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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) Building of Informatics, Technology and Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal Komputer, Informasi dan Teknologi Nusantara Journal of Multidisciplinary Science
Chairisni Lubis
Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Other

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KLASIFIKASI JENIS PENYAKIT PADA DAUN TOMAT DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK El Primo Gemilang; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 10, No 1 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i1.17839

Abstract

Tomato is one of the farming commodities in Indonesia, easy to plant but easy to get sick. Analizing the disease in plain view still not yet achieve high accuracy result, so we use the help of Convolutional Neural Network (CNN) algorithm. This research is quantitative, with image of a single tomato leaf that is infected as the input. The constructed model gains an accuracy of 58.33% with 12.716 image consisting of Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Target Spot, Spider Mites, Mosaic Virus, Yellow Leaf Curl Virus, Septoria Leaf Spot and healthy leaf. The conclusion from this research is that classification of Tomato leaf disease using CNN can help achieve a higher accuracy but using LeNet-5 as the model architecture is not very effective.
ALAT MONITORING DAN PENGENDALI SAKLAR PERALATAN LISTRIK JARAK JAUH Sindy Sindy; Chairisni Lubis; Tony Tony
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i1.3089

Abstract

This design, use the mobile phone as a controller and microcontroller as a central control system. Microcontroller will be executed on the orders received from the modem which connected to the microcontroller. Several types of sensors are used to monitor them, such as LDR sensors to detect light, LM35 sensor to detects the room temperature and the MQ-2 sensor to detect gas leaks.    The test results show that the system is already capable of controlling electrical equipment and monitor the lights and temperature conditions. In addition, the system can report a gas leak and fire to the phone number that has been saved.
PERANCANGAN APLIKASI SISTEM PAKAR UNTUK MENDIAGNOSIS GANGGUAN AUTISME PADA ANAK BALITA DENGAN MENGGUNAKAN METODE DEMPSTER SHAFER Bezaliel Rumengan; Chairisni Lubis; Tony Tony
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 2 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i2.3303

Abstract

Expert System is one of Artificial Intelligence methods that uses the knowledge from an expert as a knowledge base. This Application is created to diagnose Autism Disorder for child under 5 years old with Dempster shafer method which user will have to input symptoms that the user feel to the application. This application can also give information about autism disorder that application cover. Based on the test result dempster shafer method worked well and produce good result in generating diagnosis about disorder matching the symptom given by the user which give a great result with diagnose accuracy for training data 95,45% and 93,33% for test data.   Key wordsDempster Shafer, Autism Disorder, Expert System
PEMILIHAN CROSSOVER PADA ALGORITMA GENETIKA UNTUK PROGRAM APLIKASI PENGENALAN KARAKTER TULISAN TANGAN Judah Suryaputra; Chairisni Lubis; Tri Sutrisno
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 1 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (229.037 KB) | DOI: 10.24912/jiksi.v6i1.2600

Abstract

Handwriting recognition system using genetic algorithm is an Optical Character Recognition system which receives input in the form of handwritten image in scanned box and produces output in the form of characters from the handwriting. Writing can be uppercase, lowercase, or numbers. The designed system consists of five main processes: preprocessing the input image, vertical and horizontal line segmentation, line segmentation and character with automatic cropping, resizing template, and character recognition using genetic algorithm. Preprocessing of input image consists of grayscale process, and thresholding. Genetic algorithm is used to find characters from the character image obtained by comparing the image with the chromosome of the train data. To use the genetic algorithm method, given the process of resizing the template first so that the image size of the characters with the same template. This system has a success rate of character segmentation of 100% and success rate on character recognition with genetic algorithm of 89,027% with one point crossover, 90,43% with two point crossover, 90,72% with uniform crossover.
PENGGUNAAN AUTOMATIC CROPPING DAN PROPAGASI BALIK NEURAL NETWORK PADA PENGENALAN KARAKTER DAN FONT Dewi Sartika; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 2 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i2.3122

