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Pemilihan Platform Film Streaming Menggunakan Metode SMARTER dan MOORA: Selection of Streaming Film Platforms Using the SMARTER Method and the MOORA Saputri, Arini; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1325

Abstract

Sektor industri perfilman telah menjadi aspek tontonan wajib dalam masyarakat, saat ini film menjadi suatu hiburan yang populer di Indonesia. Kemajuan teknologi dan digitalisasi memfasilitasi akses mudah menonton film, masa transisi dari penggunaan DVD/VCD ke Blu-Ray sebagai media untuk menikmati film yang mendapatkan daya tarik pada masanya. Perkembangan internet dan platform online yang semakin pesat telah mengubah industri dunia perfilman, banyak sekali bermunculan berbagai layanan streaming yang menawarkan kemudahan untuk menonton film kapan saja dan dimana saja. Maraknya kemudahan menonton film streaming dengan tersedianya berbagai platform film masih banyak terdapat perbedaan beberapa aspek baik tampilan maupun layanan yang ditawarkan, sehingga penelitian ini memberikan wawasan dan rekomendasi mengenai opsi streaming yang baik. Dalam penelitian ini menggunakan metode MOORA dan SMARTER Kedua metodologi menghasilkan hasil yang sebanding pada nilai tertinggi yaitu Netflik sebagai platform film streaming paling aman dengan skor 0,421 pada metode SMARTER dan 0,582 pada metode MOORA , dan mengalami selisih perbedaan yang tidak terlalu signifikah terkait peroleh nilai tertinggi kedua, Dimana pada metode SMARTER di peroleh oleh Disney Hotstar dengan nilai 0,377sedangkan pada metode MOORA nilai tertinggi kedua di peroleh oleh Iflix dengan nilai0,297sehingga kedua metode ini sangat ideal untuk digunakan.
Implementasi Algoritma K-Nearest Neighbor untuk Prediksi Penjualan Alat Kesehatan pada Media Alkes: Implementation of the K-Nearest Neighbor Algorithm to Predict Sales of Medical Devices in Medical Devices Nijunnihayah, Uktupi; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Novalia, Elfina
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1326

Abstract

Media Alkes Perusahaan ini bergerak dalam bidang industri Alat Kesehatan. Perusahaan ini menyediakan berbagai produk seperti jarum kursi roda, alat infus, alat monitor tekanan darah, dan lain-lain. Media Alkes juga aktif menerapkan strategi bisnis untuk memenuhi kebutuhan pelanggan. Namun sering terjadi kekurangan stok dan barang menumpuk di dalam perusahaan ini. Peneliti telah mengelola dan menganalisis data penjualan yang ada untuk memahami kebutuhan pelanggan terhadap Alat Kesehatan. Dalam menghadapi tantangan tersebut, peneliti mengusulkan algoritma K-Nearest Neighbor untuk memprediksi penjualan Alat Kesehatan di Media Alat Kesehatan. Informasi mengenai jumlah penjualan Alat Kesehatan dengan kriteria Sangat laris, Cukup laris dan Kurang laris dapat dilihat melalui data penjualan tahun 2020 hingga tahun 2022 pada Media Laporan Penjualan Alat Kesehatan. Penelitian dilakukan dengan menerapkan metode K-Nearest Neighbor (KNN) baik dengan perhitungan secara manual maupun menggunakan sistem RapidMiner. Hasil dari prediksi yang menggunakan sistem RapidMiner menunjukkan tingkat akurasi sebesar 95,00% dari data yang disebut penjualan. Dengan hasil prediksi yang didapat yang Sangat bagus tersebut, metode ini dapat dijadikan sebagai acuan dalam merencanakan penjualan di masa depan. Dengan menerapkan prediksi ini, perusahaan dapat mengelola stok barang dengan secara efisien dan menghindari kehabisan stok serta memuat barang yang tidak diinginkan.
Peningkatan Minat Digital Skill Menggunakan Algoritma K-Medoids Clustering Pada Karyawan Zulfiana, Rizka; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.4994

