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CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM SETYAWAN, ARIF FITRA; Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.

Impact of Statistical and Semantic Features Extraction for Emotion Detection on Indonesian Short Text Sentences Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul; Setyawan, Arif Fitra
CommIT (Communication and Information Technology) Journal Vol. 19 No. 1 (2025): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v19i1.11680

The ability to detect emotions in short texts is crucial because interpreting emotions on platforms like Twitter can offer insight into social trends and responses to specific events. Additionally, examining emotions in product reviews assists companies in comprehending customer sentiment, allowing them to improve the quality of their products and services. Most research on Indonesian language emotion detection utilizes statistical feature extraction, with limited discussion on the impact of both statistical and semantic feature extraction. Thus, the research aims to detect emotions in short texts equipped with an analysis of the impact of statistical and semantic features. Analysis of the impact of statistical and semantic features on short texts is necessary to identify the most effective approaches, improve detection accuracy, and ensure that the developed systems can better handle the variety and complexity of informal language. The data used are a public dataset originating from Twitter texts and product review texts in e-commerce. The research utilizes statistical features such as Term Frequency Inverse Document Frequency (TF-IDF) and semantic features such as Bidirectional Encoder Representations from Transformers (BERT). The evaluation results show that using semantic features significantly improves the performance of emotion detection in short texts by 13–24%. It is higher than using statistical features. Deep Learning (DL) algorithms based on neural networks have also been proven to outperform Machine Learning (ML) algorithms in detecting emotions in short text. The experimental results and outlines show the potential directions for future development.

Penerapan Teknologi Artificial Intelligence Sebagai Media Inovatif dalam Mendukung Proses Pembelajaran dan Kreativitas Siswa Paket C di PKBM Bangkit Kota Semarang Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
Jurnal Pengabdian Masyarakat Nusantara (JPMN) Vol. 5 No. 1 (2025): Februari - Juli 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpmn.v5i1.4595

The rapid development of information technology requires educational institutions, including Community Learning Activity Centers (PKBM), to innovate in improving the quality of learning and fostering student creativity. PKBM Bangkit Kota Semarang as a non-formal educational institution still faces obstacles in the form of low learning motivation and limited technology-based learning media. This community service activity aims to design and implement Artificial Intelligence (AI) technology as an innovative media to support the learning process and encourage the creativity of Package C students. The implementation method includes identifying partner needs, designing and developing AI media, training and mentoring, implementing in class, and monitoring and evaluation. The results of the community service show that AI-based learning media has succeeded in increasing learning participation, facilitating access to materials, encouraging independent learning, and receiving positive responses from tutors and students. These findings demonstrate that the application of AI in PKBM has the potential to be an innovative solution for strengthening the quality of non-formal education in the digital era.

UNDERSTANDING PUBLIC OPINION ON POLITICAL CANDIDATES THROUGH TWITTER SENTIMENT ANALYSIS: A COMPARATIVE STUDY OF FEATURE EXTRACTION Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9993

Presidential elections are crucial in a country's political dynamics and are increasingly discussed on social media platforms like Twitter. However, sentiment analysis of public opinion on these platforms faces significant challenges, such as large data volumes, diverse formats, and the complexity of informal language. The key challenge is choosing the most appropriate feature extraction technique and classification algorithm to address the unique characteristics of Indonesian-language tweets in the context of presidential elections. This study aims to compare the effectiveness of two feature extraction approaches—semantic based on BERT (Bidirectional Encoder Representations from Transformers) and statistical based on TF-IDF (Term Frequency-Inverse Document Frequency)—in sentiment analysis of Indonesian-language tweets related to the presidential election, using four classification algorithms: Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, and Decision Tree. The experimental results demonstrate that the combination of TF-IDF with SVM provides the best performance, with an accuracy of 85.1% and a macro f1-score of 0.81, outperforming the BERT approach used statically. These findings indicate that statistical approaches such as TF-IDF remain relevant and practical for short social media texts and emphasize the importance of choosing a method that suits the characteristics of the data and the context of the analysis.

