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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

Chairisni Lubis

Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia

Author-ID : 246258

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

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Prediksi Harga Saham dengan Menggunakan Algoritma Hybrid Neural Network Chairisni Lubis; Eddy Sutedjo; Bowo Setiadi
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2005
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Prediksi harga saham adalah suatu proses menganalisis dan menentukan harga suatu saham di masayang akan datang. Dengan analisis teknikal, prediksi harga saham di masa datang dapat ditentukan daripembelajaran pola fluktuasi harga saham tersebut di masa lampau. Pada penelitian ini, akan diteliti keakuratandua algoritma Neural Network yang sering digunakan dalam memprediksi harga saham dan satu algoritmahybrid yang ingin diketahui kelebihannya dibandingkan kedua algoritma tersebut. Algoritma pertama adalahalgoritma Backpropagation Network, dimana algoritma ini mempunyai keakuratan prediksi yang tinggi biladata pembelajarannya relatif stabil. Algoritma kedua adalah algoritma Self Organizing Maps Kohonen, dimanaalgoritma ini mempunyai keakuratan prediksi yang cukup tinggi walaupun pola data pembelajarannya bersifatfluktuatif. Algoritma ketiga adalah algoritma Hybrid, dimana algoritma ini merupakan kombinasi dari keduaalgoritma diatas, dan diharapkan dapat memberikan keakuratan prediksi yang sangat tinggi, baik untuk datapembelajaran yang bersifat stabil maupun fluktuatif.Setiap algoritma melakukan proses prediksi saham dengan menggunakan data dari perusahaan AstraInternational Tbk., Gudang Garam Tbk., dan Telekomunikasi Tbk. Kemudian untuk mengetahui keakuratanprediksi harga saham setiap algoritma, dilakukan pengujian dengan menggunakan data saham yang terdiri daridata baru dan data pelatihan untuk mendapatkan nilai kesalahan mutlak. Hasil dari penelitian ini membuktikanbahwa algoritma Hybrid dapat memberikan keakuratan prediksi harga saham yang lebih tinggi dibandingkandengan algoritma Backpropagation Network maupun algoritma Self Organizing Maps Kohonen.Kata kunci: Neural Network, algoritma Hybrid, algoritma Backpropagation Network, algoritma Self OrganizingMaps Kohonen, prediksi saham

KLASIFIKASI PENYAKIT MATA MENGGUNAKAN CNN William William; 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.v10i1.17834

The eye is one of the organs of the human senses, namely the sense of sight. Eyes has an important function in capturing visual information that is used for daily activities. Eye health is important because vision can't be replaced by anything.Previously, a doctor made a diagnosis of an eye disease using retinal fundus images. But it takes expertise and a long time. Therefore, a classification system was made using the Convolutional Neural Network (CNN). The CNN network is used to recognize the visual pattern of image pixels with minimal preprocessing.The variables used during testing are data and batch size for the CNN training process. The data variables consist of 50 images from each class which are reproduced using mirroring with a total of 1,000 images; 50 images from each class reproduced using rotation totaling 2,000 images; and 275 normal images, 55 diabetic images, 250 glaucoma images, 250 cataract images, and 170 hypertension images totaling 1,000 images. Batch size variables used were 25 and 32. After all models were tested, it was concluded that the model trained using 1,600 images and 32 batch size gave the best results, namely loss: 0.1228 and accuracy: 0.9100.

SISTEM PAKAR DIAGNOSIS PENYAKIT TELINGA HIDUNG DAN TENGGOROK Sifra M.B. Pattiasina; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 4, No 2 (2016): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Expert system is a system that is trying to adopt human knowledge into a computer, so that the computer can resolve the issue like done by experts. System design expert for disease diagnosis ear, nose and throat with the utilization of the basis knowledge on file in database. Expert systems to diagnose diseases of ear, nose and throat using Forward Chaning methods and Generate and Test Algorithms which aims to carry out a search about indications in order to diagnose type of disease. Forward Chaining using Certainty Factor to calculate the level of certainty of the disease diagnose.Generate and Test Algorithm to do a searching for indication in the form of question. The system will provide questions to be answered. Data that is recognizable will adjust the rules to matched then be calculated using Certainty Factor and gives weight of the resulting answers to diseases diagnose.

