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All Journal Jurnal Informatika SemanTIK : Teknik Informasi International Journal of New Media Technology Jurnal ULTIMATICS JUMANJI (Jurnal Masyarakat Informatika Unjani) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Manajemen Informatika JISKa (Jurnal Informatika Sunan Kalijaga)
Ika Purwanti Ningrum
Universitas Halu Oleo
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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PENGENALAN WAJAH MENGGUNAKAN METODE LINEAR DISCRIMINANT ANALYSIS DAN K NEAREST NEIGHBOR Fandiansyah Fandiansyah; Jayanti Yusmah Sari; Ika Purwanti Ningrum
Jurnal Informatika Vol 11, No 2 (2017): Juli
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1207.972 KB) | DOI: 10.26555/jifo.v11i2.a5998

Abstract

Pengenalan wajah merupakan sistem biometrika yang banyak digunakan untuk pengenalan individu pada penggunaan mesin absensi atau akses kontrol. Hal ini karena wajah merupakan salah satu ciri biometrika yang paling umum digunakan untuk mengenali seseorang. Selain itu, pengenalan wajah tidak mengganggu kenyamanan seseorang saat pengambilan citra. Namun, ada dua hal yang menjadi masalah pengenalan wajah yaitu proses ekstraksi fitur dan teknik klasifiksi yang digunakan. Penelitian ini menggunakan linear discriminant analysis (LDA) dan k nearest neighbor untuk membangun sistem pengenalan wajah. LDA digunakan untuk membentuk satu set fisherface, di mana seluruh citra wajah direkonstruksi dari kombinasi fisherface dengan bobot yang berbeda-beda. Nilai bobot suatu citra testing akan dicocokkan dengan nilai bobot citra di database menggunakan metode klasifikasi k nearest neighbor. Sistem ini dibangun menggunakan bahasa pemograman Java. Sistem telah diuji menggunakan database sebanyak 66 citra wajah dari 22 individu. Hasil pengujian menunjukkan metode LDA dan k nearest neighbor cukup optimal untuk melakukan pengenalan wajah dengan akurasi pengenalan citra wajah normal mencapai 98.33% dan akurasi pengenalan citra wajah yang diberi noise sebesar 86,66%.
Identification of Authenticity and Nominal Value of Indonesia Banknotes Using Fuzzy KNearest Neighbor Method Ricky Ramadhan; Jayanti Yusmah Sari; Ika Purwanti Ningrum
IJNMT (International Journal of New Media Technology) Vol 6 No 1 (2019): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1592.145 KB) | DOI: 10.31937/ijnmt.v6i1.989

Abstract

The existence of counterfeit money is often troubling the public. The solution given by the government to be careful of counterfeit money is by means of 3D (seen, touched and looked at). However, this step has not been perfectly able to distinguish real money and fake money. So there is a need for a system to help detect the authenticity of money. Therefore, in this study a system was designed that can detect the authenticity of rupiah and its nominal value. For data acquisition, this system uses detection boxes, ultraviolet lights and smartphone cameras. As for feature extraction, this system uses segmentation methods. The segmentation method based on the threshold value is used to obtain an invisible ink pattern which is a characteristic of real money along with the nominal value of the money. The feature is then used in the stage of detection of money authenticity using FKNN (Fuzzy K-Nearest Neighbor) method. From 24 test data, obtained an average accuracy of 96%. This shows that the system built can detect the authenticity and nominal value of the rupiah well.
Deteksi Area Wajah Manusia Pada Citra Berwarna Berbasis Segmentasi Warna YCbCr dan Operasi Morfologi Citra Moh La Andi Rais Imran Yatim; Jayanti Yusmah Sari; Ika Purwanti Ningrum
Ultimatics : Jurnal Teknik Informatika Vol 11 No 1 (2019): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1835.192 KB) | DOI: 10.31937/ti.v11i1.1029

