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All Journal Jurnal Gaussian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Kreativitas PKM METIK JURNAL Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Riset Pembangunan : Media Hasil Penelitian dan Kajian Pembangunan Daerah Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Multica Science and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Research Applications in Community Services Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Statistika dan Komputasi (STATKOM) Eksponensial STATISTIKA

Surya Prangga

Universitas Mulawarman

Author-ID : 3771048

Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Clustering Titik Panas Bumi Pada Potensi Kebakaran Hutan Menggunakan K-Affinity Propagation Primantoro, Sudhan; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1299

K-Affinity Propagation is a development of affinity propagation from Brendan J. Frey and Delbert Dueck. The purpose of this research is to cluster geothermal hotspots on potential forest fires in Indonesia using K-Affinity Propagation for the period July 2022 and obtain optimal cluster results using standard deviation with ratio calculations. The optimal cluster results are 4 clusters, with the number of members in cluster 1 being 12 members with copies in West Sumatera Province, the number of members in cluster 2 being 12 members with copies in Southeast Sulawesi Province, the number of members in cluster 3 being 4 members with copies in Central Sulawesi Province, the number of members in cluster 4 being 1 member with copies in North Sulawesi Province. The optimal cluster results using standard deviation with the smallest ratio value is cluster 4 with a ratio value of 0.057.

Penerapan Algoritma Divisive Analysis dalam Pengelompokan Provinsi di Indonesia Berdasarkan Prevalensi Stunting Suyono, Ari Krisna; Hayati, Memi Nor; Siringoringo, Meiliyani; Prangga, Surya; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1341

Cluster analysis is an analysis that aims to group data (objects) based only on the information contained in the data that describes objects and the relationships between the objects. Divisive analysis is a clustering method using a top-down approach which starts by placing all objects into one cluster or what is called a hierarchical root and then dividing the cluster root into several smaller clusters. This research aimed to group 34 provinces in Indonesia into 2,3, and 4 clusters based on stunting prevalence data and factors causing stunting in 2022 using a divisive analysis algorithm. The results showed that for 2 clusters, cluster 1 consisted of 32 provinces with low stunting prevalence, and cluster 2 consisted of 2 provinces with high stunting prevalence. For 3 clusters, cluster 1 consisted of 26 provinces with moderate stunting prevalence, cluster 2 consisted of 6 provinces with low stunting prevalence, and cluster 3 consisted of 2 provinces with high stunting prevalence. For 4 clusters, cluster 1 consisted of 21 provinces with moderate stunting prevalence, cluster 2 consisted of 5 provinces with low stunting prevalence, cluster 3 consisted of 6 provinces with high stunting prevalence, and cluster 4 consisted of 2 provinces with very high stunting prevalence.

Peramalan Jumlah Wisatawan Mancanegara di Provinsi Kalimantan Timur Menggunakan Fuzzy Backpropagation Neural Network Aprilianti, Rina; Purnamasari, Ika; Prangga, Surya
Jurnal Statistika dan Komputasi Vol. 2 No. 1 (2023): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v2i1.1592

