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All Journal JUPITER ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Information Technology and Its Utilization
Lukman Syafie
Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Mechanical Engineering

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Pengenalan Angka Tulisan Tangan Menggunakan Jaringan Syaraf Tiruan Herman, Herman; Syafie, Lukman; Indra, Dolly
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.059 KB)

Abstract

Current technological developments spur the application of pattern recognition in various fields, such as the introduction of signature patterns, fingerprints, faces, and handwriting. Human handwriting has differences between one another and often handwriting is difficult to read or difficult to recognize and this can hamper daily activities, such as transaction activities that require handwriting. Even one of the biometric features of everyone is handwriting. One method that can be used to recognize handwriting patterns in the field of computer science is artificial neural networks (ANN) with the learning algorithm is backpropagation. Artificial neural networks are able to recognize something based on the past. This means that past data will be studied so as to be able to make decisions on new data. To recognize handwriting patterns using artificial neural networks, the characteristics of handwritten objects are extracted using an invariant moment. The results of training using artificial neural networks indicate that the correlation coefficient value is obtained on the number of hidden layer neurons by 30. The highest correlation coefficient value is 0.61382. The test results on the test data obtained an accuracy rate of 11.67% of the total test data.
Analisa Penerapan Algoritma Brute Force Dalam Pencocokan String Purnawansyah, Purnawansyah; Syafie, Lukman
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 3, No 2 (2018): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)
Publisher : Mulawarman University

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Abstract

Kebutuhan untuk menemukan informasi yang berguna dan cepat dalam suatu data yang besar sangat dibutuhkan. Karena kompleksitas data yang begitu banyak maka diperlukan suatu metode atau cara untuk dapat mencari suatu informasi yang diperlukan. Untuk melakukan pencarian sebuah data atau informasi tidak terlepas dari pencocokan string dimana dari hasil pencocokan inilah akan ditemukan pola kalimat yang dicari. Dalam penelitian ini membahas tentang penerapan algoritma brute force dalam melakukan pencocokan sebuah string. Algoritma ini melakukan pencocokan string dengan menggeser satu persatu pattern dan menyesuaikannya dengan teks hingga antara pattern dan teks memiliki pola yang sama. Hasil analisis dari penelitian ini berupa uji coba pencocokan string dengan algoritma brute force dengan studi kasus menggunakan mesin pencarian (search engine) dengan bahasa pemrograman PHP untuk pencocokan string
KLASIFIKASI DOKUMEN REPOSITORY SECARA OTOMATIS MENGGUNAKAN METODE BAYESIAN NETWORK Lukman Syafie
JUPITER Vol. 15 No. 2 (2016): JUPITER
Publisher : UPT Perpustakaaan Universitas Hasanuddin

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Abstract

Penelitian ini bertujuan untuk: (1) membangun algoritma klasifikasi Bayesian Network, (2) merancang simulasi klasifikasi yang mampu mengklasifikasi dokumen repository secara otomatis berdasarkan algoritma klasifikasi yang dibuat, dan (3) menganalisis kinerja hasil simulasi algoritma klasifikasi Bayesian Network untuk klasifikasi dokumen repository. Metode penelitian yang digunakan adalah metode eksperimen dengan bentuk penelitiankuantitatif. Hasil kajian diharapkan bermanfaat dalam membantu mengklasifikasikan suatu dokumen secara otomatis, serta menjadi dasar pembuatan sistem klasifikasi yang lebih kompleks.Kata Kunci: Bayes Network, Klasifikasi, Dokumen, Algoritma
Pengenalan Angka Tulisan Tangan Menggunakan Jaringan Syaraf Tiruan Herman Herman; Lukman Syafie; Dolly Indra
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.317.201-206

Abstract

Current technological developments spur the application of pattern recognition in various fields, such as the introduction of signature patterns, fingerprints, faces, and handwriting. Human handwriting has differences between one another and often handwriting is difficult to read or difficult to recognize and this can hamper daily activities, such as transaction activities that require handwriting. Even one of the biometric features of everyone is handwriting. One method that can be used to recognize handwriting patterns in the field of computer science is artificial neural networks (ANN) with the learning algorithm is backpropagation. Artificial neural networks are able to recognize something based on the past. This means that past data will be studied so as to be able to make decisions on new data. To recognize handwriting patterns using artificial neural networks, the characteristics of handwritten objects are extracted using an invariant moment. The results of training using artificial neural networks indicate that the correlation coefficient value is obtained on the number of hidden layer neurons by 30. The highest correlation coefficient value is 0.61382. The test results on the test data obtained an accuracy rate of 11.67% of the total test data.
SCHEDULING USING GENETIC ALGORITHM AND ROULETTE WHEEL SELECTION METHOD CONSIDERING LECTURER TIME Herman Herman; Lukman Syafie; Irawati Irawati; Lilis Nur Hayati; Harlinda Harlinda
Journal of Information Technology and Its Utilization Vol 2, No 1 (2019)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.2.1.2243