Abstract

Face detection is one of the most important preprocessing steps in facial recognition systems used in biometric identification. Face detection is used to determine the location, size and number of faces in an image or video in various positions and backgrounds. One method used in face detection systems is segmentation based on skin color. In this study YCbCr skin color segmentation method and morphological operations were used. Based on the results of experiments conducted on 38 images, the system obtained an accuracy of 63.15%
Sistem Pakar Penyakit Kulit Pada Manusia Menggunakan Metode Case Base Reasoning (CBR) Dengan Algoritma Sorensen Coefficient Asdar Asdar; Rizal Adi Saputra; Ika Purwanti Ningrum
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 6 No 1 (2022): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jumanji.v6i1.112

Abstract

Permasalahan yang sering terjadi pada masyarakat pada dunia kesehatan adalah ketersediaan pakar yang memiliki pengetahuan tertentu, seperti dokter spesialis kulit, yang tidak mudah diperoleh ataupun waktunya yang terbatas, ataupun terkendala dengan kurangnya biaya bagi masyarakat yang ingin berobat ataupun bertemu dengan pakar. Oleh karena itu perlu dibangun suatu sistem yang dapat membantu penderita, dokter atau siapapun yang bergerak dibidangnya untuk dapat meringankan pekerjaannya. Sistem tersebut adalah sistem pakar, sistem pakar adalah sistem yang mampu menirukan penalaran seorang pakar ke dalam komputer sehingga dapat menyelesaikan masalah yang seperti biasa dilakukan oleh para ahli. Penelitian ini menggunakan metode case base reasoning untuk metode penalarannya dan menggunakan algoritma sorensen coeffcient untuk mencari nilai kedekatan kasus baru dengan kasus lama dari jenis penyakit kulit yang disebabkan oleh jamur, bakteri, virus, parasit, alergi dan luka bakar dengan kasus sebanyak 130 kasus yang terbagi menjadi 104 kasus data latih dan 26 kasus sebagai data uji. Berdasarkan hasil pengujian pada sistem ini memiliki tingkat akurasi terbesar 100% dan terendah 83.33%.
Pengenalan Pola Huruf Hijaiyah dengan Metode Local Binary Pattern Menggunakan Algoritma Fuzzy K-Nearest Neighbor Asdar; Rizal Adi Saputra; Ika Purwanti Ningrum
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 7 No. 1 (2022): Januari 2022
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (315.958 KB) | DOI: 10.14421/jiska.2022.7.1.68-74

Abstract

A letter is a form, stroke, or symbol writing system. Any information obtained from a sentence depends on the letters are written clearly. Finding written hijaiyah letters can be recognized by humans, but will be difficult if a computer tries to recognize them. The reason system is difficult is because of the large variety of different letters. This study aims to make it easier for someone to learn to recognize hijaiyah letters by using the Local Binary Pattern method for the feature extraction process. The results of feature extraction will take the maximum value of the histogram of each letter. And results feature extraction will be carried out classification process using the Fuzzy K-Nearest Neighbor algorithm until finally hijaiyah letters can be recognized. Based on experimental results that have been carried out, the highest level of accuracy is obtained when the amount of training data is 154 data and the number of data testing is 29 data, resulting in an accuracy rate of 96.55%.
IDENTIFIKASI LANDMARK ORBITAL CEPHALOMETRY MENGGUNAKAN METODE FUZZY C-MEANS CLUSTERING Hasnawati Munandar; Ika Purwanti Ningrum; Jayanti Yusmah Sari
semanTIK Vol 4, No 1 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (634.464 KB) | DOI: 10.55679/semantik.v4i1.4486

