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All Journal Jurnal Ilmiah Kursor Knowledge Engineering and Data Science Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Bangsa
Admojo, Fadhila Tangguh
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Comparison of Naïve Bayes and Random Forest Algorithm in Webtoon Application Sentiment Analysis Admojo, Fadhila Tangguh; Risnanto, Slamet; Windiawati, Ai Wulan; Innuddin, Muhammad; Mualfah, Desti
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10636

Abstract

The Webtoon application has become one of the popular platforms for reading comics digitally. Webtoons, as a form of digital comics, present various types of comic content. The success of a Webtoon application depends greatly on understanding the preferences and views of its users. User evaluations of Webtoon applications can provide valuable insight into user satisfaction levels, as well as identify problems that need to be fixed by developers. In this research, Sentiment Analysis was applied to user reviews of the Webtoon Application on the Google Play Store. This research uses two different classification algorithms, namely Naïve Bayes and Random Forest, with the aim of comparing their performance in the context of sentiment analysis of user reviews of Webtoon applications. The results of this research are expected to provide an overview of the most suitable algorithm for conducting sentiment analysis classification in Webtoon applications. In collecting the dataset, we involved webtoon user reviews covering various sentiments, such as positive, negative, and neutral. However, in this analysis, the focus is given to two types of sentiment, namely positive and negative. We apply Naïve Bayes and Random Forest algorithms to perform sentiment classification on the reviews. Performance evaluation is carried out by considering metrics such as accuracy, precision, recall, and F1-score. The results of implementing these two algorithms are an accuracy of 74% Naïve Bayes, and 88% Random Forest. It can be concluded that the Random Forest algorithm is superior to the Naïve Bayes algorithm. With this, the Random Forest algorithm becomes a recommendation for classifying sentiment analysis for Webtoon applications with greater accuracy.
Klasifikasi Aroma Alkohol Menggunakan Metode KNN Admojo, Fadhila Tangguh; Ahsanawati
Indonesian Journal of Data and Science Vol. 1 No. 2 (2020): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.477 KB) | DOI: 10.33096/ijodas.v1i2.12

Abstract

Alkohol adalah senyawa-senyawa dimana satu atau lebih atom hidrogen dalam sebuah alkana digantikan oleh sebuah gugus -OH. Alkohol memiliki ikatan yang mirip air. Alkohol terdiri dari molekul polar. Dalam senyawa alkohol, oksigen mengemban muatan negatif parsial. Alkohol telah digunakan oleh orang di seluruh dunia, dalam makanan standar, untuk higienis / alasan medis, untuk relaksan dan efek euforia, untuk tujuan rekreasi, untuk inspirasi artistik, sebagai aphrodisiacs, dan untuk alasan lain. Alkohol memiliki beberapa jenis senyawa diantaranya adalah octanol, propanol, Butanol, propanol, dan isobutanol. Oleh karena itu dibutuhkan sensor untuk mendeteksi jenis bahan kimia pada suatu cairan berdasarkan aromanya dengan menerapkan salah satu metode klasifikasi yaitu K-Nearest Neighbor (KNN). Pengujian system ini terdiri dari pengujian pengaruh nilai K dan pengaruh nilai crossvalidation. Hasil dari pengujian pengaruh nilai K menghasilkan akurasi optimum senilai 100% pada nilai K=3 dan 100% pada nilai K=4
Analisis Performa Algoritma Stochastic Gradient Descent (SGD) Dalam Mengklasifikasi Tahu Berformalin Admojo, Fadhila Tangguh; Sulistya, Yudha Islami
Indonesian Journal of Data and Science Vol. 3 No. 1 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i1.42

