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EKSTRAKSI FITUR PADA CITRA TANDA TANGAN SEBAGAI CIRI IDENTITAS PEMILIKNYA MENGGUNAKAN DISCRETE FOURIER TRANSFORM Fitriani, Wina; Nafan, Muhammad Zidny; Usada, Elisa
Proceeding SENDI_U 2018: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.778 KB)

Abstract

Tanda tangan merupakan tanda untuk melambangkan nama yang dituliskan dengan tangan penulis itu sendiri sebagai penanda pribadi. Pada umumnya tanda tangan digunakan untuk menyetujui suatu kesepakatan pada lembaran dokumen sebagai bukti dari identitas dan kemauan pemilik tanda tangan. Lazimnya, pembubuhan tanda tangan dilakukan dalam kegiatan administrasi negara seperti transaksi penarikan uang secara tunai, penyetoran, kliring giro, dan sebagainya. Untuk mengetahui kepemilikan tanda tangan tersebut maka dibutuhkan suatu identifikasi. Identifikasi pola tanda tangan dibutuhkan untuk mengenali dan membedakan tanda tangan dari masing-masing orang berdasarkan ciri khas dari tanda tangan tersebut. Biometrik dapat digunakan sebagai metode dasar identifikasi berdasarkan karakteristik alami manusia.Dengan berkembangnya teknologi saat ini, identifikasi pola tanda tangan tidak hanya dapat dilakukan secara manual, tetapi juga dapat dilakukan dengan bantuan komputer. Namun komputer tidak serta merta dapat langsung melakukan proses identifikasi, melainkan dibutuhkan proses pengenalan pola terlebih dahulu yang dapat dilakukan dengan mengekstraksi fitur tanda tangan. Salah satu fitur yang dapat diekstrak dari tanda tangan adalah hasil dari transformasi. Discrete Fourier Transform (DFT) merupakan salah satu metode ekstraksi yang digunakan dalam identifikasi pola tanda tangan dengan memanfaatkan nilai mean dan standard deviation (std).
EKSTRAKSI FITUR PADA CITRA TANDA TANGAN SEBAGAI CIRI IDENTITAS PEMILIKNYA MENGGUNAKAN DISCRETE FOURIER TRANSFORM Fitriani, Wina; Naf'an, Muhammad Zidny; Usada, Elisa
Proceeding SENDI_U 2018: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.778 KB)

Abstract

Tanda tangan merupakan tanda untuk melambangkan nama yang dituliskan dengan tangan penulis itu sendiri sebagai penanda pribadi. Pada umumnya tanda tangan digunakan untuk menyetujui suatu kesepakatan pada lembaran dokumen sebagai bukti dari identitas dan kemauan pemilik tanda tangan. Lazimnya, pembubuhan tanda tangan dilakukan dalam kegiatan administrasi negara seperti transaksi penarikan uang secara tunai, penyetoran, kliring giro, dan sebagainya. Untuk mengetahui kepemilikan tanda tangan tersebut maka dibutuhkan suatu identifikasi. Identifikasi pola tanda tangan dibutuhkan untuk mengenali dan membedakan tanda tangan dari masing-masing orang berdasarkan ciri khas dari tanda tangan tersebut. Biometrik dapat digunakan sebagai metode dasar identifikasi berdasarkan karakteristik alami manusia.Dengan berkembangnya teknologi saat ini, identifikasi pola tanda tangan tidak hanya dapat dilakukan secara manual, tetapi juga dapat dilakukan dengan bantuan komputer. Namun komputer tidak serta merta dapat langsung melakukan proses identifikasi, melainkan dibutuhkan proses pengenalan pola terlebih dahulu yang dapat dilakukan dengan mengekstraksi fitur tanda tangan. Salah satu fitur yang dapat diekstrak dari tanda tangan adalah hasil dari transformasi. Discrete Fourier Transform (DFT) merupakan salah satu metode ekstraksi yang digunakan dalam identifikasi pola tanda tangan dengan memanfaatkan nilai mean dan standard deviation (std).
PERANCANGAN APLIKASI ANDROID IDENTIFIKASI TANDA TANGAN MENGGUNAKAN MULTI LAYER PERCEPTRON Novandra, Gagas; Nafâ??an, Muhammad Zidny; Laksana, Tri Ginanjar
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 3, No 1 (2018)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.426 KB) | DOI: 10.29100/jipi.v3i1.660

