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All Journal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Jurnal Khatulistiwa Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Sebatik Martabe : Jurnal Pengabdian Kepada Masyarakat Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Santika : Jurnal Ilmiah Sains dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informatika dan Komputer Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Informatika Terpadu Indexia Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal of Practical Computer Science (JPCS) Prioritas : Jurnal Pengabdian Kepada Masyarakat Synergy : Jurnal Ilmiah Multidisiplin Samakta: Jurnal Pengabdian Kepada Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer Ruru JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Sistem Informasi (JASISFO)
Asriyanik .
Universitas Muhammadiyah Sukabumi, Sukabumi
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PELATIHAN STRATEGI PEMASARAN TERPADU TERINTEGRASI DIGITAL MELALUI PEMANFAATAN MEDIA SOSIAL PADA UMKM KOTA SUKABUMI Kokom Komariah; Leonita Siwiyanti; Asriyanik Asriyanik; Asep M.Ramdan; Risma Nurmilah
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 6 (2023): martabe : jurnal pengabdian kepada masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i6.1887-1892

Abstract

Peran Digitalisasi di era teknologi pada saat ini  sudah menjadi kebutuhan masyarakat khususnya pelaku usaha dalam mempromosi kan produknya melalui platform online, Media Sosial, Website, dll, Tetapi tidak semua pelaku usaha  menggunakan teknologi tersebut, banyak hambatan yang mereka temukan dalam mempromosikan produknya oleh karena itu  tujuan pengabdian ini yaitu memberikan pelatihan dan peningkatan keterampilan  dalam hal pemasaran terpadu dan pemanfaatan media sosial melalui digital marketing  di UMKM wilayah Kota sukabumi yang tergabung dalam Anggota SEA (Sukabumi Enterpreuner Association), Aktivitas yang dilakukan melalui Pelatihan Strategi Pemasaran terpadu dan Pemanfaatan Media Sosial
PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST Asriyanik
Jurnal Ilmu Komputer Ruru Vol. 2 No. 1 (2025): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
Machine Learning-Based Classification for Scholarship Selection Asriyanik Asriyanik; Agung Pambudi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7393

Abstract

University of Muhammadiyah Sukabumi (UMMI) is a university that accepts KIP scholarship every year. However, KIP student applicants always exceed the quota, so it requires a re-selection process to determine KIP Shcolarship Awardee. UMMI does not have a clear method to support decisions in the selection process for KIP Shcholarship Awardee. To solve this problem, a classification modeling process will be carried out from previous data using machine learning algorithms, namely with Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The general method for its development uses the SEMMA method (Sample, Explore, Modify, Model, Assess). Starting with collecting a dataset of KIP recipients studying at UMMI from 2021-2022 which amounted to 519 data with 16 attributes. From the results of exploration, the main attributes that became features for modeling were DTKS Status, P3KE Status, Combined income of father and mother and achievement. These attributes are converted into numeric data for easy data modeling. The results of K-Fold Cross-Validation for the DT model in the case of classification of KIP Kuliah recipients resulted in an accuracy of 78.44% of the entire test dataset, a precision of 0.73107 indicating that 73.11% of the model's predictions were correct, recall (sensitivity level) of 78.45% and an F1 score of 73.20%. The results of modeling and validation with SVM are 80.17% accuracy, 84.44% precision and 80.17% recall. The SVM model shows slightly better in terms of accuracy and precision, both models show competitive performance in classifying KIP scholarship recipients studying at UMMI.
Co-Authors Abhista Hibatullah, Akbar Adi Sunarto, Asril Adiwijaya, Fahmi Adzkia, Hawarizmi Ummul Afiansyah, Rifan Agung Pambudi Agung Pambudi Akyas Hifdzi Rahman, Rifqi Alifatih, Auriel Haiqal Asep Budiman Kusdinar Asep M.Ramdan Asep Muhamad Ramdan Asril Adi Sunarto Azhilla Margiani Saraswati Budhy Adzy, Luthfy Budiman Kusdinar, Asep Dafa Satria Sidik, Muhamad Dang Kurniawan, Dito DANNY RAMADHAN Daris Riyadi Dasep M Luay Didik Indrayana Din Azwar Uswatun Edward, MA Algifari Eka Fitriah, Tika Elwanda Putra, Isra Fadhil Faizal Akbar Fahmi Nurfalah Fajar Hikmal Gunawan Fathia Frazna Az-Zahra Fathia Frazna Azzahra Frananda Adiezwara Ramadhan, Mohamad Frazna Azzahra, Fathia Frazzna Az-zahra, Fathia Ilmi Barokah Indra Griha Tofik Isa iqbal setiawan Isa, Indra Griha Tofik Iwan Rizal Setiawan Jamaludin, Firdaus kania, euis Kokom Komariah Kokom Komariah Larasati Mayan Pramesti Lelah Lelah Lelah Lelah Leonita Siwiyanti lucky valiant M. Rizky Suherlan MA Algifari Edward Maulana Muhammad Rizky Mohamad Nurizki Mohamad Ridwan Mokhamad Hendayun Mubharak, Gilang Fauzul Muhammad Drajat Ramdhani muhammad musyfik Muhammad Zaynurroyhan Mulud Muchamad, Reski musyfik, muhammad Nesta Suandana, Ilham Nur Asiah Ramdani Nuraeni, Fika Nurmillah, Risma Prajoko . Prajoko Prajoko Putra, Muhammad Rafli Afandi Rahmawati, Verra Sri Yulia Ramadhan, Vito Rambe, Sarah Syakira Ramdan, Adam Rijal Agus Rusmana Risma Nurmilah Riyadi, Daris Rustiandi, Ryan Santiastry, Sany Sarah Novia Hermawanti Siti Nurazizah Soebandi, Andry Subhan, Roby Azhari Suhendar Syafira Zahara Syah Rizal Fauzy Syahputra, M Ramdhan Triwulandari, Syane widi aulia rohmah Winda Apriandari Winda Apriyandari Zahra, Fathia Frazna Az