Latar Belakang: Pariwisata merupakan salah satu bidang ekonomi yang menjadi sumber penerimaan devisa bagi negara. Banyaknya wisatawan merupakan salah satu faktor yang dapat berpengaruh terhadap perkembangan pariwisata. Sepanjang tahun 2021, jumlah wisatawan mancanegara di Provinsi Kalimantan Timur mengalami penurunan. Penurunan tersebut merupakan dampak dari mewabahnya COVID-19. Peneliti melakukan peramalan jumlah wisatawan mancanegara di Kalimantan Timur menggunakan Fuzzy Backpropagation Neural Network (FBPNN) guna mengantisipasi kenaikan maupun penurunan jumlah wisatawan di masa mendatang. FBPNN adalah metode peramalan Neural Network (NN) yang menggunakan algoritma pembelajaran backpropagation dimana nilai input dan output-nya berupa derajat keanggotaan himpunan fuzzy. Tujuan: Meramalkan jumlah wisatawan mancanegara di Kalimantan Timur pada bulan Januari 2022 sampai dengan Mei 2022. Metode: Metode yang digunakan adalah Fuzzy Backpropagation Neural Network (FBPNN). Hasil: Berdasarkan hasil prediksi FBPNN dengan proporsi 80%:20% untuk data training diperoleh Root Mean Square Error (RMSE) sebesar 113,61 sedangkan untuk RMSE data testing dipeoleh adalah sebesar 108,45. Kesimpulan: Adapun kesimpulan penelitian yaitu metode Fuzzy Backpropagation Neural Network dapat digunakan untuk meramalkan jumlah wisatawan dengan nilai RMSE yang dihasilkan oleh data testing lebih kecil jika dibandingkan dengan nilai RMSE yang dihasilkan oleh data training.

PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019) Pratiwi, Reni; Hayati, Memi Nor; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method

Pengelompokan Judul Laporan Skripsi Berbasis Text Mining dengan Metode Fuzzy K-Means Nur Azizah, Noviani; Purnamasari, Ika; Prangga, Surya
METIK JURNAL (AKREDITASI SINTA 3) Vol. 8 No. 1 (2024): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v8i1.808

Text mining merupakan salah satu cabang dari data mining. Text mining dapat menganalisa dokumen, menentukan kesamaan di antara dokumen dan mengelompokkan dokumen. Pengelompokan dokumen dapat dilakukan melalui metode text mining yang dapat dikombinasikan dengan fuzzy k-means. Fuzzy k-means mampu menempatkan suatu data dimana data tersebut masuk sebagai anggota keseluruhan klaster berdasarkan derajat keanggotaan yang terletak di interval [0,1], serta dapat menunjukkan hasil penempatan klaster yang lebih akurat. Tujuan penelitian ini adalah menentukan kelompok optimal dan hasil pengelompokan yang terbentuk pada judul laporan skripsi mahasiswa Program Studi Statistika, Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman tahun 2020-2022. Pada penelitian ini menggunakan davies-bouldin index sebagai uji validasi hasil pengelompokan. Berdasarkan hasil analisis, kelompok optimal yang terbentuk adalah klaster enam dengan nilai davies-bouldin index sebesar 3,646. Terdapat 6 kelompok dari hasil analisis dengan rincian klaster ke-1 sebanyak 24 judul laporan skripsi, klaster ke-2 sebanyak 8 judul laporan skripsi, klaster ke-3 sebanyak 17 judul laporan skripsi, klaster ke-4 sebanyak 39 judul laporan skripsi, klaster ke-5 sebanyak 17 judul laporan skripsi dan klaster ke-6 sebanyak 29 judul laporan skripsi.

Handling Imbalanced Data in K-Nearest Neighbor Algorithm using Synthetic Minority Oversampling Technique-Nominal Continuous Anjani Anjani; Hayati, Memi Nor; Surya Prangga
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5142

Classification is a part of data mining that aims to predict the class of data using a trained machine learning model. K-Nearest Neighbor (K-NN) is one of the classification methods that uses the concept of distance to the nearest neighbor in creating classification models. However, K-NN has limitations in handling imbalanced class distributions. This core problem can be addressed by applying a class balancing technique. One such technique is the Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC), which is suitable for datasets containing both nominal and continuous variables. The aim of this research is to classify Honda motorcycle loan customer data at Company Z using the K-NN method combined with SMOTE-NC to address data imbalance. This research method is experimental, using a 10-fold cross-validation approach to partition training and testing data. The input variables include gender, occupation, length of installment, income, installment amount, motorcycle price, and down payment, while the output variable is payment status (current or non-current). The results of this research are: the optimal K value for classification using K-NN with SMOTE-NC is K = 1, with an average APER (Average Probability of Error Rate) of 0.143. The best result is found in subset 8 with an APER value of 0.033. In this subset, out of 61 data points, 34 current-status customers are correctly classified as current, and 25 non-current-status customers are correctly classified as non-current, with only one misclassification in each class. The conclusion of this study is that the combination of SMOTE-NC and K-NN (K=1) provides high classification accuracy for imbalanced data, and can be effectively used to support credit risk assessment in motorcycle financing.

