Article Per Year (5 Year)
p-Index From 2021 - 2026
4.447
P-Index
This Author published in this journals
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

Published : 59 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 59 Documents
Search

 2   3   4   5   6 
Klasifikasi Penyakit Gigi Karies Dan Kalkulus Menggunakan Convolutional Neural Network Excelcis Oroh; Chairisni Lubis
Nusantara Journal of Multidisciplinary Science Vol. 1 No. 4 (2023): NJMS - November 2023
Publisher : PT. Inovasi Teknologi Komputer

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

Abstract

Gigi merupakan salah satu organ tubuh yang penting bagi manusia. Gigi berfungsi untuk mengunyah makanan, berbicara, dan menjaga estetika wajah. Namun, gigi juga rentan terhadap berbagai penyakit, seperti karies, periodontitis, dan gigi berlubang. Karies adalah penyakit gigi yang paling umum terjadi di dunia. Karies disebabkan oleh bakteri yang menghasilkan asam yang dapat merusak enamel gigi dan Kalkulus adalah penumpukan plak dan mineral di permukaan gigi. Kalkulus dapat menyebabkan iritasi gusi dan meningkatkan risiko terjadinya periodontitis.Sebab itu, diperlukan suatu program untuk membantu masyarakat umum mengidentifikasi penyakit gigi karies dan kalkulus agar dapat memberikan perawatan yang maksimal sesuai dengan penyakitnya masing-masing. Penelitian kali ini menggunakan salah satu metode dari Deep Learning yaitu Convolutional Neural Network (CNN) digunakan untuk melakukan klasifikasi penyakit gigi kedalam tiga kelas, penyakit gigi karies, penyakit gigi kalkulus. Hasil pengujian menunjukkan model yang dibuat mendapatkan tingkat akurasi sebesar 94%.
Identification of Cactus Species Using the MobileNet Convolutional Neural Network Model William Mulyadi; Chairisni Lubis
Jurnal Komputer, Informasi dan Teknologi Vol. 4 No. 2 (2024): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v4i2.1933

Abstract

The objective of this design is to create a system capable of accurately and precisely classifying different types of succulent cacti. The system design aims to create a succulent cactus classification application using a Convolutional Neural Network (CNN) with the MobileNet architecture. The process involves collecting cactus images, dividing them into training and test datasets, and developing a CNN model to recognize patterns in succulent cactus species. The training data is used to train the CNN model, and the test data evaluates the model's accuracy. The trained model, stored in Tf.lite format, successfully classifies 15 cactus types, achieving high accuracy by employing preprocessing steps like resizing, normalization, and background removal. Over 1,200 cactus images were taken with a smartphone, categorized into 15 classes, and prepared to ensure optimal lighting, angle, background, and resolution (224x224 pixels). The MobileNet model was chosen for its high accuracy and efficiency. Hardware used includes a Samsung A54 smartphone and an Intel i7 laptop, with software such as Python, Kotlin, and Android Studio facilitating development. This design ensures the application’s accessibility, making it a valuable tool for cactus enthusiasts and the general public to easily identify different succulent cactus types. Testing of the cactus species classification program using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture yielded strong results, achieving over 90% accuracy in classifying 15 cactus species. The highest training accuracy of 0.9837 was achieved at 150 epochs without early stopping, outperforming other epoch configurations. The model successfully classified species across five main genera—Kalanchoe, Crassula, Echeveria, Haworthia, and Euphorbia. This high accuracy highlights the model's effectiveness, making it a useful tool for cactus enthusiasts and the public to accurately identify and distinguish cactus species.
PENGENALAN TULISAN KARAKTER MANDARIN (HANZI) DENGAN MENGGUNAKAN METODE EFFICIENTDET Roberto Davin; Chairisni Lubis; Tony
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): 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.v13i1.32853

Abstract

In today's era of globalization and digitalization, the need to understand and recognize language differences is becoming increasingly important, especially with the increasing cross-cultural interactions. Mandarin, as the second international language, one of the languages ​​of the United Nations (UN), and one of the languages ​​with the largest number of native speakers in the world, has a complex writing system consisting of thousands of characters called Hanzi. Each of these characters not only represents a sound but also a specific meaning, making the recognition of Mandarin characters a challenge. In this study, a system was developed to recognize 200 Chinese characters using the EfficientDet method, an object detection model developed by researchers from Google Research. EfficientDet is known for its efficiency in detecting objects with high accuracy while maintaining fast processing speed. In conclusion, the system obtained overall mAP results of 58%, mAR 42.64%, IoU 49.18%, precision 75%, recall 64%, and F1-score 65%.
Classification of diseases in snake plants using convolutional neural network Athalia, Kensa; Tiffany; Kevin Adhi Dhamma Setiawan; Bertrand Ferrari; Chairisni Lubis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3201