Abstract

Orthodontics is a branch of dentistry. In orthodontic treatment, several analyzes are needed, one of them is cephalometry analysis. Basically, cephalometry analysis is done manually, but this method requires a lot of time. In line with this, a computerized application is needed as a solution. In cephalometry analysis some landmarks are used as reference fields. Orbital is one of the landmarks that are difficult to identify. Orbital is located at the lowest point between the region of the eye cavity and the lower edge of the Orbital bone. One way to find Orbital landmarks is to segment the Orbital region. The segmentation aims to clearly separate the eye regions and Orbital bone in the cephalogram image. Fuzzy C-Means Clustering method is a segmentation method that can produce a clearer region partition. For this reason, the Fuzzy C-Means Clustering method is applied to get better segmentation results of the Orbital region so that the results of identification of the correct Orbital landmarks are obtained. Based on the results of testing on 90 image data provide an accuracy of 82.2% identification results with a cropping template size of 180 x 180 pixels.Keywords—Cephalometry Analysis, Fuzzy C-Means Clustering,  Landmark Cephalometry, Orbital  
Sistem Pengenalan Suara Dengan Metode Mel Frequency Cepstral Coefficients Dan Gaussian Mixture Model Ababil Azies Sasilo; Rizal Adi Saputra; Ika Purwanti Ningrum
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v11i2.6655

Abstract

ABSTRAK – Teknologi biometrik sedang menjadi tren teknologi dalam berbagai bidang kehidupan. Teknologi biometrik memanfaatkan bagian tubuh manusia sebagai alat ukur sistem yang memiliki keunikan disetiap individu. Suara merupakan bagian tubuh manusia yang memiliki keunikan dan cocok dijadikan sebagai alat ukur dalam sistem yang mengadopsi teknologi biometrik. Sistem pengenalan suara adalah salah satu penerapan teknologi biometrik yang fokus kepada suara manusia. Sistem pengenalan suara memerlukan metode ekstraksi fitur dan metode klasifikasi, salah satu metode ekstraksi fitur adalah MFCC. MFCC dimulai dari tahap pre-emphasis, frame blocking, windowing, fast fourier transform, mel frequency wrapping dan cepstrum. Sedangkan metode klasifikasi menggunakan GMM dengan menghitung likehood kesamaan antar suara. Berdasarkan hasil pengujian, metode MFCC-GMM pada kondisi ideal memiliki tingkat akurasi sebesar 82.22% sedangkan pada kondisi tidak ideal mendapatkan akurasi sebesar 66.67%. Kata Kunci – Suara, Pengenalan, MFCC, GMM, Sistem
Implementasi Metode Mesin Rekomendasi User Based Filtering pada Sistem Penyewaan Alat Pertambangan Muhamad Faza Almaliki; Ika Purwanti Ningrum; Rizal Adi Saputra
Jurnal Manajemen Informatika JAMIKA Vol 13 No 1 (2023): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v13i1.8459

Abstract

The problem with most mining companies is a lack of information on the availability of ready-to-use (purchase or rent) heavy equipment and information on providers of heavy equipment spare parts, heavy equipment owners who have difficulty finding a market to sell or rent their equipment. Transaction processes that have been taking place so far have used telephone lines, chats and person-to-person emails where this is felt to be less effective and efficient. This research methodology contains stages regarding the process and procedure of data collection, method analysis procedures, system development procedures as well as the time and place of research. The purpose of this study is to implement the user based filtering recommendation machine method on mining equipment rental systems. The results of the study show that the recommendation system that has been built has succeeded in providing appropriate recommendations to customers who have made mining equipment rental transactions based on the parameters of the results of the recommendation accuracy test. The algorithm of the user based filtering recommendation engine method can be applied to the mining tool recommendation system that has been built. Based on testing the accuracy of the recommendation values, each value obtained in the calculation of the Mean Absolute Error (MAE) is 0.00936 so that the resulting recommendation accuracy is 99.99%. Then for the value of the Mean Absolute Percentage Error (MAPE) obtained a value of 27.84%, and for the value of the Mean Squared Error (MSE) obtained was 0.01702.
Co-Authors Ababil Azies Sasilo Asdar Asdar Asdar Fandiansyah Fandiansyah Hasnawati Munandar Jayanti Yusmah Sari Moh La Andi Rais Imran Yatim Muhamad Faza Almaliki Ricky Ramadhan Rizal Adi Saputra