TRAFFIC ACCIDENT VICTIM CLASSIFICATION IN BONTANG USING NW-KNN AND BACKWARD ELIMINATION Mangalik, Gerald; Nariza Wanti Wulan Sari; Surya Prangga; Wiwit Pura Nurmayanti; Ika Purnamasari
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/yfbspb33

Traffic accidents have been a serious problem caused by various factors such as road conditions, driver behavior, and weather. To understand the pattern of victim severity, a classification approach capable of handling imbalanced data and irrelevant features was needed. This study aimed to classify the status of accident victims using the Neighbor Weighted K-Nearest Neighbor (NW-KNN) method, equipped with backward elimination for feature selection. Backward elimination was employed to reduce insignificant features and improve accuracy.The case study for this research involved the status of accident victims in Bontang City, with a sample size of 93 cases. There were nine features in this study: accident victim status, accident time, road density, road function, road surface condition, speed limit at the location, road slope, and road status.The research results showed that the best parameter combination for classification using the NW-KNN method with backward elimination was K = 7 and E = 3. The "type of accident" feature was eliminated, leaving 8 features. Classification results using the NW-KNN method with backward elimination yielded an accuracy of 88.89%, demonstrating an improvement in classification performance for identifying the status of traffic accident victims. Thus, this method proved to be an effective approach for traffic accident analysis in Bontang City.

REGRESI NONPARAMAETRIK SPLINE PADA DATA LAJU PERTUMBUHAN EKONOMI DI KALIMANTAN Purnaraga, Tirta; Sifriyani, Sifriyani; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Economic Growth Rate (EGR) is an important indicator for measuring the success of an economy's development. The welfare and progress of an economy is determined by the amount of growth shown by changes in the quantity of goods and services produced nationally. High economic growth is a goal that is expected to be achieved in a developing country. Many factors affect EGR in Kalimantan, so it is necessary to do modeling to find out the factors that significantly affect EGR. This study uses 6 factors that are suspected to influence EGR, namely the labor force participation rate, the number of large and medium industries, the average length of schooling, regional income and expenditure budgets, general allocation funds and rice productivity. The data is 2017 data obtained from the Central Bureau of Statistics in 5 provinces in Kalimantan. The method used to model the LPE is spline nonparametric regression and the optimal knot point is 3 knot points based on the smallest Generalized Cross Validation (GCV) value of 1.208. The research results, the best model is obtained with a R2 value of 82.15 percent and a Mean Square Error (MSE) of 0.805. The results of the study provide information that the factors that influence the LPE are the level of labor force participation, the number of large and medium industries, the average length of schooling, regional income and expenditure budgets, general allocation funds and rice productivity.

IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI KALIMANTAN MENGGUNAKAN REGRESI PANEL Zarkasi, Rifka Nurfaiza; Sifriyani, Sifriyani; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Pembangunan merupakan salah satu cara untuk meningkatkan kualitas kehidupan demi terciptanya masyarakat yang sejahtera. Pemerintah terus melakukan pembangunan di segala aspek seperti aspek pendidikan, kesehatan, dan kehidupan yang layak. Untuk mengukur keberhasilan pembangunan salah satu indikator yang bisa digunakan adalah Indeks Pembangunan Manusia (IPM). Dalam perhitungan IPM, telah melibatkan komponen ekonomi maupun non ekonomi. Penelitian ini bertujuan untuk meneliti faktor-faktor yang mempengaruhi IPM Kalimantan pada tahun 2014-2017. Karena data yang digunakan merupakan data panel yaitu gabungan antara data cross-section dan data time-series, maka IPM dimodelkan dengan regresi panel. Untuk mengestimasi model digunakan pendekatan Fixed Effect Model (FEM). Pemodelan IPM menghasilkan nilai sebesar 99,54 persen. Hasil penelitian menunjukkan bahwa untuk meningkatkan IPM dapat dilakukan dengan cara meningkatakan angka harapan hidup, rata-rata lama sekolah, harapan lama sekolah, dan pengeluaran per kapita.

