Lab-IS/filkom-research-papers huggingface dataset:
Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization.
Authors: Tirana Noor Fatyanosa, Wayan Firdaus Mahmudy
Journal: International Journal on Electrical Engineering & Informatics
Year: 2019
Topics: Optimization; Evolution Strategies
Abstract: The biggest obstacle faced by cattleman in cattle fattening is the high feed cost. Cattleman has to formulate cattle feed that meets the nutrient requirements of their cattle and minimize the feed costs. These problems belong to a class of constrained optimization. Various heuristic and deterministic algorithms have been used for solving the constrained optimization problems and applied to the feedstuffs composition. However, there is still some instability in finding mainly a reliable technique that can steadily discover solutions, which are truly near the global optimum of the problem. This paper proposes a combined Modified Evolution Strategies (MES) and Linear Programming (LP) method, named MESLP for optimizing the beef cattle feed. From this study, this novel method obtained a better result than either LP, Random Search, Genetic Algorithm, or Evolution Strategies with higher fitness and lower price.
Classification of unexposed potsherd cavities by using deep learning
Authors: Israel Mendonça, Mai Miyaura, Tirana Noor Fatyanosa, Daiki Yamaguchi, Hanami Sakai, Hiroki Obata, Masayoshi Aritsugi
Journal: Journal of Archaeological Science: Reports
Year: 2023
Topics: Deep Learning; Computer Vision
Abstract: The aim of the Deep Research for Akkon project is to use machine-learning techniques, specifically convolutional neural networks (CNNs), to analyze the large corpus of archaeological pottery fragments belonging to the Late Jomon period (ca. 4000 to 3200 aBP) in a non-invasive and non-destructive manner. In Japan, recent research conducted by using the ”impression method” revealed that ceramic vessels belonging to that era contain impressions of plant seeds. Studying these impressions allows archaeologists to better understand the cultural practices of the Jomon people and the path of rice diffusion in Japan. Most of the analysis was conducted by visually inspecting X-ray images. However, results based on using only X-rays are often inconclusive, leading researchers to use other invasive techniques that damage the potsherds. In this paper, we present a method that classifies X-ray images of potsherds by …
Crowdsource, crawl, or generate? creating sea-vl, a multicultural vision-language dataset for southeast asia
Authors: Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo' Sapan, Taki Hasan Rafi, Bin Wang, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Izzani
Journal: ACL 2025
Year: 2025
Topics: Data Collection; Natural Language Processing
Abstract: Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA culturally-relevant images, more than 50 times larger than other existing datasets. Through SEA-VL, we aim to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.
Meta-learning for vessel time series data imputation method recommendation
Authors: Tirana Noor Fatyanosa, Neni Alya Firdausanti, Putu Hangga Nan Prayoga, Minoki Kuriu, Masayoshi Aritsugi, Israel Mendonça
Journal: Expert Systems with Applications
Year: 2024
Topics: Meta-learning; Time Series Analysis
Abstract: A missing data problem is inevitable when collecting time series datasets from marine sensors. Due to this, sensor data is not reliable enough to assist decision-making. To impute missing values, a number of methods are available. Choosing the best imputation method, however, is not a trivial task, as it usually involves domain expertise and trial-and-error iterations. Additionally, if imputations are done carelessly, they produce a high error, resulting in incorrect assumptions by stakeholders. In this paper, a meta-learning approach is presented that can be used to extract characteristics of the underlying data, and based on that, a less error-prone imputation method is recommended. Ten commercial ocean-going vessel datasets are used to evaluate our proposed method. A total of 29,527 data samples were generated, comprising 22 inputs and 1 target. The proposed method achieves a weighted F1-Score of 87.5 …
Hybrid Feature Selection Framework for Building Resource Efficient Intrusion Detection Systems Model in the Internet of Things
Authors: Tirana Noor Fatyanosa, Mahendra Data
Year: 2023
Topics: Machine Learning; Internet of Things; Feature Selection
Abstract: The Internet of Things (IoT) infrastructure exhibits numerous security vulnerabilities which have led to an increasing number of cyber-attacks. This problem has led to the development of Intrusion Detection Systems (IDSs) explicitly tailored for detecting cyber-attacks on IoT infrastructure, particularly IDSs that utilize machine learning models. To achieve high-performance machine learning models, the training data is pivotal. However, IoT infrastructure has limited computational resources, especially CPU, memory, and power. One solution is reducing the amount of data by selecting only certain features that have a significant impact on the quality of the machine learning models. This study proposes a novel hybrid framework to select the important features to build an efficient IDS model for IoT using the XGBoost algorithm. The performance of the proposed framework was rigorously tested using the network TON-IoT …
Analisis Sentimen Masyarakat terhadap Kereta Cepat Whoosh pada Platform X menggunakan IndoBERT
Authors: Gibran Hakim, Tirana Noor Fatyanosa, Agus Wahyu Widodo
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Sentiment Analysis; Natural Language Processing
Abstract: Infrastruktur kereta api cepat di Indonesia relatif tertinggal dibandingkan negara lain. Perkembangannya baru dimulai dengan proyek kereta cepat Jakarta-Bandung (Whoosh) yang baru dimulai pada 2015 dan selesai pada 2023. Proyek kerja sama Indonesia-China ini sangat menarik perhatian publik, terutama di media sosial seperti X. Penelitian ini bertujuan mengklasifikasikan sentimen publik sebagai positif, negatif, atau netral menggunakan pemrosesan bahasa alami (NLP). IndoBERT sebagai model NLP yang dilatih khusus untuk bahasa Indonesia, digunakan untuk menganalisis sentimen secara efektif. Penelitian ini melakukan pengujian pada dataset Whoosh hasil scraping pada platform X dengan konfigurasi hyperparameter seperti jumlah epoch, learning rate, dan batch size. Performa terbaik dicapai dengan konfigurasi 3 epoch, learning rate 2e-5, dan batch size 32 yang menghasilkan metrik evaluasi accuracy, recall, precision, dan f1-score sebesar 0, 78. Hasil ini menunjukkan learning rate yang lebih kecil memberikan pembelajaran yang stabil, sementara batch size yang lebih besar memberikan estimasi gradien yang konsisten. Namun, model kesulitan mengklasifikasikan sentimen netral sehingga sering salah mengklasifikasikannya sebagai positif atau negatif. Hasil ini juga menunjukkan adanya overfitting, di mana model tampil baik pada data pelatihan tetapi menurun pada data pengujian yang menunjukkan fokus berlebihan pada detail dan noise dari data pelatihan. Penelitian ini menyoroti pentingnya pemilihan model dan konfigurasi hyperparameter dalam analisis sentimen.
