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All Journal Jurnal Penyuluhan Imaji: Jurnal Seni dan Pendidikan Seni Plumula : Berkala Ilmiah Agroteknologi Jurnal Bina Praja JITK (Jurnal Ilmu Pengetahuan dan Komputer) Media Ilmu Kesehatan Jurnal Pengabdian Magister Pendidikan IPA Jurnal Kehutanan Papuasia (Journal of Papuasia Forestry) Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Agrifitia: Journal of Agribusiness Plantation Jurnal Wana Tropika Jurnal Pengabdian Masyarakat IPTEK Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Elementary School: Jurnal Pendidikan dan Pembelajaran ke-SD-an DEDIKASI : Jurnal Pengabdian Kepada Masyarakat Research Horizon Jurnal Pendidikan Surya Edukasi (JPSE) Coreid Journal Jurnal Ikadbudi : Jurnal Ilmiah Bahasa, Sastra, dan Budaya Daerah JURNAL KEBANGSAAN REPUBLIK INDONESIA Journal of Elektronik Sistem InformasI Jurnal Pendidikan Seni dan Industri Kreatif EMPIRISMA: JURNAL PEMIKIRAN DAN KEBUDAYAAN ISLAM PAUDIA: JURNAL PENELITIAN DALAM BIDANG PENDIDIKAN ANAK USIA DINI Jurnal Wana Tropika
Purwadi
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Biomasa dan Cadangan Karbon Tanaman Berkayu di Hutan Desa Sungai Pelang, Kabupaten Ketapang, Kalimantan Barat Subarkah, Khansa Falere; Suhartati, Tatik; Bowo Woesono, Hastanto; Purwadi
Jurnal Wana Tropika Vol 15 No 2 (2025): November
Publisher : Fakultas Kehutanan Institut Pertanian STIPER Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55180/jwt.v15i2.2343

Abstract

The Sungai Pelang Village Forest, Block 11 Sub-block G, possesses a diversity of vegetation consisting of various species with different growth stages and has the potential to store carbon stocks. This research aims to estimate the biomass and carbon stocks of various woody plant species. The estimation uses a non-destructive method. The sampling method employed was the transect line method with a 10% sampling intensity, resulting in 13 observation plots with a distance of 63 meters between plots. The data collected included diameter, tree height, and the specific gravity of the species found. The results showed that the estimated total biomass is 87,40 tons/ha, and the carbon stock is 41,08 tons/ha, which is dominated by the tumih wood (Combretocarpus rotundatus (Miq.) Danser) and Geronggang (Cratoxylum arborescens).
Biomasa dan Cadangan Karbon Tanaman Berkayu di Hutan Desa Sungai Pelang, Kabupaten Ketapang, Kalimantan Barat Subarkah, Khansa Falere; Suhartati, Tatik; Bowo Woesono, Hastanto; Purwadi
Jurnal Wana Tropika Vol 15 No 2 (2025): November
Publisher : Fakultas Kehutanan Institut Pertanian STIPER Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55180/jwt.v15i2.2343

Abstract

The Sungai Pelang Village Forest, Block 11 Sub-block G, possesses a diversity of vegetation consisting of various species with different growth stages and has the potential to store carbon stocks. This research aims to estimate the biomass and carbon stocks of various woody plant species. The estimation uses a non-destructive method. The sampling method employed was the transect line method with a 10% sampling intensity, resulting in 13 observation plots with a distance of 63 meters between plots. The data collected included diameter, tree height, and the specific gravity of the species found. The results showed that the estimated total biomass is 87,40 tons/ha, and the carbon stock is 41,08 tons/ha, which is dominated by the tumih wood (Combretocarpus rotundatus (Miq.) Danser) and Geronggang (Cratoxylum arborescens).
Implementasi IoT Untuk Monitoring Iklim dan Cuaca dengan AWS Cloud Pada Daerah Wisata Lau Kawar Setiawan, Dedi; Muhammad Syahril; Jufri Halim; Wahyu Riansah; Suardi Yakub; Purwadi
Jurnal Pengabdian Masyarakat IPTEK Vol. 6 No. 1 (2026): Edisi Januari 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v6i1.12502

Abstract

Perkembangan teknologi Internet of Things (IoT) dan layanan cloud seperti AWS Cloud menghadirkan peluang untuk pemantauan lingkungan secara real time pada destinasi wisata. Penelitian ini bertujuan merancang dan mengimplementasikan system monitoring iklim dan cuaca berbasis IoT dengan integrasi AWS Cloud di area wisata Lau Kawar, Kabupaten Karo, Sumatera Utara. Sistem terdiri dari sensor suhu, kelembapan, tekanan udara, dan curah hujan yang terhubung ke modul mikrokontroler (misalnyaESP32) dan mengirim data melalui koneksi Wi-Fi ke AWS IoT Core. Data disimpan di layanan database dan divisualisasikan melalui dashboard web/mobile. Hasil menunjukkan bahwa sistem mampu memonitor parameter lingkungan dengan latensi rendah (< 2 menit) dan akurasi cukup baik untuk mendukung keputusan pengelola wisata terkait kenyamanan pengunjung dan mitigasi kondisi cuaca ekstrim. Dengan demikian, penerapan IoT + Cloud dapat menjadi solusi efektif untuk pengelolaan destinasi wisata berbasis data.
DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING Anggraeni, Ratih; Purwadi; Subarkah, Pungkas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7472

