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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Jambura Journal of Mathematics Variance : Journal of Statistics and Its Applications ESTIMASI: Journal of Statistics and Its Application Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Mathematics Education and Science Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Statistika dan Komputasi (STATKOM) Eksponensial
Rito Goejantoro, Rito
Statistics Study Program, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Samarinda
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Peramalan Jumlah Penumpang Di Bandara Soekarno-Hatta Menggunakan Metode Deseasonalized Fatmi’aturro’isah, Nurul; Purnamasari, Ika; Goejantoro, Rito
Jurnal Statistika dan Komputasi Vol. 2 No. 2 (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.v2i2.2276

Abstract

Latar Belakang: Transportasi udara merupakan salah satu sektor usaha yang menopang bidang perekonomian di Indonesia. Pada sektor transportasi khususnya penumpang pesawat udara sering kali mengalami fluktuasi yang tidak menentu. Oleh karena itu, perlu suatu metode untuk mengatasi adanya fluktuasi tersebut dan metode yang dapat digunakan yaitu metode deseasonalized. Deseasonalized merupakan bagian dari metode dekomposisi yang bertujuan untuk menghilangkan variasi musiman sehingga memungkinkan untuk fokus pada trend jangka panjang. Metode deseasonalized didasarkan pada fakta bahwa apa yang terjadi akan berulang dengan pola yang sama. Tujuan: Meramalkan jumlah penumpang pesawat di Bandara Soekarno-Hatta pada tahun 2022. Metode: Metode yang digunakan adalah Deseasonalized. Hasil: Berdasarkan hasil prediksi dengan menggunakan metode deseasonalized di dapatkan nilai tingkat akurasi Mean Absolute Percentage Error (MAPE) sebesar 25,32% dan diperoleh hasil peramalan sepanjang tahun 2022 bahwa jumlah penumpang pesawat di Bandara Soekarno-Hatta tahun 2022 berpola cenderung naik dengan hasil peramalan untuk kuartal 1 sebesar 2.514.681 penumpang, kuartal 2 sebesar 2.073.318 penumpang, kuartal 3 sebesar 2.315.309 penumpang dan kuartal 4 sebesar 2.447.735 penumpang. Kesimpulan: Metode deseasonalized dapat digunakan untuk meramalkan jumlah penumpang pesawat di Bandara Soekarno-Hatta dengan nilai MAPE  yang dihasilkan cukup baik.
A PENERAPAN METODE SUBTRACTIVE FUZZY C-MEANS PADA TINGKAT PARTISIPASI PENDIDIKAN JENJANG SEKOLAH MENENGAH ATAS/SEDERAJAT DI KABUPATEN/KOTA PULAU KALIMANTAN TAHUN 2018 Suerni, Widya -; Hayati, Memi Nor; Goejantoro, Rito
VARIANCE: Journal of Statistics and Its Applications Vol 2 No 2 (2020): 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/variancevol2iss2page63-74

Abstract

Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equivalent level in the Regency / City of Kalimantan Island in 2018 using three variables, namely the Crude Participation Rate (CPR), the School Participation Rate (SPR) and the Net Enrollment Rate (NER). Based on the three validity indices, namely Partition Coefficient Index (PCI) Validity Index, Modified Partition Coefficient Index (MPCI), and Xie & Beni Index (XBI) in the SFCM method, the optimal cluster were two clusters. Keywords: clustering, education, Subtractive Fuzzy C-Means
Klasifikasi Naïve Bayes Pada Data Status Kesejahteraan Rumah Tangga Penerima Manfaat di Kecamatan Samarinda Ilir Tahun 2023 Lupinda, Indah Cahyani; Goejantoro, Rito; Hayati, Memi Nor; Hidayatullah, Aji Syarif
EKSPONENSIAL Vol. 16 No. 1 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i1.1489

Abstract

Data mining is the process of extracting useful information and patterns from very large amounts of data. Based on the task or work performed, data mining is divided into cluster analysis, association analysis, anomaly detection, and predictive modeling. Predictive modeling consists of two types, namely regression and classification. Classification is a method for determining the membership of an object in a class based on available data. There are several methods for classification, one of which is naïve Bayes with the advantages of being easy to build and having good performance. This research aims to determine the results of the accuracy of the naïve Bayes classification on data on the welfare status of beneficiary households in Samarinda Ilir District in 2023. Based on the research results, it can be seen that the accuracy level of the naïve Bayes classification on this data is 0.8316 or 83.16%. The results of accuracy measurements show that the naïve Bayes classification of this data has a fairly high level of accuracy.
ANALISIS CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR Astuti, Putri Sri; Hayati, Memi Nor; Goejantoro, Rito
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.631 KB) | DOI: 10.30598/barekengvol15iss4pp735-744

