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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 Lontar Komputer: Jurnal Ilmiah Teknologi 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 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 Sewagati
Anny Yuniarti
Institut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Pengenalan Wajah Menggunakan Two Dimensional Linear Discriminant Analysis Berbasis Optimasi Feature Fusion Strategy Sahmanbanta Sinulingga; Chastine Fatichah; Anny Yuniarti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 3 No 1 (2016): JATISI SEPTEMBER 2016
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.761 KB) | DOI: 10.35957/jatisi.v3i1.59

Abstract

The era of technology today,, research on biometric image is not common to do. One well researched biometric image is a face recognition (face recognition). Problems on the human face recognition is a diversity of features or shape between one another face to face. Therefore, the need for facial feature extraction and classification using a particular method so that the classification can be recognized correctly.In this study proposed feature extraction method that can overcome the problems of non-linear automatic data contained in the face image, called the Two Dimensional Linear Discriminant Analysis based on Feature Fusion Strategy (TDLDA-FFS). Not stopping on feature extraction, classification methods proposed also faces that can overcome the problems of the adaptive matrix which aims to study the benefit of weight on each - each input with the method Relevanced Generalized Learning Vector quantization (GRLVQ).This research integrates methods TDLDA-FFS and GRLVQ for face recognition. With the combination of both methods are proven to provide optimal results with a level of recognition accuracy ranged between 77.78% to 82.22% with a pilot using a databaseof facial images from the Institute of Business and Information Stikom Surabaya. While the test uses a database derived from YaleB Database achieve accuracy levels ranging from 88.89% to 94.44%.
Ensemble Method for Indonesian Twitter Hate Speech Detection M. Ali Fauzi; Anny Yuniarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp294-299

Abstract

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.
Knowledge Dictionary for Information Extraction on the Arabic Text Data Saputra, Wahyu Syaifullah Jauharis; Arifin, Agus Zainal; Yuniarti, Anny
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

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

Abstract

Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%.
Effect of Number of Face Images based on Illumination Variation in the Training Process on Face Recognition Results Budi Nugroho; Anny Yuniarti; Eva Yulia Puspaningrum
Prosiding International conference on Information Technology and Business (ICITB) 2019: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 5
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

The research is related to face recognition which is influenced by illumination factor. The method used is the Robust Regression, which has a better performance than many other methods. The empirical experiment, which uses Yale Face Database B Cropped, is conducted to determine the effect of number of face images in the training process on face recognition perfomance. The hypothesis proposed in this research is the greater number of face images will result in higher facial recognition performance. The empirical experiment was conducted on this research to prove the hypothesis. Based on experiments that have been done, in general, the process of data training with many images will result in high performance of face recognition. But, this trend only occurs in images in the similar illumination condition. Illumination variation of face images also have significant impact on face recognition results. The process of training data with images of illumination variations (from several subsets of the face database) results in better face recognition performance than the process of training data with images of similar illumination conditions (from a subset of the face database). By using 19 images from subset 5 of the face database, face recognition accuracy is obtained at 95.11%. Whereas by only using 5 images from several subsets, obtained face recognition accuracy up to 96.10%. Even by using 7 images from several subsets, the accuracy obtained is up to 99.47%.Keywords: Face Recognition Performance, Robust Regression, Data Training
Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy I Made Satria Bimantara; Anny Yuniarti
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62874

Abstract

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.
Effective Coronary Artery Disease Prediction Using Bayesian Optimization Algorithm and Random Forest Amrullah, Muhammad Syiarul; Yuniarti, Anny
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5554

