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All Journal Semantik Pro Bisnis Telematika JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jambura Journal of Electrical and Electronics Engineering Community Engagement and Emergence Journal (CEEJ) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Soft Computing Exploration Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pendidikan Sains dan Komputer Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Health, Engineering and Technology Jurnal Ilmiah Multidisiplin Indonesia Journal of Information Technology and Cyber Security Jurnal Teknik Informatika dan Teknologi Informasi International Journal of Machine Learning (IJOML)
Purwadi Purwadi
Magister Ilmu Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto
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Sentiment Analysis of TikTok User Comments on The Free Nutritious Meal Program Using Support Vector Machine Lina Nur Afifah; Sri Rahayu; Purwadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1879

Abstract

This study aims to analyze user sentiment when leaving comments on TikTok about the Free Nutritious Food Program (MBG) to understand how the public views the program. Comment data was obtained through online collection and then divided into three groups: positive, negative, and neutral. Before further processing, the data went through a text cleaning and stemming stage to reduce word variation. The data was then represented using the TF-IDF method before being classified with a Support Vector Machine algorithm. The evaluation results showed that using stemming provided more accurate results than without using stemming, thereby improving the model's ability to recognize sentiments contained in comments using informal language. Additional analysis using word clouds, n-grams, and topic modeling provided an overview of words and issues frequently appearing in public discussions regarding the program.
Automatic Bell Using Esp8266 and Telegram Method as a Reminder for Laboratory Time at the AMIKOM Purwokerto University Assistant Forum Aulia Suryaning Tyas; Refida Putri; Purwadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1880

Abstract

The purpose of this research is to create an automatic bell system that uses an ESP8266 microcontroller integrated with Telegram as a reminder for practical sessions at the Amikom Purwokerto University Assistant Forum. This system is necessary because assistants need to balance laboratory responsibilities and academic activities. Using an Internet of Things-based approach, this system combines NodeMCU ESP8266, DS3231 Real-Time Clock (RTC) module, buzzer, and Telegram Bot notification service. The research process includes identifying needs, reviewing literature, designing the system, implementing, and testing. The bell operates automatically according to the schedule stored in the RTC, while the Telegram bot sends reminders 15 minutes before the practicum begins. Test results show that the bell consistently activates at the right time without delay, and that Telegram notifications are sent according to the configured schedule. These results indicate that the proposed system can meet the functional requirements for accuracy, reliability, and effective communication. Potential for further development in this system includes integration with an automatic attendance feature.
Classification of Pneumonia Using CNN and Vision Transformer Ma`dan Shomsomi; Widhaksa Triawan; Purwadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1906

Abstract

Pneumonia remains one of the leading causes of mortality among children worldwide. This study aims to evaluate the performance of two deep learning architectures, Convolutional Neural Network (CNN) and Vision Transformer (ViT), for pneumonia classification using chest X-ray images. Four training scenarios were examined, consisting of MobileNetV2 baseline, MobileNetV2 fine-tuned, ViT baseline, and ViT fine-tuned models. The dataset was obtained from the Chest X-Ray Images (Pneumonia) collection and was processed through augmentation and preprocessing to produce a balanced set of 9,000 images. Baseline models were trained using a feature extraction approach, while fine-tuning was conducted by selectively unfreezing internal layers. Experimental results show that all models achieved accuracy above 95%. The MobileNetV2 baseline reached 97.63%, while its fine-tuned counterpart did not yield further improvement, achieving 97.41%. In contrast, the Vision Transformer demonstrated substantial performance gains, where partial fine-tuning produced the highest accuracy of 98.59% with an f1-score of 0.99. These findings indicate that ViT with targeted fine-tuning is more effective in capturing global representations within X-ray images, making it a strong candidate for computer-aided pneumonia detection systems supported by artificial intelligence.
Comparative Analysis of Data-Level and Cost-Sensitive Learning in IndoBERT-Based Sentiment Analysis of Ruangguru App Reviews Ade Toti Febrian; Purwadi Purwadi; Adam Prayogo Kuncoro
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39885

