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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Manajemen dan Organisasi Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Agrikultura Jurnal Teknik ITS IPTEK Journal of Proceedings Series TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Seminar Nasional Informatika (SEMNASIF) Dental Journal (Majalah Kedokteran Gigi) Jurnal Ilmiah Pertanian Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Makara Journal of Technology Journal of Business, Social and Technology Sewagati
Anny Yuniarti
Institut Teknologi Sepuluh Nopember
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KAMUS BAHASA ARAB – INDONESIA ONLINE DENGAN PEMECAHAN SUKU KATA MENGGUNAKAN METODE PARSING Anny Yuniarti; Aris Tjahyanto; Imam Kuswardayan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 3, No 1 Januari 2004
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (290.743 KB) | DOI: 10.12962/j24068535.v3i1.a125

Abstract

Kebutuhan umat Islam akan fasilitas penunjang belajar bahasa Arab di Indonesia masih belum terpenuhi dengan optimal. Kamus bahasa Arab yang beredar di pasaran sulit dipahami karena minimnya pengetahuan tentang ilmu tata bahasa Arab di kalangan umat Islam. Pada penelitian ini dikembangkan sebuah perangkat lunak yang berfungsi menerjemahkan kata berbahasa Arab dengan metode parsing sehingga dapat mencakup kata-kata yang telah mengalami perubahan bentuk dari bentuk dasarnya. Karena kata bahasa Arab memiliki turunan kata yang jumlahnya cukup besar, dan supaya kamus efisien, maka tidak semua turunan kata disimpan dalam basisdata. Oleh sebab itu diperlukan suatu cara untuk mengenali pola kata, dan cara mengetahui bentuk dasar suatu kata. Keseluruhan perangkat lunak ini diimplementasikan berbasis web sehingga memudahkan pengaksesan pengguna. Dan pengguna tidak memerlukan proses instalasi perangkat lunak atau sistem operasi tertentu. Pembuatan perangkat lunak ini didahului dengan perancangan proses dan perancangan interface. Kemudian rancangan tersebut diimplementasikan menjadi sebuah perangkat lunak yang siap untuk dipakai. Perangkat lunak yang sudah jadi tersebut telah diuji coba sesuai dengan spesifikasi kebutuhan dan kemampuan yang dimiliki yaitu melakukan manajemen pada basisdata rules dan basisdata kamus. Dengan demikian perangkat lunak ini dapat dipakai sebagai kamus bahasa Arab digital. Kata kunci : Parser, Bahasa Arab, Unicode.
PENGHILANGAN NOISE PADA CITRA BERWARNA DENGAN METODE TOTAL VARIATION Anny Yuniarti; Nanik Suciati; Fetty Tri A.
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 5, No 1 Januari 2006
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.114 KB) | DOI: 10.12962/j24068535.v5i1.a199

Abstract

Saat ini multimedia telah menjadi teknologi yang cukup dominan. Tukar menukar informasi dalam bentuk citra sudah banyak dilakukan oleh masyarakat. Citra dengan kualitas yang baik sangat diperlukan dalam penyajian informasi. Citra yang memiliki noise kurang baik digunakan sebagai sarana informasi, oleh karena itu diperlukan suatu metode untuk memperbaiki kualitas citra. Metode yang digunakan dalam penelitian ini adalah metode total variation untuk penghilangan noise yang dapat diterapkan untuk model warna nonlinier, yaitu Chromaticity-Brightness (CB) dan Hue-Saturation-Value (HSV). Filter total variation disebut filter yang bergantung pada data citra karena koefisien filternya diperoleh dari pemrosesan data citra dengan rumusan yang baku. Sehingga filter mask untuk masing-masing piksel memiliki kombinasi koefisien yang berbeda. Metode ini menggunakan proses iterasi untuk menyelesaikan persamaan dasar yang nonlinier. Uji coba dilakukan dengan menggunakan 30 data dengan berbagai jenis noise, yaitu gaussian, salt and pepper dan speckle. Uji coba pembandingan dengan metode filter median dan filter rata-rata. Dari percobaan ini menunjukkan bahwa metode total variation menghasilkan citra yang lebih baik daripada metode filter median maupun filter rata-rata, terutama pada citra yang terdegradasi dengan noise gaussian dan speckle. Kata kunci : Denoising, Total variation, Nonlinear Color Model
KLASTERISASI DOKUMEN MENGGUNAKAN WEIGHTED K-MEANS BERDASARKAN RELEVANSI TOPIK Muhammad Riduwan; Chastine Fatichah; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 17, No. 2, Juli 2019
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v17i2.a892

