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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Techno Nusa Mandiri : Journal of Computing and Information Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) INTI Nusa Mandiri REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Ririn Restu Aria
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Social Sciences

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KLASTERISASI ANGKATAN KERJA DI INDONESIA BERDASARKAN USIA MENGGUNAKAN METODE ALGORITMA K-MEANS Ririn Restu Aria
INTI Nusa Mandiri Vol 18 No 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5056

Abstract

The concept of the population is divided into two groups, namely the working age population and the population not working age. Indonesia, which has 34 provinces, has an unequal distribution of labor force due to the level of economic growth that is still not evenly distributed in several sectors. Labor is the most important and influential element in managing and controlling the economic system. In this study the method used in the grouping of provinces was based on the workforce in 34 provinces using the K-Means algorithm. The purpose of grouping data is done to get a province grouping that has a workforce in Indonesia by grouping / clustering into 3 groups based on age groups using the K-Means algorithm. Based on the calculations, the results of cluster 0 were 6 provinces, cluster 1 as many as 3 provinces and cluster 2 were 25 provinces. The K-Means algorithm can be used to understand the workforce problems and make it easier to describe the characteristics or characteristics of each group. Based on these results, the local government can give more attention to the regions with the smallest workforce such as the Province of Central Sulawesi, East Kalimantan, Jambi so that economic growth in various sectors can be increased so that the welfare of the workforce, especially in terms of work in the field of work can be easily obtained.
Classification of Domestic Flight Passengers at Main Airports Using the K-Means Clustering Method Syaoqiyah, Syifa Siti; Anisa, A; Selvina, Yudhi Yulianti Selvina; Rahmadenti, Nadhia Ayu; Aria, Ririn Restu
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.340

Abstract

The aviation business in Indonesia has recently experienced quite significant growth, which can be seen from the fact that many people tend to choose air transportation to travel and connect them to cities in Indonesia. With air transportation, the time spent traveling to one area or city can be reduced. accomplished in a short time. This causes the number of passengers per flight to be quite high, especially in domestic flights which occur at the main airport. This research will use the K-Means Clustering algorithm to find out the schedule for the busiest month for the highest domestic airlines at major airports. The data source for this research comes from the central statistics agency regarding the number of domestic airline passengers at major airports. The criteria used in this research are divided into 3 clusters, namely high, medium, and low. The results of this research show that the highest number of passengers (C1) occurs in January to April, while the moderate number of passengers (C2) occurs in May to December, and the lowest number of passengers (C3) occurs in August to November.
Classification of Domestic Flight Passengers at Main Airports Using the K-Means Clustering Method Syaoqiyah, Syifa Siti; Anisa, A; Selvina, Yudhi Yulianti Selvina; Rahmadenti, Nadhia Ayu; Aria, Ririn Restu
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.340

Abstract

The aviation business in Indonesia has recently experienced quite significant growth, which can be seen from the fact that many people tend to choose air transportation to travel and connect them to cities in Indonesia. With air transportation, the time spent traveling to one area or city can be reduced. accomplished in a short time. This causes the number of passengers per flight to be quite high, especially in domestic flights which occur at the main airport. This research will use the K-Means Clustering algorithm to find out the schedule for the busiest month for the highest domestic airlines at major airports. The data source for this research comes from the central statistics agency regarding the number of domestic airline passengers at major airports. The criteria used in this research are divided into 3 clusters, namely high, medium, and low. The results of this research show that the highest number of passengers (C1) occurs in January to April, while the moderate number of passengers (C2) occurs in May to December, and the lowest number of passengers (C3) occurs in August to November.
Co-Authors A Anisa Annisa Nur Rosdiana Asep Saepudin Herdian, Barlin I Gusti Bagus Wiksuana Indra Riyana Rahadjeng Indra Riyana Rahadjeng Nur Sarita Rahadjeng, Indra Riyana Rahadjeng, Indra Riyana Rahmadenti, Nadhia Ayu Selvina, Yudhi Yulianti Selvina Susi Susilowati Susi Susilowati Susi Susilowati Susliansyah Susliansyah Susliansyah, Susliansyah Syaoqiyah, Syifa Siti Yasmin, Ghaisani