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All Journal Techno LPPM Seminar Nasional Informatika (SEMNASIF) Knowledge Engineering and Data Science Indonesian Journal of Information System BAREKENG: Jurnal Ilmu Matematika dan Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer International Journal of Natural Science and Engineering Progresif: Jurnal Ilmiah Komputer Variance : Journal of Statistics and Its Applications PROGRES PENDIDIKAN
Tahalea, Sylvert Prian
Universitas Pembangunan Nasional Veteran Yogyakarta
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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TOURISM ON INSTAGRAM: A SOCIAL NETWORK ANALYSIS Tahalea, Sylvert Prian; Salouw, Elvis; Wibowo, Astrid Wahyu Adventri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.402 KB) | DOI: 10.30598/barekengvol16iss1pp197-204

Abstract

This research aims to analyze and describe the development of tourism in Maluku Province, Indonesia on Instagram. The data used in this study are hashtags from several excellent tourist attractions or tourist priorities set by the maluku province tourism office. The data is then processed using social network analysis to find the level of importance and connectedness of tourism hashtags with other hashtags used in image captions on Instagram posts. The results showed that there are nine hashtags that have an important role in the network because they have high values in the measurement of degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The hashtags are #maluku, #ambon, #natsepa, #pulauosi, #pulaubair, #beach, #repost, #indonesia, and #namalatu. Two of nine hashtags have a high betweenness centrality value, namely #natsepa that represent natsepa beach tourism and #namalatu that represents namalatu beach tourism. Both of these tours have a high value betweenness centrality with a different form of hashtags, namely #natsepa.id and #namalatu02. This research conducted using social network analysis degree measurements such as degree, betweeness, closeness, and eigenvector to analyze insight of tourism topics in Instagram. The result of this research can give insights to the tourism actors, especially in Maluku Province, of how the hashtags are connected and related. The relation of the hashtags can be used as social media marketing strategy.
DYNAMIC ANALYSIS OF SEITR MATHEMATICAL MODEL ON THE SPREAD OF HEPATITIS B DISEASE IN AMBON CITY Larubun, Swine Enggelina; Leleury, Zeth Arthur; Lesnussa, Yopi Andry; Tahalea, Sylvert Prian; Warong, Maria Marlein
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1989-2000

Abstract

Hepatitis B is a disease caused by infection with the HBV (Hepatitis B Virus) virus that commonly infects the liver and can develop into liver cancer. The disease can be transmitted through blood, semen, breast milk, saliva, vaginal fluids, and sperm. One effective way to prevent Hepatitis B disease is by vaccination. This study will construct a mathematical model, such as the SEITR model, to study the spread of Hepatitis B disease in Ambon City. The SEITR epidemic model is a disease spread model that divides the population into five subpopulation classes, namely the susceptible individual subpopulation class, the exposed individual subpopulation class, the infected individual subpopulation class, the treatment individual subpopulation class, and the recovered individual subpopulation class. Based on the dynamic system analysis conducted, two equilibrium points were obtained, namely the disease-free equilibrium point and the endemic equilibrium point. In addition, based on the data and simulation results, it can be concluded that the spread of Hepatitis B in Ambon City depends on the transmission rate from infected individuals to susceptible individuals
Forecasting the Poverty Rates using Holt’s Exponential Smoothing Agusdin, Riza Prapascatama; Tahalea, Sylvert Prian; Permadi, Vynska Amalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.2672

Abstract

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
CLUSTERING SHRIMP DISTRIBUTION IN INDONESIA USING THE X-MEANS CLUSTERING ALGORITHM Fadhilah, Rahmi; Matdoan, M. Y.; Safira, Dinda Ayu; Tahalea, Sylvert Prian
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): 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/variancevol6iss1page49-54

Abstract

Shrimp is one of the marine biological resources available in almost all Indonesian waters and is one of the mainstay export commodities from the fisheries sub-sector. This is expected to improve the welfare of the community, so it is necessary to cluster the distribution of shrimp in Indonesia. Clustering is a data mining technique used to group data or partition datasets into subsets. One of the best clustering algorithms is X-means. X-means clustering is used to solve one of the main disadvantages of K-means clustering, namely the need for prior knowledge of the number of clusters (K). The purpose of this research is to obtain the results of clustering the distribution of shrimp in Indonesia using the X-means clustering algorithm. The data used in this study comes from the publication of Marine and Coastal Resources Statistics 2022 by the Central Bureau of Statistics of the Republic of Indonesia. This study obtained the results that there are 3 clusters in the clusterization of shrimp distribution in Indonesia. Cluster 0 consists of 1 province, cluster 1 consists of 27 provinces, and cluster 2 consists of 6 provinces.
Co-Authors Agusdin, Riza Prapascatama Awang Hendrianto Pratomo Azhari SN Azhari SN Bambang Yuwono Fadhila, Hasna Luthfiana Fadhilah, Rahmi I Nyoman Tri Anindia Putra Ketut Sepdyana Kartini Larubun, Swine Enggelina Matdoan, M. Y. Nur Heri Cahyana Nur Heri Cahyana Putu Wirayudi Aditama Safira, Dinda Ayu Salouw, Elvis Vynska Amalia Permadi Warong, Maria Marlein Wibowo, Astrid Wahyu Adventri Wilis Kaswidjanti Yopi Andry Lesnussa Zeth Arthur Leleury, Zeth Arthur