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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Tekno Insentif MIND (Multimedia Artificial Intelligent Networking Database) Journal Systemic: Information System and Informatics Journal IJAIT (International Journal of Applied Information Technology) Infotek : Jurnal Informatika dan Teknologi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Nurhasanah, Youllia Indrawaty
Institut Teknologi Nasional, Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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SISTEM PENGENALAN SUARA BAHASA INDONESIA UNTUK MENGENALI AKSEN DAERAH Adhitya Yoga Pratama Idwal; Youllia Indrawaty Nurhasanah; Dina Budhi Utami
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 3 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i3.688

Abstract

In performing theater sometimes feature regional stories that use the characteristic accents of the displayed areas and there is a theater that uses the linguist to know the accent from the area so that the characters who want to staged to deepen the role. However, sometimes theater does not have a linguist to know the accent used is correct or not. Therefore, it takes a technology that can recognize the Indonesian voice using accents that can help linguists recognize regional accents called speech recognition. Voice recognition process is divided into two main parts of the method of feature extraction and pattern recognition methods. In this research we use Linear Predictive Coding (LPC) characteristic extraction method and pattern recognition method using Vector Quantization (VQ). The results of this test, the built application can be used to recognize the sound of Indonesian language using accent areas of Malay and sunda. To recognize sounds more effective with sounds that use the sentence because the value of voice features that use more sentences than the value of voice traits that use the word. Thus, the results obtained with a high accuracy using the sentence that is 80% for Malay Accents sentence and 80% for the Sundanese accent sentence. Then, for the accuracy of using the word "pergi" is 40% for the word using accent Malay and 60% for the word using Sundanese accent. Meanwhile, for the accuracy of using the word "persib" which is 90% for the word using accents Malay and 70% for words using Sundanese accents. Keywords—Speech Recognition, Accents, Linear Predictive Coding, Vector Quantization
Pendeteksian Markerless Pada Aplikasi Augmented Reality (AR) Tuntunan Shalat Sesuai Mazhab Syafi’i Menggunakan Algoritma FAST Adriadi Karya Anugerah; Youllia Indrawaty Nurhasanah; Rio Korio Utoro
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

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Abstract

Praying is a pillar of Islam based on the law that is divided into two kinds namely Mandatory and Sunnah. People usually are not aware of how to perform prayers properly and correctly, because prayer education is still given conventional only using the book as a guide. Therefore it is a necessary alternative learning media using augmented reality technology which is applied to mobile android, so users can learn the guidance of prayer anywhere and anytime. The application that was built in this research is based on interactive multimedia using augmented reality technology, where the user not only read and view the picture but they can also see the motion animation along with the sound of the movement prayer. The user also can notice the whole movement of prayers from all sides, both from the front, side or the back. Testing was conducted on 20 respondents with age criteria of 10 until 40 years. The result shows 80% respondents that had tried the application agree that the application has an attractive appearance, for the information presented is clear, and it is easy to use and responsive. The conclusion shows that the application is feasible to use.
Integrasi Logika Fuzzy dengan Teknologi Cerdas: Tinjauan Sistematis atas Peluang, Tantangan, dan Arah Masa Depan NURHASANAH, YOULLIA INDRAWATY; KURNIA, EMA; SUTARTI, SUTARTI
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.1-17

