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All Journal Jurnas Nasional Teknologi dan Sistem Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mandiri IT NERO (Networking Engineering Research Operation)
Tias, Rahmawati Febrifyaning
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics

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Dynamic Time Warping Pada Metode K-Means Untuk Pengelompokan Data Trend Penjualan Produk Wijaya, Fairizal Aaron Wahyu Tanamas Satria; Prasetyo, Eko; Tias, Rahmawati Febrifyaning
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10, No 2 (2024): Agustus 2024
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i2.2024.100-109

Abstract

Pasar swalayan menghasilkan transaksi penjualan dengan pelanggan dengan laju pertumbuhan data tinggi. Data transaksi sejumlah barang memiliki pola berbeda, ada yang tinggi dan ada yang rendah. Perbedaan ini mengakibatkan pola penjualan antar satu produk dengan produk lainnya berbeda. Pola ini juga mengakibatkan pengelolaan stok dan promosi juga berbeda. Maka, untuk memudahkan pengelolaan barang, perlu dilakukan pengelompokan trend penjualan otomatis menggunakan K-Means. Namun, metode perhitungan jarak (dissimilarity) standar tidak tepat digunakan pada data periodik. Penelitian ini bereksperimen mengelompokkan data periodic trend penjualan produk menggunakan metode K-Means dan DTW (Dynamic Time Warping) untuk perhitungan jarak. DTW melakukan perhitungan jarak dengan memperhatikan data histori periode sebelum dan setelah data yang sedang dihitung. Pelibatan data disekitarnya akan membentuk jarak sesuai dengan pola data periodik. Hasil pengelompokan data tahun 2021, Cluster 1 dengan 417 data memiliki pola naik dari awal tahun kemudian menurun, selanjutnya perlahan naik kembali di pertengahan hingga akhir tahun. Cluster 2 dengan 2403 data memiliki pola stabil di awal tahun, ketika di pertengahan tahun mulai naik, kemudian di akhir tahun turun namun kembali naik. Cluster 3 dengan 289 data memiliki pola menurun saat awal tahun, namun terlihat stabil hingga akhir tahun. Hasil ekeperimen pengelompokan trend penjualan menggunakan metode K-Means dengan jarak DTW menunjukkan hasil yang baik dan memiliki pola data dalam kelompok yang sesuai karakteristik alami. Hal ini didukung oleh validitas Davies-Bouldin index -1.215 dimana nilai yang semakin kecil mendekati nol menunjukkan kualitas cluster yang dicapai baik.
Classification of Cash Direct Recipients Using the Naive Bayes with Smoothing Prasetyo, Eko; Al-Adni, Muhammad Faris; Tias, Rahmawati Febrifyaning
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3584

Abstract

Direct Cash Assistance is a social program distributed to residents meeting specific requirements. The village government determines the recipients using a conventional system through village meetings. This approach is greatly influenced by the decision-holders’ subjectivity with non-transparent thinking. This research aims to solve the problem of classifying Direct Cash Assistance recipients by applying probability-based classification. The research method used is smoothed Nave Bayes, which improves Nave Bayes by adding a constant to avoid zero classification. The datasets use variables such as age, type of work, and criteria for receiving assistance. The last variable includes five nominal data, which debilitates Nave Bayes by not obtaining a posterior probability as a prediction class result. We used Direct Cash Assistance data from the Sedati sub-district, Sidoarjo district, East Java. The results of research with original Nave Bayes and smoothed Nave Bayes classification show that smoothed Nave Bayes has good prediction performance with an accuracy of 95.9% with a data split of 60:40. Smoothed- Nave Bayes also solves the problem of 8 data without predictive classes. The prediction results show that Smoothed Nave Bayes performs better than standard Nave Bayes. This research contributes to refining Nave Bayes to complement probability-based classification by adding refinement constants to avoid zero classification.
Dynamic Time Warping Pada Metode K-Means Untuk Pengelompokan Data Trend Penjualan Produk Wijaya, Fairizal Aaron Wahyu Tanamas Satria; Prasetyo, Eko; Tias, Rahmawati Febrifyaning
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 2 (2024): Agustus 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i2.2024.100-109

Abstract

Pasar swalayan menghasilkan transaksi penjualan dengan pelanggan dengan laju pertumbuhan data tinggi. Data transaksi sejumlah barang memiliki pola berbeda, ada yang tinggi dan ada yang rendah. Perbedaan ini mengakibatkan pola penjualan antar satu produk dengan produk lainnya berbeda. Pola ini juga mengakibatkan pengelolaan stok dan promosi juga berbeda. Maka, untuk memudahkan pengelolaan barang, perlu dilakukan pengelompokan trend penjualan otomatis menggunakan K-Means. Namun, metode perhitungan jarak (dissimilarity) standar tidak tepat digunakan pada data periodik. Penelitian ini bereksperimen mengelompokkan data periodic trend penjualan produk menggunakan metode K-Means dan DTW (Dynamic Time Warping) untuk perhitungan jarak. DTW melakukan perhitungan jarak dengan memperhatikan data histori periode sebelum dan setelah data yang sedang dihitung. Pelibatan data disekitarnya akan membentuk jarak sesuai dengan pola data periodik. Hasil pengelompokan data tahun 2021, Cluster 1 dengan 417 data memiliki pola naik dari awal tahun kemudian menurun, selanjutnya perlahan naik kembali di pertengahan hingga akhir tahun. Cluster 2 dengan 2403 data memiliki pola stabil di awal tahun, ketika di pertengahan tahun mulai naik, kemudian di akhir tahun turun namun kembali naik. Cluster 3 dengan 289 data memiliki pola menurun saat awal tahun, namun terlihat stabil hingga akhir tahun. Hasil ekeperimen pengelompokan trend penjualan menggunakan metode K-Means dengan jarak DTW menunjukkan hasil yang baik dan memiliki pola data dalam kelompok yang sesuai karakteristik alami. Hal ini didukung oleh validitas Davies-Bouldin index -1.215 dimana nilai yang semakin kecil mendekati nol menunjukkan kualitas cluster yang dicapai baik.
Quality of Service (QoS) Analysis using Wireshark on the LAN Network at An Najiyah High School Surabaya Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.273

Abstract

In the realm of information technology, Indonesia has entered the fourth generation, or 4G, which is a fast, widely available internet network that can be used to advance a variety of industries, including the agricultural, social, cultural, economic, and even educational ones. Additionally, from 2020 to the beginning of 2022, you will need to be connected to the internet in order to stay productive during the Covid-19 virus outbreak. This is especially true for the education sector, since online teaching and learning activities are essential for maintaining productivity. An Najiyah Surabaya High School needs reliable internet access in order to provide better support for its online learning students. An Najiyah High School Surabaya employs QOS (Quality of Service) to monitor network quality and data traffic transferred over the network. Three QoS parameters—packet loss, throughput, and delay—will be used in this research's analysis. concentrate on keeping an eye on the local area network (LAN); the value is then retrieved following the network's monitoring. When text data transmission on a LAN network was tested, the results indicated that the network quality at SMA Na Najiyah Surabaya was very good, with values of 2.6 Mbps for throughput, o% packet loss, and 0% and 0.12 ms delay.
A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx) Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
NERO (Networking Engineering Research Operation) Vol 10, No 2 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i2.30548

Abstract

KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools
Co-Authors Al-Adni, Muhammad Faris Eko Prasetyo Hamidah, Mas Nurul Heni Pujiastuti Rifki Fahrial Zainal Wijaya, Fairizal Aaron Wahyu Tanamas Satria