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Public Perception of Buying and Selling Bitcoin Using Lexicon Sentiment Analysis Muhammad Rahman Ali; Wijaya, Rifki; Yunanto, Prasti Eko
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.980

This study investigates public perceptions of Bitcoin (BTC) trading using sentiment lexicon analysis. The rapid growth of cryptocurrency trading has attracted significant public interest and investment, making it crucial to understand the sentiments and opinions surrounding BTC transactions. By employing sentiment lexicon methods, this research analyzes tweets and social media posts to determine public sentiment. The study aims to identify trends and patterns in public opinion, providing insights into how sentiment impacts BTC trading behavior. Preliminary results indicate a correlation between positive sentiment and increased trading activity, while negative sentiment correlates with market declines. This research contributes to a better understanding of the role of public sentiment in the volatile cryptocurrency market.

Pengenalan Robotika sebagai Media Pengembangan Keterampilan Berpikir Komputasional pada Siswa Sekolah Menengah Atas Alam Bandung Purboyo, Tito Waluyo; Naufal , Dziban; Putra , M. Darfyma; Wijaya, Rifki; Komara, Riza Aria; Rambe , Muhammad Anugra Rizky; Putti, Fasya Hanifah
Almufi Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2: Desember (2024)
Publisher : Yayasan Almubarak Fil Ilmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63821/ajpkm.v4i2.387

Pengenalan robotika sebagai media pembelajaran memiliki potensi besar dalam mengembangkan keterampilan berpikir komputasional, yang merupakan kompetensi esensial bagi siswa dalam menghadapi tantangan di dunia modern. Program pengabdian masyarakat ini bertujuan untuk memperkenalkan dan melibatkan siswa Sekolah Menengah Atas Alam Bandung secara aktif dalam berpikir komputasional melalui aktivitas pembelajaran berbasis robotika. Dalam kegiatan ini, siswa diajak untuk memahami konsep dasar robotika dan menerapkannya dalam pemecahan masalah secara kreatif dan logis. Pendekatan yang digunakan juga mencakup pengenalan sistem kendali sederhana, yang memanfaatkan teknologi sinyal kelistrikan tubuh untuk mengendalikan robot sebagai alat peraga multidisiplin, sehingga mendorong minat siswa terhadap bidang sains dan rekayasa. Program ini diharapkan dapat memberikan kontribusi positif dalam mengembangkan kemampuan siswa untuk berpikir kritis dan inovatif, serta membekali mereka dengan keterampilan yang relevan di era digital.

Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data Wijaya, Rifki; Kosala, Gamma
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1880-1888

With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.

Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning Nur, Zikri Kholifah; Wijaya, Rifki; Wulandari, Gia Septiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7761

This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Logistic Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.

Perbandingan Analisis Sentimen pada Ulasan Aplikasi Sirekap Menggunakan Support Vector Machines dan Naive Bayes Khalid, khalid; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 12 No 1 (2025): April 2025
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v12i1.5196

This research analyzes the sentiment reviews of the SIREKAP application on the Google Play Store using two machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The dataset used consists of 19,925 reviews that have gone through preprocessing stages, including text cleaning, stopword removal, stemming, and tokenization. To overcome data imbalance, oversampling and undersampling techniques were applied. Furthermore, TF-IDF is used for feature extraction, converting text into numerical representation. The dataset is divided into 80% training data (15,940 data) and 20% test data (3,985 data). The results show that oversampling provides better performance than undersampling. In the oversampling method, the SVM algorithm achieved the highest accuracy of 95%, with consistent precision, recall, and F1-score values across all sentiment classes. The Naïve Bayes algorithm also performed quite well, with an accuracy of 77% on the oversampled data. In contrast, in the undersampling method, both algorithms have the same accuracy of 61%. This study confirms that the combination of oversampling technique and SVM algorithm is the best approach to handle imbalanced data and provides important insights into user perception of the SIREKAP application.

Palm Oil Seed Origin Classification Based on Thermal Images and Agricultural Data Using Convolutional Neural Network Natha, Si Gede Ngurah Chandra Adi; Wirayuda, Tjokorda Agung Budi; Wijaya, Rifki
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4880

The traceability of palm oil seed origins plays a vital role in ensuring transparency, legality, and sustainability across the palm oil supply chain. Recent advances in deep learning have created new opportunities to improve classification systems by leveraging both visual and contextual data. This study proposes a deep learning-based model for classifying the origin of palm oil seeds by integrating thermal imagery with agricultural data. Two convolutional neural network (CNN) architectures, ResNet50 and MobileNet, were evaluated under three experimental setups: using only thermal images, combining thermal images with agricultural features (socio-economic, soil, and spectral fruit characteristics), and applying hyperparameter tuning to the best-performing model. The results show that ResNet50 consistently outperformed MobileNet, particularly in multimodal configurations. The highest performance was achieved using ResNet50 with the Adam optimizer, a learning rate of 0.001, and a batch size of 16, resulting in training accuracy of 99.75%, validation accuracy of 99.92%, and test accuracy of 100.00%. Evaluation metrics confirmed the model’s robustness with precision, recall, and F1-score all reaching 100.00%. This research highlights the significant potential of combining thermal imagery and agricultural data in CNN-based models for accurate and reliable classification of palm oil seed origins. The approach can support traceability systems in the palm oil industry, offering a scalable and data-driven solution for ensuring supply chain integrity and sustainability.