SEGMENTASI DOKUMEN DENGAN MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK Veronica Santoso Khalim; Chairisni Lubis; Agus Budi Dharmawan
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.v3i1.3289

The development of knowledge in the field of artificial intelligence is highly developed, such as research on segmentation of documents. Segmentation of documents makes the user becomes easy to obtain the required information. Segmentation is to divide an image into several regions based on the similarity of the form or object. Segmentation can also means, an image recognized into small blocks and recognized into a kind whether text or not. The design of this application that have utility as a maker of computer programs that can automatically segmenting types contained in the mixture and recognize text document or image automatically as well, make the document easier to be taken and analyzed its contents. Segmentation this document is based on a block by using backpropagation and histogram smoothing for smoothing the histogram. Input form document image obtained with the help of the scanner with JPEG and BMP file formats. After testing by using the Automatic Cropping, it is known that the accuracy rate of 82.1% for the first size is 1048x195 and 58.7% for the second size is 1048x52. While testing using Windowing process, known accuracy rate of 21%. The introduction of more accurate to type text rather than images. Key wordsBackpropagation, Histogram, Segmentation, Smoothing

SISTEM PAKAR DIAGNOSIS KERUSAKAN KOMPUTER DAN INTERNET DENGAN CERTAINTY FACTOR BERBASIS WEB Hendra Liana; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

The application that I designed is an expert system to diagnose the faulty or error in computer. The two main components of problems that I address are hardware and software. There are two main feature of this expert system, which is consists of development environment and consultation environment. The development environment is made purposely to manage the diagnoses, symptoms, and relations from the problems that user may approach. As in the consultation environment, user can use it by inserting the input into the consultation table which is provided in the program, then the system will calculate the result based on the certainty factor method with forward chaining system. The final result of this application is a view of the symptoms that choosen by user, and display the probability of diagnoses, but only the diagnoses with the biggest certainty factor value will be choosen as a final result, and accompanied by a final solution.

APLIKASI CERTAINTY FACTOR PADA AKUISISI PENGETAHUAN PENYAKIT KEJIWAAN SKIZOFRENIA Lucy Komala; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 2, No 2 (2014): 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.v2i2.3214

Data aquicition is the most important component in Expert System Development. In this recearch, we used Certainty Factor for data aquicition. Data obtained will be used for establish rules, then the rules would be the value calculated in advance using the method of certainty factor. Calculations with certainty factor have a value MB, MD, and CF. After the value of MB, MD, and CF obtained the value will be stored in the existing database. Testing result get 88.75% for accuracy. Key words Certainty factor, Rule-Based Expert System, Schizophrenia

DETEKSI KONDISI ORGAN GINJAL DAN LAMBUNG MELALUI IRIS MATA MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK Fanjie Hidayat; 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.v3i2.3307

Iridology is a science based on analysis of signs contained in the network structure of the human body such as iris as a reflection of the health condition. According to the science of iridology, iris eye is an extension of the brain. The organs in the body sends vibrations to the body cells and is recorded in the brain. These recordings can then be viewed through the iris of the eye that relate directly to the brain. This project build software for detection of kidney and stomach conditions through the iris with Backpropagation Neural Network. The first process in this application is looking for feature extraction on the iris image by preprocessing stage. The value from feature extraction will be calculated by Backpropagation Neural Network to find the kidney and stomach conditions, normal or abnormal from the iris image. The test results reached 94.44% from data in database and 72.22% from the untrained data. Key wordsIridologi, Gray Level Cooccurrence Matrix, Backpropagation Neural Network