Abstract

Tahu berformalin adalah salah satu jenis makanan yang sering mengandung bahan-bahan kimia yang dapat mengawetkan daripada tahu tanpa formalin. Pada tahu berformalin dapat memberikan tekstur lebih kenyal dan berwarna putih bersih. Penelitian ini bertujuan untuk mengklasifikasikan tahu berformalin dan tahu tidak berformalin. Pada paper ini menggunakan algoritma Stochastic Gradient Descent atau dalam penerapannya lebih dikenal dengan SGD Classifier yang merupakan bagian dari algoritma machine learning untuk klasifikasi, regresi maupun jaringan syaraf tiruan serta algoritma ini sangat efisien pada dataset berskala besar. Penelitian ini mencoba menerapkan algoritma SGD pada dataset tahu berformalin dengan jumlah dataset yakni 11000 yang dimana 5500 data tahu berformalin dan 5500 data tahu tidak berformalin. Setelah dilakukan beberapa tahapan dalam pengujian dengan algoritma SGD maka diperolah hasil akurasi, presisi, recall, f1-score pada model yang masing-masing 82.6% untuk akurasi, 81.7% untuk presisi, 84.1% untuk recall, 83.5% untuk f1-score dan dilakukan pengujian menggunakan 10 data yang tidak termasuk dalam data latih memperoleh performansi rata-rata akurasi sebesar 70%, presisi 71%, recall 70% dan f1-score 70%.
Analisis performa metode Naïve Bayesh Classifier pada Electronic Nose dalam identifikasi formalin pada tahu Admojo, Fadhila Tangguh; Jabir, Siti Rahma
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.67

Abstract

Penelitian ini bertujuan untuk menganalisis performa metode Naive Bayes Classifier (NBC) dalam identifikasi formalin pada tahu menggunakan Electronic Nose. Hasil dari penelitian ini menunjukkan bahwa performa NBC cukup moderat, dengan nilai akurasi sekitar 0,59 hingga 0,60, presisi sekitar 0,67 hingga 0,68, recall sekitar 0,59 hingga 0,60, dan F1-score sekitar 0,55. Ini menunjukkan bahwa model mampu mengklasifikasikan beberapa titik data dengan benar, tetapi tidak semua. Walaupun demikian masih ada ruang untuk perbaikan dan perlu dipertimbangkan untuk mencoba metode lain untuk meningkatkan hasil identifikasi formalin pada tahu. Hasil ini menunjukkan bahwa metode Naive Bayes Classifier pada Electronic Nose masih belum dapat memberikan hasil yang optimal dalam identifikasi formalin pada tahu, dan hasil yang diperoleh masih tidak lebih baik dari penelitian sebelumnya
Estimating Obesity Levels Using Decision Trees and K-Fold Cross-Validation: A Study on Eating Habits and Physical Conditions Admojo, Fadhila Tangguh; Nurul Rismayanti
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.126

Abstract

This study harnesses the predictive capabilities of machine learning to explore the determinants of obesity within populations from Mexico, Peru, and Colombia, using a Decision Tree algorithm bolstered by 5-fold cross-validation. Our comprehensive analysis of 2111 individuals' lifestyle and physical condition data yielded accuracy, precision, recall, and F1-scores that notably peaked in the third and fifth folds. The findings affirmed the significance of dietary habits and physical activity as substantial predictors of obesity levels. The variability in model performance across the folds underscored the importance of robust cross-validation in enhancing the model's generalizability. This research contributes to the burgeoning field of data science in public health by providing a viable model for obesity prediction and laying the groundwork for targeted health interventions. Our study's insights are pivotal for public health officials and policymakers, serving as a stepping stone towards more sophisticated, data-driven approaches to combating obesity. The study, however, recognizes the inherent limitations of self-reported data and the need for broader datasets that encompass more diverse variables. Future research directions include the analysis of longitudinal data to establish causal relationships and the comparison of various machine learning models to optimize predictive performance
Pelatihan Pemanfaatan Teknologi Informasi untuk Monitoring Pembelajaran Jarak Jauh di SMKN 6 Palembang Azdy, Rezania Agramanisti; Ajismanto, Fahmi; Saputri, Nurul Adha Oktarini; Admojo, Fadhila Tangguh; Barovih, Guntoro
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 10 (2023): Desember
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i10.500