Abstract

Jumlah penelitian di dunia mengalami perkembangan yang pesat, setiap tahun berbagai peneliti dari penjuru dunia menghasilkan karya ilmiah seperti makalah, jurnal, buku dsb. Metode klasterisasi dapat digunakan untuk mengelompokkan dokumen karya ilmiah ke dalam suatu kelompok tertentu berdasarkan relevansi antar topik. Klasterisasi pada dokumen memiliki karakteristik yang berbeda karena tingkat kemiripan antar dokumen dipengaruhi oleh kata-kata pembentuknya. Beberapa metode klasterisasi kurang memperhatikan nilai semantik dari kata. Sehingga klaster yang terbentuk kurang merepresentasikan isi topik dokumen. Klasterisasi dokumen teks masih memiliki kemungkinan adanya outlier karena pemilihan fitur teks yang tidak optimal. Oleh karena itu dibutuhkan pemrosesan data yang tepat serta metode yang mengoptimalkan hasil klaster. Penelitian ini mengusulkan metode klasterisasi dokumen menggunakan Weighted K-Means yang dipadukan dengan Maximum Common Subgraph. Weighted k-means digunakan untuk klasterisasi awal dokumen berdasarkan kata-kata yang diekstraksi. Pembentukan Weighted K-Means berdasarkan perhitungan Word2Vec dan TextRank dari kata-kata dalam dokumen. Maximum common subgraph merupakan tahap pembentukan graf yang digunakan dalam penggabungan klaster untuk menghasilkan klaster baru yang lebih optimal. pembentukan graf dilakukan dengan perhitungan nilai Word2vec dan Co-occurrence dari klaster. Representasi topik dokumen tiap klaster dapat dihasilkan dari pemodelan topik Latent Dirichlet Allocation (LDA). Pengujian dilakukan dengan menggunakan dataset artikel ilmiah dari Scopus. Hasil dari analisis Koherensi topik menunjukkan nilai koherensi usulan metode adalah 0.532 pada dataset 1 yang bersifat homogen dan 0.472 pada dataset 2 yang bersifat heterogen.
CLASSIFICATION OF LUNG AND COLON CANCER TISSUES USING HYBRID CONVOLUTIONAL NEURAL NETWORKS Chilyatun Nisa'; Nanik Suciati; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1225

Abstract

Colon and lung cancers are two highly lethal kinds of cancer which can often coexist and pose a new challenge for accurate diagnosis. While research often concentrates on detecting a single cancer in a specific organ, this study proposes an innovative machine-learning approach to identify both colon and lung cancers. The objective is to create a hybrid machine learning classification model to enhance diagnostic precision. The LC25000 dataset comprises 25,000 color histopathological image samples of lung and colon cell tissues, indicating the presence or absence of cancer (adenocarcinoma). Image features are extracted using the pre-trained VGG-16 model. The cancer type is identified through three machine learning classification algorithms: Stochastic Gradient Descent (SGD), Random Forest (RF), and K-Nearest Neighbor (KNN). The model's evaluation employed a 10-fold cross-validation technique, with CNN-SGD exhibiting the highest performance based on evaluation metrics. On a scale of 0 to 100, it scored 99.8 for Area Under Curve (AUC) and 98.88 for Classification Accuracy (CA). CNN-RF, a model with performance closely following CNN-SGD, demonstrates training times 58.3 seconds faster than CNN-SGD. Meanwhile, CNN-KNN ranks last among the models evaluated in this study based on its F1, recall, AUC, and CA scores.
Audio Feature Analysis and Selection for Deception Detection in Court Proceedings Muhammad Meftah Mafazy; Chastine Fatichah; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1250

Abstract

Deception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model's accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%.
Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques Rizky Damara Ardy; Anny Yuniarti; Christy Atika Sari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1255

Abstract

This study aims to enhance face detection performance in 360-degree videos by utilizing advanced image augmentation techniques with the YOLOv8 algorithm, which is effective for real-time object detection. Acknowledging the unique challenges posed by equirectangular projection, this research introduces a novel equirectangular augmentation method specifically designed for this medium. Our findings demonstrate a remarkable 1.346% improvement in detection accuracy in Equirectangular Projection (ERP) settings compared to default YOLOv8 augmentation strategies. This significant enhancement not only addresses the geometric distortions inherent in panoramic video formats but also emphasizes the critical need for tailored augmentation approaches to improve face detection in complex environments. By showcasing the effectiveness of these customized methods, this research contributes to the growing field of deep learning applications for immersive video technologies, with implications for sectors like security, virtual reality, and interactive media. Ultimately, this work highlights the potential of innovative augmentation techniques to ensure robust face detection in challenging visual contexts.
Explainable BERT Embeddings for Veracity Assessment in Criminal Investigations Thoha Haq; Chastine Fatichah; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1327

