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All Journal Agromet ComEngApp : Computer Engineering and Applications Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Ilmiah FIFO Jurnal Sisfokom (Sistem Informasi dan Komputer) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian kepada Masyarakat Nusantara JUSTIN (Jurnal Sistem dan Teknologi Informasi) IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Rekayasa Komputasi Terapan Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Nasional
Eliyani Eliyani
Universitas Mercu Buana
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Social Sciences

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Penerapan Clustering dalam Data Science Untuk Mengembangkan Keterampilan Analitik di SMK Media Informatika Eliyani, Eliyani; Rifqi, Muhammad; Dwiasnati, Saruni
Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2024): Desember 2024
Publisher : Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/japamas.v3i2.162

Abstract

The current and future job market requires a workforce skilled in data science, including analytical techniques such as clustering. With the development of Industry 4.0, skills in data analysis are becoming increasingly important, and education must adapt to meet this need. Currently, there are still many students at the vocational high school level who do not understand the concept of data science, including clustering techniques, so they have difficulty understanding how data can be generated for decision making from each scheme. The method that can be used in this Community Service is an introduction to the basic concepts of data science and simple training in the use of simple data science tools. The goal that can be raised for the Community Service that we do is to provide students with a basic understanding of what data science is as one method for making important data decisions. The contribution that can be generated from the Community Service that we do is that students will have basic skills in knowledge related to data science, especially in understanding and applying clustering techniques for data analysis. The result of the Community Service that we do is that students understand the basic concepts of data science and clustering and how this method is applied in the analysis of the data obtained.
Pengelompokan wilayah produksi tuna, cakalang, tongkol dan udang di Indonesia menggunakan algoritma K-Means Dwiasnati, Saruni; eliyani, Eliyani; Arif, Sutan Mohammad; Avrizal, Reza
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp128-137

Abstract

The research was intended to cluster the production areas of Indonesia's fishery products especially Skipjack Tuna, Tuna, Mackarel Tuna, and shrimp using data science techniques. The algorithm used was K-means Clustering. The data used was annual production data for each province for the last 3 years (2019 – 2021). Determination of the number of clusters using the Elbow Method. For each commodity, three clusters were obtained, namely clusters with low production, medium production, and high production. For Skipjack Tuna, there were 19 provinces belonging to the low cluster, 13 provinces being medium, and 2 provinces being high. For Tuna, there were 22 provinces in the low cluster, 9 provinces in the middle, and 3 provinces in the high cluster. For Mackarel Tuna, low was 19 provinces, medium was 12 provinces, and high was 3 provinces. For shrimp, 23 provinces were low, 7 provinces were medium, and 4 provinces were high. High production clusters for Skipjack Tuna were North Sulawesi and North Maluku Provinces, Tuna were North Sulawesi, North Maluku and Maluku Provinces, for Mackarel Tuna were Aceh, East Java and Maluku Provinces, and for shrimp were North Sumatra, West Kalimantan, South Kalimantan and East Kalimantan Provinces.
Diabetes Classification Algorithm Optimization Using Particle Swarm Optimization on Naïve Bayes, C4.5 and Random Forest Maulana, Reffy; Eliyani, Eliyani
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2431

Abstract

The global rise in diabetes prevalence presents a significant public health concern, emphasizing the need for accurate and efficient early detection systems. This study investigates the performance of three classification algorithms—Naïve Bayes, C4.5, and Random Forest—for predicting diabetes and explores the impact of hyperparameter tuning via Particle Swarm Optimization (PSO) on model performance. The research employs the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset from the Centers for Disease Control and Prevention (CDC), which includes a wide range of health-related and demographic variables from adult respondents across the United States. Each algorithm was tested under two conditions: with default parameters and after optimization using PSO. Experimental results demonstrate that the Random Forest algorithm, even without optimization, yielded the highest accuracy at 95.15%, whereas Naïve Bayes showed the weakest performance. However, applying PSO significantly improved the performance of initially suboptimal models, particularly Naïve Bayes and C4.5. Specifically, Naïve Bayes accuracy increased from 80.80% to 82.24% (a 1.44% increase), and C4.5 accuracy increased from 91.22% to 91.31% (a 0.09% increase). In contrast, the effect of optimization on Random Forest was minimal, showing a slight decrease in accuracy to 94.37%, indicating the model’s robustness in its default configuration. These findings underscore the importance of algorithm selection and tailored optimization strategies in enhancing the accuracy of diabetes classification systems.
Pengembangan Jadwal Shift Staf Editor Video pada Stasiun Televisi Nasional Trans7 berbasis Android menggunakan Algoritma Ant Colony dengan Firebase Rizal, Rizal; Eliyani, Eliyani
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 2: April 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020721394