Abstract

Program aplikasi yang dibuat adalah sebuah program pengenalan teks berbasis jaringan saraf tiruan dengan menggunakan metode Backpropagation Neural Network. Data input berupa file citra grayscale yang merupakan hasil scan dengan format bitmap (.bmp). Setelah proses grayscale, tahap selanjutnya melakukan proses threshold. Kemudian setelah proses threshold selesai, tahap selanjutnya adalah automatic cropping. Automatic cropping yang digunakan adalah automatic cropping dengan histogram. Digunakan automatic cropping agar proses segmentasi menghasilkan potongan yang tepat dengan objek yang ingin dipotong. Ekstraksi ciri yang dipakai adalah Global Histogram yang juga sebagai input dari metode backpropagation neural network. Hasil pengenalan menggunakan metode BackPropagation menghasilkan persentase keberhasilan lebih dari 96%. Kata Kunci : Automatic Cropping, Backpropagation, Global Histogram .
SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT KEJIWAAN BIPOLAR DENGAN MENGGUNAKAN METODE BAYESIAN NETWORK Olivia Prima Putri; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 2 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i2.3323

Abstract

Expert System is one of artificial Intelligence which uses the knowledge of an expert as its knowledge base. In this research, expert system  designed to diagnosing bipolar disorder  using the method of bayesian network. The result of this application is  the type of psychiatric diseases  bipolar and the value of the probability of each disease. In this research, the  probability of bipolar disorder test data according to medical records obtained yield was 39.39% and the results of testing using test data with new symptoms obtained yield was 55.55%. Key words Bayesian network, bipolar disorder,expert system
SISTEM PAKAR TROUBLESHOOTING KERUSAKAN PERANGKAT KERAS DAN PERANGKAT LUNAK KOMPUTER Sullivan Sullivan; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 2 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i2.3328

Abstract

This Application is created to diagnose problem of Personal Computer’s Hardware and Software with Certainty Factor, which User will have to input Symptoms that the user have. The Symptoms will be process using Certainty Factor. Certainty Factor method give accuracy about 95%.  Key words Certainty Factor, Gejala kerusakan Komputer,  Sistem pakar.
PENGENALAN SUARA UNTUK MENCARI JUDUL LAGU DENGAN BACKPROPAGATION NEURAL NETWORK Ilham Samuel; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 1 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i1.3271

Abstract

Voice recognition is a method which can be used to identify the title of a song. Song title recognizition is a difficult process because not every song our brain can remember and sometime only a fragment of song's lyrics or just a part of tone that can remember. This software is made as a solution to voice recognition to find the title of a song with backpropagation neural network method. The first method of this application is to find resemble extraction for every fragment of a song to get important resemble and then BPNN are going to look the similarity resemblance from every song's fragment. Recognition is executed by looking for similiraty of song's fragment, the most similar fragment will decided as the title of a song. The result of voice that had been tested are the voice which already trained the degree of recognition up to 100% an the voice which has not been trained have degree of recognition up to 42.1% Kata Kunci:Pengenalan Suara, Mel Frequency Cepstrum Coefficeints, Backpropagation Neural Network
APLIKASI PERAMALAN HARGA PENUTUPAN DAN PERKIRAAN ARAH PADA ONLINE FOREX Stevanndy Trisdiyanto; Chairisni Lubis; Bobby Tumbelaka
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i1.3090

Abstract

This application uses artificial neural networks that generate value of the closing price on the day and uses three indicators of technical analysis to help estimate the movement of trading foreign currency price. The data used for input values is from price movements that occurred the previous day. Closing price refers to the combination of the opening price, highest price, lowest price, closing price and volume. After getting the value of the output in the form of today's closing price, then the next will be combined with the results of the estimated direction of movement of currency rates obtained by using three indicators : indicators Bollinger Bands, indicators DeMarker and indicators Moving Average Convergence / Divergence (MACD). The test results showed that the percentage of success is good for 85.22% and get a profit $ 9,448. 
PENGENALAN TULISAN TANGAN HANGUL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Oktavianus Oktavianus; Chairisni Lubis; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v9i1.11589

