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PENERAPAN METODE TERM FREQUENCY INVERSE DOCUMENT FREQUENCY (TF-IDF) DAN COSINE SIMILARITY PADA SISTEM TEMU KEMBALI INFORMASI UNTUK MENGETAHUI SYARAH HADITS BERBASIS WEB (STUDI KASUS: HADITS SHAHIH BUKHARI-MUSLIM) Victor Amrizal
JURNAL TEKNIK INFORMATIKA Vol 11, No 2 (2018): Jurnal Teknik Informatika
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (783.015 KB) | DOI: 10.15408/jti.v11i2.8623

Abstract

ABSTRAK Hadits merupakan sumber ajaran Islam disamping Al-Qur’an. Tanpa hadits, syari’at Islam tidak dapat dimengerti secara utuh dan tidak dapat dilaksanakan. Namun dewasa ini, tidak sedikit orang yang keliru dalam memahaminya, hal tersebut disebabkan oleh banyaknya orang yang memahami hadits sebatas mengandalkan teks lahiriyah saja. Salah satu hal yang dapat kita tempuh untuk mengetahui makna yang terkandung dalam hadits adalah dengan mempelajari syarah hadits guna meminimalisir kesalahan penafsiran terhadap suatu hadits. Sejauh ini aplikasi syarah hadits yang ada masih terbatas, yaitu dalam bahasa full arab yang tidak semua orang dapat memahaminya. Sedangkan untuk bahasa indonesia hanya ada lidwa dan arbain, namun masih sangat luas jangkauannya. Oleh karena itu, diperlukan suatu sistem untuk solusi permasalahan tersebut, yaitu  Sistem Temu Kembali Informasi yang dapat dimanfaatkan karena memberikan alternatif berupa metode similarity yang dapat digunakan untuk melakukan pencarian dokumen relevan dengan yang kita inginkan. Metode similiarity yang digunakan adalah cosine similarity dengan pembobotan kata menggunakan metode TFIDF dan menerapkan teks preprocessing terlebih dahulu untuk memperkecil term sehingga bisa mempercepat proses perhitungan term. Teks preprocessing tersebut meliputi tokenizing, stopword removal atau filtering, dan stemming. Hasil uji coba dengan pengujian confusion matrix didapatkan: recall 88.7%, precision 100%, accuracy 88,73 %, dan error rate 11,27 %.   ABSTRACT Hadith is a source of Islamic teachings besides the Qur'an. Without using the hadith, the syari'at of Islam can not be fully understood and can not be implemented. But today, many people are mistaken in understanding it, it is caused by the many people who understand the hadith to rely on text lahiriyah only. One of the things that we can take to know the meaning contained in the hadith is to study syarah hadith in order to minimize misinterpretation of a hadith. So far the application of syarh hadith is still limited. Because so far the existing applications are still full Arab language that not everyone can understand it.. As for the Indonesian language there are only lidwa and arbain, but still very wide reach. Therefore, we need a system for the solution of the problem, that is Information Retrieval System which can be utilized because it provides an alternative in the form of similarity method that can be used to search documents relevant to what we want. The similiarity method used is cosine similarity with word weighting using TFIDF method and applying preprocessing text first to minimize term so that it can speed up the term calculation process. The preprocessing text includes tokenizing, stopword removal or filtering, and stemming. The results of testing with confusion matrix test obtained: 88.7% recall, precision 100%, accuracy 88.73%, and error rate 11.27%. 
Analisis Sentimen Kinerja KPU Pemilu 2019 Menggunakan Algoritma K-Means Dengan Algoritma Confix Stripping Stemmer A Sidang Amirul Haj; Victor Amrizal; Arini
Journal of Innovation Information Technology and Application (JINITA) Vol 2 No 1 (2020): JINITA, June 2020
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.914 KB) | DOI: 10.35970/jinita.v2i1.119

Abstract

The performance of the KPU in the 2019 elections was a lively conversation between the general public and the political elite. Many people or parties commented on the process of calculating the results of the 2019 elections through social media. KPU's Facebook fan page is also busy being attacked by positive or negative comments. Sentiment analysis of public opinion can be investigated to find out how much percentage of a positive and negative sentiment of this policy through comments that have been sent to social-social media. Data analyzed were 200 data taken from Facebook KPU, divided into 150 training data and 50 test data. This study uses the K-Means algorithm with a value of k = 2 to determine the final sentiments of positive and negative, the Levenshtein Distance algorithm for word normalization and the Confix Stripping Stemmer algorithm in the stemming process. The results obtained from the public sentiment on the performance of the KPU are more negative than positive. The results of the accuracy obtained from the use of the K-Means algorithm are 84% with a lower accuracy value compared to the combination of the algorithm above, namely 86%. Suggestions for further research should use even more data and use the k-fold cross-validation accuracy calculation technique as a further trial