Abstract

ABSTRAKPengembangan sistem logika fuzzy telah mengalami kemajuan pesat sejak awal diperkenalkan. Studi ini menyajikan tinjauan literatur untuk mengeksplorasi berbagai metodologi logika fuzzy dan aplikasi di berbagai sektor, seperti sistem kontrol, prediksi cuaca, diagnosa medis, dan lainnya. Kajian ini juga mencakup integrasi fuzzy dengan teknologi modern seperti IoT, Big Data, dan kecerdasan buatan (AI), yang telah mendorong penerapan lebih luas dan efisien. Selain menyoroti pencapaian, makalah ini membahas tantangan dalam interpretabilitas, efisiensi komputasi, dan adaptabilitas metode fuzzy dalam menghadapi kompleksitas teknologi dan data modern. Studi ini mengkaji pentingnya pengembangan lebih lanjut terhadap integrasi dengan AI untuk memastikan relevansi dan kontribusi logika fuzzy terhadap solusi cerdas di masa depan. Dengan demikian, penelitian ini menyediakan arah yang strategis untuk eksplorasi lebih lanjut, terutama terkait tantangan teknis dan peluang inovasi dalam domain ini.Kata kunci: AI, Big Data, IoT, Logika Fuzzy, Tantangan TeknologiABSTRACTThe development of fuzzy logic systems has progressed rapidly since its introduction. This study presents a review of recent literature to explore various fuzzy logic methodologies and applications in various sectors, such as control systems, weather prediction, medical diagnosis, and others. The review also covers the integration of fuzzy with modern technologies such as IoT, Big Data, and AI, which has driven wider and more efficient applications. In addition to highlighting achievements, the paper discusses challenges in computational efficiency, and adaptability of fuzzy methods in the face of modern technological and data complexity. The study emphasises the importance of further development towards interpretability and integration with AI to ensure the relevance and contribution of fuzzy logic to future intelligent solutions. Thus, this research provides a strategic direction for further exploration, especially regarding technical challenges and innovation opportunities in this domain.Keywords: AI, Big data, Fuzzy Logic, IoT, Technology Challenges
Decision Support Systems to Selection of Diet Type Using Fuzzy Sugeno and Naïve Bayes Method Nurhasanah, Youllia Indrawaty; Nana Hermana, Asep; Arga Hutama, Mahesa
IJAIT (International Journal of Applied Information Technology) Vol 01 No 02 (November 2017)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v1i02.894

Abstract

Sugeno Fuzzy algorithm is one of the algorithms contained on Fuzzy Inference System, that used to describe the condition between the two pieces of the decisions represented in the form of rules IF - THEN, where the output is constant or linear equations. While the Naive Bayes algorithm is an algorithm that uses data classification to a particular class based on the probability of each data class. Both of these algorithms can be implemented on a Decision Support System (DSS) for diet selection, using Fuzzy Sugeno as an additional determinant of energy and Naive Bayes method as decision maker. This is because the need for food intake and diet has become a problem for humans. To prevent excess intake of food it needs dietary adjustments or so-called diet. But in daily life, people sometimes hard to determine the type of diet that is suitable for them. So we need a system that can determine the type of diet that is suitable for a person. The data that used as a reference for decision support are age, daily caloric requirement, Body Mass Index (BMI), blood pressure, cholesterol, uric acid and blood sugar levels. Results of system testing showed from a sample of 30 data there are 26 appropriate data and 4 inappropriate data to determine the type of diet by the system with the success rate of 86.7%.
Iqro Reading Learning System through Speech Recognition Using Mel Frequency Cepstral Coefficient (MFCC) and Vector Quantization (VQ) Method Nurhasanah, Youllia Indrawaty; Amelia Dewi, Irma; Ade Saputro, Bagus
IJAIT (International Journal of Applied Information Technology) Vol 02 No 01 (May 2018)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v2i01.1173