Detection of AI-Generated Images from Various Generators Using Gated Expert Convolutional Neural Network
Authors: R Ahmad Fattah Saskoro, Novanto Yudistira, Tirana Noor Fatyanosa
Journal: IEEE Access
Year: 2024
Topics: Computer Vision; Deep Learning
Abstract: The rapid advancement of artificial intelligence (AI), particularly in text-to-image generative models, has led to a proliferation of synthetic images. This progress, while remarkable, raises concerns about misuse in fraudulent activities. To address this issue, we propose a Convolutional Neural Network (CNN)-based approach for classifying AI-generated images from multiple generators. We introduce a gated CNN model that leverages mixed datasets for improved training efficiency and performance. This approach eliminates the need for extensive tuning with each new dataset and mitigates the risk of catastrophic forgetting. Our experiments demonstrate that the gated CNN model slightly outperforms traditional single CNN models, providing a more robust solution for identifying AI-generated images. This paper presents a comprehensive comparison of methods and offers insights into enhancing the classification of AI …
Retrieval-Augmented Generation for Indonesian Criminal Law Information Using the LLaMA Model
Authors: Ariel Zakly Pratama, Arwin Marinta, Bagus Triyudanto, Saman Muhamad, Tirana Noor Fatyanosa
Journal: Innovative Informatics and Artificial Intelligence Research
Year: 2025
Topics: Natural Language Processing; Large Language Models
Abstract: Law is a set of rules that regulate boundaries of behavior within society, is coercive in nature, and imposes sanctions on violators. One of its branches is criminal law, which focuses on violations of norms with the threat of sanctions. However, the legal system often provides excessive discretion to the judiciary, making legal outcomes difficult to predict and potentially disadvantageous, especially for individuals who lack legal understanding or cannot afford legal representation. This disparity poses a significant challenge to legal systems in many countries. In this study, we demonstrate that artificial intelligence (AI) based on Retrieval-Augmented Generation (RAG) can serve as an innovative solution to support a more equitable enforcement of the law. This technology integrates information retrieval and data-driven text generation to help the public understand their rights, access valid legal information, and obtain relevant legal guidance. Based on the implementation and testing of a criminal law chatbot using the LLaMA language model, questions, generated answers, and the relevance of the chatbot's responses were evaluated. Out of ten tested questions, eight received relevant responses, while two were deemed irrelevant. Additionally, the chatbot's capability to retrieve legal documents based on user-provided prompts was assessed. Of ten input prompts, the chatbot successfully identified eight relevant documents, achieving a hit rate of 80%. Thus, the application of RAG in this legal chatbot can provide an innovative solution to support law enforcement. In the future, the use of AI in legal systems has the potential to reduce information disparities …
Implementasi Chatbot Menggunakan Long Short Term Memory (LSTM) dengan Pendekatan Retrieval-Based
Authors: Ja'far Shidqul Azzam, Fitra A Bachtiar, Tirana Noor Fatyanosa
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Natural Language Processing; Deep Learning
Abstract: Implementasi Chatbot Menggunakan Long Short Term Memory (LSTM) dengan Pendekatan Retrieval-Based | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Implementasi Chatbot Menggunakan Long Short Term Memory (LSTM) dengan Pendekatan Retrieval-Based 1.Beranda / 2.Arsip / 3.Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 / 4.Artikel Implementasi Chatbot Menggunakan Long Short Term Memory (LSTM) dengan Pendekatan Retrieval-Based Penulis Ja'far Shidqul Azzam Universitas Brawijaya Fitra A. Bachtiar Tirana Noor Fatyanosa Kata Kunci: chatbot, LSTM, FastText, cosine similarity, prediksi jawaban Abstrak Naskah ini akan diterbitkan di SIET Referensi Adamopoulou, E., & Moussiades, L. (2020). An Overview of Chatbot Technology. In I…
Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data
Authors: Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Hana Ratnawati
Journal: Healthcare Informatics Research
Year: 2025
Topics: Machine Learning; Bioinformatics