Abstract

Sering terjadinya suatu kasus pemalsuan tanda tangan disebabkan karena metode yang digunakan untuk mengidentifikasi tanda tangan masih kurang baik dan tidak akurat. Hal ini dikarenakan identifikasi tanda tangan kebanyakan masih dilakukan dengan cara melihat langsung tanda tangan, beserta nama pemilik tanda tangan yang tercantum di bagian bawah tanda tangan pada sebuah dokumen. Mengidentifikasi tanda tangan dengan cara manual tentu memiliki banyak kelemahan seperti ketelitian dan ketepatan saat identifikasi yang kurang absah, sehingga pemalsuan tanda tangan sangat mungkin terjadi. Penelitian ini menggunakan metode Artificial Neural Network yang akan diterapkan pada aplikasi identifikasi tanda tangan. Neural Network merupakan metode yang dapat mendeteksi pola rumit dan tidak mengikuti seragkaian instruksi yang diberikan peneliti. Namun metode ini mampu belajar dengan sendirinya saat menghadapi permasalahan. Metode ini memiliki kelebihan yaitu kemampuan untuk memodelkan fungsi linear, komputasi paralel, dan mempunyai sifat mentolerir kesalahan (fault tolerance). Penelitian ini diharapkan dapat membantu suatu lembaga, baik itu lembaga pemerintahan maupun lembaga swasta dalam mengidentifikasi pemilik dari suatu tanda tangan yang ada pada dokumen-dokumen penting seperti dokumen pencairan dana dan dokumen surat-menyurat. Sehingga kasus pemalsuan tanda tangan dapat diminimalisir. Selain hal tersebut dalam penelitian ini juga diharapkan agar nantinya sistem identifikasi tanda tangan dapat diterapkan pada suatu lembaga atau instansi.
Kecenderungan Tanggapan Masyarakat terhadap Ekonomi Indonesia berbasis Lexicon Based Sentiment Analysis Nafan, Muhammad Zidny; Amalia, Andika Elok
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 3, No 4 (2019): Oktober 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v3i4.1283

Abstract

Sentiment analysis aims to find opinions, identify sentiments expressed, and then classify their polarity values. One method of sentiment analysis is Lexicon-based. This study implements the Lexicon based sentiment analysis to analyze the polarity of public responses to the topic of the development of "the Indonesian economy". The dataset is collected from social media from 2017 to 2019. Preprocessing used is folding cases, deleting newline characters, changing non-standard words, deleting mentions, deleting hashtags, removing URL strings, changing word negation, and translating text into English with TextBlob library. Then extract the sentiment values from adjectives, adverbs, nouns, and verbs found in the text. Based on the results of sentiment analysis, it can be seen that there are 63.6% positive responses from the public to the development of the Indonesian economy, 7.4% negative responses, and 29% neutral.
Typo handling in searching of Quran verse based on phonetic similarities Purwita, Naila Iffah; Bijaksana, Moch Arif; Lhaksmana, Kemas Muslim; Naf’an, Muhammad Zidny
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 6, No 2 (2020): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v6i2.2065

Abstract

The Quran search system is a search system that was built to make it easier for Indonesians to find a verse with text by Indonesian pronunciation, this is a solution for users who have difficulty writing or typing Arabic characters. Quran search system with phonetic similarity can make it easier for Indonesian Muslims to find a particular verse.  Lafzi was one of the systems that developed the search, then Lafzi was further developed under the name Lafzi+. The Lafzi+ system can handle searches with typo queries but there are still fewer variations regarding typing error types. In this research Lafzi++, an improvement from previous development to handle typographical error types was carried out by applying typo correction using the autocomplete method to correct incorrect queries and Damerau Levenshtein distance to calculate the edit distance, so that the system can provide query suggestions when a user mistypes a search, either in the form of substitution, insertion, deletion, or transposition. Users can also search easily because they use Latin characters according to pronunciation in Indonesian. Based on the evaluation results it is known that the system can be better developed, this can be seen from the accuracy value in each query that is tested can surpass the accuracy of the previous system, by getting the highest recall of 96.20% and the highest Mean Average Precision (MAP) reaching 90.69%. The Lafzi++ system can improve the previous system.
Penerapan Cosine Similarity dan Pembobotan TF-IDF untuk Mendeteksi Kemiripan Dokumen Muhammad Zidny Naf'an; Auliya Burhanuddin; Ade Riyani
Jurnal Linguistik Komputasional Vol 2 No 1 (2019): Vol. 2, No. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.917 KB) | DOI: 10.26418/jlk.v2i1.17

