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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 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

Author-ID : 2469461

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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Algoritma Pengklusteran Pautan Tunggal Rito Goejantoro
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 4, No 3 (2009): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Analisis kluster adalah teknik yang digunakan untuk menggabungkan objek-objek ke dalam grup atau kluster sedemikian sehingga setiap grup atau kluster homogen terhadap karakteristik tertentu, dan setiap grup harus berbeda dari grup lainnya terhadap karakteristik yang sama. Metode pengelompokan pautan tunggal adalah salah satu metode analisis kluster. Ada beberapa jenis algoritma pengklusteran pautan tunggal. Beberapa modifikasi dapat membuat algoritma menjadi lebih efisien.

PENGELOMPOKAN KABUPATEN/KOTA DI PULAU KALIMANTAN BERDASARKAN INDIKATOR KESEJAHTERAAN RAKYAT MENGGUNAKAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS Nur Annisa Fitri; Memi Nor Hayati; Rito Goejantoro
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 1 (2021): September 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i1.14416

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.

KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019) Amanah Saeroni; Memi Nor Hayati; Rito Goejantoro
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): 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.8.2.2020.88-94

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.

Peramalan Produksi Kelapa Sawit Menggunakan Winter's dan Pegel's Exponential Smoothing dengan Pemantauan Tracking Signal Dwi Agoes Setiawan; Sri Wahyuningsih; Rito Goejantoro
Jambura Journal of Mathematics Vol 2, No 1: Januari 2020
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Analisis data time series menggunakan metode Winter’s exponential smoothing dan Pegel’s exponential smoothing merupakan analisis data yang dipengaruhi oleh pola data musiman. Winter’s exponential smoothing merupakan metode peramalan yang mengasumsikan pola data bersifat trend aditif sedangkan Pegel’s exponential smoothing menyajikan sembilan model klasifikasi yang memisahkan faktor trend dan musiman. Penelitian ini bertujuan untuk memperoleh model yang tepat dan hasil peramalan dari data produksi kelapa sawit Provinsi Kalimantan Timur periode Januari 2014 sampai Desember 2017. Hasil peramalan diverifikasi menggunakan metode tracking signal. Hasil penelitian menunjukkan bahwa model musiman multiplikatif tanpa trend pada metode Pegel’s exponential smoothing dengan nilai MAPE sebesar 7,04% memiliki akurasi peramalan yang lebih baik daripada metode yang lainnya. Berdasarkan pemantauan menggunakan tracking signal diperoleh satu hasil peramalan yang bersifat bias. Model musiman multiplikatif tanpa trend dapat digunakan untuk meramalkan 3 bulan ke depan yaitu Januari, Februari dan Maret Tahun 2018. Hasil peramalan 3 bulan ke depan mengalami penurunan secara berturut-turut.

Perancangan Aplikasi Peramalan untuk Metode Exponential Smoothing Menggunakan Aplikasi Lazarus (Studi Kasus: Data Konsumsi Listrik Kota Samarinda) Hairi Septiyanor; Syaripuddin Syaripuddin; Rito Goejantoro
ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 2, Juli, 2021 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v2i2.13364

Exponential smoothing is forecasting method used to predict the future. Lazarus is an open source software based on free pascal compiler. at this research, program Lazarus be design used exponential smoothing method to predict electricity consumption data in Samarinda City from September to November 2018. Purposed of this researched is to determine the procedure of building an exponential smoothing forecasting application and obtained forecasting result using the built application. Procedure of built the application are designed interface, designed properties and filled coding. The optimum smoothing parameters were obtained used the golden section method. Based on the analysis, electricity consumption data in Samarinda City shows a trend pattern, then the forecasting was used double exponential smoohting (DES) method are DES Brown and DES Holt. The best forecasting method for at this researched is DES Holt, because DES Holt method produced MAPE 0,0659% less than DES Brown method produced MAPE 0,0843%.