Abstract: Objectives Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods We propose using the high dimensionality of genetic variations (ie, SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature …
Penyaringan Informasi Sensitif pada Sistem Chatbot Berbasis Retrieval Augmented Generation dengan Metode Named Entity Recognition
Authors: Kesid Dewa Wicaksana, Rizal Setya Perdana, Tirana Noor Fatyanosa
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Natural Language Processing; Named Entity Recognition
Abstract: Retrieval Augmented Generation (RAG) telah berkembang sebagai pendekatan inovatif dalam chatbot dengan menggabungkan Large Language Models (LLMs) dan sumber pengetahuan eksternal. Namun, tantangan besar muncul terkait kebocoran informasi sensitif, khususnya dalam aplikasi yang membutuhkan perlindungan privasi. Penelitian ini mengembangkan chatbot berbasis RAG dengan penyaringan informasi sensitif menggunakan Named Entity Recognition (NER). Model DistilBERT yang telah di-fine-tune untuk tugas NER pada dataset sintetis, diimplementasikan untuk mengenali entitas sensitif seperti nama, alamat, dan nomor identitas. Proses penelitian mencakup pengembangan pipeline RAG, integrasi model NER, serta evaluasi kinerja dengan metrik precision, recall, dan f-measure. Hasil menunjukkan performa tinggi dari model fine-tuned DistilBERT, dengan precision 0,965, recall 0,965, dan f …
Analisis Sentimen Terhadap Makanan Manis Di Platform X Menggunakan TF-IDF dan Naive Bayes
Authors: Nurul Annisa Murnastiti, Tirana Noor Fatyanosa
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Sentiment Analysis; Machine Learning
Abstract: Perkembangan media sosial telah mempermudah masyarakat dalam berbagi pendapat mengenai berbagai topik, termasuk makanan manis. Analisis sentimen terhadap pendapat tersebut memberikan wawasan berharga bagi industri makanan untuk memahami persepsi konsumen, mengidentifikasi preferensi, dan merancang strategi pemasaran. Penelitian ini bertujuan untuk menganalisis sentimen pada ulasan makanan manis menggunakan algoritma Naive Bayes. Data yang digunakan dikumpulkan dari platform X, meliputi ulasan berbahasa Indonesia. Proses analisis meliputi pembersihan data, tokenisasi, penghapusan stopwords, stemming, dan ekstraksi fitur menggunakan TF-IDF. Sentimen dibagi menjadi tiga kategori: positif, negatif, dan netral. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 67, 1%, dengan kontribusi signifikan pada pengelompokan sentimen positif sebagai kategori dominan. Temuan ini memberikan wawasan penting bagi produsen makanan dalam memahami persepsi konsumen terhadap makanan manis serta menjadi referensi bagi penelitian di bidang analisis sentimen dan pemasaran digital.
Analisis Sentimen Terhadap Program Indonesian International Student Mobility Award (IISMA) di Platform X/Twitter Menggunakan TF-IDF dan SVM
Authors: Diagne Alya Fidian, Tirana Noor Fatyanosa, Achmad Ridok
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Sentiment Analysis; Machine Learning
Abstract: Indonesian International Student Mobility Award (IISMA) adalah program Merdeka Belajar Kampus Merdeka (MBKM) yang bertujuan memperluas wawasan dan membangun hubungan internasional bagi mahasiswa Indonesia. Penelitian ini menganalisis sentimen masyarakat terhadap IISMA di platform X menggunakan metode analisis sentimen berbasis Support Vector Machine (SVM) dengan kernel linear, polinomial, dan Radial Basis Function (RBF). Dataset diperoleh melalui web scraping dan diolah menggunakan text mining dengan ekstraksi fitur TF-IDF. Hasil menunjukkan bahwa model SVM mampu mengklasifikasikan sentimen ke dalam tiga kelas yaitu positif, netral, dan negatif, namun kesulitan dalam memprediksi kelas negatif akibat ketidakseimbangan dataset. Kernel RBF memiliki kinerja terbaik dengan precision 70, 52% dan akurasi 62, 92%, sedangkan kernel linear menghasilkan F1-score tertinggi sebesar 53%, menunjukkan keseimbangan antara precision dan recall. Pengujian kinerja model pada teks berbahasa Indonesia dan Inggris menunjukkan bahwa teks berbahasa Inggris memberikan hasil lebih baik dengan akurasi 71, 11% dibandingkan 62, 44% pada teks berbahasa Indonesia yang didukung oleh kemampuan TextBlob dalam mengenali pola sentimen bahasa Inggris secara lebih akurat. Hasil ini menggarisbawahi pentingnya strategi khusus untuk meningkatkan kinerja analisis sentimen pada teks berbahasa Indonesia, termasuk pelabelan manual atau penggunaan alat analisis sentimen yang mendukung bahasa tersebut.
