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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 793 Documents
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PERAMALAN JUMLAH PENUMPANG PENERBANGAN DOMESTIK PADA TIGA BANDARA UTAMA INDONESIA MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Nabila Zahra Tamami; Puspita Kartikasari; Deby Fakhriyana
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.243-254

Abstract

Forecasting the number of airplane passengers requires a model related to time and location because the data on the number of airplane passengers is space time series data. Generalized Space Time Autoregressive (GSTAR) is an approach to forecast time series data that show a connection between time and location. The GSTAR model can be applied to data with heterogeneous location characteristics.  In this research, the GSTAR model is used to form a forecasting model for the number of domestic flight passengers at three major airports in Indonesia Juanda Airport, Soekarno Hatta Airport, and Hasanuddin Airport, using optimal location weighting. The residual of the GSTAR model satisfies the white noise assumption. The location weights used are uniform weights, inverse distance weights, and cross-correlation normalization. The outcome model is constructed with a first-order differencing, an autoregressive order of 1, and spatial order of 1, resulting in the formation of the GSTAR (11)I(1) model. The most optimal model generated is the GSTAR (11)I(1) model utilizing inverse distance weights due to its smallest sMAPE value compared to other weightings, which stands at 8,71%. This sMAPE value signifies a highly precise forecast accuracy.
PERBANDINGAN PERAMALAN MENGGUNAKAN DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES MARKOV CHAIN UNTUK DATA INFLASI INDONESIA Ica Rahma Kurniawati; Suparti Suparti; Tarno Tarno
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.224-233

Abstract

Inflation is one measure of a country’s economic condition. The inflation rate in Indonesia had decreased to 1.33% in June 2021 when the Covid-19 pandemic was hitting. Forecast of the inflation rate is needed for monitoring economic condition. The Double Exponential Smoothing Holt method can be used on data that contains trend elements and uses two smoothing parameters in the analysis, while the Fuzzy Time method Series Markov Chain is a development of the Fuzzy Time Series method with the Markov chain concept which does not have any conditions in its analysis. The results of forecasting analysis from the Double Exponential Smoothing Holt method obtained an MAE value of 0.35957, while from the Fuzzy Time Series Markov Chain method obtained an MAE value of 0.22462. Thus, the Fuzzy Time Series Markov Chain model is a better model in forecasting than the Double Exponential Smoothing Holt method. That model is reused to analyze forecasting and get a MAPE value of 8.79% which means that the Fuzzy Time Series Markov Chain model predicts Indonesia’s inflation data very well.
PENGELOMPOKAN DAERAH RAWAN KRIMINALITAS DI INDONESIA TAHUN 2021 MENGGUNAKAN METODE K-MEANS & FUZZY C-MEANS Wulan Cahya Rahma Rahmadani; Tatik Widiharih; Tarno Tarno
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.234-242

Abstract

Crime is one of the aspects that can influence national stability and security. In this study, crime data is used to cluster crime-prone areas and is considered whether these areas require extra surveillance or not. This research employs the K-means and Fuzzy C-Means methods. The K-means method groups data based on the similarity of data with cluster centroids. This algorithm is relatively efficient in complexity and easy to understand. K-means explicitly allocates data to specific clusters. On the other hand, Fuzzy C-Means is capable of placing a data point that lies between two or more other clusters into a single cluster. This is due to each data point having a degree of membership to determine its grouping, making the chances of failure to converge or stable cluster centers very low. The optimal number of clusters is selected using validation through the Davies Bouldin Index and Calinski Harabasz Index. The research results indicate that clustering crime-prone areas using both methods yield the same outcome of 2 clusters. Cluster 1 exhibits a higher crime rate compared to cluster 2, as indicated by the higher average values of group members. The lowest Davies Bouldin Index is 1.04948, and the highest Calinski Harabasz Index is around 24.36783.

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