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All Journal BIMASTER CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Kebijakan Perikanan Indonesia Sarwahita : Jurnal Pengabdian Kepada Masyarakat Variance : Journal of Statistics and Its Applications Insan Cita Jurnal Pengabdian Kepada Masyarakat JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Debataraja, Naomi Nessyana
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia Debataraja, Naomi Nessyana; Kusnandar, Dadan; Nusantara, Rossie Wiedya
CAUCHY Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress)
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i2.13266

Abstract

Geographically weighted regression (GWR) is an exploratory analytical tool that creates a set of location-specific parameter estimates. The estimates can be analysed and represented on a map to provide information on spatial relationships between the dependent and the independent variables. A problem that is faced by the GWR users is how best to map these parameter estimates. This paper introduces a simple mapping technique that plots local t-values of the parameters on one map. This study employed GWR to evaluate chemical parameters of water in Pontianak City. The chemical oxygen demand (COD) was used as the dependent variable as an indicator of water polution. Factors used for assessing water pollution were pH (X1), iron (X2), fluoride (X3), water hardness (X4), nitrate (X5), nitrite (X6), detergents (X7) and dissolved oxygen, DO, (X8). Samples were taken from 42 locations. Chemical properties were measured in the laboratory. The parameters of the GWR model from each site were estimated and transformed using Geographic Information Systems (GIS). The results of the analysis show that X1, X2, X3, X5, and X7 influence the amount of COD in water. The resulting map can assist the exploration and interpretation of data.
Pemodelan M-Adaptive Generalized Poisson Regression Spline Pada Kasus MDR-TB Di Kalimantan Barat Irvandi, Firzakalpa Syafiq; Debataraja, Naomi Nessyana; Yudhi, Yudhi
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 2 (2023): Jurnal Derivat (Agustus 2023)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.4481

Abstract

Tuberculosis is a disease caused by the Mycobacterium tuberculosis. Multi-Drug Resistant Tuberculosis (MDR-TB) is the term used to describe Mycobacterium tuberculosis that is resistant to one or more Anti-TB drugs. This study aims to determine the factors that affect the number of patients recovering from MDR-TB, by modeling the number of MDR-TB cured patients using Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) method. The predictor variables are the average age (X1), percentage of patients who fail category 2 treatment (X2), percentage of patients who fail category 1 treatment (X3), percentage of patients relapsed (X4), percentage of patients neglecting treatment (X5), and percentage history of close contact with other patients (X6). A combination of BF (Basis function), MI (Maximum interaction), and MO (Minimum observation), the BF value is two to four times of predictor variables, MI has value of 1,2, and 3, and MO has value of0,1,2, and 3. From the result, the best model was obtained from the combination of BF=24, MI=3, and MO=1, with GCV values of 0,3504 and R2 of 88,3%, and there are 14 BF that affect the response variable . The most influential predictors variables in a row, are X6, X3, X5, and X2.  Keywords: Poisson, basis function, GCV
ANALISIS POLA SPASIAL KEBAKARAN HUTAN DI KALIMANTAN BARAT MENGGUNAKAN GETIS-ORD (GI*) STATISTIC DAN INDEKS MORAN Prianti, Sabrina Eka; Kusnandar, Dadan; Debataraja, Naomi Nessyana; Martha, Shantika
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 6 (2024): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v13i6.89234

