Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Progresif: Jurnal Ilmiah Komputer Journal of Informatics Development
Cahyasari Kartika Murni
Institut Teknologi Dan Bisnis Widya Gama Lumajang
Computer Science & IT Control & Systems Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Uji Klasifikasi Algoritma Naive Bayes Classification dalam Analisis Sentimen Ulasan Puncak B29 Lumajang Fadhel Akhmad Hizham; Cahyasari Kartika Murni; Marita Qori’atunnadyah
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1618

Abstract

B29 Peak is a tourist attraction which is located in Argosari Village, Senduro District, Lumajang Regency, which is the highest peak in the Bromo sand sea area with a height of 2,900 meters above sea level. Based on Google Maps, B29 Peak has the best review score for tourist attraction in Lumajang Regency, with an average score of 4.7 stars and reviewed by 3,368 users until October 25th, 2023. Sentiment amnalysis was carried out to group the very large number of user reviews as an evaluation in order to correct deficiencies and improve the quality of the tourist attraction. Grouping sentiment analysis is using the Naïve Bayes Classification. The result of classification obtained the average score of accuracy is 91.06%, precision 96.57%, recall 94.07% and F1-score 95.31%. While the best result is on the 150 data testing, with the accuracy of 92.67%, precision 97.20%, recall 95.21% and F1-score 96.19%.Keyword: Sentimen Analisis; B29 Peak; Naïve Bayes Classification AbstrakPuncak B29 merupakan salah satu objek wisata yang terletak di Desa Argosari, Kecamatan Senduro, Kabupaten Lumajang, yang merupakan puncak tertinggi di Kawasan lautan pasir Bromo dengan ketinggian 2.900 meter di atas permukaan laut. Berdasarkan situs Google Maps, Puncak B29 memiliki skor ulasan yang terbaik untuk tempat wisata Kabupaten Lumajang, dengan skor ulasan rata-rata 4,7 bintang dan diulas oleh 3.368 pengguna hingga 25 Oktober 2023. Sentimen analisis dilakukan untuk mengelompokkan ulasan pengguna yang jumlahnya sangat banyak sebagai evaluasi agar dapat memperbaiki kekurangan dan meningkatkan kualitas dari tempat wisata tersebut. Pengelompokkan sentimen analisis menggunakan algoritma Naïve Bayes Classification. Hasil klasifikasi diperoleh nilai rata-rata akurasi sebesar 91,06%, presisi 96,57%, recall 94,07% dan F1-score 95,31%. Sementara untuk perolehan hasil terbaik terdapat pada pengujian dataset sebanyak 150 data, dengan akurasi sebesar 92,67%, presisi 97,20%, recall 95,21% dan F1-score 96,19%.Kata kunci: Sentimen Analisis; Puncak B29; Naïve Bayes Classification 
Implementation and Performance Evaluation of a Pretrained Convolutional Neural Network (CNN) for Face Recognition on a Student Dataset Vanessya Tri Agustin; Fadhel Akhmad Hizham; Cahyasari Kartika Murni
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2079

Abstract

Face recognition has become an important technology in computer vision and is widely applied in various fields, including identity verification in educational environments. However, variations in lighting, facial pose, and facial expressions remain major challenges in achieving accurate recognition. This study aims to implement and evaluate the performance of a pretrained Convolutional Neural Network (CNN) for student face recognition using a dataset collected directly from ITB Widya Gama Lumajang. The research employed the pretrained CNN model provided by the face_recognition and dlib libraries. The implementation process consisted of image preprocessing, face detection, face encoding, and face matching. System performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results demonstrated that the pretrained CNN model achieved an overall recognition accuracy of 96.73% and consistently produced high precision, recall, and F1-score values. Although variations in lighting, facial pose, facial expressions, and image quality affected recognition performance, the system was able to identify most student faces accurately. These findings indicate that the pretrained CNN method is effective and reliable for student face recognition and has the potential to support identity verification applications in educational environments.
Mapping Public Conversation on Indonesia's Free Nutritious Meal Program on Twitter (X) Using TF-IDF and K-Means Clustering Cahyasari Kartika Murni; Faishal Basbeth; Silviana Widya Lestari
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2081

