Article Per Year (5 Year)
p-Index From 2021 - 2026
3.346
P-Index
This Author published in this journals
All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Bakti Saraswati BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer WIDYABHAKTI Jurnal Ilmiah Populer Math Educa Journal Jurnal Matematika UNAND To Maega: Jurnal Pengabdian Masyarakat Jurnal Varian Madaniya Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian kepada Masyarakat ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Etihad: Journal of Islamic Banking and Finance COVIT Journal of Innovation Research and Knowledge EXPLORE Jurnal Manajemen dan Ekonomi Terapan Amaluna: Jurnal Pengabdian Masyarakat
Hendayanti, Ni Putu Nanik
Institut Teknologi Dan Bisnis STIKOM Bali
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search
Journal : Jurnal Varian

 1 
PEMODELAN JUMLAH UANG BEREDAR DAN INFLASI NASIONAL DENGAN VECTOR ERROR CORRECTION MODEL (VECM) Ni Putu Ni Putu Nanik Hendayanti; Maulida Nurhidayati
Jurnal Varian Vol 1 No 1 (2017)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v1i1.44

Abstract

Vector Autoregresive Model (VAR) is one of the simultaneous time series models. VAR is a system of equations in which each of the variables is a linear function of the lag (past) the independent variable it self are the values of other variables of the lag in the system. Sometimes, several models of VAR may contain relationships between variables, this relationship caused VAR model become not representative. Model Vector Error Correction (VEC) can overcome this problem. Economy is one of the main foundations of the power of a country. However, economic stability does not always run smoothly because of many factors, both internal factors or external factors. One of the main indicators used to see the development of the economy of a country is the level of the rate of inflation. Inflation is continous tendency of prices to increase in against market demands in general of the community. There are many factors that may influence the on set of inflation i.e. money suply. This research aims to model the number of money supply and inflation on nationwide with Vector Error Correction Models (VECM). The results showed that the estimated VECM to function there are short term inflation the value of error correction short term to long term of 0.000235. On the analysis of the short term, changes in the money supply earlier in the month gave a negative influence to changes in inflation this month of 0.207. While the change in inflation months earlier gave a positive influence to changes the money supply in the month of 0.000570.
PEMODELAN DATA DERET WAKTU DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN LOGISTIC SMOOTHING TRANSITION AUTOREGRESSIVE Gusti Ayu Made Arna Putri; Ni Putu Nanik Hendayanti; Maulida Nurhidayati
Jurnal Varian Vol 1 No 1 (2017)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v1i1.50

Abstract

Time series analysis is a statistical analysis that can be applied on data related to time. Modeling of time series data is widely associated with the process of forecastinga certain characteristics in the coming period. Most inflation data modeling is done using a linear time series models such as Autoregressive Integrated Moving Average (ARIMA). In fact only the ARIMA model can be applied to models of linear time series data. Models of ARIMA hasn't been able to give good results when the data being analyzed is a nonlinear time series data. The inflation data, data that has a tendency to form patterns of nonlinear data so the application of nonlinear time series models can be done on the inflation data. Logistic model Smooting Threshold Autoregressive (LSTAR) is a time series model can be applied to data that follow nonlinearmodel. LSTAR then developed on data-financial and economic data such as inflation. If the inflation data are modelled with expected LSTAR approach can get a better result because already done smoothing in it. This research aims to know the best model that can be used to perform data modeling inflation. The results showed that the results of the comparison of the MSE and the RMSE for the model of ARIMA and LSTAR. Based on these results it is known that the model MSE has a value and LSTAR RMSE smaller compared to ARIMA. So the model more appropriate LSTAR is used to model the data of inflation
Ketepatan Klasifikasi Penerima Beasiswa STMIK STIKOM Bali dengan Hybrid Self Organizing Maps dan Algoritma K-Mean Ni Putu Nanik Hendayanti; Gusti Ayu Made Arna Putri; Maulida Nurhidayati
Jurnal Varian Vol 2 No 1 (2018)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v2i1.316

Abstract

Data Mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data yang sangat besar. Salah satu teknik yang dikenal dalam Data Mining yaitu clustering. Pengertian clustering dalam Data Mining adalah pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap di lama cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lain. Salah satu metode klasifiaksi atau clustering adalah Self Organizing Maps (SOM). SOM merupakan metode artificial neural network yang digunakan untuk mengelompokkan (clustering) data berdasarkan karakteristik/fitur-fitur data. Metode pengelompokan yang menggunakan konsep jarak dan memiliki karakteristik yang hampir sama dengan SOM yaitu metode K-means. Penelitian ini bertujuan untuk mengembangkan suatu metode yang merupakan hybrid dari SOM dan K-means yang digunakan untuk menentukan ketepatan suatu klasifikasi. Sebelum diujikan pada data asli, metode hybrid SOM dan K-Means diujikan lebih dulu pada data benchmark sehingga dapat diketahui berapa persen ketepan yang dihasilkan. Kemudian dilanjutkan dengan penerapan metode hybrid SOM dan K-means pada data penerimaan beasiswa di STMIK STIKOM Bali. Penelitian ini bertujuan untuk menentukan ketepatan klasifikasi penerima beasiswa STMIK STIKOM Bali dengan metode hybrid SOM dan K-means. Hasil penelitian menunjukkan bahwa metode Kmeans dan SOM memberikan hasil yang sama yang akibatnya metode SOM-Kmeans juga memberikan hasil yang sama. Alasannya, metode SOM-Kmeans menggunakan nilai centroid dari hasil SOM, dan hasil yang diperoleh pada metode Kmean memiliki hasil yang sama dengan SOM akibatnya metode SOM-Kmeans menghasilkan hasil yang sama dengan kedua metode sebelumnya.
Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart Gede Suwardika; I Ketut Putu Suniantara; Ni Putu Nanik Hendayanti
Jurnal Varian Vol 2 No 2 (2019)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v2i2.361