Abstract

Kebakaran hutan adalah suatu keadaan dimana api menghanguskan sebagian atau keseluruhan hutan sehingga menimbulkan kerusakan yang mengakibatkan kerugian terhadap perekonomian dan nilai lingkungan hidup. Kebakaran hutan dapat terjadi berulang tiap tahunnya. Penelitian ini bertujuan untuk menentukan daerah signifikansi hotspot, pola spasial dan korelasi antar titik api di Kalimantan Barat. Daerah signifikansi hotspot dianalisis dengan Getis Ord (Gi*) Statistic. Pola spasial dan korelasi antar titik api dianalisis dengan Indeks Moran. Data yang digunakan yaitu data titik api dari instrumen MODIS citra satelit Terra dan Aqua. Hasil analisis Getis Ord (Gi*) Statistic menunjukkan konsentrasi titik api tertinggi tahun 2018 terjadi pada Kabupaten Kayong Utara, Kubu Raya, Mempawah, Sambas dan Kota Pontianak. Tahun 2019 terjadi pada Kabupaten Kayong Utara, Ketapang dan Sambas. Tahun 2020 terjadi pada Kabupaten Bengkayang, Kubu Raya, Landak, Mempawah dan Sambas. Tahun 2021 terjadi pada Kota Pontianak, Kabupaten Kubu Raya dan Mempawah. Tahun 2022 terjadi pada Kabupaten Bengkayang, Landak, Mempawah, Sambas dan Kota Singkawang. Kejadian kebakaran hutan berulang di Kalimantan Barat yang tergolong dalam signifikansi hotspot paling banyak terjadi di Kabupaten Sambas yaitu tahun 2018, 2019, 2020, dan 2022. Selain itu, analisis Indeks Moran menunjukkan bahwa Kebakaran Hutan di Kalimantan Barat tahun 2018-2022 terdapat autokorelasi spasial antar titik api dan pola spasialnya menghasilkan pola yang berkelompok (clustered).  Kata Kunci :  titik api, Getis Ord (Gi*) Statistic, Indeks Moran.
PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI JUMLAH KEMATIAN IBU HAMIL DENGAN REGRESI ZERO INFLATED GENERALIZED POISSON (ZIGP) Perangin Angin, Christi Alemsa; Debataraja, Naomi Nessyana; Sulistianingsih, Evy
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 4 (2024): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v13i4.77971

Abstract

Angka Kematian Ibu (AKI) adalah parameter kesejahteraan wanita, parameter kesejahteraan sebuah negara serta menggambarkan hasil capaian pembangunan sebuah negara. Sejak tahun 2018 hingga 2021 AKI di Kapuas Hulu mengalami peningkatan. Pada tahun 2018 AKI di Kapuas Hulu sebesar 71 per 100.000 kelahiran hidup (KH) dan tahun 2021 naik menjadi 173 per 100.000 KH, sedangkan target Sustainable Development Goals (SDGs) adalah kurang dari 70 per 100.000 KH. Data AKI tersebut merupakan data diskrit yang berdistribusi Poisson. Namun jika dilihat berdasarkan kecamatan yang ada di Kapuas Hulu, masih terdapat AKI yang nol kematian. Proporsi data nol yang berlebihan pada variabel respon dapat menyebabkan adanya masalah zero inflation. Nilai nol yang berlebihan dalam Regresi Poisson dapat menyebabkan terjadinya pelanggaran asumsi equidispersi. Hal tersebut dapat diatasi dengan Regresi ZIGP. Penelitian ini bertujuan untuk memodelkan dan menentukan faktor kematian ibu hamil di Kabupaten Kapuas Hulu tahun 2018-2021 menggunakan Regresi ZIGP. Data yang digunakan pada penelitian ini adalah data profil kesehatan di 23 kecamatan yang ada di Kabupaten Kapuas Hulu. Variabel respon ( ) yang digunakan yaitu jumlah kematian ibu hamil. Data tersebut terlebih dahulu dilakukan pengujian asumsi equidispersi. Jika terjadi pelanggaran asumsi equidispersi, maka langkah selanjutnya yaitu melakukan pengujian asumsi Zero Inflation. Apabila terjadi pelanggaran asumsi Zero Inflation, maka pemodelan dapat dilakukan dengan menggunakan Regresi ZIGP. Proporsi nilai nol pada variabel tersebut sebanyak 92%, sehingga terdapat masalah Zero Inflation dan mengindikasikan terjadinya overdispersi pada Regresi Poisson. Berdasarkan hasil pemodelan terbaik dengan Regresi ZIGP, persentase kunjungan ibu hamil pertama (K1) merupakan faktor yang berpengaruh terhadap jumlah kematian ibu hamil.  Kata Kunci: AKI, ZIGP, Overdispersi.
APPLICATION OF THE QUEST AND CHAID METHODS IN CLASSIFYING STUDENT GRADUATION Banu, Syarifah Syahr; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Satyahadewi, Neva
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page155-164