PEMODELAN ANGKA HARAPAN HIDUP DAN ANGKA KEMATIAN BAYI DI KALIMANTAN DENGAN REGRESI NONPARAMETRIK SPLINE BIRESPON Padatuan, Aprianti Boma; Sifriyani, Sifriyani; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Penelitian ini menggunakan model regresi nonparametrik birespon dengan pendekatan spline truncated. Model tersebut digunakan untuk menyelesaikan permasalahan analisis regresi yang bentuk kurvanya tidak diketahui. Pendekatan spline truncated memiliki fungsi polinomial tersegmen yang memberikan sifat fleksibilitas. Data yang digunakan dalam penelitian ini terdiri dari dua variabel respon yaitu Angka Harapan Hidup (AHH) dan Angka Kematian Bayi (AKB) di Pulau Kalimantan. Tujuan penelitian adalah untuk menentukan model regresi nonparametrik spline truncated birespon pada data AHH dan AKB dan mengetahui faktor-faktor yang mempengaruhi AHH dan AKB. Hasil penelitian diperoleh model terbaik yaitu model regresi nonparametrik spline linier birespon dengan nilai R2 sebesar 80,51 persen dan model spline tiga titik knot dengan nilai Generalized Cross Validation (GCV) minimum 7,1454. Faktor-faktor yang mempengaruhi AHH dan AKB adalah persentase keluarga menerapkan Perilaku Hidup Bersih dan Sehat (PHBS), persentase bayi diberi Air Susu Ibu (ASI) usia 0-6 bulan, laju pertumbuhan ekonomi, persentase persalinan yang dibantu oleh tenaga medis dan persentase penduduk miskin.

Co-Authors Aisha, Putri Anjani Anjani Annisa Hayatunnufus Annisa Zuhri Apridayanti Apridayanti, Annisa Zuhri Aprilianti, Rina Binda Aprilia Suryani Casuarina, Indah Putri Dani, Andrea Tri Rian Darnah Darnah Darnah, Darnah Dini Elizabeth Dwi Husnul Mubiin Erlyne Nadhilah Widyaningrum Fauziyah, Meirinda Febriana Rinda Sihotang Febriyanti, Nur Afifah Goenjatoro, Rito Hillidatul Ilmi Ika Purnamasari Ika Purnamasari Ilmi, Hillidatul Irfan Fadil Juliarto, Handy Kurniawan Khairun Nida Khoiril Anwar M. Fathurahman Mangalik, Gerald Meiliyani Siringoringo Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Muhammad Rais Muhammad Wahdeni Pramana Muhammad Wahdeni Pramana Nariza Wanti Wulan Sari Ni Luh Ari Yusasrini Nur Azizah, Noviani Nurul Istiqomah Oroh, Chiko Zet Padatuan, Aprianti Boma Patiallo, Monalisa Randang Permana, Jordan Nata Pratiwi, Reni Primantoro, Sudhan Purnaraga, Tirta Raihani, Risti Rito Goejantoro, Rito Sekar Nur Utami Selsa Amelia Sifriyani, Sifriyani Siringoringo, Meiliyani Siti Mahmuda Solikhah, Arifatus Sukamto, Ika Sumiyarsi Suyitno Suyitno Suyono, Ari Krisna Syamsiar, Syamsiar Wiwit Pura Nurmayanti Zarkasi, Rifka Nurfaiza

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