imbalance class needs to be done. One method that often used in nominal predictor data is SMOTE-N. For accuracy improving, a hybrid SMOTE-N and ADASYN-N was developed. SMOTE-N-ENN and ADASYN-N were developed for accuracy improvement. In this study, SMOTE-N, SMOTE-N-ENN and ADASYN-N will be compared in handling imbalance class in the classification of premarital sex among adolescent using base class CART. The conclusion obtained regarding the best method for handling class imbalance is ADASYN-N because it provides the highest AUC compared to SMOTE-N and SMOTE-N-ENN. The best decision tree provides information that factors that can predict adolescents having premarital sexual relations are dating style, knowledge of the fertile period, knowledge of the risk of young marriage, gender, recent education, and area of residence.
Co-Authors A. Tuti Rumiati Adatul Mukarohmah Adatul Mukarromah Aldho Riski Irawan Almuhayar, Mawanda Anindya Apriliyanti Pravitasari Brodjol Sutija Suprih Ulama Dedy Dwi Prastyo Dian Sukma Pratiwi Dio Ariadi Dwi Rantini Dwiatmono Agus Widodo Erma Oktania Permatasari Febrian Kristianda Ferriastuti, Widiana Heri Kuswanto Hia, Supriadi Husna Miratin Nuroini I Gusti Bagus Ngurah Diksa Idrus Syahzaqi Irhamah Irhamah - Irwan Susanto Ismaini Zain Iswari Hariastuti Iswari Hariastuti Kinanthi Sukma Wening Kinanthi Sukma Wening Made Ayu Dwi Octavanny Marina Marsudi Putri Mochamad Ihsan Ananto Mohammad Alfan Alfian Riyadi Mohammad Alfan Alfian Riyadi Novarani Putri Saraswati Nur Asiyah Nur Iriawan Oktaviana, Pratnya Paramitha Pratnya Paramitha O Pratnya Paramitha Oktaviana Qori’atunnadyah, Marita Raditya Novidianto Rahayu Prihatini Saputri Rhifda Zukhrufi Riska Prakasita Sahitayakti Rouhan, Asva Abadila Rumaisa Kruba Salman Alfarizi Pradana Andikaputra Santi Wulan Purnami Setiawan Setiawan Shofi Andari Sinta Septi Pangastuti Soehardjoepri Soehardjoepri Susila, Muktar Redy T. Dwi Ary Widhianingsih Taufik Azmi Ulfa Siti Nuraini Veniola Forestryani Vita Ratnasari Wahyuni Suryaningtyas, Wahyuni Wibawati Wibawati Widhianingsih, Tintrim Dwi Ary Wiwiek Setya Winahju