Anak Baik: A Low-Cost Approach to Curate Indonesian Ethical and Unethical Instructions
Authors: Sulthan Abiyyu Hakim, Rizal Setya Perdana, Tirana Noor Fatyanosa
Year: 2025
Topics: Natural Language Processing; Data Curation
Abstract: This study explores the ethical challenges faced by Indonesian Large Language Models (LLMs), particularly focusing on their ability to distinguish between ethical and unethical instructions. As LLMs become increasingly integrated into sensitive applications, ensuring their ethical operation is crucial. A key contribution of this study is the introduction of the Anak Baik dataset, a resource designed to enhance the ethical reasoning capabilities of Indonesian LLMs. The phrase “Anak Baik”, meaning “Good Boy”, symbolizes the ideal of ethical behavior, as a well-behaved child refrains from engaging in harmful actions. The dataset comprises instruction-response pairs in Indonesian, crafted for Supervised Fine-Tuning (SFT) tasks. It includes examples of both ethical and unethical responses to guide models in learning to generate responses that uphold moral standards. Leveraging Low-Rank Adaptation (LoRA) on models such as Komodo and Cendol shows a significant improvement in ethical decision-making processes. This enhanced performance is quantitatively validated through substantial increases in BLEU and ROUGE scores, indicating a stronger alignment with socially responsible behavior.
Variational Mode Decomposition and Linear Embeddings are What You Need For Time-Series Forecasting
Authors: Hafizh Raihan Kurnia Putra, Novanto Yudistira, Tirana Noor Fatyanosa
Journal: arXiv preprint arXiv:2408.16122
Year: 2024
Topics: Time Series Analysis; Signal Processing
Abstract: Time-series forecasting often faces challenges due to data volatility, which can lead to inaccurate predictions. Variational Mode Decomposition (VMD) has emerged as a promising technique to mitigate volatility by decomposing data into distinct modes, thereby enhancing forecast accuracy. In this study, we integrate VMD with linear models to develop a robust forecasting framework. Our approach is evaluated on 13 diverse datasets, including ETTm2, WindTurbine, M4, and 10 air quality datasets from various Southeast Asian cities. The effectiveness of the VMD strategy is assessed by comparing Root Mean Squared Error (RMSE) values from models utilizing VMD against those without it. Additionally, we benchmark linear-based models against well-known neural network architectures such as LSTM, Bidirectional LSTM, and RNN. The results demonstrate a significant reduction in RMSE across nearly all models following VMD application. Notably, the Linear + VMD model achieved the lowest average RMSE in univariate forecasting at 0.619. In multivariate forecasting, the DLinear + VMD model consistently outperformed others, attaining the lowest RMSE across all datasets with an average of 0.019. These findings underscore the effectiveness of combining VMD with linear models for superior time-series forecasting.
Variational Mode Decomposition and Linear Embeddings are What You Need For Time-Series Forecasting
Authors: Hafizh Raihan Kurnia Putra, Novanto Yudistira, Tirana Noor Fatyanosa
Journal: arXiv e-prints
Year: 2024
Topics: Time Series Analysis; Signal Processing
Abstract: Time-series forecasting often faces challenges due to data volatility, which can lead to inaccurate predictions. Variational Mode Decomposition (VMD) has emerged as a promising technique to mitigate volatility by decomposing data into distinct modes, thereby enhancing forecast accuracy. In this study, we integrate VMD with linear models to develop a robust forecasting framework. Our approach is evaluated on 13 diverse datasets, including ETTm2, WindTurbine, M4, and 10 air quality datasets from various Southeast Asian cities. The effectiveness of the VMD strategy is assessed by comparing Root Mean Squared Error (RMSE) values from models utilizing VMD against those without it. Additionally, we benchmark linear-based models against well-known neural network architectures such as LSTM, BLSTM, and RNN. The results demonstrate a significant reduction in RMSE across nearly all models following VMD …
A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem
Authors: Israel Mendonça, Tirana Noor Fatyanosa, Masayoshi Aritsugi, Pedro Henrique González Silva
Year: 2024
Topics: Optimization; Data Mining
Abstract: This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational …
ImputAnom: Anomaly Detection Framework Using Imputation Methods for Univariate Time Series
Authors: Tirana Noor Fatyanosa, Mahendra Data, Neni Alya Firdausanti, Putu Hangga Nan Prayoga, Israel Mendonça, Masayoshi Aritsugi
Year: 2023
Topics: Anomaly Detection; Time Series Analysis
Abstract: Anomaly detection plays a crucial role in various domains such as cybersecurity, fraud detection, and industrial asset condition monitoring. In these fields, identifying abnormal patterns or outliers is paramount for the business they support. This paper presents a new framework that utilizes imputation methods to effectively identify anomalies. To evaluate the performance of the proposed framework, experiments were conducted on different datasets that contain anomalies from different domains. Experimental results demonstrate the effectiveness of the framework in helping to detect anomalies. It provides improvements between 8.17% and 165.21% for all datasets. Experimental results also confirm the effectiveness of the proposed framework and its potential to be applied in real-world scenarios.
Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
Authors: Sari Dewi Budiwati, Al Hafiz Akbar Maulana Siagian, Tirana Noor Fatyanosa, Masayoshi Aritsugi
Journal: Applied Sciences
Year: 2021
Topics: Natural Language Processing; Machine Translation
Abstract: Phrase table combination in pivot approaches can be an effective method to deal with low-resource language pairs. The common practice to generate phrase tables in pivot approaches is to use standard symmetrization, i.e., grow-diag-final-and. Although some researchers found that the use of non-standard symmetrization could improve bilingual evaluation understudy (BLEU) scores, the use of non-standard symmetrization has not been commonly employed in pivot approaches. In this study, we propose a strategy that uses the non-standard symmetrization of word alignment in phrase table combination. The appropriate symmetrization is selected based on the highest BLEU scores in each direct translation of source–target, source–pivot, and pivot–target of Kazakh–English (Kk–En) and Japanese–Indonesian (Ja–Id). Our experiments show that our proposed strategy outperforms the direct translation in Kk–En with absolute improvements of 0.35 (a 11.3% relative improvement) and 0.22 (a 6.4% relative improvement) BLEU points for 3-gram and 5-gram, respectively. The proposed strategy shows an absolute gain of up to 0.11 (a 0.9% relative improvement) BLEU points compared to direct translation for 3-gram in Ja–Id. Our proposed strategy using a small phrase table obtains better BLEU scores than a strategy using a large phrase table. The size of the target monolingual and feature function weight of the language model (LM) could reduce perplexity scores.
