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All Journal EKSAKTA: Journal of Sciences and Data Analysis Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Pendidikan dan Pembelajaran Square : Journal of Mathematics and Mathematics Education Community Development Journal: Jurnal Pengabdian Masyarakat Soeropati: Journal of Community Service Journal of Data Insights Prosiding Seminar Nasional Unimus
Indah Manfaati Nur
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Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Data Insights

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Poverty Level Grouping in West Java Province with the K-Means Clustering Method: Pengelompokan Tingkat Kemiskinan di Provinsi Jawa Barat dengan Metode K-Means Clustering Amelia; Nur, Indah Manfaati; Rizky, Muhammad; Milasari, Septiana Putri
Journal of Data Insights Vol 1 No 2 (2023): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v1i2.152

Abstract

Poverty in a region will have an impact on hampering national development. Poverty is an economic disease that is often faced by every country, including Indonesia. According to information obtained from the Central Bureau of Statistics, we can gather data on the poverty rate in all provinces of Indonesia, with a particular focus on the province of West Java. West Java province is one of the provinces with the highest population density on the island of Java, which is ranked 2nd after the province of DKI Jakarta and ranks 4th for the province with a high percentage of poor people after DI. Yogyakarta, Central Java, and East Java. Consequently, it is crucial for the regional government to identify areas with high, moderate, or low poverty rates. This information will enable the local government to formulate appropriate policies and prioritize interventions to address poverty effectively. In this study, the K-Means clustering method was used to classify poverty rates based on two variables, namely the community development index and the open unemployment rate using the help of RStudio software. The findings indicated that the application of the elbow method in West Java province resulted in the identification of three distinct clusters of districts/cities that stood out as the most prominent. Cluster 1 (districts/cities with relatively high poverty rates), cluster 2 (districts/cities with low poverty rates), cluster 3 (districts/cities with high poverty rates). Regencies/cities that fall into the category with a high poverty rate are Sukabumi, Cianjur, Garut, Tasikmalaya, Ciamis, Kuningan, Cirebon, Majalengka, Indramayu, Subang, West Bandung, and Pangandaran.
Forecasting Starbucks Indonesia Share Prices with Methods ARIMA: Memprediksi Harga Saham Starbucks Indonesia dengan Metode ARIMA Fellya Naza Nurcahyani; Septiana Putri Milasari; Indah Manfaati Nur
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.309

Abstract

Starbucks is the largest coffee shop company in the world from the United States. This increase has become a trend in drinking coffee consumption among young people in a lifestyle while discussing. This indicates that the increase in the number of Starbucks stores is one of the drivers of Starbucks share prices among investors. Starbucks shares have the code SBUX as the issuer code. Starbucks Corporation is a coffee company and global coffeehouse chain. Satrbucks is an international company (MNCs) that anticipates various risks. The ARIMA forecasting method is different from other forecasting methods. This method uses an iterative approach to identify the most appropriate model from all possible existing models and this model can use all types of data. The ARIMA method was chosen for this research because this method is very suitable for short-term forecasting, where the products produced by the PT have a short expiration date. The result of the MAPE value is 3.218%, which means the accuracy is good because it is less than 10%.
Co-Authors Abdurakhman Abdurakhman Abdurakhman AHMADI Aini, Anissa Nur Al Haris, M. Ali Imron Amelia Amri, Saeful Ayomi, Nun Maulida Suci Eko Andy Purnomo Fauzi, Fatkhurokhman Fellya Naza Nurcahyani Fisabilillah, Muh. Irodat Himmaturrohmah, Laily Indah Fitriyani Lestari, Talia Indah Lisa Anggraini M. Al Haris Masudah, Nurhidayatul Milasari, Septiana Putri Mochamad Hasyim muhammad rizky, muhammad Nugrahanto, Rifqi Nur, M. Saifudin Octario Ashar, Andika Udistiyan Permatasari, Shella Heidy Prizka Rismawati Arum Ridwanulhaq, Alfina Fauziah Rochdi Wasono S.A.P., Rangga Sa’adillah Septiana Putri Milasari Supriadin Supriadin