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All Journal Jurnal Ilmu Komputer dan Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI JISKa (Jurnal Informatika Sunan Kalijaga) Scientific Journal of Informatics Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) SinarFe7 Jurnal ELTEK Jurnal Informatika: Jurnal Pengembangan IT
Khadijah Fahmi Hayati Holle
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science Other

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Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) Holle, Khadijah Fahmi Hayati
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.998 KB) | DOI: 10.18860/mat.v8i2.3537

Abstract

The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta.
Sistem Automatic Text Summarization Menggunakan Algoritma Textrank Zamzam, Muhammad Adib; Crysdian, Cahyo; Hayati Holle, Khadijah Fahmi
MATICS Vol 12, No 2 (2020): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v12i2.8372

Abstract

Text summarization (perangkuman teks) adalah pendekatan yang bisa digunakan untuk meringkas atau memadatkan teks artikel yang panjang menjadi lebih pendek dan ringkas sehingga hasil rangkuman teks yang relatif lebih pendek bisa mewakilkan teks yang panjang. Automatic Text Summarization adalah perangkuman teks yang dilakukan secara otomatis oleh komputer. Terdapat dua macam algoritma Automatic Text Summarization yaitu Extraction-based summarization dan Abstractive summarization. Algoritma TextRank merupakan algoritma extraction-based atau extractive, dimana ekstraksi di sini berarti memilih unit teks (kalimat, segmen-segmen kalimat, paragraf atau passages), lalu dianggap berisi informasi penting dari dokumen dan menyusun unit-unit (kalimat-kalimat) tersebut dengan cara yang benar. Hasil penelitian dengan input 50 artikel dan hasil rangkuman sebanyak 12,5% dari teks asli menunjukkan bahwa sistem memiliki nilai recall ROUGE 41,659 %. Nilai tertinggi recall ROUGE tertinggi tercatat pada artikel 48 dengan nilai 0,764. Nilai terendah recall ROUGE tercatat pada artikel  37 dengan nilai 0,167.
Air Quality Forecasting in DKI Jakarta Using Artificial Neural Network Asfilia Nova Anggraini; Nisa Kholifatul Ummah; Yessy Fatmasari; Khadijah Fahmi Hayati Holle
MATICS Vol 14, No 1 (2022): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v14i1.13863

Abstract

The increase in the use of motorized vehicles increases air pollution conditions, especially in big cities such as the capital city of Indonesia, Jakarta. The pollution that pollutes this city contains various kinds of chemical particles that are harmful to living things when they enter the body. several efforts to reduce this pollution have been carried out, one of which is by identifying the pollutants contained in the air. This study uses data obtained from monitoring stations to predict the content of pollutants in the air at some time in the future. the method used is data mining forecasting with a neural network model. by using rapid miner obtained several graphic descriptions of pollutant conditions in Jakarta that go up and down. pollutant levels of SO2, CO, PM10 and NO2 all increased in the November-December period and at the same time period, ozone was at its lowest point. Results from Prediction air quality using Artificial Neural Network with 5 parameters, shown on this pollutant PM10 had an RMSE of 9,477; SO2 had an RMSE 5,474; CO had an RMSE 8,392; O3 had an RMSE 18,250; NO2 had an RMSE 5,171. Can be concluded that the RMSE value of O3 is higher than the others and the lowest value of NO2.
Co-Authors Adhitya Prayoga Permana Agung Teguh Almais Agung Teguh Wibowo Almais Agus Zainal Arifin Ainafatul Nur Muslikah Akbar Roihan Alfianita, Rizha Allin Junikhah Annisa Rizkiana Putri Asfilia Nova Anggraini Aziza, Miladina Rizka Cahyo Crysdian Diana Purwitasari Ekaputri, Enggarani Wahyu Fatchurrohman Fatchurrohman Hardiana Riski RIswanto Hayyun Lathifaty Yasri Junikhah, Allin Khamaida Safinah Kurniyatul Ainiyah Kurniyatul Ainiyah Mohammad Malik Fajar Munna, Daurin Nabilatul Mustofa, Ahmad Habibil Nadila Sugianti Nisa Kholifatul Ummah Novrindah Alvi Hasanah Putri, Hikmatul Maulidia Romadhoni, Yuliana Salma Fatia Ulfi Andrian Sari Wibowo, Muhammad Ismail Arjun Yayang Galuh Yessy Fatmasari Yuliana Romadhoni Yunifa Miftachul Arif Yusuf Ansori Zamzam, Muhammad Adib