Abstract

Public programs now generate their own online commentary, and that commentary is worth reading systematically rather than anecdotally. This paper maps the topic structure of Indonesian-language conversation about the Free Nutritious Meal program (Makan Bergizi Gratis, MBG) on Twitter (X). We scraped 3,459 tweets posted in January and February 2025, and cleaned them through case folding, filtering, tokenization, stopword removal, and stemming, which left 2,926 documents, or 84.59% of the raw set. These were weighted with Term Frequency-Inverse Document Frequency (TF-IDF) into a 2,926 x 5,000 document-term matrix and partitioned with K-Means. The Elbow Method placed the optimal solution at K = 3, with an SSE of 2,760.3617 and a Silhouette Score of 0.0092. Three topics emerged: institutional endorsement tied to food security and self-sufficiency (94 tweets, 3.21%), everyday talk about how the program is being delivered and paid for (2,012 tweets, 68.76%), and the benefits of the program for children and their health (820 tweets, 28.02%). The Silhouette Score sits close to zero. We read this as a property of the corpus rather than a failure of the procedure: every document discusses the same program, so the clusters share a large common vocabulary and their boundaries are soft. The partition should therefore be treated as a first thematic map rather than a clean separation, and contextual representations such as Word2Vec, FastText, or IndoBERT are the obvious next step.
Comparative Analysis of Transformer Models for Emotion Classification in Indonesian Text Data Faishal Basbeth; Cahyasari Kartika Murni; Abdur Ro’uf
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2082

Abstract

This study presents a comparative analysis of three transformer-based models, BERT, RoBERTa, and DistilBERT, for emotion classification on Indonesian text. The Indo4B dataset, which consists of five emotion labels (anger, fear, happiness, love, and sadness), is used as the benchmark corpus. All three models were trained using fine-tuning and hyperparameter-tuning procedures. The evaluation was conducted using accuracy, F1-score, model size, and training time as the primary performance metrics. The results indicate that RoBERTa achieves the best overall performance, with an accuracy of 90.83% and an F1-score of 91. Meanwhile, DistilBERT demonstrates approximately 60% faster training time compared to BERT, with only a 0.61% decrease in accuracy. These findings suggest that DistilBERT offers a highly efficient alternative while maintaining competitive classification performance.
Zero Crossing Rate (ZCR) Feature Extraction in Speech Emotion Recognition Using a Convolutional Neural Network Silviana Widya Lestari; Maysas Yafi Urrochman; Cahyasari Kartika Murni
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2083

Abstract

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction (HCI), healthcare, and automated feedback systems (Hashem et al., 2023). However, extracting relevant acoustic features from complex speech signals remains a significant challenge (Lieskovská et al., 2021). This paper investigates the effectiveness of the Zero Crossing Rate (ZCR) feature extraction method combined with a 1D Convolutional Neural Network (CNN) architecture for speech emotion classification. By evaluating emotional speech data across benchmark datasets (RAVDESS, CREMA, SAVEE, and TESS), audio signals were preprocessed using data augmentation techniques, including noise injection, time stretching, pitch shifting, and time shifting. The ZCR feature representation was fed into a CNN model to classify seven core emotional states: anger, disgust, fear, happiness, sadness, surprise, and neutral. The proposed ZCR-CNN approach achieved a classification accuracy of 61.96% with a training loss of 0.8214 and testing loss of 0.8123. Although ZCR effectively captures high-frequency and noisiness variations indicative of specific emotional states, its performance highlights the trade-offs of relying on single-domain time features compared to spectral features (Singh & Goel, 2022).
Co-Authors Abdur Ro’uf Fadhel Akhmad Hizham Fadhel Akhmad Hizham Faishal Basbeth Marita Qori’atunnadyah Maysas Yafi Urrochman Silviana Widya Lestari Vanessya Tri Agustin