Abstract

Perusahaan bisnis bidang broadcasting atau tepatnya stasiun televisi memiliki strategi untuk menayangkan tayangan acara yang paling ditunggu oleh penonton setianya, yaitu berupa tayangan acara yang up to date dan cepat dalam penyiarannya. Oleh sebab itu, diperlukan staf pada bagian pasca produksi untuk bekerja secara cepat dan sesuai dengan prosedur tayangan. Untuk mempersiapkan tayangan terbaru secara cepat, diperlukan sistem penjadwalan staf editor video yang jumlahnya besar secara cepat, tepat, dan dapat dilihat dengan instan oleh para editor video sehingga memudahkan editing video setiap harinya. Algoritma yang cocok untuk menghasilkan jadwal secara cepat dengan jumlah yang besar dengan tidak ada data yang bentrok adalah ant colony atau algoritma koloni semut. Algoritma ant colony ini mengacu pada cara hidup semut yang berkelompok dalam mencari makanan sehingga dapat kembali lagi ke tempat semula dengan jalur yang sama dan cepat. Data masukan yang digunakan dalam penelitian ini adalah nama editor dan ruangan, serta luaran berupa jadwal per shift untuk setiap ruangan untuk suatu periode tertentu. Penelitian ini menggunakan basis data MySQL dan firebase. Jadwal editor yang telah diolah pada aplikasi back-end kemudian diubah ke dalam bentuk aplikasi android, dengan demikian jadwal tersebut dapat dilihat oleh seluruh staf editor video melalui smartphone masing-masing. Pengujian dilakukan terhadap hasil perhitungan algoritma, fungsional sistem, integrasi, dan penerimaan yang dikembalikan pada sistem dengan mencari kesalahan pada interface perangkat lunak, dan pengujian penggunaan langsung kepada pengguna. Hasil pengujian menunjukkan bahwa algoritma ant colony dapat digunakan untuk menyusun jadwal editor video dengan cepat dan tepat, semua fitur berjalan dengan baik, dan tingkat kepuasan pengguna cukup tinggi. AbstractBroadcasting or television business companies have a strategy to broadcast programs up to date and quickly. Therefore, staff in the post-production section are required to work quickly and in accordance with the show procedures. To prepare the latest shows quickly, a video editor staff scheduling system is needed. That scheduling system should be fast, precise, and can be seen instantly by video editors so that the editing processes can be done easily every day. The suitable algorithm to generate a schedule quickly in large numbers with no data clashing is ant colony.Ant colony algorithm refers to the way of ants when looking for food in a group, then they can return again to their original place with the same path and fast. The input data used in this study is the name of the editor and the room, and output in the form of a schedule per shift for each room for a certain period. This research uses MySQL and firebase databases. The editor's schedule that has been processed in the back-end application is then converted into an android application, therefore the schedule can be seen by all video editor staff via their own smartphones. Tests are carried out on the results of algorithm calculations, functional systems, integration, and feedback to the system by looking for errors on the software interface, and direct use testing to users. The test results show that the Ant Colony algorithm can be used to compile a schedule of video editors quickly and precisely, all features run well, and the level of user satisfaction is quite high.
Transformasi Digital UMKM Meruya Selatan melalui Sistem Rekomendasi Produk Eliyani, Eliyani; Dwiasnati, Saruni; Yuliarty, Popy
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i2.8315

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan di Kelurahan Meruya Selatan sebagai upaya untuk meningkatkan kapasitas pelaku UMKM dalam memanfaatkan teknologi digital melalui penerapan sistem rekomendasi produk berbasis data sains. Program ini dirancang untuk memperkenalkan peran sistem rekomendasi dalam ekosistem e-commerce, khususnya dalam mengidentifikasi konsumen untuk peningkatan penjualan serta memperkuat loyalitas pelanggan. Kegiatan dilaksanakan pada tanggal 25–26 April 2025 dengan melibatkan 25 peserta pelaku UMKM yang bergerak di sektor kuliner. Hasil identifikasi awal menunjukkan bahwa mayoritas peserta belum memiliki toko daring dan masih mengalami keterbatasan dalam pencatatan, pengelolaan, serta pemanfaatan data penjualan. Pelaksanaan pelatihan dilakukan secara partisipatif melalui penyampaian pengenalan materi, pengenalan jenis sistem rekomendasi, serta pemahaman dasar algoritma yang umum diterapkan dalam praktik bisnis digital. Evaluasi menunjukkan tingkat kepuasan peserta dengan nilai rata-rata sebesar 2,1 yang termasuk dalam kategori “Baik”. Peserta memberikan tambahan wawasan, meningkatkan motivasi, serta memperkuat pemahaman dalam mengadopsi teknologi digital untuk pengelolaan usaha. Temuan ini menegaskan bahwa peningkatan literasi digital dan pemanfaatan data sains memiliki peran strategis dalam mempercepat transformasi digital UMKM dan meningkatkan daya saing pada ekonomi digital. Selain itu, hasil kegiatan menekankan pentingnya pendampingan berkelanjutan, penguatan sinergi dengan program pemerintah daerah, serta penyusunan roadmap pengembangan berbasis komunitas yang terintegrasi secara bertahap.
Co-Authors Anis Cherid ARIF, SUTAN MOHAMMAD Avrizal, Reza Erlangga, Jerry Fatimah, Nona I Handoko Malik Hadid Maulana, Reffy Muhammad Dery Setiawan Muhammad Rifqi Nia Kurnia Asih Popy Yuliarty, Popy Rizal Rizal Rudiarto, Sabar Saruni Dwiasnati Subeki, Fajar Syarif Hidayat, Rafi Wawan Gunawan Yonny Koesmaryono Yudo Devianto