Abstract

Classification is the process of grouping objects that have the same characteristics into several categories. This study applies a combination of classification algorithms, namely Bootstrap Aggregating K-Nearest Neighbor in credit scoring analysis. The aim is to classify the credit payment status of electronic goods and furniture at PT KB Finansia Multi Finance in 2020 and determine the level of accuracy produced. Credit payment status is grouped into 2 categories, namely smoothly and not smoothly. There are 7 independent variables that are used to describe the characteristics of the debtor, namely age, number of dependents, length of stay, years of service, income, amount of payment, and payment period. The application of the classification algorithm at the credit scoring analysis is expected to assist creditors in making decisions to accept or reject credit applications from prospective debtors. The results showed that the accuracy obtained from the Bootstrap Aggregating K-Nearest Neighbor algorithm with a proportion of 90:10, m=80%, C=73, and K=5 was the best, which was 92.308%.
COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFICATION METHODS FOR STATUS OF TODDLER NUTRITION DATA AT BAQA SAMARINDA SEBERANG COMMUNITY HEALTH CENTER Annabaa Aulia, Muzizah; Goejantoro, Rito; Hayati, Memi Nor
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.1-13

Abstract

Classification is a job of assessing data objects to put them into a certain class from a number of available classes. The naïve Bayes method is a statistical classification that can be used to estimate the probability of membership in a class. Meanwhile, the K-Nearest Neighbor (K-NN) method is a supervised method used for classification. The aim of this research is to obtain classification results of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center in 2022 using the naïve Bayes algorithm and the K-NN algorithm. Based on the calculation results for classification of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center using accuracy calculations and confusion matrices, the highest accuracy was obtained using the naïve Bayes method of 82.15% and a Press's Q value of 168 with a training data proportion of 90%: testing data of 10%. Meanwhile, the results of accuracy calculations and the confusion matrix obtained the highest accuracy in the K-NN method of 90.57% at values 3-NN, 5-NN, 7-NN, 9-NN and Press's Q value of 187.65 with a training data proportion of 90% and testing data 10%. From the results of this analysis, it was concluded that the K-NN method worked better than the naïve Bayes method in classifying the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN DATA JUMLAH KEJADIAN DAN DAMPAK BENCANA BANJIR MENGGUNAKAN METODE FUZZY C-MEANS Hayati, Memi Nor; Goejantoro, Rito; Siringoringo, Meiliyani; Purnamasari , Ika; Yuniarti, Desi; Nida, Khairun; Messakh, Gerald Claudio
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm167

Abstract

Cluster analysis is a technique used to find groups of similar data objects. The Fuzzy C-Means (FCM) method is a data grouping method where the existence of each data in a cluster is determined by the degree of membership. This study aims to determine the optimal number of clusters based on the Modified Partition Coefficient (MPC) validity index and to determine the optimal grouping results of 34 provinces in Indonesia based on data on the number of events and the impact of floods in 2017-2021. The optimal number of clusters using the FCM method is based on MPC value consists of 2 clusters, namely the first cluster consisting of 27 provinces in Indonesia and the second cluster consisting of 7 provinces in Indonesia.
Implementasi Metode Fuzzy Possibilistic C-Means pada Pengelompokan Provinsi di Indonesia Berdasarkan Data Jumlah Kejadian dan Dampak Bencana Banjir Nida, Khairun; Hayati, Memi Nor; Goejantoro, Rito
Journal of Mathematics Education and Science Vol. 7 No. 1 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i1.1919