Abstract

The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.
Penerapan Support Vector Regression (Svr) Dalam Memprediksi Jumlah Kunjungan Wisatawan Domestik Ke Bali Ni Putu Nanik Hendayanti; I Ketut Putu Suniantara; Maulida Nurhidayati
Jurnal Varian Vol 3 No 1 (2019)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v3i1.506

Abstract

Bali is one of the most popular tourism sectors in Indonesia. In the arena of international tourism, the island of Bali is considered as the most famous national destination compared to other destinations. The high level of domestic tourism visits to Bali annually must be strictly noted especially for local governments and Bali provincial tourism agencies in optimizing facilities, infrastructure to the safety of tourists Visit. Therefore, it takes a method that can predict the number of tourists visiting Bali annually. One method used to predict the number of tourists visiting Bali is Support Vector Regression (SVR). SVR is a method to estimate a mapped function from an input object to a real amount based on the training data. SVR has the same properties about maximizing margins and kernel tricks for mapping nonlinear data. Results of this research. Based on forecasting using MAPE value training data obtained by 11.34% while use data testing of MAPE value obtained by 7.30%. Based on the resulting MAPE value can be categorized well for the number of tourism visitors.
Perbandingan Pembobotan Seemingly Unrelated Regression – Spatial Durbin Model Untuk Faktor Kemiskinan Dan Pengangguran Luh Putu Safitri Pratiwi; Ni Putu Nanik Hendayanti; I Ketut Putu Suniantara
Jurnal Varian Vol 3 No 2 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v3i2.596

Abstract

Hukum I Tobler menduga segala sesuatu di suatu wilayah berhubungan erat dengan wilayah lainnya sehingga pemodelan analisis spasial lebih tepat digunakan untuk memodelkan faktor yang berpengaruh terhadap kemiskinan dan pengangguran di suatu wilayah dengan memperhatikan efek spasialnya Salah satu metode spasial yang bisa digunakan ialah Seemingly Unrelated Regression-Spatial Durbin Model (SUR-SDM). Di dalam penelitian SUR SDM diperlukan suatu pembobot yang digunakan untuk menghitung koefisien autokorelasi. Matriks pembobot yaitu matriks yang elemen-elemennya adalah nilai pembobot yang diberikan untuk perbandingan setiap daerah tertentu. Metode penentuan matriks pembobot dalam penelitian ini dengan menggunakan Queen Contiguity dan pembobot customize. Penelitian ini bertujuan untuk mendeskripsikan kemiskinan dan pengangguran serta faktor – faktor yang diduga mempengaruhinya menggunakan metode SUR-SDM dengan bobot Queen Contiguity dan Customize. Adapun variabel-variabel yang digunakan yaitu Variabel respon terdiri dari persentase rumah tangga miskin (%) (y1) dan angka pengangguran (%)(y2). Sedangkan variabel bebasnya yaitu terdiri dari: persentase jumlah sarana pelayanan kesehatan meliputi posyandu, poliklinik, puskesmas, puskesmas pembantu, dokter praktek, klinik bersalin, dan pos KB (%) (x1), persentase jumlah sarana sekolah meliputi TK, SD, SLTP, SMU, dan SMK (%) (x2), persentase penduduk yang bekerja di sektor pertanian (%) (x3), persentase rumah tangga yang menggunakan air bersih (PDAM) (%) (x4), dan rasio penduduk yang belum tamat SD (x5). Hasil yang didapat yaitu pemodelan SUR-SDM dengan bobot Customize menghasilkan nilai R-Square yang lebih kecil dibandingkan bobot queen di kedua variable respon yaitu sebesar 80.60% dibandingkan queen sebesar 80.64 untuk variable kemiskinan dan untuk variable pengangguran bobot Customize mengasilkan nilai 92.51% lebih kecil disbanding queen sebesar 92.53%
Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali Ni Putu Nanik Hendayanti; Maulida Nurhidayati
Jurnal Varian Vol 3 No 2 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v3i2.668