NAÏVE BAYES AND SUPPORT VECTOR MACHINE BASED ON OPTIMIZATION FOR PUBLIC SENTIMENT ANALYSIS POST-2024 ELECTION Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10147

The 2024 election has sparked an explosion of public opinion across various digital platforms, but the complexity and large volume of data make it difficult for policymakers to understand public sentiment in a timely manner. Therefore, an accurate and efficient sentiment analysis method is needed to automatically classify public opinion. This study aims to analyze and compare the performance of the Naïve Bayes algorithm and an optimized Support Vector Machine (SVM) in classifying post-election public sentiment. The research method includes collecting 10,000 text data entries from various data sources, conducting text preprocessing, extracting features using the TF-IDF method, applying both algorithms with parameter tuning, and generating their performance using accuracy, precision, recall, and F1 score metrics. The results show that the optimized SVM algorithm delivers superior performance, achieving 88.24% accuracy, compared to 82.35% for Naïve Bayes. These findings indicate that SVM is more effective in handling complex public opinion sentiment classification, thus serving as a valuable reference for post-election policymaking

Penerapan Teknologi Artificial Intelligence Sebagai Media Inovatif dalam Mendukung Proses Pembelajaran dan Kreativitas Siswa Paket C di PKBM Bangkit Kota Semarang Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
Jurnal Pengabdian Masyarakat Nusantara (JPMN) Vol. 5 No. 1 (2025): Februari - Juli 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpmn.v5i1.4595

The rapid development of information technology requires educational institutions, including Community Learning Activity Centers (PKBM), to innovate in improving the quality of learning and fostering student creativity. PKBM Bangkit Kota Semarang as a non-formal educational institution still faces obstacles in the form of low learning motivation and limited technology-based learning media. This community service activity aims to design and implement Artificial Intelligence (AI) technology as an innovative media to support the learning process and encourage the creativity of Package C students. The implementation method includes identifying partner needs, designing and developing AI media, training and mentoring, implementing in class, and monitoring and evaluation. The results of the community service show that AI-based learning media has succeeded in increasing learning participation, facilitating access to materials, encouraging independent learning, and receiving positive responses from tutors and students. These findings demonstrate that the application of AI in PKBM has the potential to be an innovative solution for strengthening the quality of non-formal education in the digital era.

Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform Ariyanto, Amelia Devi Putri; Fari Katul Fikriah; Arif Fitra Setyawan
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 17 No. 1 (2024): Pixel :Jurnal Ilmiah Komputer Grafis dan Ilmu Komputer
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v17i1.2010

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

Pelatihan Desain Grafis Siswa PKBM Bangkit di Kota Semarang untuk Kemandirian Ekonomi Kreatif Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.750

Perkembangan ekonomi digital menuntut penguasaan keterampilan desain grafis sebagai bagian dari strategi komunikasi visual. Kegiatan pengabdian ini bertujuan untuk meningkatkan keterampilan desain grafis dasar siswa PKBM Bangkit di Kota Semarang guna mendukung kemandirian ekonomi kreatif. Metode pelaksanaan meliputi identifikasi kebutuhan, perencanaan berbasis praktik, pelatihan penggunaan aplikasi Canva, dan evaluasi partisipatif. Pelatihan dilakukan secara interaktif dan aplikatif, dengan materi yang mudah diakses dan disesuaikan dengan konteks kehidupan peserta. Hasil menunjukkan bahwa sebagian besar peserta merasa Canva mudah digunakan, dan memahami materi dengan cukup jelas. Peserta mampu menghasilkan desain sederhana untuk kebutuhan pribadi dan komunitas. Kegiatan ini membuktikan bahwa PKBM memiliki potensi sebagai wadah pengembangan keterampilan digital yang inklusif. Temuan ini memperkuat pentingnya integrasi pelatihan kreatif dalam pendidikan nonformal untuk mendukung pemberdayaan ekonomi masyarakat secara berkelanjutan.

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