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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi INFORMAL: Informatics Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of New Media Technology Jurnal ULTIMATICS Jurnal ULTIMA Computing GERVASI: Jurnal Pengabdian kepada Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Unri Conference Series: Community Engagement Community Empowerment Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Info Kripto Jurnal Ilmiah Edutic : Pendidikan dan Informatika Proceedings Series of Educational Studies Mekongga: Jurnal Pengabdian Masyarakat Media of Computer Science

Jayanti Yusmah Sari

Universitas Sembilanbelas November Kolaka

Author-ID : 501844

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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

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%.

SIMILARITY BASED ENTROPY ON FEATURE SELECTION FOR HIGH DIMENSIONAL DATA CLASSIFICATION Jayanti Yusmah Sari; Mutmainnah Muchtar; Mohammad Zarkasi; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Abstract Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we have computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features. The experiment result shows that proposed method is able to deal with high dimensional data problem with average accuracy of 80.5%.

MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN Fransisca J Pontoh; Jayanti Yusmah Sari; Amil A Ilham; Ingrid Nurtanio
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.

LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION Jayanti Yusmah Sari; Chastine Fatichah; Nanik Suciati
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris). Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP) method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP), a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI) detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

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

Klasifikasi Data Aging Tunggakan Nasabah Menggunakan Metode Decision Tree Pada ULaMM Unit Kolaka Sarimuddin Sarimuddin; Jayanti Yusmah Sari; Muhammad Mail; Muh. Ariyandhi Masalu; Reski Surya Aristika; Nurfagra Nurfagra
INFORMAL: Informatics Journal Vol 5 No 1 (2020): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v5i1.16964

This study aims to classify aging of loan data using the decision tree method based on plafond, outstanding principal, and the amount of loan. The subjects in this study were the debtor of ULaMM (Unit Layanan Modal Mikro), unit of Kolaka, PT. PNM (Persero) Kendari Branch. The number of samples used is 100 data debtors. Based on the results of the research conducted, it was found that the classification analysis using the Decision Tree has an accuracy rate of 95.00%, while the classification analysis using the Gradient Boosted Tree has an accuracy level of 90.00%. From the results of the analysis that has been done, it can be concluded that for the data in this study, the classification method using the Decision Tree is better than the Gradient Boosted Tree method.

Digitalisasi Pelayanan Publik Desa Palewai Dengan Sistem Informasi Desa Suharsono Bantun; Jayanti Yusmah Sari; Noorhasanah Z; Syahrul Syahrul; Arief Budiman
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.25185

One of the basic functions of government is to provide public services. Even during the Covid-19 pandemic, these needs must still be met and optimally adjusted by service providers. The Palewai Village Office, is one of the local government agencies that does not escape its obligation to improve public service standards during this pandemic. However, based on observations, it was found that the performance of public services in the office was not optimal because population data processing was still manual and data storage media still used physical documents. Therefore, an online information system is needed that can provide clear and up-to-date information, as well as make it easier for people to fill out forms from anywhere so that there is no crowd in the service room. This study aims to develop a Website- Based Village Information System at the Palewai Village Office which will handle population data, services for making cover letters and distributing population data and current information for the community. The results of this study are a website-based village information system that can manage community data effectively and efficiently and can be accessed quickly and easily to provide information related to services at the Palewai Village office.

Visualisasi Letak Geografis Provinsi Di Indonesia Berbasis Augmented Reality Untuk Siswa SD Suharsono Bantun; Jayanti Yusmah Sari; Qammaddin Qammaddin; Rahmat Karim
INFORMAL: Informatics Journal Vol 6 No 1 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i1.20282

The learning implementation in the subject of ¬social science¬, especially the topic of Indonesia's geographical location at SD Negeri 99 Pongrakka is still carried out in a classical way, namely using the lecture method with learning media in the form of depictions on the blackboard or just observing pictures through textbooks. These methods make the learning process less interesting and seem passive because there is no direct interaction between the topic and the students. To create interactive learning, we propose the development of learning media by utilizing Augmented Reality technology. The system proposed in this study can display the geographical location of the province in 3 dimensions by means of the user pointing the marker at the camera then the camera detects the marker and the object will be displayed directly on the monitor screen according to the marker used. After testing the system based on the Software Requirement Specification, it was able to be concluded that the application of visualizing the geographical location of the province based on Augmented Reality could not only make learning more interactive but also more interesting, so that it could be used as an option for learning methods.