Abstract

Coronary artery disease (CAD) continues to be a major global health issue, demanding more effective diagnostic techniques. This study introduces a detailed framework for CAD detection that integrates data preprocessing, feature engineering, and model optimization to enhance diagnostic accuracy. Our methodology encompasses comprehensive data cleansing to eliminate inconsistencies, transformations for better feature representation, feature reduction to highlight relevant variables, data augmentation for balanced class distribution, and optimization strategies to boost model performance. We employed a random forest classifier, trained via 5-fold cross-validation, to develop a robust model. The efficacy of this model was tested through two key experiments: firstly, by comparing its performance on preprocessed versus raw data, and secondly, against previous studies. Results demonstrate that our model significantly surpasses the one trained on raw data, achieving an accuracy of 93.00% compared to 86.16%. Moreover, when compared with existing research, our random forest model excels with an accuracy of 93.00%, a F1 Score of 93.00%, and a recall of 94.00%. Despite the superior precision of the Hybrid PSO-EmNN model found in other research, our results are promising. They underscore the potential of advanced feature engineering to further refine the effectiveness of CAD detection models. The study concludes that meticulous data preprocessing and model optimization are crucial for enhancing CAD diagnostics. Future research should focus on incorporating more sophisticated feature engineering techniques and expanding the dataset to improve the model’s precision and overall diagnostic capabilities.
Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features Arifin, Miftahol; Yuniarti, Anny; Suciati, Nanik
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1529

Abstract

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.
Accounting Treatment of Coffee as Bearer Plant Asset at Perumda Perkebunan Kahyangan Jember Suteja, Diana; Febriana Suryawati, Rindah; Soetedjo, Soegeng; Yuniarti, Anny; Sauri, Sofyan; Puspitasari, Leny
Jurnal Manajemen dan Organisasi Vol. 15 No. 4 (2024): Jurnal Manajemen dan Organisasi
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jmo.v15i4.52261

Abstract

This study explores the accounting treatment of coffee plant as a bearer plant asset at Perumda Perkebunan Kahyangan Jember, a local state-owned enterprise in East Java Province Indonesia. As the agricultural sector plays a crucial role in Indonesia's economy, the proper classification, recognition, measurement, and disclosures of coffee plant as bearer plants are critical for financial transparency and effective management. This study examines how relevant Indonesian Financial Accounting Standards (i.e. PSAK 16 Revision 2011 on Fixed Assets, PSAK 69 on Agriculture, PSAK 14 on Inventory, and PSAK 48 on Impairment of Assets) and International Financial Reporting Standard (i.e. IAS 41) applied in the financial reporting of coffee as a bearer plant at Perumda Perkebunan Kahyangan Jember. Using a qualitative research method with the case study approach, this study provides empirical evidence on how the implementation of such accounting standards, and the obstacle faced by the company presented based on the perspective of recognition, measurement, and disclosure relevant to coffee plant.
KLIKHIU: APLIKASI PENDUKUNG PEMASARAN DENGAN STRATEGI JARINGAN DAN STANDARISASI HARGA Imam Kuswardayan; Darlis Herumurti; Anny Yuniarti; Hadziq Fabroyir; Siska Arifiani
Jurnal Widya Laksmi: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2024): Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat)
Publisher : Yayasan Lavandaia Dharma Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59458/jwl.v4i1.66