Abstract

snake plant has an important role in human life, as well as in increasing the aesthetic value of the environment. Limited knowledge about diseases in snake plants has a crucial result in improper handling and control when the plant is attacked by disease. Advances in deep learning technology and Convolutional Neural Network (CNN) have presented high opportunities with their advantages in recognizing patterns and features from image data. This research will use a CNN model with VGG-19 architecture to classify diseases in the leaves of the snake plant. It is expected that by using the pre-trained VGG-19 model, the model can recognize complex visual patterns in snake plants. Diseases to be classified include several types of diseases that often attack snake plants such as anthracnose, rust, water soaked lesion, and healthy plants for comparison. The highest value of training accuracy reached a value of 98.08%, validation accuracy of 94.02%, and testing accuracy reached 94%.
Deteksi YOLOv8 dan Pengenalan Wajah Menggunakan RESNET50 Pada Gereja Dony, Dony; Lubis, Chairisni
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.9757

Abstract

Face recognition and object detection technologies have been used and developed rapidly in various fields such as security, facilities management, and surveillance. Churches, as a place where many people gather, often face challenges in seating management and monitoring congregation attendance, which is still done traditionally or manually. This traditional approach not only requires a lot of time and effort, but is also prone to human error. Therefore, a system was designed to be able to detect the availability of chairs and identify the faces of the congregation automatically, using the YOLOv8 method and a Convolutional Neural Network (CNN) based on the ResNet-50 model for face detection and recognition. The test results from the 3 groups tested obtained an average accuracy of 85.26% and a detection accuracy of 95.46% with the YOLOv8 model training reaching 97% mAP50 and the ResNet50 model with an accuracy of 99.54% and a validation accuracy of 99.37%.
IMPLEMENTASI VIRTUAL MOUSE BERBASIS HAND GESTURE RECOGNITION DENGAN MEDIAPIPE DAN CONVOLUTIONAL NEURAL NETWORK Suki, Steven; Lubis, Chairisni; Pragantha, Jeanny
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 1 (2025): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i1.53138

Abstract

Hand gesture recognition merupakan bentuk interaksi manusia-komputer yang inovatif dengan memanfaatkan gerakan tangan sebagai input. Penelitian ini bertujuan mengembangkan dan mengevaluasi aplikasi virtual mouse berbasis gerakan tangan untuk mengatasi keterbatasan perangkat input konvensional seperti masalah mobilitas dan ketergantungan hardware. Sistem dirancang menggunakan kombinasi hand landmark detection dengan MediaPipe untuk ekstraksi fitur tangan dan Custom Convolutional Neural Network (CNN) untuk mengenali pola gerakan. Hasil evaluasi menunjukkan performa model yang sangat baik dengan akurasi pelatihan mendekati 99% dan akurasi validasi mencapai 99,9%. Dalam pengujian aplikasi, sistem berhasil mengenali gestur dengan sempurna (100%) pada latar belakang putih, sedangkan pada latar belakang bervariasi tingkat keberhasilannya mencapai 66%. Temuan ini membuktikan bahwa solusi virtual mouse berbasis gerakan tangan dapat berfungsi efektif dalam kondisi terkendali, meskipun masih memiliki tantangan pada lingkungan dengan latar belakang kompleks.
Pengenalan Cuaca Indonesia Berdasarkan Citra Langit Menggunakan CNN Arsitektur MobileNetV2 Kurniawan, Darryl Matthew; Lubis, Chairisni
Computatio : Journal of Computer Science and Information Systems Vol. 9 No. 1 (2025): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v9i1.34021

Abstract

Negara Indonesia memiliki cuaca yang bervariasi dan memiliki dampak yang akan berpengaruh pada kehidupan sehari-hari. Pengenalan cuaca dengan memanfaatkan citra langit merupakan salah satu solusi yang efektif untuk mendapatkan informasi cuaca berdasarkan dengan kondisi langit. Penelitian ini akan menggunakan salah satu arsitektur CNN yaitu MobileNetV2 untuk melakukan klasifikasi ke 4 kategori cuaca yaitu cerah, berawan, mendung, dan hujan, disertai dengan prediksi data numerik berupa suhu, kelembaban udara, kecepatan angin, sinar UV, dan tekanan udara. Dataset yang digunakan akan berupa citra langit dan data numerik yang diperoleh dengan pemantauan cuaca selama kurang lebih 2 bulan. Hasil eksperimen menunjukkan bahwa model yang dirancang dapat memperoleh akurasi validasi sebesar 78%, dengan nilai MSE untuk data numerik 0.6478, dan total loss validasi 1.547. Hasil ini menunjukkan bahwa model yang dirancang memiliki potensi untuk dapat melakukan pengenalan cuaca secara efektif di Indonesia dengan meningkatkan ukuran dataset dan optimasi lebih lanjut.
Sistem Informasi Pemetaan Warisan Budaya Kawasan Banten Lama Berbasis Android Dewayani, Ery; Lubis, Chairisni; Mulyawan, Bagus
Computatio : Journal of Computer Science and Information Systems Vol. 3 No. 2 (2019): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v3i2.5554

