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All Journal Jurnal Informatika SMARTICS Journal EDUPEDIA Jurnal Syntax Fusion : Jurnal Nasional Indonesia
Muhammad Faisal
Universitas Islam Negeri Maulana Malik Ibrahim
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Deteksi Plagiarisme pada Dokumen Jurnal Menggunakan Metode Cosine Similarity Rito Putriwana Pratama; Muhammad Faisal; Ajib Hanani
SMARTICS Journal Vol 5 No 1: SMARTICS Journal (April 2019)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.316 KB) | DOI: 10.21067/smartics.v5i1.2848

Abstract

Plagiarisme merupakan tindakan mengambil gagasan, mengambil hasil riset, mengakuisisi hasil riset, dan meringkas suatu tulisan tanpa menyebutkan sumbernya. Metode cosine similarity merupakan salah satu metode yang dapat digunakan untuk menghitung nilai kemiripan antar dokumen. Tahapan yang dilakukan sistem untuk menghasilkan nilai kemiripan antar dokumen yaitu dengan membandingkan dokumen jurnal yang di upload dengan dokumen repository yang diperoleh dari hasil grabbing data DOAJ dan tersimpan di database. Dalam perhitungan metode yang dilakukan akan diperoleh presentase nilai kemiripan antar dokumen. Setelah itu akan dihitung kembali untuk mencari nilai kemiripan dokumen jurnal antar publisher yang ada di dokumen repository. Berdasarkan skenario uji coba yang dilakukan dengan menghitung jumlah dokumen relevan terambil dibagi dengan jumlah dokumen yang ada dalam database kemudian dikali 100%, maka diperoleh nilai recall pada Aplikasi Deteksi Plagiarisme Menggunakan Metode Cosine Similarity yaitu 13%. Sedangkan untuk memperoleh nilai precision dilakukan skenario pengujian dengan menghitung jumlah dokumen relevan terambil dibagi dengan jumlah dokumen relevan dalam pencarian kemudian dikali 100% diperoleh hasil 8%.
Analisis Perbandingan Metode K-Nn Dan Decision Tree Dalam Klasifikasi Kenyamanan Thermal Bangunan Ilham Yusuf Akbar; Muhammad Faisal; Usman Pagalay
Jurnal Syntax Fusion Vol 2 No 06 (2022): Jurnal Syntax Fusion: Jurnal Nasional Indonesia
Publisher : Rifa' Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54543/fusion.v2i06.197

Abstract

Thermal comfort and good air quality can have a positive influence on a person's health and activities. Human knowledge will determine the right quality for a building or room that can only be used at a minimum first if it is based on the value of temperature and humidity standard measuring instruments that display raw data so that a system is needed that makes it easy for humans to determine thermal building comfort. Most systems that discuss thermal comfort revolve around temperature and humidity monitoring still do not use a machine learning model to maximize data analysis. In this study, a comparison of machine learning models is designed that is able to provide a classification to predict the category or label given to a data set consisting of the thermal comfort variable. The output produced in this comparison is the result of labeling predictions from 3 temperature comfort levels by testing 30% praise data and 70% training data. From the results of the segmentation accuracy level using K-Nearest Neighbor, the accuracy reaches 100% with the highest accuracy at a value of K = 1, while the Decision tree comparison method also gets an accuracy value that reaches 100%, this shows that thermal comfort can be applied in the classification machine learning method
Menilai Kecenderungan Nilai Antar Mata Pelajaran Menggunakan Association Rule Pada Rapidminer Adi Nurrachman; Muhammad Faisal; Fachrul Kurniawan
EDUPEDIA Vol 5, No 2 (2021): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/ed.v5i2.868

Abstract

The focus of this research is trying to assess the tendency of scores between subjects using the association rule method using Rapidminer. The Association Rule method itself is often called Market Basket Analysis. The results showed that there was a tendency that students with good grades in English had good Mathematics scores with a 100% confidence level with a support value of 0.333
Klasifikasi Penyakit Diabetes Menggunakan Algoritma Decision Tree Nurussakinah Nurussakinah; Muhammad Faisal
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.15989

Abstract

Penelitian ini mengembangkan metode klasifikasi diabetes dengan menggunakan algoritma Decision Tree. Data klinis dari pasien diabetes dan non-diabetes dianalisis, termasuk atribut seperti usia, BMI, tekanan darah, dan tes gula darah. Algoritma Decision Tree berhasil mengklasifikasikan diabetes dengan akurasi tinggi, sehingga dapat membantu dokter dalam mendiagnosis dan mengelola penyakit tersebut. Pada penelitian ini menunjukkan bahwa metode klasifikasi dengan Decision Tree efektif dalam mengidentifikasi penyakit diabetes. Dalam pengujian menggunakan data uji, model klasifikasi mampu mengenali pasien diabetes dengan tingkat akurasi yang memuaskan. Metode ini memiliki potensi untuk meningkatkan diagnosis dini dan pengelolaan penyakit diabetes, tetapi perlu dilakukan penelitian lebih lanjut untuk memvalidasi dan memperluas penggunaannya dalam populasi yang lebih luas. This study developed a diabetes classification method using the Decision Tree algorithm. Clinical data from diabetic and non-diabetic patients were analyzed, including attributes such as age, BMI, blood pressure, and blood sugar tests. The Decision Tree algorithm successfully classifies diabetes with high accuracy, so it can assist doctors in diagnosing and managing the disease. This study shows that the classification method with the Decision Tree is effective in identifying diabetes. In testing using test data, the classification model is able to identify diabetic patients with a satisfactory level of accuracy. This method has the potential to improve the early diagnosis and management of diabetes, but further research is needed to validate and expand its use in a wider population. 
Co-Authors Adi Nurrachman Ajib Hanani Fachrul Kurniawan Ilham Yusuf Akbar Nurussakinah Nurussakinah Rito Putriwana Pratama Usman Pagalay