Abstract

Harga produk di platform online seringkali berada di bawah standar. Hal ini terjadi karena beberapa faktor, seperti persaingan yang tinggi diantara sesama penjual, biaya operasional sistem penjualan dengan platform online yang rendah, serta kurangnya pengawasan terhadap kualitas produk. Dalam beberapa kasus, penjual mungkin menjual produk dengan harga yang lebih rendah dari harga pasar untuk menarik pelanggan. Namun, harga yang terlalu rendah dapat menimbulkan kecurigaan terhadap kualitas produk. Selain itu, biaya operasional yang rendah dapat mengurangi kualitas layanan dan pengiriman produk. Oleh karena itu, untuk menjaga kesejahteraan pelaku usaha dalam ekosistem perdagangan digital yang semakin berkembang, dikembangkan sebuah aplikasi yang dapat memastikan bahwa produk perusahaan dapat dipasarkan dengan harga yang sesuai standar yang berlaku. Aplikasi yang dikembangkan, KlikHIU, memiliki fitur utama yang dapat mencegah penjual menetapkan harga jual produk yang dipasarkannya di bawah harga standar. Hal ini bertujuan untuk memastikan persaingan yang sehat antara penjual. Aplikasi KlikHIU juga dapat menghubungkan penjual dengan jaringan distribusi. Dengan aplikasi KlikHIU yang dikembangkan, perusahaan dapat memberikan kepastian kepada konsumen tentang harga dan kualitas produk yang mereka beli.
KONSELING KARIR SISWA SEKOLAH MENENGAH ATAS MENGGUNAKAN TEKNOLOGI REALITAS VIRTUAL hadziq fabroyir; Achmad Fahriza; Muhammad Rayyaan Fatikhahur Rakhim; Achmad Chabiburrohman; Ahmad Raihan Muzakki; Fawwaz Abdulloh Al-Jawi; Siska Arifiani; Anny Yuniarti
Jurnal Widya Laksmi: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2024): Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat)
Publisher : Yayasan Lavandaia Dharma Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59458/jwl.v4i1.67

Abstract

Pada pengabdian masyarakat ini, teknologi realitas virtual atau virtual reality (VR) dimanfaatkan untuk mendukung proses konseling karir siswa sekolah menengah atas (SMA). Tim pengabdi bekerja sama dengan Madrasah Aliyah Negeri Insan Cendekia (MAN IC) Pasuruan yang menghadapi masalah rendahnya akurasi rekomendasi dan relevansi karir bagi siswa. Evaluasi dampak penggunaan teknologi VR dalam konseling karir dan pemilihan jurusan kuliah dilakukan terhadap 55 siswa MAN IC Pasuruan. Metode melibatkan pemaparan materi tentang VR, penyelenggaraan konseling menggunakan VR, pengerjaan psikotes, serta pengisian kuesioner minat bakat oleh siswa. Hasil tes dievaluasi lebih lanjut menggunakan Weighted Standard Deviation dan Jenks Natural Breaks. Evaluasi menunjukkan dominasi variasi tingkat kecocokan sedang (di rentang 80-90) antara minat bakat, potensi akademik, dan hasil tes VR. Proses ini memberikan pemahaman yang lebih mendalam tentang profil siswa dalam konteks minat, bakat, dan potensi akademik mereka, memperkuat pendekatan komprehensif dalam mendukung konseling karir di tingkat SMA.
Co-Authors 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 Ardy, Rizky Damara 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 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 Hudan Studiawan I Made Satria Bimantara I Made Widiartha I Putu Gede Hendra Suputra Imam Kuswardayan Ishardan Ishardan Isye Arieshanti Kelly Rossa Sungkono Khairun Nisa Kostidjan, Okky Darmawan Lutfiani Ratna Dewi M. Ali Fauzi M. Ali Fauzi Mafazy, Muhammad Meftah Maulana, Hendra MIFTAHOL ARIFIN, MIFTAHOL Mohamad Dion Tiara Muhammad I. Rosadi, Muhammad I. Muhammad Rayyaan Fatikhahur Rakhim Muhammad Riduwan Nadya Anisa Syafa Nafiiyah, Nur Nanik Suciati Nisa', Chilyatun Oviyanti Mulyani Pasnur Pasnur Purwanto, Yudhi Puspitasari, Leny Ratri Enggar Pawening Reginawanti Hindersah Ridho Rahman Hariadi Rindah Febriana Suryawati Sahmanbanta Sinulingga Saiful Bahri Musa Saprina Mamase Saputra, Wahyu Syaifullah Jauharis Siska Arifiani Soegeng Soetedjo Sofyan Sauri, Sofyan Takashi Nakamoto Wahyu Syaifullah Jauharis Saputra Wibowo, Della Aulia Wijayanti Nurul K Wijayanti Nurul Khotimah