Abstract

Analisis cluster merupakan salah satu teknik dalam data mining yang digunakan untuk menemukan kelompok objek data yang serupa. Metode Fuzzy Possibilistic C-Means (FPCM) adalah salah satu metode clustering yang merupakan pengembangan dari algoritma Fuzzy C-Means (FCM) dan Possibilistic C-Means (PCM) dengan menggunakan kelebihan dari pemodelan fuzzy dan possibilistic. Penelitian ini bertujuan untuk mengetahui jumlah cluster optimal berdasarkan indeks validitas Modified Partition Coefficient (MPC) serta mengetahui hasil pengelompokan optimal 34 Provinsi di Indonesia berdasarkan data jumlah kejadian dan dampak bencana banjir pada tahun 2017-2021. Menurut Badan Nasional Penanggulangan Bencana (BNPB) sejak tahun 2017 hingga 2021 jumlah bencana alam yang terjadi di Indonesia mencapai 18.658 kejadian di mana bencana banjir termasuk kategori bencana yang besar dengan persentase total kejadian 28% sejak tahun 2017 hingga 2021. Oleh sebab itu, perlu dilakukan pengelompokan Provinsi di Indonesia berdasarkan dampak bencana banjir sebagai upaya mitigasi dalam mengenali risiko bencana banjir. Jumlah cluster optimal dengan menggunakan metode FPCM berdasarkan indeks validitas MPC adalah sebanyak 2 cluster yaitu cluster pertama beranggotakan 19 Provinsi di Indonesia dan cluster  kedua beranggotakan 15 Provinsi di Indonesia. Cluster pertama didominasi oleh provinsi di Kepulauan Sumatera yang sebagian besar kawasannya terdiri dari dataran tinggi dan pegunungan, serta provinsi yang terletak di Kepulauan Papua dan Maluku yang memiliki jumlah penduduk lebih kecil dibandingkan dengan provinsi lain. Sementara pada cluster kedua didominasi oleh provinsi dengan jumlah pemukiman bantaran sungai yang cukup tinggi.
Co-Authors Abidin, Ahmad Aliful Aditiya Risky Tizona Amanah Saeroni Andrea Tri Rian Dani Annabaa Aulia, Muzizah Ardyanti, Hesti Ariessela, Syeli Astuti, Putri Sri Athifaturrofifah Athifaturrofifah Cahyani, Era Tri Candra, Yossy Christyadi, Santo Dani, Andrea Tri Rian Darnah Darnah Andi Nohe Darnah, Darnah Desi Yuniarti Deviyana Nurmin Devy Sintya Putri Dewi Wulan Sari Dini Elizabeth Dwi Agoes Setiawan Dwi Husnul Mubiin Dwi Indra Yunistya Dyah Arumatica Novilla Etri Pujiati Fatmi’aturro’isah, Nurul Febriyanti, Nur Afifah Fidia Deny Tisna Amijaya Hairi Septiyanor Hidayatullah, Aji Syarif Ika Purnamasari Ika Purnamasari Ilham Adnan Kasoqi Irene Lishania Irfan Fadil Isgiarahmah, Afryda Juliartha, Made Angga Katianda, Kristin Rulin Khairun Nida Khoiril Anwar Lupinda, Indah Cahyani M. Fathurahman Mahmudi Mahmudi Martua Tri Januar Sinaga Meiliyani Siringoringo Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Messakh, Gerald Claudio Mochammad Imron Awalludin Muhammad Rahmad Fadli Muhammad Rais Muhammad Yafi Mulyta Anggraini Murdani, Endah Mulia Ni Wayan Rica A Nida, Khairun Novalia, Viona Nur Annisa Fitri Nur Azizah Nurdayanti Nurdayanti Nurhasanah Nurhasanah Nurmin, Deviyana Nurul Rahmahani Oktri Mayasari Permana, Jordan Nata Primantoro, Sudhan Putra, Eko Prasatyo Putri, Nurlia Sucianti Rachman, Dezty Adhe Chajannah Rahmaulidyah, Fatihah Noor Rinaldi, Rival Satriya, Andi M Ade Sekar Nur Utami Septilasse, Rebeka Norcaline Sifriyani, Sifriyani Siringoringo, Meiliyani Siti Mahmuda Soraya, Raihana Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Suerni, Widya - Surya Prangga Suyitno Suyitno Suyitno Suyitno Syafitri, Febriana Syamsiar, Syamsiar Syaripuddin Syaripuddin Syaripuddin Syaripuddin Wasono Wasono Wasono, Wasono Widyawati Widyawati Yenni Safitri Yudha Muhammad Faishol Yuki Novia Nasution Yuki Novia Nasution, Yuki Novia Yuliasari, Pratiwi Dwi Yuniarti, Desi