Abstract

Graduation is the final result of the learning process during the course. Student graduation time is affected by many factors. Whether or not the time of student graduation is appropriate is an important thing that must be considered. Graduating well and on time is one measure of success in the learning process. This research aims to build a student graduation classification model by applying the QUEST (Quick, Unbiased, and Efficient, Statistical Tree) and CHAID (Chi-squared Automatic Interaction Detection) methods, examining the factors that affect student graduation, and comparing the classification results of the two methods. Both methods produce output in the form of tree diagrams, making it easier to interpret. Based on the classification tree formed from the two methods, four final nodes of the classification tree were generated, and three categories were grouped. Factors that affect student graduation include age and IPK. The classification results show that the percentage of classification accuracy for student graduation with QUEST and CHAID methods is 76.1%.
APPLICATION OF C4.5 ALGORITHM WITH FEATURE SELECTION IN CLASSIFICATION OF DISCHARGE STATUS OF HEAD INJURY PATIENTS ., Putri; Sulistianingsih, Evy; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page165-174

Abstract

Head trauma is a medical emergency that can cause brain damage and disability, leading to death. The discharge status of injured patients is classified into two: alive and dead. The purpose of this study is to apply the C4.5 algorithm without feature selection and by using Chi-Square and Mutual Information feature selection to show independent variables that significantly influence the discharge status of head injury patients. This research data is secondary data of patients who suffered head injuries at Dr. Abdul Aziz Hospital, Singkawang City, in 2019-2021. The independent variables used were age, gender, length of hospitalization, etiology of head injury, Suprasellar Cistern, and Glasscow Coma Scale, with the dependent variable being discharge status. Based on the study results, the Chi-Square feature selection results identified two variables that had a significant effect. In contrast, for the Mutual Information feature selection results, five variables had a significant impact on the dependent variable. The C4.5 Algorithm classification model without feature selection produces an accuracy of 88.57%, the C4.5 Algorithm classification model with Chi-Square feature selection produces an accuracy of 88.57%, and the C4.5 Algorithm classification model with Mutual Information feature selection produces an accuracy value of 91.42% with the highest accuracy obtained from the results of the C4.5 Algorithm model formation with Mutual Information feature selection.
Peningkatan Keterampilan Analisis Data Bagi Fungsional BPS di Kalimantan Barat Melalui Pelatihan SEM dengan AMOS Martha, Shantika; Andani, Wirda; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Imro'ah, Nurfitri; Satyahadewi, Neva; Tamtama, Ray; Perdana, Hendra; Kusnandar, Dadan
Bahasa Indonesia Vol 22 No 01 (2025): Sarwahita : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/sarwahita.221.9