Abstract

Historically, the study of Qur'an in Indonesia evolved along with the spread of Islam. Learning methods of reading the Qur'an have been found ranging from al-Baghdadi, al-Barqi, Qiraati, Iqro', Human, Tartila, and others, which can make it easier to learn to read the Qur'an. Currently, the development of speech recognition technology can be used for the detection of Iqro vol 3 reading pronunciations. Speech recognition consists of two general stages of feature extraction and speech matching. The feature extraction step is used to derive speech-feature and speech-matching stages to compare compatibility between test sound and train voice. The speech recognition method used to recognize Iqro readings is extracting speech signal features using Mel Frequency Cepstral Coefficient (MFCC) and classifying them using Vector Quantization (VQ) to get the appropriate speech results. The result of testing for speech recognition system of Iqro reading has been tested for 30 peoples as a sample of data and there are 6 utterances indicating the information failed, so the system has a success rate of 80%.
Perbandingan Varian Model EFFICIENTNETV2 pada Citra Histologi Osteosarcoma Nurhasanah, Youllia Indrawaty; Nurfayza, Rifasya Ayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Osteosarcoma merupakan jenis kanker tulang ganas yang menyerang ujung tulang panjang dan berpotensi menyebar ke organ lain (metastasis). Diagnosis dini berperan penting untuk mendukung hasil pengobatan yang optimal. Penelitian ini menggunakan Convolutional Neural Network (CNN) untuk menganalisis dan mengklasifikasikan citra histologi tulang Osteosarcoma, dengan membandingkan kinerja tiga varian model EfficientNetV2 (S, M, dan L). Dataset yang digunakan adalah citra histologi Osteosarcoma yang telah didigitalisasi dan dataset ini diproses melalui tahap preprocessing, augmentasi, serta training menggunakan ketiga model, lalu diproses pula melalui konfigurasi hyperparameter. Evaluasi kinerja model dilakukan berdasarkan akurasi, Presisi, Recall, dan F1-Score. Hasil penelitian menunjukkan bahwa EfficientNetV2-S mencapai akurasi tertinggi sebesar 88,86% dengan efisiensi yang lebih baik, sedangkan EfficientNetV2-Memiliki stabilitas klasifikasi yang lebih baik dengan F1-Score yang lebih konsisten dengan akurasi sebesar 88,80%. Sementara itu, EfficientNetV2-L menunjukkan hasil akurasi yang kompetitif tetapi memerlukan sumber daya komputasi yang lebih besar. Analisis lebih lanjut menunjukkan bahwa pemilihan model tidak hanya bergantung pada akurasi, tetapi juga mempertimbangkan ukuran model dan kebutuhan komputasi. Hasil penelitian ini menunjukkan bahwa EfficientNetV2-S merupakan pilihan optimal berdasarkan akurasi dan efisiensi, sedangkan EfficientNetV2-M lebih unggul dalam stabilitas klasifikasi. Hasil penelitian ini dapat menjadi referensi dalam pengembangan sistem berbasis deep learning untuk diagnosis kanker tulang di masa depan.   Abstract Osteosarcoma is a malignant bone cancer that primarily affects the ends of long bones and has a high potential for metastasis to other organs. Early diagnosis is crucial to improving treatment outcomes and patient prognosis. This study employs a Convolutional Neural Network (CNN) to analyze and classify histological images of Osteosarcoma by comparing the performance of three variants of the EfficientNetV2 model (S, M, and L). The dataset that used in this study is a digitized Osteosarcoma histology images, and will be processed through the preprocessing, data augmentation, and model training using the three different EfficientNetV2 variants. Additionally, hyperparameter tuning is performed to optimize model performance. The evaluation of model performance is conducted based on accuracy, precision, recall, and F1-score. The results showed that EfficientNetV2-S achieved the highest accuracy of 88.86% with better efficiency, while EfficientNetV2-S had better classification stability with a more consistent F1-Score with an accuracy of 88.80%. Meanwhile, EfficientNetV2-L showed competitive accuracy results but required more computational resources. Further analysis reveals that model selection not only depends on accuracy, but also considers model size and computational requirements. The results show that EfficientNetV2-S is the optimal choice based on accuracy and efficiency, while EfficientNetV2-M is the optimal choice in classification stability. The results of this study can serve as a reference in the development of deep learning-based systems for bone cancer diagnosis in the future.  
Identifikasi Nada antara Suling Sunda dan Suling Rekorder dengan Menggunakan Metode Frequency Cpstral Coefficients (MFCC) dan Dynamic Time Warping (DTW) Suryadikarsa, Fawwaz Muhammad; Nurhasanah, Youllia Indrawaty; Dewi, Irma Amelia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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Abstract