Abstract

Berbagai sumber pendapatan yang dapat dihasilkan dalam suatu daerah, salah satunya yaitu dalam sektor pariwisata. Seperti halnya sektor yang lain, sektor pariwisata juga memberikan banyak sumbangan bagi pembangunan ekonomi di suatu daerah maupun negara tujuan wisata. Indonesia memiliki banyak tujuan wisata daerah yang sudah terkenal hingga mancanegara salah satunya yaitu Pulau Bali. Bali merupakan daerah yang sudah memiliki kedudukan yang sejajar dengan daerah-daerah tujuan wisata lainnya yang ada di dunia. Sebagai suatu daerah yang sangat berpotensi dalam pengembangan wisata, maka pemerintah memberikan perhatian yang khusus dalam pengembangan pariwisata di Pulau Bali. Maka dari itu, perlu adanya peramalan jumlah kunjungan wisatawan mancanegara ke Bali yang nantinya bisa bermanfaat bagi pemerintah daerah maupun dinas pariwisata. Dalam hal ini, akan digunakan dua metode untuk meramalkan jumlah kunjungan wisatawan mancanegara ke Bali. Adapun metode yang digunakan yaitu Seasonal ARIMA dan Support Vector Regression (SVR). Hasil peramalan data out sampel dengan menggunakan metode SARIMA dan SVR menunjukkan bahwa metode SARIMA memiliki nilai MAPE lebih kecil dari pada SVR. Nilai MAPE motode SARIMA adalah 5,33% sedangkan metode SVR sebesar 19,74%. Begitu juga nilai MSE dan MAE dari metode SARIMA lebih kecil dari metode SVR. Dari Penelitian yang dilakukan dapat disimpulkan bahwa model SARIMA merupakan motode yang lebih baik untuk memprediksi jumlah kunjungan wisatawan mancanegara ke Bali.
The Implementation of Fuzzy Time Series in Forecasting The Number of Tourist Visits Aziza, Istin Fitriana; Soraya, Siti; Sahdan, Sahdan; Husain, Husain; Hendayanti, Ni Putu Nanik; Harsyiah, Lisa
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.4890

Abstract

The development of tourism in West Nusa Tenggara (NTB) Province is supported by its geographical conditions, including scattered small islands (gilis), a tropical climate, and the cultural peculiarities of the Sasak and Mbojo Tribes, thereby becoming an attraction in the development of global tourist destinations. Tourism development in NTB Province would be more attractive with the establishment of the Mandalika National Tourism Development Strategic Area (KSPPN). This research aims to predict the number of tourist visits. A method to forecast the number of tourist visits in NTB Province is needed to assist the government in preparing appropriate facilities and infrastructure in the event of a possible surge in tourist visits. The method used in this study is the Fuzzy Time Series to predict the number of tourist visits in NTB Province. The data used in this study were secondary data sourced from the NTB government tourism office. The result of this research was that the Fuzzy Time Series method was effective in predicting the number of tourist visits in NTB Province, with an accuracy of 90.29%. The forecast result, generated using the Fuzzy Time Series method, was not significantly different from the actual data; in other words, it was almost identical to the actual data. The forecast for tourist visits to the NTB province in the 48th period remains unchanged until the 53rd period, namely 80,739.7 people. The FTS method used in this study cannot be applied to data with long-term seasonal patterns. A suggestion for future researchers is to develop a classical FTS that captures additional long-term seasonal patterns. 
Co-Authors Bagus Made Sabda Nirmala Cahyani, Merlinda Putri Eka Deviana Dewa Gede Bayu Krisna Putra Dian Pramana Erwin Suhendra Gde Yoga Hary Sedana Gede Suwardika Gusti Ayu Aghivirwiati Gusti Ayu Desi Saryanti Gusti Ayu Made Arna Putri Gusti Ngurah Ady Kusuma Habib Ratu Perwira Negara Harsyiah, Lisa Herawati, Baiq Candra Husain Husain I Gede Suardika I Gusti Ayu Desi Saryanti I Gusti Ayu Desi Saryanti I Gusti Ngurah Adi Kusuma I Gusti Ngurah Ady Kusuma I Ketut Putu Suniantara I Ketut Putu Suniantara I Komang Budiarta I Made Ari Santosa I Made Hendra M. Pramayasa I Made Hendra Mahajaya Pramayasa I Made Pasek Pradnyana Wijaya I Made Rudita I Made Sudarsana I Wayan Gede Oksarya Ega Satrya Ismoen, Muhaimin Istin Fitriana Aziza Kadek Dwi Pradnyani Novianti Kadek Dwi Pradnyani Novianti Kadek Masakazu Kadek Masakazu LUH PUTU SAFITRI PRATIWI Made Liandana, Made Made Satria Wibawa Mokan, Zaimudin Al Mahdi Ni Kadek Sukerti Ni Luh Gede Pivin Suwirmayanti, S.Kom, MT, Ni Luh Gede Pivin Ni Putu Meina Ayuningsih Ni Wayan Ekayanti Nurhidayati, Maulida P.A. Andiena Nindya Putri Phyta Rahima Primajati, Gilang Putu Adi Guna Permana Putu Adi Guna Permana Putu Devi Novayanti Ricky Aurelius Nutanto Diaz, Ricky Aurelius Rosalia Hadi Sahdan, Sahdan Siti Soraya Sufahani, Suliadi Firdaus T.M. Kusuma