Abstract

User reviews of online learning applications such as Ruangguru provide valuable information for evaluating service quality, user experience, and digital learning effectiveness. Although IndoBERT has demonstrated strong performance in Indonesian sentiment analysis, previous studies generally compared imbalance handling techniques using different datasets, model architectures, and experimental protocols, making the relative effectiveness of data-level and cost-sensitive learning approaches difficult to evaluate objectively under the same Transformer backbone. This study compares a Data-Level Approach using Latent-SMOTE with a Cost-Sensitive Learning Approach using Class-Weighted Loss on an identical IndoBERT architecture. The dataset consists of 3,767 Ruangguru user reviews collected from Google Play Store and processed through text preprocessing, IndoBERT tokenization, stratified train-validation-test splitting, and evaluation using Accuracy, Precision, Recall, Macro F1-score, confusion matrix, Cochran's Q Test, and McNemar Test. Experimental results show that the Baseline model achieved the highest Accuracy (90.05%), while the Cost-Sensitive Learning approach obtained the highest Macro F1-score (0.6275), outperforming both the Baseline (0.5658) and the Data-Level approach. These findings indicate that class-weighted optimization improves minority-class recognition without modifying the original training distribution, whereas Latent-SMOTE enhances minority representation but does not outperform Class-Weighted Loss. McNemar testing further confirms that the improvements over the Baseline are statistically significant. The main contribution of this work is an objective comparison of Data-Level and Cost-Sensitive Learning approaches using the same IndoBERT backbone, dataset, preprocessing pipeline, hyperparameters, and evaluation protocol. In addition, the study applies Latent-SMOTE in the latent feature space and complements performance evaluation with statistical significance testing, providing stronger empirical evidence for handling imbalanced Indonesian sentiment datasets
Spatial Validation Analysis And Prediction Of Service Reach Of Trans Banyumas Purwokerto-Banyumas Corridor 4 Ahead Of Operation In January 2026 Dendi Putra Prakoso; Purwadi Purwadi
International Journal of Health Engineering and Technology Vol. 4 No. 5 (2026): IJHESS JANUARY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v4i5.525

Abstract

The expansion of Trans Banyumas services through Corridor 4 to the Banyumas area has entered the final stage with an operational schedule set for January 1, 2026. This study conducted a pre-operational evaluation to validate route readiness and predict service coverage using Geographic Information Systems (GIS). By modeling the route along the Bulupitu Purwokerto Terminal to Banyumas Terminal which includes 49 Bus Stops (TPB), the analysis used QGIS 3.40.11 with Network Analysis and Service Area Analysis methods. The results show that the route has high efficiency with an estimated travel time of 45-60 minutes. Service Area analysis with a radius of 400 meters confirmed that the TPB is able to cover 6 major hospitals, 8 educational institutions, and all major tourist destinations in the Old Town of Banyumas. Spatial validation concluded that the route is very feasible for operation, with high potential to support the revitalization of heritage areas and the integration of public transportation in the Greater Banyumas area
Co-Authors Abdul Jahir Adam Prayogo Kuncoro Ade Toti Febrian Adhisa Nanda Kurnia Akto Hariawan Alya Zhafira Ammar Fauzan, Ammar Andi Dwi Riyanto Andina, Anisa Nur ANNISA HANDAYANI Arief Kurnia Ramadhani Arvita Tivanny Ellen Atiqah Noor Zhaafirah Aulia Suryaning Tyas Bachtiar Mujaddidi Bagus Adhi Kusuma Bagus Adhi Kusuma Berlilana Berlilana Dendi Putra Prakoso Desi Riyanti Dhanar Intan Surya Saputra Dias Puspita Anggareni Ecca Andini Naretawati Fairus Aufa Baharita Ferix Aziz Susandi Fini Ikhfiani Fadilah Giat Karyono Hamzah Tsabit Akdama Hendra Marcos Hidayah, Debby Ummul Ibrahim, Farrel Ilham Robbani Ilham Suryo Saputra Imam Tahyudin Imam Tahyudin Intan Surya Saputra, Dhanar Iskoko, Angga Jaka Wijaya Kusuma jordy, Roy Jordy Kuat Indartono Lina Nur Afifah M. Syaiful Amin Mardiyanto Mardiyanto Ma`dan Shomsomi Mohammad imron Muliasari Pinilih, Muliasari Nandia Triyusvita Nor Azman Abu Nor Azman Bin Abu Novitasani Putri Othman Bin Mohd Othman Bin Mohd Panca Ragil Prasetyo Pandu W, Muhammad Arfianto Puas Triawan Pungkas Subarkah Putri Oktavianingsih Putri Vidia Lestari Rahayu, Dania Gusmi Rahman Rosyidi Ratih Anggraeni Refida Putri Rehandika Priambudi Rohmah, Umdah Aulia Rujianto Eko Saputro Sitaresmi Wahyu Handani Solihatun Havidah Sri Rahayu Widhaksa Triawan Yulia Risma Yanti Yusmedi Nurfaizal