Suling adalah sebuah instrumen musik yang biasa digunakan oleh para pemain musik ataupun masyarakat pada umumnya. Suling sunda merupakan alat musik tradisional asal Pasundan ini mampu menghipnotis yang mendengarkannya karena nada khasnya yang indah, suling rekorder adalah alat musik modern dengan bunyi seperti peluit. Tetapi tidak banyak orang tahu bahwa nada pada suling sunda bisa juga dimainkan pada suling rekorder, sehingga pelajar yang mengikuti dengung (seni sunda) harus membawa 2 buah suling ke sekolah apabila bertepatan dengan kelas musik. Identifikasi nada antara suling sunda dan rekorder ini adalah sebuah sistem yang digunakan untuk membandingkan dan mencocokan frekuensi nada yang sama antara suling sunda dan suling rekorder, agar musik yang dimainkan di suling sunda bisa juga dimainkan di suling rekorder. Penelitian ini dibuat dengan menggunakan algoritma Mel Frequency Cepstral Coefficient (MFCC) sebagai metode proses ekstraksi ciri dan algoritma Dynamic Time Warping (DTW) sebagai identifikasi nada dengan perbedaan waktu pada saat perekaman. Berdasarkan hasil penelitian yang dilakukan, sistem mengidentifikasi nada suling sunda ke suling rekorder dengan total tingkat akurasi sebesar 70% dengan data latih diambil dari seorang ahli, dan sistem gagal  mengidentifikasi nada suling sunda ke suling rekorder dengan total 30%. Ketidaksesuaian identifikasi nada diakibatkan jarak ekstrasi ciri antar nada yang berdekatan dan karena suling sunda bisa menggunakan nada rendah, standar, dan tinggi dan untuk penelitian ini hanya nada standar saja yang digunakan dan pada saat pengambilan data uji semua peniup adalah orang awam terhadap meniup suling sehingga kerap terjadi kesalahan pada saat proses pengambilan data uji. AbstractFlute is a musical instrument commonly used by music players or public people. Sundanese flute is a traditional musical instrument from Pasundan that is can hypnotize people who hear it because of the beautiful special tone, flute recorder is a modern musical instrument with sounds like whistle. But not many people know the tone  Sundanese flute can be played using recorder flutes, so that students who follow the dengung (Sundanese art) must bring 2 flutes to school when it coincides with the music class. The identification between tone of Sundanese flute and flute recorder is a system to compare and match frequency same tones between Sundanese flute and recorder flute so the music that is usually played on Sundanese flute can also be played on the flute recorder. This research was made using an algorithm Mel Frequency Cepstral Coefficient (MFCC) to perform feature extraction and algorithm processes Dynamic Time Warping (DTW) is used to identify the time difference of recording. Based on results of research, the system can identifies Sundanese flute tones to refine recorders with total accuracy rate of 70% with training data taken from an expert, and the system fails to identify the tone flute Sunda to flute recorder with a total of 30%. Incompatibility matching tones caused by the distance between adjacent tones and because Sundanese flutes can use low, standard, and high tones and for this study only standard tones are used and when taking test data all blowers are laymen to blow flutes so that errors often occur during the process of taking test data.
Co-Authors Ade Saputro, Bagus Adhitya Yoga Pratama Idwal Adri Pramana Putra Putra Adriadi Karya Anugerah Afis Siswantini Afriyanti Dwi Kartika Amelia Dewi, Irma Andhika Pahlevi Andriana Andriana Andriana Zulkarnain Anugerah, Adriadi Karya Arga Hutama, Mahesa Asep Chaesar Trianto ASEP NANA HERMANA Asep Nana Hermana Bagus Ade Saputro D, Irma Amelia DEWI, IRMA AMELIA Dian Purnomo Dian Purnomo Dina Budhi Utami Dina Budhi Utami, Dina Budhi Dita Permatasari Dita Permatasari Ersa Triansyah Fabio Fahri Pratama Fadhlin Prasetia Febriandi, Arry Hafizh Achmad Dinan Harianja, Yandri Hermana, Asep Nana Idwal, Adhitya Yoga Pratama Irham Sidik Permana Irma Amelia D Irma Amelia Dewi Irma Amelia Dewi Jasman Pardede KHARISMA, MUHAMMAD Khemal Rizky Ramadhan Kurnia Ramadhan Putra Kurnia, Ema LUKMAN, RIZKI Mahesa Arga Hutama Milda Gustiana Husada Muhammad Ichwan Muhammad Ichwan MUHAMMAD KHARISMA Nana Hermana, Asep Naufal, Muhammad Thoriq Ninda Halimah Alghoniyyah Nurfayza, Rifasya Ayu Pahlevi, Andhika Permana, Irham Sidik Prasetia, Fadhlin Putra, Adri Pramana Putra Ramadhan, Khemal Rizky riansyah, rizki rahmat Rio Korio Utoro rizki rahmat riansyah Siswantini, Afis Sofia Umaroh Sofia Umaroh Suryadikarsa, Fawwaz Muhammad Sutarti Sutarti Tristiyono, Anggi Yasmin Math Khader Zulkarnain, Andriana