Abstract

Plagiarisme merupakan tindakan mengambil sebagian atau seluruh ide seseorang berupa dokumen maupun teks tanpa mencantumkan sumber pengambilan informasi. Penelitian ini bertujuan untuk mendeteksi kemiripan dokumen teks menggunakan algoritma cosine similarity dan pembobotan TF-IDF sehingga dapat digunakan untuk menentukan nilai plagiarisme. Dokumen yang digunakan untuk perbandingan teks ini adalah abstrak bahasa Indonesia. Hasil penelitian yaitu saat dilakukan stemming nilai kemiripan lebih tinggi rata-rata 10% daripada tidak dilakukan proses stemming. Penelitian ini menghasilkan nilai similaritas diatas 50% untuk dokumen yang tingkat kemiripannya tinggi. Sedangkan untuk dokumen dengan tingkat kemiripan rendah atau tidak berplagiat menghasilkan nilai similarity dibawah 40%. Dengan metode yang digunakan pada preprocessing yang terdiri dari case folding, tokenizing, stopword removeal, dan stemming. Setelah proses preprocessing maka tahap selanjutnya dilakukan perhitungan pembobotan TF-IDF dan nilai kemiripan menggunakan cosine similarity sehingga mendapatkan nilai persentase kemiripan. Berdasarkan hasil percobaan algoritma cosine similarity dan pembobotan TF-IDF mampu menghasilkan nilai kemiripan dari masing-masing dokumen pembanding
Sentiment Analysis of Cyberbullying on Instagram User Comments Muhammad Zidny Naf'an; Alhamda Adisoka Bimantara; Afiatari Larasati; Ezar Mega Risondang; Novanda Alim Setya Nugraha
Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2019.2.20

Abstract

Instagram is a social media for sharing images, photos and videos. Instagram has many active users from various circles. In addition to sharing submissions, Instagram users can also give likes and comments to other users' posts. However, the comment feature is often misused, for example it is used for cyberbullying which includes one act against the law. But until now, Instagram still does not provide a feature to detect cyberbullying. Therefore, this study aims to create a system that can classify comments whether they contain elements of cyberbullying or not. The results of the classification will be used to detect cyberbullying comments. The algorithm used for classification is Naïve Bayes Classifier. Then for each comment will pass the preprocessing and feature extraction stages with the TF-IDF method. For evaluation and testing using the K-Fold Cross Validation method. The experiment is divided into two, namely using stemming and without stemming. The training data used is 455 data. The best experimental results obtained an accuracy of 84% both with stemming, and without stemming.
Pengaruh Semantic Expansion pada Naïve Bayes Classifier untuk Analisis Sentimen Tokoh Masyarakat Muhamad Satria Adhi; Muhammad Zidny Nafan; Elisa Usada
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (770.016 KB) | DOI: 10.29207/resti.v3i2.901

Abstract

Sentiment analysis is a field of study that analyzes one's opinions, sentiments, evaluations, attitudes and emotions that are conveyed in written text. There are several factors that cause low accuracy results from sentiment analysis. These factors such as less optimal stemming process, word negation process that does not produce maximum results, writing errors in the dataset, and others. These problems can be overcome by optimizing the process of normalizing words, negation, stemming, and adding methods of semantic expansion. The purpose of adding the Semantic Expansion method and improvement in the process is to increase the accuracy value of the Sentiment Analysis process. This study aims to create a sentiment analysis model from public comments on a public figure (Ridwan Kamil) using the Naïve Bayes Classifier algorithm. Based on the test results in the sentiment analysis model using the Naïve Bayes Classifier method with the addition of the semantic expansion method it is proven that it can improve accuracy. The accuracy obtained using the semantic expansion method is 72%. While the value of accuracy without semantic expansion is 70%.
Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa Wahyu Andi Saputra -; Muhammad Zidny Naf’an; Asyhar Nurrochman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.544 KB) | DOI: 10.29207/resti.v3i3.1338

Abstract

Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.
Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means Ridho Ananda; Muhammad Zidny Naf’an; Amalia Beladinna Arifa; Auliya Burhanuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.312 KB) | DOI: 10.29207/resti.v4i1.1531

Abstract

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.