PENERAPAN METODE K – HARMONIC MEANS DALAM PENGELOMPOKAN KABUPATEN/KOTA (Studi Kasus: Kemiskinan di Pulau Kalimantan Tahun 2020) Dwi Indra Yunistya; Rito Goejantoro; Fidia Deny Tisna Amijaya
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

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

Poverty is one of the problems that faced by every country in the world, especially in developing countries, one of them is Indonesia. Poverty alleviation that is currently planned is no longer uniform, but it is necessary to pay attention to the condition of each dimension causing poverty in an area, so it is necessary to group districts/cities on the Kalimantan Island based on poverty. Cluster analysis is classifying the data (objects) only based on the information discovered in the data that describes the objects and the relations between them. The method used in this research is K-Harmonic Means method. K-Harmonic Means is a non-hierarchical clustering algorithm that uses the average harmonic distance from each data point to the cluster center. This study aims to classify the District/City in Kalimantan Island based on poverty indicators and obtain the silhouette coefficient value from the optimal cluster analysis. Based on the results of the analysis of the K-Harmonic Means method, the optimal number of clusters is 2 clusters with parameter (p) of 4. Cluster 1 consists of 11 Districts/Cities and Cluster 2 consists of 45 Districts/Cities. Silhouette coefficient value for data validation of District/City clustering results on Kalimantan Island using the K-Harmonic Means method, namely 2 clusters with parameter (p) of 4 is 0.323 which states that the resulting cluster structure in this grouping is a weak structure.

Pelatihan Penggunaan Fungsi Hitung Dasar dan Logika Matematika Statistika untuk Penyelesaian TIU Ika Purnamasari; Meiliyani Siringoringo; Sri Wahyuningsih; Memi Nor Hayati; Suyitno Suyitno; Rito Goejantoro; Surya Prangga
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 6, No 1 (2023): Volume 6 No 1 Januari 2023
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v6i1.8423

ABSTRAK Pegawai Negeri Sipil (PNS) yaitu warga negara Indonesia yang memenuhi syarat tertentu, diangkat sebagai Pegawai ASN secara tetap oleh pejabat pembina kepegawaian untuk menduduki jabatan pemerintahan. Pada proses perimaaan CPNS, terdapat dua tahapan Seleksi yaitu SKD dan SKB. Pada SKD, pemerintah memberlakukan passing grade yang menjadi penentu kelulusan ke tahap SKB. Salah satu jenis tes pada tahap SKD yaitu TIU yang merupakan tes untuk mengukur tingkat intelegensi dalam analisa numerik, verbal, figural, serta kemampuan untuk berpikir logis dan analitis. Tujuan kegiatan pelatihan yaitu memberikan informasi kepada masyarakat umum, khusunya masyarakat yang akan mengikuti tes seleksi SKD CPNS 2021 tentang penggunaan fungsi hitung dasar dan logika dalam mengerjakan soal TIU dengan lebih mudah, cepat dan tepat. Berdasarkan hasil penilaian pada saat pelatihan, peserta dapat menunjukkan adanya peningkatan pemahaman dalam menyelesaikan soal TIU dengan mudah, cepat dan tepat. Hal ini terlihat dari peningkatan nilai skor posttes yang jauh lebih tinggi dibandingkan saat pretes. Kedepannya diharapkan adanya kegiatan lanjutan dengan intensif agar peserta kegiatan dapat terbiasa dalam pemecahan soal dengan cepat. Kata Kunci: ASN; PNS; SKB; SKD; TIU ABSTRACT Civil Servants (PNS) is an Indonesian citizen who meets certain conditions, appointed as an ASN employee regularly by the office of staffing to occupy government positions. In the CPNS acceptance process, there are two stages of selection, namely SKD and SKB. In SKD, the government imposes a passing grade that determines graduation to the SKB stage. One type of test at the SKD stage is TIU which is a test to measure the level of intelligence in numerical analysis, verbal ability, figural ability, and the ability to think logically and analytically. The purpose of the training is to provide information to the general public, especially the public who will take the 2021 SKD CPNS selection test on the use of fundamental calculation functions and logic in working on TIU problems more simply, quickly, and precisely. Based on the yield of the assessment at the time of training, participants can show an increased understanding of solving TIU problems simply, quickly, and precisely. The posttest score is much higher than during pretests. In the future, expected that this training can continue intensive so that participants can get used to solving problems more quickly. Keywords: ASN; PNS; SKB; SKD; TIU

ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP STATUS PEMBAYARAN KREDIT BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN REGRESI LOGISTIK Memi Nor Hayati; Surya Prangga; Rito Goejantoro; Darnah; Ika Purnamasari
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm66

Electronic goods and furniture for some people are currently seen as basic needs that must be met. High prices make it difficult for people to meet their needs with cash purchases, so they choose credit purchases using the services of finance companies in purchasing goods. This study aims to determine the factors that influence the status of credit payments for electronic goods and furniture at PT. KB Finansia Multi Finance Bontang 2020 uses logistic regression. Based on the results of the analysis, it was found that the predictor variables that had a significant effect on the credit payment status response variable were length of stay (domicile) at the address borne by the debtor when applying for credit (X3) and the amount of credit payments charged by the debtor per month (X6). The value of the Apparent Error Rate (APER) of 29.323% indicates that the logistic regression model obtained is also good for solving cases of current and non-current classification of credit payment status.

Estimasi Parameter Model ARIMA untuk Peramalan Debit Air Sungai Menggunakan Least Square dan Goal Programming Dewi Wulan Sari; Rito Goejantoro; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.755 KB)

Forecasting is a technique to make a desicion in the future considered by data from the past and present. This forecasting is in hydrology sector which is river flow forecasting. River flow forecasting is one way to anticipate the instability of the river flow. The aim of this research was to determine the best ARIMA model based on analysis of the river flow of Karang Mumus, Samarinda. This research will explain the procedure of ARIMA model building using the Least Square and Goal Programming to predict the river flow of Karang Mumus, Samarinda. The data used montly from January until December. The model of ARIMA (2,1,2)to predict the river flow of Karang Mumus using Goal Programming is : Zt=μ-0,0492Zt-1-0,0523Zt-2-0,9969Zt-3+0,9247at-1+0,9339at-2+at ARIMA (2,1,2) for river flow forecasting using Goal Programming is : Zt=1,17Zt-1-0,17Zt-2+at+0,31at-1 The best ARIMA model for river flow forecasting of Karang Mumus is ARIMA (2,1,2) using Least Square method. Result for river flow forecasting of Karang Mumus river in Samarinda from January until Desember 2015 are 1.733 m3, 1.729 m3, 1.730 m3, 1.730 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, dan 1.730 m3.

Klasifikasi Data Nasabah Asuransi Dengan Menggunakan Metode Naive Bayes Dyah Arumatica Novilla; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is the logical grouping of objects according to the characteristics of their similarities. Naive Bayes is a method for predicting future opportunities based on past experiences. This study discusses the classification of insurance customer data of PT. Prudential Life Branch of Samarinda in 2017. With the aim to know whether the method of Naive Bayes can classify data of insurance customers of PT. Prudential Life in 2017 using the R program and to determine the accuracy of the results of data testing I and data testing II. As a result, Naive Bayes method can classify data of insurance customers of PT. Prudential Life with 80% accuracy for 25 data testing I and 74.67% for 75 data testing II.

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 Gerald Claudio Messakh 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 Mochammad Imron Awalludin Muhammad Rahmad Fadli Muhammad Rais Muhammad Yafi Mulyta Anggraini Murdani, Endah Mulia Ni Wayan Rica A 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 Sri Wahyuningsih Suerni, Widya - Surya Prangga Suyitno Suyitno Suyitno Suyitno Syafitri, Febriana 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

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