Abstract

Scheduling lectures is not something easy, considering many factors that must be considered. The factors that must be considered are the courses that will be held, the space available, the lecturers, the suitability of the credits with the duration of courses, the availability of lecturers' time, and so on. One algorithm in the field of computer science that can be used in lecture scheduling automation is Genetic Algorithms. Genetic Algorithms can provide the best solution from several solutions in handling scheduling problems and the selksi method used is roulette wheel. This study produces a scheduling system that can work automatically or independently which can produce optimal lecture schedules by applying Genetic Algorithms. Based on the results of testing, the resulting system can schedule lectures correctly and consider the time of lecturers. In this study, the roulette wheel selection method was more effective in producing the best individuals than the rank selection method.
IMPLEMENTATION OF MOMENT INVARIANT IN RECOGNIZING OF ELECTRICAL METER NUMBERS Lukman Syafie; Herman Herman; Nur Alam; Tasmil Tasmil
Journal of Information Technology and Its Utilization Vol 3, No 1 (2020)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.3.1.3194

Abstract

Implementation of computer vision can be done in the introduction of images or pictures of characters of numbers or letters. Based on this, then the computer vision can be used in the introduction of numbers on the electric meter or commonly called kWh meter. The underlying thing for the electric meter to be the object of research is to look at the situation, where the electric meter recorder keeps the record using the camera. Furthermore, the value shown on the electric meter will be inputted manually. Manual input requires a relatively long time because the amount of electricity meter input value is not small data. One method that can be used in recognizing the shape of the image in computer vision is the invariant moment. The results of this study indicate that the quality of the image gives effect, both in terms of the extraction of features and the accuracy of the recognition of the figure on the image of the electric meter. In addition to this, the threshold value of the euclidian distance method should also be used to limit the recognition process.
RABIN-CARP IMPLEMENTATION IN MEASURING SIMALIRITY OF RESEARCH PROPOSAL OF STUDENTS Herman Herman; Lukman Syafie; Tasmil Tasmil; Muhammad Resha
Journal of Information Technology and Its Utilization Vol 3, No 1 (2020)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.3.1.3210

Abstract

Plagiarism is the use of data, language and writing without including the original author or source. The place where palgiate practice occurs most often is the academic environment. In the academic world, the most frequently plagiarized thing is scientific work, for example thesis. To minimize the practice of plagiarism, it is not enough to just remind students. Therefore we need a system or application that can help in measuring the level of similarity of student thesis proposals in order to minimize plagiarism practice. In computer science, the Rabin-Karp algorithm can be used in measuring the level of similarity of texts. The Rabin-Karp algorithm is a string matching algorithm that uses a hash function as a comparison between the search string (m) and substrings in text (n). The Rabin-Karp algorithm is a string search algorithm that can work for large data sizes. The test results show that the use of values on k-gram has an effect on the results of the measurement of similarity levels. In addition, it was also found that the use of the value 5 on k-gram was faster in executing than the values 4 and 6.
IMPLEMENTASI SIMPLE ADDITIVE WEIGHTING DALAM PEMILIHAN KARYA SENI KALIGRAFI TERBAIK DI PONDOK PESANTREN DARUL AMAN GOMBARA MAKASSAR Andi Mattangkilang, Ainun; Lilis Nur Hayati; Lukman Syafie
JURNAL ILMU KOMPUTER Vol 9 No 1 (2023): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v9i2.255

Abstract

Penentuan penilaian kaligrafi terbaik masih dilakukan secara manual (tulis tangan) karena belum adanya sistem penilaian secara berbasis website. Demikian juga belum ada penentuan metode dalam menentukan pengambilan keputusan untuk menentukan kaligrafi terbaik, dimana guru yang mengajar pada mata pelajaran khat sangat membutuhkan sebuah aplikasi atau sistem yang mendukung penilaian kaligrafi terbaik dari para santri di pondok pesantren. Aplikasi atau sistem yang akan dibuat oleh peneliti ini akan menentukan penilaian setiap kriteria-kriteria. Kriteria pada jenis khat itu ditentukan dari kaidah, warna, motif, kerapian, dan nilai estetika. Berdasarkan hal tersebut untuk menentukan kaligrafi terbaik sesuai dengan kriteria yang akan ditentukan, maka penelitian ini menggunakan metode Simple Additive Weighting (SAW). Metode SAW atau yang dikenal penjumlahan terbobot yang merupakan metode yang digunakan untuk mencari nilai bobot pada rating kinerja setiap alternatif pada setiap atribut. Dalam perhitungannya membutuhkan proses normalisasi matriks keputusan ke suatu skala yang akan dibandingkan dengan semua kriteria dari setiap alternative. Dengan adanya analisis penerapan metode SAW untuk pemilihan kaligrafi terbaik, dapat mempermudah, mempercepat serta memberikan hasil rekomendasi yang akurat untuk penilaian kaligrafi terbaik sehingga dapat membantu dalam proses pengambilan keputusan dan menentukan kaligrafi yang terbaik yang dibuat oleh santri. Dengan ini hasil pengujian black box (beta) dari cara perhitungan tersebut diperoleh skor dan pernyataan sebagai berikut: soal nomor 1 = 4,60%, soal nomor 2 = 5,00%, soal nomor 3 = 4,20 %, soal nomor 4 = 4,80%, soal nomor 5 = 4,80%, soal nomor 6 = 4,90%. Maka diperoleh nilai rata-rata 4,71% dengan nilai indeks 78,50% yang termasuk dalam baik.
Co-Authors Andi Mattangkilang, Ainun Dolly Indra Harlinda Lahuddin Herman Herman Herman Herman Irawati Irawati Lilis Nur Hayati lilis nurhayati Muhammad Resha Nur Alam Purnawansyah Purnawansyah Tasmil Tasmil Tasmil Tasmil Yundari, Yundari