Public Perception of Buying and Selling Bitcoin Using Lexicon Sentiment Analysis Muhammad Rahman Ali; Wijaya, Rifki; Yunanto, Prasti Eko
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.980

This study investigates public perceptions of Bitcoin (BTC) trading using sentiment lexicon analysis. The rapid growth of cryptocurrency trading has attracted significant public interest and investment, making it crucial to understand the sentiments and opinions surrounding BTC transactions. By employing sentiment lexicon methods, this research analyzes tweets and social media posts to determine public sentiment. The study aims to identify trends and patterns in public opinion, providing insights into how sentiment impacts BTC trading behavior. Preliminary results indicate a correlation between positive sentiment and increased trading activity, while negative sentiment correlates with market declines. This research contributes to a better understanding of the role of public sentiment in the volatile cryptocurrency market.

Mapping Gestures Based on Text Emotion Classification for a Virtual Chatbot for Early Marriage Consultation in Lombok Using RoBERTa Model Ramadhan, Adam Zahran; Wijaya, Rifki; Shaufiah, Shaufiah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5038

To address the persistent issue of early marriage among Indonesian adolescents, this study proposes a virtual counseling chatbot that classifies emotional cues in text using a fine-tuned IndoRoBERTa model. The emotion classification framework is designed to support counseling-based prevention efforts by moving beyond basic sentiment analysis and adopting five functional emotional categories such as ‘Enthusiastic’, ‘Gentle’, ‘Analytical’, ‘Inspirational’, and ‘Cautionary’ to align with psychological counseling styles. Built on fine-tuned IndoRoBERTa architecture, the model was trained in two phases: first with 2,500 manually validated samples yielding 92.8% accuracy, and then with 12,500 auto-labeled entries, resulting in 91.3% accuracy. Performance was assessed using accuracy, precision, recall, and F1-score. A gesture mapping layer was also integrated to enhance empathetic response generation. Each emotion label was paired with a predefined body gesture, grounded in counseling theory, to support future development of multimodal virtual agents capable of expressing emotions both textually and physically. The novelty lies in combining context-aware emotion classification with gesture mapping, enabling future development of expressive, culturally relevant, and empathetic virtual chatbot agents.

Mapping Facial Expressions Based on Text for Virtual Counseling Chatbot Using IndoBERT Model Padilah, Rifki; Wijaya, Rifki; Shaufiah, Shaufiah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5039

Early marriage in Lombok remains a serious issue, with a prevalence rate of 16.59% in 2021, the second highest in Indonesia. Limited access to counseling services, especially in rural areas, poses a significant prevention challenge. This study developed a virtual counseling chatbot system capable of mapping text-based emotions to facial expressions to improve the effectiveness of counseling for early marriage prevention. The methodology involved training an IndoBERT model on a synthetic dataset to analyze conversation texts. The model was designed to classify user input into five functional emotion categories: Enthusiasm, Gentleness, Analytical, Inspirational, and Cautionary. Performance evaluation revealed that the IndoBERT model achieved an outstanding accuracy of 94% in its final phase. This result significantly surpassed other models evaluated, such as CNN (71.6%) and KNN (79%), confirming the superiority of the chosen approach The study concludes that the high-accuracy IndoBERT model is a robust foundation for empathetic virtual agents. This research provides a significant contribution to the fields of Affective Computing and Human-Computer Interaction by demonstrating an effective framework for mapping nuanced, functional emotions from Indonesian text to facial expressions. The proposed system not only offers a scalable technological solution for mental health challenges like early marriage prevention but also highlights the impact of advanced, context-aware NLP models in creating more human-like and empathetic user interactions.

Deteksi Influenza Berdasarkan Heart Rate Steps, Dan Resting Heart, Heart Rate Menggunakan Multi-Layer Perceptron Ginting, Dewi Swarni Br; Purboyo, Tito Waluyo; Wijaya, Rifki
eProceedings of Engineering Vol. 10 No. 1 (2023): Februari 2023
Publisher : eProceedings of Engineering

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

Abstrak— Menurut Michael K.Abraham Influenza itu memiliki tiga jenis yaitu (A, B dan C) yang di subtipekan berdasarkan permukaannya, ada protein yang Bernama neuraminidase (N) dan ada juga yang Bernama hemagglutinin (H) di tipe A dan B, untuk fusi hemagglutinin-esterase pada tipe C. Influenza A mempunyai dua subtype dan yang untuk saat ini subtypenya lebih dominan beredar kepada manusia, termasuk (H1/N1) dan (H3/N2).[1] Dalam sepuluh tahun trakhir ini, pencari kegiatan yang bisa dikenakan semakin populer buat memantau detak jantung heart rate (HR). Tetapi heart rate (HR) tidak bisa menggunakan tafsirkan tanpa konteks yang benar. Nilai heart rate (HR) terukur 70 bpm bisa dianggap tinggi buat seorang atlet ketika tidur, atau rendah buat seseorang yang umumnya tidak berlatih, setelah berjalan jauh.[7] Dalam penelitian tugas akhir ini penulis menggunakan algortima Multi-layer Perceptron (MLP) ialah ANN (Artificial Neural Network) pada perceptron. Yang berbentuk ANN (Artificial Neural Network) feedforward memakai satu atau lebih hidden layer. Data yang di gunakan dalam penelitian ini merupakan sebuah alat wearable device sebagai jam tangan. Dari penelitian yang sudah di lakukan yang menggunakan algoritma Multi-Layer Perceptron, peneliti mendapatkan accuracy pada pasien 1 yaitu 66% dan untuk pasien 2 yaitu 81%. Sehingga dapat di simpulkan bahwan system ini berjalan sesuai dengan tujuan. Kata kunci— Influenza, Heart rate, Deteksi influenza, TimeSeries Heart rate, Multi-Layer perceptron

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