Effects of the Number of Hyperparameters on the Performance of GA-CNN
Authors: Masayoshi Aritsugi, Tirana Noor Fatyanosa
Journal: 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
Year: 2020
Topics: Machine Learning; Convolutional Neural Networks
Abstract: The performance of a machine learning algorithm is highly dependent on its hyperparameters. However, hyperparameter optimization is not a trivial task as it is problem-specific. The difficulty rises when coupled with a larger number of hyperparameters resulting in high search space dimensions. The common understanding seems to be that the optimization is only done on limited hyperparameters. Indeed, a larger number of hyperparameters have not been commonly utilized in hyperparameter optimization. This study investigates the role of hyperparameters by using a genetic algorithm (GA) as the main optimization method for a convolutional neural network (CNN). The novelty of this study is two-fold. Firstly, we defined 20 hyperparameters and their ranges, specifically for text classification. Secondly, we conducted experiments with different numbers of hyperparameters and different numbers of optimized hyperparameters. GA-CNN was evaluated using a disaster tweets dataset and compared to the other methods, ie, grid search, random search, TPE, TPOT, CNN, LSTM, CNN-LSTM, and BERT. The experimental results demonstrated that the proposed method shows better performance over other methods. The results also showed that a larger number of hyperparameters and layer-specific hyperparameter values are indeed important.
Klasifikasi emosi berdasarkan ciri wajah menggunakan convolutional neural network
Authors: Achmad Yusuf, Randy Cahya Wihandika, Candra Dewi
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Deep Learning; Computer Vision
Abstract: Selain sebagai identitas seseorang, wajah juga merupakan alat pendukung dalam bersosialisasi secara langsung. Seseorang dapat menyampaikan emosi yang dialami menggunakan ekspresi yang dimunculkan oleh wajah. Emosi merupakan perasaan untuk mendorong individu atau merupakan respons suatu stimulus. Pada bidang consumer research, pengujian konsumen adalah metode yang digunakan untuk mengetahui memprediksi penerimaan produk oleh konsumen pada suatu pasar. Walaupun telah melalui tahap pengujian konsumen secara ekstensif sebelum memasuki pasar, tingkat kegagalan produk makanan baru masih tinggi. Hal ini menunjukkan bahwa metode pengujian konsumen secara tradisional tidak mampu memprediksi performa pasar dan penerimaan produk oleh konsumen dalam jangka panjang. Untuk dapat mengetahui perilaku konsumen dengan lebih dalam, penggunaan pengukuran emosi banyak digunakan dalam pengujian konsumen karena emosi memengaruhi perilaku konsumen. Pada permasalahan ini klasifikasi emosi berdasarkan ciri wajah dinilai cocok untuk membantu meningkatkan kualitas pengujian konsumen. Metode yang digunakan pada penelitian ini adalah Convolutional Neural Network (CNN). Data yang digunakan adalah data yang diperoleh dari Extended Cohn-Kanade Dataset (CK+) yang diambil dari 210 subjek dengan total gambar yang digunakan sebanyak 327 gambar. Pengujian penelitian menggunakan K-fold Cross Validation dengan nilai k sebesar 4. Hasil pengujian menunjukkan nilai learning rate tertentu dapat melatih arsitektur lebih baik dibandingkan dengan nilai learning …
Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery)
Authors: Noval Dini Maulana, Budi Darma Setiawan, Candra Dewi
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Regression Analysis
Abstract: Roti merupakan salah satu jenis makanan yang digemari oleh Masyarakat Indonesia. Salah satu bukti pentingnya roti bagi masyarakat Indonesia adalah impor tepung terigu yang terus meningkat. Salah satu perusahaan pembuat roti yang saat ini sedang berkembang adalah Harum Bakery. Kendala yang sering dihadapi Harum Bakery adalah sistem peramalan permintaan pelanggan yang masih manual dan terkesan mengira-ngira. Proses peramalan tersebut sangat jelas berpengaruh besar terhadap proses penjualan. Dengan adanya peramalan penjualan roti, diharapkan dapat membantu toko roti Harum Bakery dalam mempersiapkan bahan baku dan segala sesuatu yang diperlukan untuk pembuatan roti. Support Vector Regression (SVR) adalah salah satu metode yang bisa digunakan dalam melakukan peramalan. Data yang digunakan adalah data penjualan roti manis, cake dan tawar dengan tipe data time series dan menggunakan 4 fitur. Pada penelitian ini metode SVR yang digunakan untuk meramal hasil penjualan menghasilkan nilai evaluasi RMSE untuk roti manis sebesar 0, 00176, roti cake sebesar 0, 00019, dan roti tawar sebesar 0, 00010.