Sistem Absensi Mahasiswa Berbasis Dorsal Hand Vein Menggunakan Local Binary Patterns dan Fuzzy k-NN Suharsono Bantun; Jayanti Yusmah Sari; Noorhasanah Z; Mardianto Mardianto; Aspian Achban
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 1 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i1.1496

After WHO announced COVID-19 as a pandemic in 2020, universities began to implement online learning, after 1 year had passed, limited face-to-face learning would begin again by prioritizing the health and safety of campus residents. The offline lecture attendance system manually by signing the attendance sheet is at risk of becoming a medium for transmitting the virus because it is touched by many lecturers and students. Biometric systems are widely applied in various fields such as security systems and employee attendance. However, it has several weaknesses, such as the possibility of sabotage in fingerprints and palm geometry, difficulty in recognizing facial objects using accessories such as hats and glasses as well as changing expressions and expensive acquisition tools in retina-based recognition applications. This study uses Local Binary Patterns (LBP) to identify the dorsal hand vein. LBP is used as a feature extraction method to optimize the feature value of the vein texture in order to obtain good accuracy and fast processing speed. To match the dorsal vein features of the test image and the image in the database, the Fuzzy k-NN method is used. The test results show a good recognition accuracy of 90.67%.

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

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.

Co-Authors A. Muhammad Idkhan Abdul Malik Abdullah Igo BD Adha Mashur Sajiah Agum Agidtama Gafar Agus Zainal Arifin Agustinus Suria Darme Amil A Ilham Andi Baso Kaswar Andi Baso Kaswar Arief Budiman Aryadi Nurfalaq Asih Setiyorini Asih Setiyorini Aspian Achban Auliani, Andi Nur Melly Bambang Pramono Bantun, Suharsono Chastine Fatichah Daiona, Abdullah Igo Baran Darme, Agustinus Suria Dewi Hastuti Dimas Febriyan Priambodo Eva Sapitra Fandiansyah Fandiansyah Fandiansyah, Fandiansyah Fransisca J Pontoh Hafidz Muhtar Hardianti Hardianti Hasnawati Munandar Henry Praherdhiono Idkhan, A. Muhammad Ika Purwanti Ningrum Ika Purwanti Ningrum Ika Purwanti Ningrum Ika Purwanti Ningrum Purnama Ika Purwanti Ningrum, Ika Purwanti Ika Putri Ningrum Ilham Andi Indar Ismail Jamaluddin Ingrid Nurtanio Isnawaty Isnawaty Jayawarsa, A.A. Ketut La Ode Hasnuddin S. Sagala Linda Purnama Muri Luh Putu Ratna Sundari Mardianto Mardianto Mardianto Mardianto Moh La Andi Rais Imran Yatim Muarif, Amar Muh. Abdi Fahmi Muh. Ariyandhi Masalu Muhammad Mail Muhammad Naim Muhammad Nur Khidfi Muhammad Syaiful Muhammad Syaiful Muhtar, Hafidz Mutmainnah Muchtar Naim, Muhammad Nanik Suciati nina sularida limin Ningrum, Ika Purwanti Nirsal Nirsal Nirsal Noorhasanah Zainuddin Novriadi, Teguh Nur Fajriah Muchlis Nur Inzani Reski Amalia Nurfagra Nurfagra Nurfalaq, Aryadi Nurfitria Ningsi Phradiansah ., Phradiansah Punaji Setyosari Purnama, Ika Purwanti Ningrum Qammaddin Qammaddin R, Ranir Aftar Rabiah Adawiyah Rabiuldien Amat, Rabiuldien Rahman, Faizal Jumain Rahmat Karim Ranir Atfar R Rapa, Wiwi Rasmiati Rasyid Reski Surya Aristika Ricky Ramadhan Rina Rembah Risnayanti Risnayanti Rizal Adi Saputra Saida Ulfa Sarimuddin, Sarimuddin Sartika Sari Sartika Sari Sehan, Sahara Selviani Selviani Suci Pricilia Lestari Suharsono Bantun Suharsono Bantun Suharsono Batun Suharsono Suhar Syaban, Kharis Syahrul Syahrul Syamsuddin Teguh Novriadi Wiwi Rapa Yuwanda Purnamasari Pasrun

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