Abstract

The binary classification of truth and lies is often a detriment in criminal investigations as statements are intentionally not entirely true nor entirely false. This ambiguity in the veracity of their claims demands more extensive methods such as explainable models. Explainable models, particularly SHapley Additive exPlanations (SHAP), can help dissect statements and narrow down information for a more thorough investigation. Data from the Miami University Deception Database, comprising of various statements and their veracity, was analyzed for its linguistic features. This research utilizes Bidirectional Encoder Representations from Transformers (BERT) Embeddings to provide contextual understanding of statements and Sentiment Lexicons to provide domain specific knowledge. Results show that the R² (coefficient of determination) of the 2-Gram embedding performed the best at 0.39 by being able to capture more context than the 1-Gram embedding while being more general than the 3-Gram and 4-Gram embeddings. Each variant of the BERT Embedding was proven to be much more effective than general word embedding such as GloVe, Word2Vec and FastText. SHAP values were able to capture key points of interest in a statement by narrowing down pivotal and decision-making points. These results highlight potential indicators of either deceptive or truthful language such as the word ‘something’ and ‘our’. These points of interest can help humans focus on key points of investigation and intervention.
Enhancing Intraoral Dental Lesion Localization via Multi-Scale Ensemble Learning Using a Robust Weighted Box Fusion Approach Hisyam Syarif; Chastine Fatichah; Anny Yuniarti; Xinyou Zeng; Abdullah Al-Haddad
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20127

Abstract

The early detection of dental diseases is essential for preventing severe oral health complications. However, automated lesion detection utilizing intraoral images remains highly challenging due to severe tooth overlap, occlusion, and visually similar anatomical structures. Under these complex conditions, conventional single-stage object detectors frequently produce redundant and inaccurate bounding boxes, which significantly degrades localization precision. To explicitly resolve this problem, this study proposes a robust multi-scale ensemble learning strategy that integrates bounding box predictions from YOLOv5 and YOLOv8 through a Weighted Boxes Fusion (WBF) mechanism. Unlike traditional post-processing techniques such as Non-Maximum Suppression (NMS) and Soft-NMS, the proposed method fuses overlapping bounding boxes by leveraging confidence-weighted spatial aggregation, thereby preserving critical detection information. Extensive experiments were conducted on a publicly validated intraoral image dataset comprising four distinct clinical classes: caries, cavity, cracks, and normal teeth. Quantitative evaluations demonstrate that the proposed WBF ensemble approach substantially outperforms single- model baselines. The integrated model achieves a mean Average Precision (mAP@0.5) of 66.14%, a Precision of 66.47%, and an Intersection over Union (IoU) of 90.83%, representing a massive improvement over the baseline mAP values of approximately 36 to 37%. Furthermore, rigorous statistical testing validates that these performance gains are highly significant (p < 0.05). Ultimately, these findings indicate that the proposed ensemble framework provides a reliable, high-precision solution for intraoral dental lesion localization, offering substantial viability for real-world clinical diagnostic applications.
Yeo-Johnson Transformation Usage in Data Preprocessing for Well Production Prediction Using Deep Neural Networks (DNN) Rizky, Alringga; Yuniarti, Anny
Journal of Business, Social and Technology Vol. 7 No. 2 (2026): Journal of Business, Social and Technology
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jbt.v7i2.607

Abstract

Background: The accurate prediction of infill well production is one of the major bottlenecks for hydrocarbon reservoir development. Traditional reservoir simulation tools are computationally expensive, taking weeks to months per scenario. Objective: This paper presents the development of a Deep Neural Network (DNN) model for prediction with hyperparameter optimization using the Tree-structured Parzen Estimator (TPE) to predict pay porosity (PORPAYX) in infill wells of the Pertamina Hulu Sanga Sanga field. Methods: A DNN model was developed to predict oil well production based on subsurface and production features from a comprehensive dataset of Pertamina Hulu Sanga Sanga reservoir characteristics and production data. Details of our method include: training the model on a robust dataset, hyperparameter tuning using the Tree-structured Parzen Estimator (TPE), and K-fold cross-validation for performance validation. Results: Scaling normalized the data in such a way that every feature had equal influence during model training, enabling better learning and accurate prediction. In contrast, fitting the model using unscaled data resulted in an R² of less than zero (a negative score), meaning that the model could not explain the variability in the data. The mean R² score of the unscaled data model was −0.08496, along with a higher MSE = 0.009057 and RMSE = 0.095148. This was due to the model's failure to process features with varying scales, which prevented proper learning and prediction. Conclusion: Residual plots confirmed that the model trained with scaled data met the assumptions of linearity and normality.
Performance of Contrast Adjustment Techniques on The Face Recognition Method with Test Data Under Varying Lighting Conditions Budi Nugroho; Hendra Maulana; Anny Yuniarti
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.130