Abstract

Berkembangnya teknologi informasi memungkinkan hasil uji terhadap pencapaian pembelajaran yang dilakukan peserta didik dilaporkan secara langsung kepada wali atau orangtua siswa. Beberapa media dan sarana dapat digunakan, dan salah satu diantaranya adalah penggunaan teknologi informasi di bidang web, yaitu Edmodo. Edmodo menyediakan feature yang disebut dengan Edmodo Parents yang dapat mengikutsertakan orangtua siswa untuk dapat menerima update progres kegiatan belajar siswa dengan mengetahui materi atau tugas apa saja yang diberikan, tugas yang telah dan belum dikumpulkan siswa, nilai yang diberikan guru terhadap tugas siswa, dan diskusi langsung dengan guru pengampu pelajaran. Pelatihan penggunaan Edmodo dilakukan di SMKN 6 Palembang dengan tujuan agar para pendidik sadar dengan adanya fitur Edmodo Parents dan dapat memberikan panduan penggunaan kepada orangtua yang belum memahami tata cara menggunakan Edmodo untuk dapat mengikuti progres perkembangan anaknya maupun berkomunikasi langsung terhadap guru pengampu pelajaran tertentu. Hasil evaluasi di akhir kegiatan pelatihan memperlihatkan bahwa terdapat peningkatan pemahaman peserta kegiatan terhadap Edmodo for Parents baik definisi maupun cara penggunaannya sebesar 42,04%.
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Systematic Literature Review on Ontology-based Indonesian Question Answering System Admojo, Fadhila Tangguh; Lajis, Adidah; Nasir, Haidawati
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p129-144

Abstract

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for accurate responses. Secondly, template-based SPARQL query construction, commonly used in Indonesian QA systems, suffers from semantic gaps and inflexibility. Advanced techniques like semantic matching algorithms and dynamic template generation can bridge these gaps and adapt to evolving ontologies. Thirdly, lexical gaps and ambiguity hinder QA systems. Bridging vocabulary mismatches between user queries and ontology labels remains a challenge. Strategies like synonym expansion, word embedding, and ontology enrichment must be explored further to overcome these challenges. Lastly, the review discusses the potential of developing multi-domain ontologies to broaden the knowledge coverage of QA systems. While this presents complex linguistic and ontological challenges, it offers the advantage of responding to various user queries across various domains. This literature review identifies crucial challenges in developing ontology-based Indonesian QA systems and suggests innovative approaches to address these challenges.
Optimal Strategy for Handling Unbalanced Medical Datasets: Performance Evaluation of K-NN Algorithm Using Sampling Techniques Salim, Yulita; Utami, Aulia Putri; Manga’, Abdul Rachman; Aziz, Huzain; Admojo, Fadhila Tangguh
Knowledge Engineering and Data Science Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i22024p176-186

Abstract

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung cancer detection accuracy, while undersampling contributed to balanced performance gains in the COVID-19 class. Metrics, including accuracy, precision, recall, and F1-score, provided insights into the model’s effectiveness. These findings highlight the positive impact of data balancing techniques on K-NN performance in imbalanced medical image classification. Continued research is essential to refine these techniques and improve medical diagnostics.
Optimizing Javanese Numeral Recognition Using YOLOv8 Technology: An Approach for Digital Preservation of Cultural Heritage Syafie, Lukman; Azis, Huzain; Admojo, Fadhila Tangguh
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.239

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.
Co-Authors Achmad Jauhari, Achmad Ahsanawati Aziz, Huzain Devie Rosa Anamisa Fahmi Ajismanto Guntoro Barovih, Guntoro Hamada Zein Huzain Azis Ika Oktavia Suzanti Jabir, Siti Rahma Lajis, Adidah Manga’, Abdul Rachman Mualfah, Desti Muhammad Innuddin Nasir, Haidawati Naswin, Ahmad Nurul Rismayanti Radhitya, Made Leo Rezania Agramanisti Azdy, Rezania Agramanisti Salim, Yulita Saputri, Nurul Adha Oktarini Slamet Risnanto Syafie, Lukman Utami, Aulia Putri Windiawati, Ai Wulan Yudha Islami Sulistya