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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Multidisciplinary on Social and Technology INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Journal : building of informatics technology and science

 1 
Klasifikasi American Sign Language Menggunakan Convolutional Neural Network Israldi, Tino; Haerani, Elin; Sanjaya, Suwanto; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2570

Abstract

Communicating is a necessity for all groups or individual because each individual should communicate with their surroundings. Communicating can also make us get information so that it can be used as a reference to be able to adapt. Verbal language used by speaking out loud is a way of communicating with individuals, but not all individuals can communicate with it, especially there are some individuals who have hearing limitations. Because of these limitations, another program that can be used is through sign language. Language requirements are languages that are usually used by people with disabilities in terms of hearing or speaking and sign language also has a fairly well-known sign language standard, namely the American Sign Language (ASL) standard. Unlike languages in the world, sign language is also often of little interest to most people because people's interest in sign language is still lacking so that most people are unable to understand their language. Sign language has many types, one of which is sign language by using hands to form letters and numbers. In overcoming these problems, the solution is to create a system that can be used to recognize sign language, the system developed is a system that used machine learning technology. This study will propose an ASL classification approach through data preprocessing and a convolutional neural network model. The proposed model can classify ASL hand posture images to be translated into the alphabet. The result of this study is an model with accuracy of 99.8% obtained from the process of merging preprocessing data and the convolutional neural network model.
S Sistem Pakar Diagnosa Gangguan Kejiwaan Menggunakan Metode Inferensi Forward Chaining dan Certainty Factor Fauzan, Muhammad; Wulandari, Fitri; Haerani, Elin; Oktavia, Lola
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3232

Abstract

The era of artificial intelligence AI technology is now an advantage because the system does all the work according to the human brain. Expert Systemis abranch ofartificial intelligencethat adapts the mind and reasoning of an expert to solve a problem and make a decision so that it draws conclusions based on the facts. From cases of psychiatric disorders, this expert system is highly recommended to make it easier to find out what type of disorder you are suffering from to assist the public and experts in diagnosing diseases quickly and accurately. For this reason, researcherscreated an expert system for diagnosingpsychiatric disordersusing the forwardchaining inferencemethod and certainty factor. Based on the results of the implementation and analysis thathave been carried out in this study, it produces a software system, namely an expert system that has an easy-to-understand display, and can assist experts in diagnosing psychiatric disorders
Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor Novitasari, Fitria; Haerani, Elin; Nazir, Alwis; Jasril, Jasril; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3610

Abstract

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.
Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas Pranata, Joni; Agustian, Surya; Jasril, Jasril; Haerani, Elin
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6335

Abstract

Masalah keterbatasan data latih menjadi tantangan utama dalam klasifikasi sentimen di berbagai bahasa, termasuk bahasa Indonesia, terutama untuk analisis sentimen terkait topik tertentu. Hal ini disebabkan oleh berbagai faktor, dan umumnya adalah kebutuhan untuk mengetahui dengan segera bagaimana sentimen terhadap suatu isu, sehingga tidak mungkin menghabiskan waktu untuk memberi label yang cukup pada data untuk proses pelatihan. Penelitian ini mengusulkan model klasifikasi sentimen dengan sumber data pelatihan yang sedikit, pada studi kasus pengangkatan Kaesang Pangarep sebagai ketua umum PSI. Algoritma Random Forest digunakan sebagai model dasar (baseline) yang dioptimasi dengan penambahan data eksternal untuk training, pemrosesan teks (text preprocessing) dan parameter tuning. Fitur input yang digunakan adalah model bahasa IndoBERT sebagai embedding kata untuk menghasilkan representasi teks yang lebih kontekstual. Hasil penelitian menunjukkan bahwa metode IndoBERT dengan Random Forest yang dioptimasi memberikan peningkatan performa yang signifikan dibandingkan baseline, sebesar 6%. Hasil klasifikasi model yang paling optimal sebesar 54% unutk F1-score dan 63% akurasi. Temuan ini menegaskan bahwa penambahan data eksternal dan optimasi parameter dapat meningkatkan kemampuan generalisasi model dalam klasifikasi sentimen bahasa Indonesia. Penelitian ini diharapkan dapat menjadi referensi metodologis bagi studi klasifikasi sentimen serupa yang menghadapi kendala ukuran dataset.
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Fauzan, Habibul Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novi Yanti Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Oktavia, Lola Oktavia Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Simatupang, Muhammad Farid Audi Rahman Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelvi Yusra, Yusra zikrillah aulia, said rizki