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All Journal Semantik Pro Bisnis Telematika JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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 Penelitian Sistem Informasi Jurnal Ilmiah Multidisiplin Indonesia Journal of Information Technology and Cyber Security Jurnal Siber Multi Disiplin Jejak digital: Jurnal Ilmiah Multidisiplin 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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Classification of Impulse Buying on TikTok Shop Live Streaming Using the XGBoost Algorithm Fini Ikhfiani Fadilah; Purwadi Purwadi
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.v8i2.39143

Abstract

The development of social commerce through the TikTok Shop platform has transformed the interaction patterns between sellers and consumers through live streaming features that enable an interactive and real-time shopping experience. This study aims to classify Impulse Buying behavior in TikTok Shop Live Streaming activities using the XGBoost algorithm. The dataset consists of 300 observations collected from live streaming sessions of the TikTok account Igameivia during the period of January–April 2026. The variables used include live streaming duration, views, live impressions, number of comments, and new followers. The number of orders and Gross Merchandise Value (GMV) variables were excluded from the model due to their potential to cause feature leakage. The research stages included data preprocessing, dataset splitting using an 80:20 ratio, XGBoost model training, and evaluation using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and ROC-AUC metrics. The results show that the model achieved an Accuracy of 81.67%, Precision of 82.93%, Recall of 89.47%, F1-Score of 86.08%, and ROC-AUC of 0.8732. These results indicate that the model has a good capability to distinguish between Impulse Buying and Non-Impulse Buying behaviors. Feature importance analysis revealed that the number of comments, live impressions, and new followers were the most influential variables in the classification process. These findings suggest that user engagement and audience reach during live streaming sessions play an important role in driving impulsive purchasing behavior. Therefore, the XGBoost algorithm can be utilized to identify Impulse Buying tendencies based on live streaming activities and support data-driven decision-making on the TikTok Shop platform.
Pelatihan Canva Oleh Mahasiswa Kampus Mengajar Menggunakan Akun Belajar Guna Meningkatkan Keterampilan Siswa Dalam Bidang Desain Di SMP PGRI 2 Somagede Purwadi Purwadi; Putri Vidia Lestari
Community Engagement and Emergence Journal (CEEJ) Vol. 5 No. 1 (2024): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v5i1.3952

Abstract

Pendidikan di Indonesia sejalan dengan perkembangan teknologi, semakin banyaknya pengguna handphone namun kebanyakan mereka tidak memanfaatkannya secara maksimal. Dalam menunjang pendidikan di Indonesia yang berbasis teknologi, pemerintah memberikan fasilitas akun belajar.id yang diberikan kepada pengajar dan pelajar di Indonesia untuk mengakses beberapa aplikasi dengan fitur lengkap. Salah satunya Canva for education yang yang memiliki akses layaknya menggunakan Canva Pro dimana jika diakses menggunakan akun belajar pengguna tidak perlu berlangganan atau membayarnya setiap bulan. Namun, dalam pemanfaatannya yang tidak maksimal oleh siswa bahkan banyak yang tidak mengetahui jika memiliki akun belajar tersebut. Tim penugasan juga mengunjungi perpustakaan dan masih kurangnya hiasan sebagai salahsatu daya tarik pengunjung. Melalui program kampus mengajar Angkatan 6 ini, kami mncoba memanfaatkan akun belajar.id tersebut untuk meningkatkan keterampilan siswa melalui pelatihan Canva bagi siswa OSIS di SMP PGRI 2 Somagede. Pada pelatihan ini difokuskan pada pembuatan poster media literasi di perpustakaan karena masih minimnya media literasi terutama di perpustakaan. Akan tetapi masih terdapat kendala pada saat pelaksanaan yaitu jaringan yang kurang baik yang mengharuskan menggunakan data seluler atau hostpot seluler untuk mendapatkan jaringan yang stabil di dalam ruang kelas. Beberapa tujuan dalam kegiatan pelatihan ini yaitu, memanfaatkan akun belajar.id, meningkatkan keterampilan dan kreativitas siswa dalam bidang desain, serta meningkatkan literasi siswa dengan pembuatan poster literasi. Metode pendekatan yang digunakan pada pelatihan ini menggunakan pendekatan kualitatif. Pelatihan dilaksanakan menggunakan metode demonstrasi dan praktek langsung oleh siswa menggunakan handphone masing-masing. Hasil dari kegiatan pelatihan ini siswa dapat mengoperasikan aplikasi Canva dengan baik, mengetahui salah satu manfaat akun belajar, dan meningkatkan keterampilan dalam bidang desain. Siswa sangat antusias mengikuti pelatihan hingga selesai dan mengharapkan kegiatan selanjutnya dalam bidang teknologi terutama desain yang ditunjukkan dengan hasil karya poster yang digunakan sebagai media literasi di perpustakaan sekolah dan desain poster di kelas ataupun feed Instagram kelas masing-masing.
Geospatial Analysis of Global Temperature and Humidity Variations Using Integrated Meteorological Data Alya Zhafira; 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.v5i2.1817