Abstract

The effect of the Korean Culture for the past few years has been increasing whereas the culture has been a part of the people’s daily lives especially for the youth. Unlike the Latin alphabet, the Hangul alphabet has the characteristics resembling strokes that’re written in blocks that make a syllable. Therefore, on this occasion a system will be made to recognize Hangul as an alternative for learning Hangul. The application design uses pre-processing such as grayscaling, thresholding, dilation and contouring. The data collected in this design uses as much as 3.960 images of the Hangul Alphabet. After GAN is used to generate images as well as Data Augmentation, the dataset reaches a total of 5.303 images which are separated into training set and testing set. The testing is done 2 times whereas the first test is tested on single letters and reached 55,58% accuracy. The second test is done with the letters that got segmented by the application which consists of 1-4 syllables whereas it reached 55,7-60% accuracy. 
Co-Authors Abdi Praja Adrian Primanta S Adrian Primanta Suciadi Agus Budi Dharmawan Agus Budi Dharmawan Agus Budi Dharmawan Agus Budidharmawan Albert Albert Anak Agung Gede Sugianthara Athalia, Kensa Bagus Mulyawan bagus Mulyawan Benny Karnadi Bertrand Ferrari Bezaliel Rumengan Bezaliel Rumengan, Bezaliel Bobby Tumbelaka Bobby Tumbelaka Bowo Setiadi Budianto Lomewa Lo Budiyanto Lomewa Lo Bunardi Budiman Calvin Geraldy Carlos, Daniel Christ Bastian Waruwu Christian Dwi Mardiyanto Dedi Trisnawarman Devid Sumarlie Dewi Sartika DEWI SARTIKA Donni Suharyanto Dony, Dony Dyah Erny Herwindiati Dyah Erny Herwindiati Eddy Sutedjo El Primo Gemilang Elvin Elvin Ery Dewayani Ery Dewayani Excelcis Oroh Fabrian Ivan Prasetya Fabyo Hartono Tamin Fanjie Hidayat Fanjie Hidayat, Fanjie Ferdinand Iskandar Friesky Christian Hendratama Jr. Helmy Thendean Hendra Liana Henri Henri Ilham Samuel Ilham Samuel, Ilham Immanuel Chandra Immanuel Chandra Ivan Wijaya Janson Hendryli Jeanny Pragantha Jefry Jefry Jefta Gani Hosea Jourdan Stanley Judah Suryaputra Kelvin Samuel Kevin Adhi Dhamma Setiawan Keyza Novianti Kristina Erlinda, Kristina Kurniawan Sulianto Kurniawan, Darryl Matthew Lely Hiryanto Listovie Cavito Lucy Komala Lucy Komala Lucy Komala, Lucy Marta Lisa, Marta Matthew Patrick Michael Antoni Michael Antoni, Michael Michiko Ang Michiko Ang Michiko Ang, Michiko Ni Putu Diah Ayu Vita Widia Murti Ni Putu Diah Ayu Vita Widia Murti, Ni Putu Diah Ayu Vita Widia Nikolas Patrick Fernando Novario Jaya Perdana Oktavianus Oktavianus Olivia Prima Putri Olivia Prima Putri, Olivia Prima Prawito Prayitno Prinzky Randy Sukanda Wijaya Renaldi Bong Riyandi Riyandi Roberto Davin Ronald Arifin Ronald Kurniawan Lawidjaya Ronald Ronald Saddhananda Sandy Danish Arkansa Sifra M.B. Pattiasina Sindy . Sindy Sindy Stevanndy Trisdiyanto Stevanndy Trisdiyanto Indrajaya Suki, Steven Sullivan Sullivan Sullivan Sullivan, Sullivan Sunardi Suwito Syawal Ludin Teny Handhayani Tiffany Tjandra, Vincent Geraldy Tony . Tony Tony Tony Tony TRI SUTRISNO Veronica Santoso Khalim Veronica Santoso Khalim, Veronica Santoso Vincent Geraldy Tjandra William Mulyadi William William Willy Wijaya Yegar Sahaduta Yoestinus Yoestinus Yuliana Soegianto Yusten Wuntoro Yusten Wuntoro Zyad Rusdi Zyad Rusdi