Perbandingan Prediksi Penggunaan Listrik dengan Menggunakan Metode Long Short Term Memory (LSTM) dan Recurrent Neural Network (RNN)
Authors: Nurfatima Selle, Novanto Yudistira, Candra Dewi
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2022
Topics: Deep Learning; Time Series Analysis
Abstract: Energi listrik telah menjadi salah satu kebutuhan yang sangat penting dan membantu kehidupan manusia di era modern saat ini. Energi listrik yang tidak dapat disimpan dalam waktu yang lama dan harus dapat selalu tersalurkan menyebabkan penyedia energi listrik harus dapat mampu menyediakan energi listrik dengan tepat. Oleh karena itu, diperlukan sebuah sistem yang mampu melakukan prediksi terhadap penggunaan listrik dengan memanfaatkan data historis penggunaan listrik sebelumnya. Sehingga PT. PLN selaku penyedia energi listrik harus dapat mampu menyesuaikan jumlah listrik yang harus disediakan dengan permintaan kebutuhan pelanggan. Penelitian ini menggunakan metode Recurrent Neural Network (RNN) dan Long Short Term Memory (LSTM) yang merupakan metode pembelajaran deep learning. Kedua metode ini mampu mengolah data dan melakukan prediksi dengan format data …
The implementation of k-means algorithm as image segmenting method in identifying the citrus leaves disease
Authors: FG Febrinanto, C Dewi, A Triwiratno
Journal: IOP Conference Series: Earth and Environmental Science
Year: 2019
Topics: Machine Learning; Computer Vision
Abstract: The purpose of this study is to identify the disease on citrus leaves. A digital imagery makes it possible to identify disease automatically. Three diseases to be identified in this research are CVPD (Citrus Vein Phloem Degeneration), Downy Mildew, and Cendawan Jelaga. The research will study the implementation of an image segmentation to analyze the citrus leaves diseases. The method that will be used to do image segmentation is K-Means. The segmentation which will be carried out consist of two kinds, namely a leaf segmentation and a disease segmentation. After segmentation process, the results of disease segmentation are classified by using the K-Nearest Neighbor (K-NN) algorithm to know its disease class. From data analysis, the results of the optimal cluster shows that the leaf segmentation consist of 2 clusters and the disease segmentation consist of 9 clusters. While the obtained optimal parameter K …
Diagnosis Tingkat Risiko Penyakit Stroke Menggunakan Metode K-Nearest Neighbor dan Naïve Bayes
Authors: Annisa Puspitawuri, Edy Santoso, Candra Dewi
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Healthcare
Abstract: Penyakit stroke merupakan penyakit yang timbul akibat terputusnya suplai darah menuju otak karena terdapat semburan pembuluh darah atau terjadi sumbatan berupa gumpalan darah. Stroke merupakan penyebab kecacatan nomor satu dan penyebab kematian nomor tiga di dunia setelah penyakit jantung dan kanker, baik di negara maju maupun berkembang. Berdasarkan data Riskedas, prevalensi stroke di Indonesia tahun 2013 akan terjadi kenaikan jika dibandingkan data Riskesdas pada tahun 2007 dengan nilai angka 8, 3%, naik mencapai angka 12, 1% per 1.000 penduduk. Untuk itu diperlukan suatu tindakan pendeteksian tingkat risiko penyakit stroke agar dapat segera diatasi sesuai dengan tingkat risikonya. Penelitian ini mengusulkan adanya suatu aplikasi diagnosis tingkat risiko penyakit stroke menggunakan metode K-Nearest Neighbor dan Naive Bayes karena data yang didapat menggunakan atribut numerik dan kategoris. Algoritme K-Nearest Neighbor digunakan untuk memproses data numerik, dan algoritme Naive Bayes digunakan untuk memproses data kategoris. Hasil penelitian menunjukkan nilai akurasi tertinggi yang diperoleh pada data kelas seimbang adalah 96.67% dengan data latih 45, data uji 30 dan nilai K= 15-22. Sedangkan pada data latih tidak seimbang, menunjukkan akurasi tertinggi sebesar 100% dengan jumlah data latih 60, data uji 30 dan nilai K= 20-30.