Abstract

Global climate monitoring is crucial for understanding variations in temperature and humidity, which directly influence ecosystems, human health, and socio-economic activities. This study presents a Geographic Information System (GIS)-based analysis and visualization of global temperature and humidity patterns using historical hourly weather data from 2012 to 2017. The dataset, obtained from open-access sources, was processed and analyzed in Google Colab using Python libraries such as pandas, geopandas, folium, and plotly. Data preprocessing involved merging city-level observations, cleaning missing values, and calculating mean temperature and humidity per location. The resulting dataset was then visualized through an interactive global map and a scatter plot to identify spatial relationships between the two climatic variables.To quantify these spatial relationships, a statistical correlation analysis was conducted, revealing a weak negative relationship between temperature and humidity (r = -0.25) across global regions.The findings reveal that regions near the equator exhibit consistently high temperatures and humidity, while higher-latitude cities show lower temperatures and more variable moisture levels. This GIS-based approach demonstrates the potential of open meteorological data for climate pattern recognition and supports reproducible workflows for environmental analysis. The results highlight the importance of integrating data science tools with GIS for accessible and scalable global climate visualization.
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
DETEKSI SENTIMEN MULTIBAHASA: INDOBERT VS ROBERTA BERBASIS TERJEMAHAN NLLB-200 Fendi Elyon Ramadhan; Giat Karyono; Purwadi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8498

Abstract

Sentiment classification in Indonesian-language social media text remains a challenge because informal spelling, code-mixing, culture-specific expressions, and class imbalance can reduce model reliability. Using a dataset containing 1,336 labeled X posts related to the 2024 Indonesian Presidential Election, this research compares two transformer workflows for three-class sentiment classification, which are fine-tuning IndoBERT on Indonesian text and a translation-based workflow, where NLLB-200 translates the same text into English before the RoBERTa training process. The dataset was divided via stratified sampling into 1,069 training examples, 133 validation examples, and 134 test examples. Both models were trained for five epochs with identical optimization settings and evaluated using accuracy, weighted precision, weighted recall, weighted F1, classwise scores, and confusion matrices. IndoBERT achieved 82.09% accuracy and 80.31% weighted F1, compared with 80.60% and 75.69% for the NLLB-200 plus RoBERTa pipeline. The largest difference occurred in the neutral class, for which IndoBERT obtained 0.26 recall and RoBERTa only 0.05. Error analysis indicates that translation artifacts and majority-class bias jointly reduced sensitivity to neutral and context-dependent expressions. Direct monolingual fine-tuning was more reliable for this dataset, although translation-based transfer remained competitive for the dominant positive class. Future work should use larger independently annotated datasets, repeated runs, translation-quality analysis, and class-aware training objectives.
Evaluasi Efektivitas Sistem Autentikasi Dua Faktor (2FA) terhadap Kepercayaan Pengguna Aplikasi Digital dengan Pendekatan Statistik Fadila Nur Syifa; Akbar Priyanto; Purwadi Purwadi
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

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

Abstract

The rapid development of digital technology has led to an increasing demand for effective security systems to protect user information. One popular method is two-factor authentication (2FA), which adds another layer of protection beyond passwords. This study aims to assess the effectiveness of 2FA implementation in increasing user trust when accessing digital services. The method used in this study was a quantitative approach, collecting data through questionnaires administered to a group of respondents who use digital applications. The collected data were then analyzed using descriptive and inferential statistical techniques to explore the relationship between 2FA use and user trust levels. The study findings indicate that most respondents feel more secure and trust systems that use 2FA compared to those that only implement single-factor authentication. Furthermore, the statistical analysis indicates a positive correlation between 2FA implementation and increased user trust. Thus, it can be concluded that user views of 2FA are generally positive. This research is expected to serve as a reference for system developers in improving user security and convenience.
Co-Authors Abdul Jahir Adam Prayogo Kuncoro Ade Toti Febrian Adhisa Nanda Kurnia Akbar Priyanto Akto Hariawan Alya Zhafira Amelia Nur Azizah Ammar Fauzan, Ammar Andi Dwi Riyanto Andina, Anisa Nur ANNISA HANDAYANI Arief Kurnia Ramadhani Arvita Tivanny Ellen Atiqah Noor Zhaafirah Augusta Dwi Putra Aulia Suryaning Tyas Azkyatul Mardiyah Bachtiar Mujaddidi Bagus Adhi Kusuma Bagus Adhi Kusuma Bangkit Ririatini Berlilana Berlilana De Ajeng Vien Saputri Denaya Fadilah Dendi Putra Prakoso Desi Riyanti Dhanar Intan Surya Saputra Dias Puspita Anggareni Dzaki Dwi Abdullah Ecca Andini Naretawati Fadila Nur Syifa Fairus Aufa Baharita Faras Alfito Dwi Fendi Elyon Ramadhan Ferix Aziz Susandi Fini Ikhfiani Fadilah Giat Karyono 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 Juwita Septiani 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 Novaneza Rasendriya 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 Rifqi Aziz Rohmah, Umdah Aulia Rujianto Eko Saputro Sitaresmi Wahyu Handani Solihatun Havidah Sri Rahayu Stella Putriseptiyani Septeragil Syahdan Jamjami Vevinciya Dila Dita Widhaksa Triawan Yulia Risma Yanti Yusmedi Nurfaizal