Abstract

In this global era, when seeking information or communicate with each other, people are using information technology to support those activities. Smartphones for example, is one of affordable technology that can be owned by common people. These days, people are using smartphones anywhere and any time. On every smartphones that being used, it has a operating systems to manages smartphones hardware and provide services that given. Android is the most popular of a mobile operating systems that being used to operate smartphones in the world. Through the popularity of android and growing smartphones users, transmitting information are more effective if the information can be accessed using smartphones. On the first year of research” Mapping Banten Lama’s Cultural Heritage Website “has already developed, and to make accessibility of the information more convenient, on the second year of research has developed application program “Android Based Information System for Mapping Banten Lama’s Cultural Heritage”. This application program is accessible through any kind of android smartphones. Prototyping method is used to develop this application program. For storing data, this application program use MySql database that operate inside server (hosting) so it can can be accessed through internet. Android studio and Java programming language are used to build this application program. Black Box Testing and User Acceptance Test (UAT) are used to test this application program. The result of this research is develop a product of software that can be accessed through android smartphones.  Pada era global saat  ini  masyarakat menggunakan teknologi informasi  dalam  mencari informasi maupun untuk berkomunikasi.  Salah satu teknologi yang  terjangkau dan dapat dimiliki oleh masyarakat umum adalah handphone. Mereka membawa dan menggunakan  handphone setiap saat dimanapun mereka berada. Perusahaan gadget  mulai mengembangkan  perangkatnya  menggunakan sistem operasi Android yang akhir-akhir ini sangat popular dan menjadi perhatian masyarakat Indonesia maupun dunia. Dengan memanfaatkan perkembangan teknologi tersebut,  dalam mengenalkan berbagai informasi kepada masyarakat umum  akan lebih efektif bila informasi dapat diakses  melalui handphone. Website Warisan Budaya Kawasan Banten Lama  sudah dibuat pada penelitian tahun pertama  dan    agar lebih  memudahkan masyarakat luas untuk akses , pada penelitian tahun kedua dikembangkan suatu  program aplikasi “Sistem Informasi Pemetaan Warisan Budaya Kawasan Banten Lama  Berbasis Android”. Program aplikasi yang dikembangkan ini dapat diakses  diberbagai tipe handphone yang berbasis Android. Metodologi pengembangan sistem menggunankan metode  Prototyping.  Basisdata  menggunakan  My Sql, yang disimpan dalam server (hosting) agar dapat diakses melalui internet. Selain  itu program aplikasi  menggunakan Android Studio dengan bahasa pemrograman Java. Metode pengujian menggunakan User Acceptance Test (UAT). Penelitian ini menghasilkan produk software yang dapat diakses melalui handphone berbasis android.
PREDIKSI KURS MATA UANG DENGAN METODE LONG SHORT TERM MEMORY (LSTM) BERBASIS ATTENTION Rusdi, Zyad; Lubis, Chairisni; Tjandra, Vincent Geraldy
Computatio : Journal of Computer Science and Information Systems Vol. 5 No. 2 (2021): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v5i2.13117

Abstract

Currency exchange is the exchange rate for current or future payments between two currencies of each country. In Indonesia, there are frequent fluctuations in the exchange rate of USD against IDR which causes instability in economic growth. This has resulted in reduced interest from foreign investors in investing in Indonesia, and has resulted in degeneration of development because the position of foreign investors is very important for economic growth. Therefore, predictions are needed to anticipate exchange rate fluctuations using the Long Short - Term Memory (LSTM) method. Some of the steps taken are collecting data, preprocessing, splitting data, build the LSTM model architecture, training the model, and testing. From the test results, the best results were obtained for the LSTM and LSTM + attention models, namely by using the parameters of 60 timestep, 32 neurons, 150 epoch, 32 batch size, and a learning rate of 0.001. The results obtained from the LSTM model are the total training time of 108.76 seconds, the loss value is 0.000162, and the RMSE result is 1.3328. The results obtained from the LSTM + attention model are the total training time of 116.05 seconds, the loss value is 0.000157, and the RMSE result is 0.6335. So it can be concluded that LSTM with attention can improve training 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