SISTEM PAKAR PENUNJANG KEPUTUSAN PENJURUSAN KULIAH BERDASARKAN KEPRIBADIAN DENGAN TEST MMPI 1 DENGAN METODE CERTAINTY FACTOR Ni Putu Diah Ayu Vita Widia Murti; Chairisni Lubis; Dedi Trisnawarman
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.v3i2.3322

Personality is the part of every human which is a prominent characteristic of the human person, the whole way a man react and interact with other human beings. Personality is most often described in terms that could be measured properties exhibited by someone. A system designed to help determine personality by Certainty Factor method. Certainty Factor method focuses on the level of confidence. The input of this system is the input value scale of MMPI (Minnesota Multiphasic Personality Inventory) 1 test and process with Certainty Factor that released the results of personality type. The Result of the 120 cases was 82.5% accuracy rate.Key wordscertainty factor, expert system, Minnesota Multiphasic Personality Inventory, personality type

APLIKASI SISTEM INFERENSI FUZZY UNTUK MENDIAGNOSIS PENYAKIT MATA BERBASIS WEBSITE Fabrian Ivan Prasetya; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol 8, No 1 (2020): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

On this research a fuzzy inference system application program will be designed to diagnose web-based diseases. In addition to diagnosing the disease, this research was conducted to increase the severity of the disease. Data collection in this study uses the literature method, documentation method, and interview method. The inference engine used is the forward chaining method and the calculation of the severity using the Sugeno’s fuzzy method with triangular and trapezoidal membership functions. Fuzzy Inference System Application Program created can be used to diagnose diseases in general that can contain various types of diseases, symptoms, rule bases and diagnosis results of a disease and its symptoms based on the symptoms of the disease suffered. In this study, the application was created by using ASP.NET and SQL Server 2019. Data testing was carried out there were two types of testing. The results of the percentage of truth in diagnosing the severity of the disease reached 92.30% and in diagnosing the disease using the triangle function reached 52.56% in the first test and by using the trapezoidal function reached 53.84% in the first test. For the second test result, the percentage of truth in diagnosing the severity of the disease was 89.65% and in diagnosing the disease using the triangle function reached 41.37% and by using the trapezoidal function reached 41.37%

PENDETEKSIAN PENGGUNAAN MASKER WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK Bunardi Budiman; Chairisni Lubis; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.

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 bagus Mulyawan Benny Karnadi Bezaliel Rumengan Bezaliel Rumengan, Bezaliel Bobby Tumbelaka Bobby Tumbelaka Bowo Setiadi Budianto Lomewa Lo Budiyanto Lomewa Lo Bunardi Budiman Calvin Geraldy Christ Bastian Waruwu Christian Dwi Mardiyanto Dedi Trisnawarman Devid Sumarlie Dewi Sartika DEWI SARTIKA Donni Suharyanto Dyah Erny Herwindiati Eddy Sutedjo El Primo Gemilang Elvin Elvin Ery Dewayani 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 Jefry Jefry Jefta Gani Hosea Jourdan Stanley Judah Suryaputra Kelvin Samuel Keyza Novianti Kristina Erlinda, Kristina Kurniawan Sulianto 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 Ronald Arifin Ronald Kurniawan Lawidjaya Ronald Ronald Saddhananda Sandy Danish Arkansa Sifra M.B. Pattiasina Sindy . Sindy Sindy Stevanndy Trisdiyanto Stevanndy Trisdiyanto Indrajaya Sullivan Sullivan Sullivan Sullivan, Sullivan Sunardi Suwito Syawal Ludin Teny Handhayani Tony . Tony Tony Tony Tony TRI SUTRISNO Veronica Santoso Khalim Veronica Santoso Khalim, Veronica Santoso Vincent Geraldy Tjandra William William Willy Wijaya Yegar Sahaduta Yoestinus Yoestinus Yuliana Soegianto Yusten Wuntoro Yusten Wuntoro Zyad Rusdi Zyad Rusdi

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