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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Jurnal Ilmu Komputer dan Informasi Berkala Ilmiah Pendidikan Biologi (BioEdu) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Jurnal Kimia dan Kemasan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Communications in Science and Technology SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPF : JURNAL PENDIDIKAN FISIKA Jurnal Ilmu Fisika Jurnal Informatika Global Jurnal Abdimas Mandiri Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics Hexatech: Jurnal Ilmiah Teknik Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Molekul: Jurnal Ilmiah Kimia Al-Ashri: Ilmu-Ilmu Keislaman AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Evi Yulianti
Pusat Teknologi Bahan Industri Nuklit-BATAN Kawasan PUSPIPTEK Serpong, Tangerang 15314, Indonesia
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Penerapan Metode Forecasting Dalam Menentukan Prediksi Jumlah Mahasiswa Baru Dengan Menggunakan Single Exponential Smoothing Rama Samudra, M.S; Marcelina, Dona; Terttiaavini; Yulianti, Evi; Coyanda, John Roni; Putri, Indah Pratiwi
Jurnal Ilmiah Informatika Global Vol. 15 No. 2: Agustus 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i2.3916

Abstract

Forecasting adalah suatu proses analisis untuk memprediksi nilai-nilai masa depan berdasarkan informasi data historis, atau tren yang telah ada. Forecasting melibatkan metode matematika, statistik, untuk menghasilkan perkiraan tentang apa yang mungkin terjadi di masa mendatang dengan mengumpulkan data historis jumlah mahasiswa baru yang mendaftar selama 11 tahun terakhir. Data harus mencakup periode waktu yang cukup lama untuk mengidentifikasi tren dan pola, kemudian analisis data historis untuk mengidentifikasi tren, musiman, atau fluktuasi lainnya. Analisis data dilakukan dengan menggunakan teknik analisis single exponential smooting akan memperhitungkan nilai alpha (konstanta smoothing) untuk menghasilkan perkiraan yang paling akurat. Analisis data menggunakan tiga nilai konstanta alpha 0.3, 0.6, 0.9 tersebut akan dipilih nilai alpha dengan nilai error terkecil, dan kemudian akan di aplikasikan pada sistem peramalan berbasis website. Melalui sistem berbasis website, informasi dapat dikelola secara efisien dan memungkinkan Universitas Indo Global Mandiri untuk mengoptimalkan proses penerimaan mahasiswa baru mereka. Dengan menerapkan metode peramalan ini dalam sistem berbasis website, Universitas Indo Global Mandiri dapat mengelola informasi secara efisien, yang pada gilirannya akan membantu mereka mengoptimalkan proses penerimaan mahasiswa baru. Analisis data dengan tingkat error terkecil, dapat diartikan bahwa metode peramalan tersebut telah memberikan hasil yang sangat mendekati dengan data aktual, sehingga dapat diandalkan untuk melakukan proyeksi ke depan.
Query keyword extraction in discriminative marginalized probabilistic neural method for multi-document summarization Subeno, Bambang; Budi, Indra; Yulianti, Evi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp907-915

Abstract

The large number of textual documents in the medical field makes it very difficult for readers to obtain comprehensive information. Users usually use a query approach to get the desired information. Using the correct query will produce relevant information. In the existing discriminative marginalized probabilistic neural method, referred to as DAMEN, used for multi-document summarization, a background sentence query is used to retrieve the top-K relevant documents and then generate a summary of these documents. However, the background sentence query used to retrieve the top-K documents did not provide accurate summary results. The author improved the DAMEN model by adding a keyword extraction process to the query background sentence. We call this model Q-DAMEN. Our model shows significant improvement over the original DAMEN method, with the best results achieved by the variation of using a keyword query entered into the discriminator component and a background sentence query entered into the generator component. The multipartieRank keyword extraction method shows the best results with a Rouge-1 value of 29.12, Rouge-2 of 0.79, and Rouge-L of 15.53. The results demonstrate that the more accurate the keywords extracted from the sentence background query, the more accurate the multi-document summaries generated.
Co-Authors Abdul Haris Abdurrohman, Jafar Abro, Fikri Adriana, Risda Agung Bambang Setio Utomo Agus Sugandha Ahmad Dahlan Alfina, Ika Alie, M. Fadhiel Aminuddin, Jamrud Anandez, Arum Adisha Putra Annas, Dicky Atmoko, Indri Aulia, Muti’a Rahma A’yun, Nidha Aulia Qurrata Bambang Subeno Berghuis, Nila Tanyela Bhary, Naradhipa Bilalodin Bilalodin Budi, Indra Busral, Busral Coyanda, John Roni Cyndika Dana Indra Sensuse Dari, Qorinah Wulan DEWI SARTIKA Dhamayanti, Dhamayanti Dwitilas, Fariz Wahyuzan Eka Qadri Nuranti Enrique, Gabriel Faradillah Fatari, Fatari Febrianto, Muhamad Rizki Fridarima, Shanny Geni, Lenggo Gupron, Akhmad Hananto, Djoko Haryadi, Arifin Nur Muhammad Hasnawati Hasnawati Hayati, Atika Trisna Heri Jodi, Heri Humairoh, Nayu Nur Husin, Husna Sarirah Imelda Saluza, Imelda Indah Permatasari Iskandar Zulkarnaen Jayawarsa, A.A. Ketut Kartika Sari Khusaenah, Nur Kurniawan, Alfin Lastri Widya Astuti, Lastri Widya Laugiwa, Matiin Lukman Hakim Madiabu, Muhammad Jihad Mannix, Ilma Alpha Marcelina, Dona Martawijaya, M. Agus Meganingrum Arista Jiwanggi Ndruru, Sun Theo Constan Lotebulo Nissa, Nuzulul Khairu Nua, Muh. Tri Prasetia Pisgamargareta, Abel Praktino, Budi Prasetyo, Ridho Pratama, Mochamad Jodi Pratiwi, Indah Putri Putri Rizqiyah Putri, Indah Pratiwi Rabiyatul Adawiyah Siregar Rachmadhanti, Elvira Nur Rachmawati, Nur Rama Samudra, M.S Ramadhan, Mustafa Ridho, Muhammad Yusuf Rohmad Salam, Rohmad Rosiana Dwi Saputri, Rosiana Dwi Sampora, Yulianti Saputra, Muklas Ade Sofyan, Muhammad Ihsan Sudaryanto Sulkiah Hendrawati Sumarsih, Rani Sri Sunardi Sunardi Suryati Syazali, Muhammad Rizki Terttiaavini Terttiavini, Terttiavini Zulham Zulham