Abstract

This Community Service activity is a form of cooperation between Statistics Study Program FMIPA UNTAN and BPS through training activities. The purpose of this PKM is to provide knowledge and insight to BPS functional employees about SEM (Structural Equation Modeling) using AMOS. This activities were carried out on Monday, August 14, 2023 in the Vicon room of the West Kalimantan provincial BPS office with 32 participants attending. The results of this training activity are expected to be applied by BPS functional employees in processing and analyzing data as research needs and work related to statistical data. The level of success in this training was measured through pre-test, post-test and participant satisfaction survey. A wilcoxon test was conducted with α = 0.05 and the result was p-value smaller than 0.01. So that the p-value < α which means rejecting H0 and it can be concluded that the average pretest score < average posttest score. In other words, the post-test results increased significantly, which means that the participants' abilities increased after the training. Based on the participant satisfaction survey, the results showed that all participants (100%) had never used AMOS software before. Overall, participants were satisfied (61.5%) and very satisfied (38.5%) with the training because they could increase their knowledge and the training materials delivered were in accordance with their needs, easy to understand and interesting, could be applied easily, and were delivered in order and systematically.   Abstrak Kegiatan Pengabdian Kepada Masyarakat (PKM) ini merupakan salah satu wujud kerjasama Prodi Statistika FMIPA UNTAN dengan BPS melalui kegiatan pelatihan. Adapun tujuan PKM ini yaitu memberikan pengetahuan dan wawasan kepada pegawai fungsional BPS tentang teknik pengolahan dan analisis data SEM (Structural Equation Modelling) dengan menggunakan AMOS. Kegiatan PKM dilaksanakan pada hari Senin, 14 Agustus 2023 di ruang Vicon kantor BPS prov Kalbar dengan jumlah peserta yang hadir 32 orang. Hasil dari kegiatan pelatihan ini diharapkan dapat diterapkan oleh pegawai fungsional BPS dalam mengolah dan menganalisis data sebagai kebutuhan penelitian maupun pekerjaan yang berhubungan dengan data statistika. Tingkat keberhasilan pada pelatihan ini diukur melalui pemberian pretest, posttest dan survey kepuasan peserta. Dilakukan uji beda menggunakan uji wilcoxon dengan α = 0.05 dan didapatkan hasil yaitu berupa p-value lebih kecil dari 0.01. Sehingga p-value < α yang berarti tolak H0 dan dapat disimpulkan rata-rata nilai pretest < rata-rata nilai posttest. Dengan kata lain hasil posttest meningkat secara signifikan yang artinya kemampuan peserta meningkat setelah dilaksanakan pelatihan. Berdasarkan survey kepuasan peserta didapatkan hasil ternyata semua peserta (100%) belum pernah menggunakan software AMOS sebelum pelatihan. Secara keseluruhan peserta merasa puas (61,5%) dan sangat puas (38,5%) mengikuti pelatihan karena dapat menambah pengetahuan serta materi pelatihan yang disampaikan sesuai dengan kebutuhan, mudah dipahami dan menarik, dapat diterapkan dengan mudah, dan disampaikan dengan urut dan sistematis.
PEMODELAN TINGKAT KUALITAS AIR DI KOTA PONTIANAK DENGAN MENGGUNAKAN MULTIVARIATE GEOGRAPHICALLY WEIGHTED REGRESSION Kusnandar, Dadan; Debataraja, Naomi Nessyana; Utari, Shindy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.308 KB) | DOI: 10.30598/barekengvol15iss3pp493-502

Abstract

Ketersediaan air bersih dan sanitasi yang layak merupakan salah satu tujuan dalam Sustainable Development Goals. Kualitas air cenderung mengalami penurunan terutama di daerah permukiman akibat tercemar limbah dari hasil kegiatan manusia. Penyebab pencemaran air bisa jadi berbeda-beda di setiap lokasi pengamatan, sehingga faktor letak geografis perlu dipertimbangkan pada proses pengambilan keputusan. Multivariat Geographically Weighted Regression digunakan untuk mengatasi adanya pengaruh heterogenitas spasial dalam data yang disebabkan oleh perbedaan kondisi lokasi yang satu dengan lokasi lain. Tujuan dari penelitian ini adalah menentukan model dan faktor-faktor apa saja yang berpengaruh terhadap kualitas air di Kota Pontianak. Data yang digunakan pada penelitian ini adalah data kualitas air di Kota Pontianak sebanyak 42 titik sampel lokasi. Variabel responnya terdiri dari Y1 (COD) dan Y2 (TDS), sedangkan untuk variabel prediktor terdiri dari X1 (warna), X2 (pH), X3 (kandungan zat besi), dan X4 (kesadahan). Hasil penelitian menunjukkan bahwa variabel yang mempengaruhi COD adalah warna, sedangkan variabel TDS dipengaruhi oleh warna dan kesadahan.
APPLICATION OF ADASYN OVERSAMPLING TECHNIQUE ON K-NEAREST NEIGHBOR ALGORITHM Marlisa, Herina; Satyahadewi, Neva; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
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/barekengvol18iss3pp1829-1838