Developing Digital Comics as Character Learning Media for Elementary School Students
Authors: Fauzatul Ma’rufah Rohmanurmeta, Candra Dewi
Journal: Jurnal Ilmu Pendidikan
Year: 2020
Topics: Education; Digital Media
Abstract: Learning is expected not only to be centered on the teacher, but students must be able to find their own knowledge. In some place Learning is still focused on the transfer of material from teacher to student (Shen, 2004). Based on the results of the presurvey, the use of the computer is rarely used in learning. It is seen that the laboratory is only used for ICT activities. Since the introduction of computers as an educational tool, computers are considered as tools that can support the educational process (Domagk et al., 2010). Now it is very important to introduce technology to students, especially for children’s education. As desired by the Indonesian government that the implementation of learning by implementing a new curriculum called the 2013 Curriculum emphasizes the importance of education in schools can develop human resources who have strong, killed character (Fahmy et al., 2015). In Indonesia, the nation’s children are educated to be intelligent people and have good character, and this is the goal of education. Learning strategies that can be used to provide edu-
Optimasi Penentuan Centroid pada Algoritme K-Means Menggunakan Algoritme Pillar (Studi Kasus: Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Timur)
Authors: Alan Primandana, Sigit Adinugroho, Candra Dewi
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Clustering
Abstract: Metode k-means clustering merupakan metode pengelompokan non hierarchy yang mengelompokkan data ke beberapa pusat klaster (centroid). Kesederhanaan metode k-means banyak digunakan diberbagai bidang karena memiliki beberapa keunggulan yaitu mudah diimplementasikan dan memiliki tingkat ketelitian yang cukup tinggi terhadap ukuran objek sehingga metode ini relatif lebih terukur dan efisien. Akan tetapi algoritme k-means awal perhitungan menggunakan nilai C (centroid) yang secara acak akan menyebabkan hasil yang acak pula. Ketergantungan pada nilai C (centroid) membuat akurasi pada algoritme k-means kurang maksimal. Hasil yang dari perhitungan k-means seringkali didapatkan dengan melakukan percobaan beberapa kali dan cenderung menghasilkan klaster yang berbeda. Tapi dalam mendapatkan hasil yang lebih baik, sulitnya menentukan batasan eksperimen. Penentuan titik pusat klaster secara acak menyebabkan metode k-means belum mampu mendapatkan hasil pengelompokan terbaik. Pada penelitian ini menjabarkan algoritma yang juga digunakan untuk mengoptimalkan pemilihan titik pusat awal pada metode k-means yaitu algoritma pillar. Algoritma ini merupakan penentuan posisi centroid awal dengan menghitung jarak akumulasi metrik antara setiap data dengan semua centroid sebelumnya. Pemilihan titik ditentukan oleh titik data yang memiliki jarak maksimum. Adapun penelitian ini melakukan penentuan centroid menggunakan algoritme Pillar kemudian hasil dari algoritme tersebut digunakan untuk titik pusat klaster pada algoritme k-means. Pada setiap klaster algoritme pillar mampu …
Performance evaluation of distance function in KNN and WKNN for classification of soil organic matter
Authors: Candra Dewi, Yoke Kusuma Arbawa
Year: 2019
Topics: Machine Learning; Agriculture
Abstract: Essential oils plantation requires sufficient nutrients and organic matter to produce high quality oil. This study introduces a new technique in identifying soil organic matter content by utilizing soil images and nearby distance-based classification algorithms such as KNN and Weighted KNN. However, the problem with distance-based algorithms is the use of distance calculation techniques, which can produce significant differences in calculations. Therefore, in this study a test was conducted on the use of different distance formulation, namely Manhattan, Euclidean and Minkowsky. The features used as input to the recognition process were extracted using the Gray Level Co-occurence Matrix (GLCM) method. The test results on 847 training data and 363 test data showed that the best distance function in K-Nearest Neighbor (KNN) and Weighted-KNN (WKNN) was Minkowsky with a rating value (r) of 5. This shows that …
Pengembangan modul literasi digital berbasis budaya lokal madiun untuk meningkatkan motivasi belajar siswa sekolah dasar
Authors: Sita Lufiah, Raras Setyo Retno, Candra Dewi
Journal: Prosiding Konferensi Ilmiah Dasar
Year: 2022
Topics: Education; Cultural Studies
Abstract: Hasil FGD (focuss group discussion) dengan beberapa guru di Kabupaten Madiun bahwa peserta didik membutuhkan stimulus atau rangsangan agar pesera didik memiliki kemampuan literasi yang memadai. Tujuan dari penelitian ini 1) mengembangkan modul literasi digital berbasis budaya lokal Madiun untuk siswa sekolah dasar 2) menguji kelayakan modul literasi digital berbasis budaya lokal Madiun 3) mengetahui tingkat kepraktisan modul literasi digital berbasis budaya lokal Madiun yang diterapkan dalam pembelajaran di sekolah dasar. Prosedur penelitian pengembangan menggunakan model ADDIE (analyze, design, development, implementation and evaluation). Subjek penelitian ini adalah 25 siswa kelas V sekolah dasar di Madiun. Hasil penelitian menunjukkan bahwa 1) modul yang dikembangkan berupa modul literasi digital berbasis budaya lokal Madiun untuk siswa sekolah dasar dengan menggunakan model ADDIE modul yang dikembangkan memenuhi kriteria sangat valid dengan presentase sebesar 91, 2% berdasarkan penilaian dari validator 2) modul yang dikembangkan kepada siswa mendapatkan respon positif dan memenuhi kriteria sangat baik dengan presentase 97%, sedangkan hasil respon guru mendapatkan respon positif dan memenuhi kriteria sangat baik sebesar 100% yang dapat digunakan dalam pembelajaran.
Implementasi Komik digital pelestarian lingkungan berbasis nilai karakter
Authors: Fauzatul Ma’rufah Rohmanurmeta, Candra Dewi
Journal: Prosiding Seminar Nasional Hasil Penelitian LPPM Universitas PGRI Madiun
Year: 2019
Topics: Education; Digital Media
Abstract: Mata pelajaran IPA adalah salah satu mata pelajaran yang bertujuan untuk mengenalkan siswa dengan lingkungan sekitar. Sehingga dengan mempelajari lingkungan sekitarnya diharapkan siswa dapat melestarikan lingkungan sekitarnya. Penerapan pembelajaran IPA selain menekankan pada aspek kognitif juga menekankan padapengalaman langsung yang dialami siswa sehari-hari. Dengan deminikian guru diharapkan dapat merancang media pembelajaran yang efektif dan kreatif bagi siswa agar siswa dapat mempelajari konsep-konsep pelestarian lingkungan dan memiliki karakter yang baik dalam pelestarian lingkungan sekitar. Pemanfaatan berbagai media pembelajaran dan sumber belajar yang salah satunya adalah media komik digital diharapkan mampu meningkatkan keefektikan pembelajaran dan kreativitas guru dalam meningkatkan hasil belajar siswa.