Abstract

General Company for Printing Money of the Republic of Indonesia is one of the state-owned enterprises that prints banknotes and other official documents. Perum Peruri also wishes to gain more insight into the technology implemented in the company. The demand for a workforce skilled in the use of technology in the work environment continues to increase over time. Perum Peruri has 16 Digital Skill categories, each of these categories has a high, medium to the lowest interest. In this problem, the data taken has not been grouped, so there is a lack of information about the number of categories that have the highest to lowest interest. By analyzing the specialization data, it will help determine which categories need improvement. The categories in Digital Skills specialization can then be improved by using this information as a reference for designing improvement strategies. Research was conducted using clustering to determine the number of categories that Perum Peruri personnel are interested in. In this study, sales data in Excel format was analyzed, and clusters based on product sales data were created using the K-Medoids approach. Sales information obtained from secondary data that manages employee specialization. Using RapidMiner, the accuracy for the three clusters designated as highest, middle, and lowest based on the clustering results was ascertained. The first cluster of 16 items analyzed consisted of 7 items with the highest ranking, the second cluster had 5 items categorized as medium, and the third cluster had 4 items classified as the lowest. Based on the results, 4 items were categorized as low, indicating the need for a socialization approach to increase interest in the Digital Skill.
Mengintegrasikan Prinsip Pembangunan Berkelanjutan dalam Pembelajaran Matematika untuk Merangsang Keterampilan Berkelanjutan pada Generasi Mendatang Kusumaningrum, Dwi Sulistya; Lestari, Santi Arum Puspita; Nurapriani, Fitria; Dwi Sulistya
Jurnal Rekayasa Sistem Industri Vol. 13 No. 1 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i1.7167.1-10

Abstract

This research constitutes a literature review employing a qualitative approach, analyzing scholarly articles, books, and other documents related to sustainable development. This article aims to summarize and analyze previous studies concerning sustainable development in the context of mathematics education, as well as strategies that can be employed to integrate the principles of sustainable education. Integrating the principles of sustainable development into education, including mathematics education, is crucial in fostering a more environmentally responsible society and promoting sustainability across all sectors. However, its implementation remains limited. Educators face various challenges, including a lack of time, resources, and understanding of sustainable education, along with a dearth of supportive teaching materials. The principles of sustainable development can serve as a framework for developing curricula and teaching practices that are more sustainable. Educators can select mathematical problems related to environmental or social issues, discuss relevant mathematical concepts in connection with these problems, and help students comprehend the impact of mathematical decisions on the environment and society. Integrating the principles of sustainable development into mathematics education not only aids in producing a generation with sustainable skills but also motivates students to learn mathematics in more engaging and meaningful ways. A learning approach centered around sustainable development can be an effective way to prepare students for a sustainable future. The article also underscores the necessity for curriculum development, training, and professional advancement for educators.
Implementation of Spatial Analysis Using KNN-5 in GIS for Mapping Mushroom Houses in Karawang Regency Guntur, Muhamad; Hananto, April Lia; Nurapriani, Fitria; Hilabi, Shofa Shofiah
Jurnal Accounting Information System (AIMS) Vol. 9 No. 1 (2026)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v9i1.1910

Abstract

This research aims to develop a Geographic Information System (GIS) to map the locations of straw mushroom farms in Karawang Regency by applying spatial analysis based on the K-Nearest Neighbors algorithm with k = 5. This approach examines the spatial proximity relationship between mushroom production sites and the nearest markets to inform location-based distribution decisions. The research method employs a quantitative approach through mapping, geographic coding, and analysis of geographic distances, which are integrated into the GIS. Spatial data are obtained from field observations and collected from mushroom farmers, then analyzed using distance calculations based on geographic coordinates. The results show that the K-Nearest Neighbors analysis with k = 5 can dynamically identify the nearest markets and more objectively represent the spatial relationship between production and markets than a static radius approach. Additionally, proximity analysis between farms reveals local-scale spatial clustering patterns, while radius analysis provides an initial overview of limited spatial accessibility. Integrating the analysis results into the Geographic Information System enables comprehensive spatial visualization and supports more efficient, data-driven decision-making for straw mushroom distribution.