Abstract

In the face recognition process influenced by lighting, the application of the image enhancement process at the preprocessing stage plays an important role in normalizing image contrast so that the quality of the input image becomes better. This step is expected to improve face recognition performance. In this study, we implement a lighting-influenced face recognition method, namely Robust Regression, and test several image enhancement techniques in the preprocessing phase to determine their effects on face recognition performance under different image lighting conditions, including Contrast-limited Adaptive Histogram Equalization (CLAHE), Histogram Equalization (Histeq), and Image Intensity Adjustment (Imadjust). HE uses a global technique that adjusts the overall intensity of the image. CLAHE uses a local technique that adjusts the intensity of pixels based on their surrounding areas. Meanwhile, the Imadjust function adjusts the intensity of image pixels based on the specified minimum and maximum values. The experiment is conducted using the AR Face Database which contains images affected by lighting factors. Lighting conditions include several categories, namely low, medium, high, and very high (extreme) lighting conditions. The experimental scenario is carried out by comparing the results of face recognition using several preprocessing techniques on each test data. The experimental results show that image enhancement techniques improve the performance of face recognition. The face recognition approach that adds the CLAHE technique to the preprocessing shows the highest performance of 95.87%. Meanwhile, the face recognition approach that adds the Imadjust technique to the preprocessing shows the lowest performance of 84.38%.
Co-Authors Abdullah Al-Haddad Achmad Chabiburrohman Achmad Fahriza Agus Arifin Agus Arifin, Agus Agus Z. Arifin, Agus Z. Agus Zainal Arifin Agus Zainal Arifin Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ahmad Raihan Muzakki Akira Asano Akira Taguchi Alifiansyah Arrizqy Hidayat Amrullah, Muhammad Syiarul Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Anita Hakim Nasution Arif Fathur Mahmuda Arifiani, Siska Arifzan Razak Aris Fanani Aris Tjahyanto Arya Yudhi Wijaya Berlian Rahmy Lidiawaty Betty Natalie Fitriatin Bilqis Amaliah Budi Nugroho Budi Nugroho Chastine Fatichah Chilyatun Nisa&#039; Christy Atika Sari Darlis Heru Mukti Darlis Herumurti Devira Wiena Pramintya Dhian Satria Yudha Kartika Diana Suteja Dini Adni Navastara, Dini Adni Eva Yulia Puspaningrum Fawwaz Abdulloh Al-Jawi Feni Siti Fauziah2 Fetty Tri A. Fiandra Fatharany Gulpi Qorik Oktagalu Pratamasunu Hadziq Fabroyir Handayani Tjandrasa Hani Ramadhan Hidiyah Ayu Ratna Ma’rufah Hisyam Syarif Hudan Studiawan I Made Satria Bimantara I Made Widiartha I Putu Gede Hendra Suputra Imam Kuswardayan Imam Kuswardayan Ishardan Ishardan Isye Arieshanti Kelly Rossa Sungkono Khairun Nisa Kostidjan, Okky Darmawan Lutfiani Ratna Dewi M. Ali Fauzi M. Ali Fauzi Maulana, Hendra MIFTAHOL ARIFIN, MIFTAHOL Mohamad Dion Tiara Muhammad I. Rosadi, Muhammad I. Muhammad Meftah Mafazy Muhammad Rayyaan Fatikhahur Rakhim Muhammad Riduwan Nadya Anisa Syafa Nafiiyah, Nur Nanik Suciati Nanik Suciati Oviyanti Mulyani Pasnur Pasnur Purwanto, Yudhi Puspitasari, Leny Ratri Enggar Pawening Reginawanti Hindersah Ridho Rahman Hariadi Riduwan, Muhammad Rindah Febriana Suryawati Rizky Damara Ardy Rizky, Alringga Sahmanbanta Sinulingga Saiful Bahri Musa Saprina Mamase Saputra, Wahyu Syaifullah Jauharis Siska Arifiani Soegeng Soetedjo Sofyan Sauri, Sofyan Takashi Nakamoto Thoha Haq Wahyu Syaifullah Jauharis Saputra Wibowo, Della Aulia Wijayanti Nurul K Wijayanti Nurul Khotimah Xinyou Zeng