Abstract

The K-Nearest Neighbor Algorithm is a commonly used data mining algorithm for classification due to its effectiveness with large datasets and noise. However, class imbalance may impact classification results, where data with unbalanced classes may classify new data based on the majority class and ignore minority class data. The research analyzed whether applying the Adaptive Synthetic (ADASYN) oversampling technique in the K-Nearest Neighbor Algorithm can handle data imbalance problems. The study looks at the resulting accuracy, specificity, and sensitivity values. ADASYN oversamples the minority class data based on the model's difficulty level of data learning using distribution weights. This research uses the Pima Indian Diabetes Dataset from the Kaggle website. The dependent variable was diabetes mellitus status, while the independent variables were number of pregnancies, glucose levels, diastolic blood pressure, insulin levels, Body Mass Index (BMI), and age. The study found that the accuracy, specificity, and sensitivity values were 72.88%, 73.42%, and 71.79%, respectively. Based on the results of the analysis, it can be concluded that using ADASYN in the K-Nearest Neighbor Algorithm to classify diabetes mellitus in Pima Indian women is good enough to address imbalanced data. It is shown that the ADASYN oversampling technique can help the K-Nearest Neighbor Algorithm to classify new data without ignoring the data of the minority class.
CLUSTER MAPPING OF HOTSPOTS USING KERNEL DENSITY ESTIMATION IN WEST KALIMANTAN Cahyani, Cristy Framedia; Kusnandar, Dadan; Debataraja, Naomi Nessyana; Martha, Shantika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2353-2362

Abstract

Forest and land fires pose a recurring concern every year in Indonesia, often taking place in West Kalimantan Province, particularly during the dry season. This study aims to use the Kernel Density Estimation (KDE) to categorize the data of the hotspots in the province of West Kalimantan according to their density and to map the cluster level of the fire risks in the region. The data utilized in this study are secondary data obtained from the images of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument, which are available on firms.modaps.eosdis.nasa.gov and provided by NASA. The data focuses on hotspots dispersed across West Kalimantan province during 2020. The variables examined in the study were the confidence level (≥80%) of forest and land fire hotspots, the distance from each point to the nearest river, and the distance from each point to the nearest road. The kernel density estimation method with a Gaussian kernel function yielded clustering results into three distinct groups according to their vulnerability levels. Low vulnerability areas comprise Cluster 1, which consists of 127 points or 50.97% of the total hotspots. Medium vulnerability areas belong to Cluster 2, which has 47 points or 30.32% of the total. Cluster 3 includes high vulnerability locations, consisting of 29 points or 18.71% of the total. The most susceptible areas to forest and land fires are located within the Ketapang regency.
Co-Authors ., Putri Andani, Wirda Anugrahnu, Joannes Fregis Philosovio Aprizkiyandari, Siti Banu, Syarifah Syahr Bertiani, Marsela Dian Cahyani, Cristy Framedia Dadan Kusnandar Desdianti, Maycandra Etika, Ezra Evy Sulistianingsih Hamdari, Ela Hendra Perdana HUDA, NUR’AINUL MIFTAHUL Imro'ah, Nurfitri IMRO’AH, NURFITRI Irvandi, Firzakalpa Syafiq Marlisa, Herina Martha, Shantika Neva Satyahadewi Nuruljannah, Auliya Perangin Angin, Christi Alemsa Pratama, Pandu Pasha Pratama, Yogi Prianti, Sabrina Eka Priyanto, Dwi Ari Rizki, Setyo Wira Rossie Wiedya Nusantara Salsabila, Yumna Hanum Simanjuntak, Martina Sumarni, Sania Lina Tamtama, Ray Utari, Shindy Yoke Lestyowati Yudhi