Persepsi guru tentang kegiatan projek penguatan profil pelajar pancasila (p5) sebagai penerapan pembelajaran berdiferensiasi pada kurikulum merdeka era digital
Authors: Aning Rahmawati, Parji Parji, Candra Dewi
Journal: Prosiding Konferensi Ilmiah Dasar
Year: 2023
Topics: Education; Curriculum Development
Abstract: Penelitian ini membahas tentang potensi dan tantangan kurikulum merdeka era digital dalam penerapan pembelajaran berdiferensiasi dan implementasi kegiatan projek Penguatan Profil Pelajar Pancasila (P5). Penelitian ini menggunakan pendekatan kualiutatif sebagai metode penelitiannya. Hasil yang diperoleh dari penelitian ini adalah kurikulum merdeka era digital menawarkan paradigma baru dalam dunia pendidikan, di mana guru dapat menjadi agen perubahan untuk menciptakan pembelajaran yang lebih adaptif dan inklusif. Melalui kesadaran akan tantangan dan peluang ini, pendidikan diharapkan dapat terus berkembang dan memberikan dampak positif dalam membentuk generasi yang unggul, berakhlak mulia, dan siap menghadapi tantangan masa depan. Integrasi kegiatan projek Penguatan Profil Pelajar Pancasila (P5) dengan pendekatan pembelajaran berdiferensiasi pada kurikulum merdeka era digital diharapkan dapat meningkatkan pemahaman dan penerapan nilai-nilai Pancasila pada siswa secara holistik, sehingga mereka tumbuh menjadi warga negara yang mencintai tanah air, berkepribadian kuat, dan mampu berkontribusi positif dalam memajukan bangsa dan negara.
Pengaruh Pembelajaran Diferensiasi Berbantuan Website Genially terhadap Motivasi Belajar Siswa
Authors: Allisya Sendy Syahputri, Candra Dewi, Heny Kusuma Widyaningrum
Journal: Seminar Nasional Sosial, Sains, Pendidikan, Humaniora (Senassdra)
Year: 2023
Topics: Education; Digital Tools
Abstract: Pembelajaran diferensiasi merupakan salah satu alternatif perbaikan proses pembelajaran yang ada. Pembelajaran diferenasiasi sejalan dengan tujuan kurikulum merdeka yang saat ini diterapkan. Penerapan pembelajaran diferensiasi yang dibantu dengan teknologi salah satunya dengan website genially mampu meningkatkan motivasi belajar siswa sehingga hasil dari pembelajaran akan lebih maksimal. Pembelajaran dengan cara permainan akan membuat motivasi belajar siswa meningkat karena pembelajaran mayoritas dilakukan dengan hanya transfer ilmu dari guru ke siswa dengan cermah atau atau jawab. Belajar dengan cara bermain serta memperhatikan karakteristik siswa akan menciptakan pembelajaran diferensiasi yang mampu meningkatkan motivasi belajar siswa. Ketika motivasi belajar siswa mengingkat maka tujuan pembelajaran akan mudah tercapai. Tidak hanya mudah tercapai juga akan lebih bermakna bagi siswa yang menjadi tujuan utama pendidikan.
Pengaruh Model Pembelajaran Blended Learning Terhadap Kemampuan Literasi Digital Siswa Kelas V Sekolah Dasar
Authors: Dwinda Susi Rahmasiwi, Candra Dewi, Pinkan Amita Tri Prasasti
Journal: Jurnal Riset Pendidikan Dasar (JRPD)
Year: 2023
Topics: Education; Blended Learning
Abstract: Literasi digital dapat menjadikan anak bertanggung jawab dalam memilih informasi dan penyebaran informasi. Untuk itu Implemetasi literasi digital SD dapat dijadikan pilihan pembelajaran yang menarik dengan menggunakan sumber digital pada saat pembelajaran. Penelitian ini bertujuan untuk mengetahui pengaruh kemampuan Literasi Digital siswa kelas V Sekolah Dasar pada mata pelajaran tematik. Model pembelajaran yang digunakan yaitu model pembelajaran Blended Learning. Metode penelitian yang digunakan dalam penelitian ini yaitu penelitian kuantitatif dengan desain Quasi Experimental Design dengan populasi siswa kelas VA dan VB SD Siti Hajar Kota Madiun dengan sampel sebanyak 25 siswa kelas VA dan 25 sisiwa kelas V B. Instrument yang digunakan yaitu dengan angket Literasi Digital. Data dianalisis menggunakan independent sample t-tes. Berdasarakan hasil penelitian dan pengolahan data menunjukkan bahwa terdapat perbedaan yang signifikan dalam kemampuan Literasi Digital Siswa Kelas V Sekolah Dasar yang mengikuti pembelajaran dengan model Blended Learning dibandingkan dengan siswa yang mengikuti pembelajaran Konvensional (taraf signifikansi 0,000< 0, 05); maka sebagaimana dasar pengambilan independent sample t test dapat disimpulkan Ho ditolak dan Ha diterima.