Abstract

The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
Geospatial Analysis of Global Temperature and Humidity Variations Using Integrated Meteorological Data Zhafira, Alya; 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; Putri, Refida; 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 Shomsomi, Ma`dan; Triawan, Widhaksa; 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.
Design and Implementation of a Web-Based Currency Converter System Using an Application Programming Interface Purwadi; Augst Nurandini; Gusnaeni Indah Pratiwi
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.v5i2.1967

Abstract

This study aims to design and implement a web-based currency converter application that utilizes an Application Programming Interface (API) to provide real-time and accurate exchange rate data. The increasing intensity of global economic activities has created a growing need for fast and reliable currency conversion, while manual conversion methods are prone to errors and data inconsistencies. This research employs the Research and Development (R&D) approach using the waterfall development model, which includes requirement analysis, system design, implementation, testing, and maintenance. The developed application provides two main features: an exchange rate calculator that performs automatic currency conversion based on real-time data, and a currency exchange history feature that presents exchange rate trends in graphical form within a selected period. Testing results indicate that the application runs reliably, delivers fast responses, and consistently displays up-to-date exchange rate information. In conclusion, the proposed application serves as an effective web-based solution for accessing accurate currency exchange information to support international financial activities.
Comparative Performance Analysis of BERT and RoBERTa for Email Spam Classification Purwadi; Hafizh Dzaky Ahya Gemilang
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.v5i2.1968

Abstract

The rapid advancement of information technology has increased the use of email as a primary digital communication medium, while also contributing to the growing volume of spam emails that threaten productivity and information security through phishing and malware. An accurate and adaptive email spam classification system is therefore required. This study aims to analyze and compare the performance of BERT and RoBERTa transformer models for email spam classification. An experimental research approach was employed using an email dataset consisting of spam and non-spam (ham) classes. The research process includes data collection, text preprocessing, model fine-tuning, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that both BERT and RoBERTa achieve high classification performance. However, RoBERTa demonstrates superior results, particularly in terms of spam recall and overall accuracy, indicating a stronger ability to detect spam emails. This advantage is attributed to RoBERTa’s optimized pre-training strategy, which improves contextual semantic understanding of email content. In conclusion, RoBERTa is more effective than BERT for email spam classification and can serve as a reliable model for developing robust transformer-based spam detection systems.
Co-Authors Aan Budi Santoso Abdul Halik Abdurrahman, Zakaria Husein Adam Hidayah Adi Suhendra, Adi Afendy Widayat Ahmad Jamaludin Alfandi, Ricky Alfito Widiansyah Angela Nitia Nefasa Anggi Pangestu Anggraini, Adinda Arujisaputra, Erwin Teguh Augst Nurandini Aulia Suryaning Tyas Azlan Baharuddin, Rismayada Bakti Wisnu Widjajani Bowo Woesono, Hastanto Boy, Ahmad Budi Prasetyo, Aris Catur Wibowo Budi Santoso, Catur Wibowo Budi Dana, Wahyu Seka Dedi Setiawan Devri Suherdi, Devri Dewi, Mutiara Indah Nirmala Dicky Nofriansyah Dimas Deworo Puruhito, Dimas Deworo Dinna Hadi Sholikah Elfitriani Eudia Christina Wulandari evan saputra, evan Fadhilah, Nurul Aini Fani Ardiani Faridah, Ayumi Firmansyah, Erick Gantara Tino Pasomba Ghassani, Rizqi Githa Noviana Gusnaeni Indah Pratiwi Hafizh Dzaky Ahya Gemilang Hariyanti, Dwi Prasetiyawati Diyah Hendra Jaya Hidayah, Rizka Iftika Miftahul Arzaqi Imam Radianto Anwar Setia Putra, Imam Radianto Anwar Setia Intan Rahmawati Ismiasih Jojok Dwiridotjahjon Jufri Halim jufri halim Karti Rahayu Kusumaningsih Kasimat, William Socrates Laksono Trisnantoro Lina Nur Afifah Lumban Gaol, Anggelina H M. Dimyati Huda Makhfud, Mukhamad Manoby, Worry Mambusy Mardhatilah, Dina Mawandha, Hangger Gahara Michael Muhammad Indika Fathiras Azhami Muhammad Syahril Muniroh Munawar Nia Irawati, Nia Nur Zaini, Nur Nurjanah, Danik Perdana Afif Luthfy Permana, Budi Prastowo, Galang Pungkas Subarkah Purnomo Edi Sasongko Putri, Refida Raditiyanto, Satria Ratih Anggraeni Ratna Wahyu Pusari . . Rini, Mei Risda Novita Sari Ritonga, Muhammad Al-fatih Rizki, Fiorentina Cahaya Rohmah, Nur Hanifatul Rosidah Sabarudin, Didin Santoso, Handri Setia Putra, Imam Radianto Anwar Shomsomi, Ma`dan Sigiro, Elyas Frankly Gregorius Siwi Istiana Dinarti, Siwi Istiana Solly Aryza SRI RAHAYU Suardi Yakub Subarkah, Khansa Falere Sugeng Wahyudiono Sugeng Wahyudiono, Sugeng Suhartati, Tatik Sutanto, Hari Prasetyo Tatik Suhartati Tini Apriani, Tini Triawan, Widhaksa Turjuan Hasugian, Jimmi Usti Fatimah Sari Sitorus Pane Wahid Akbar Basudani Wahyu Nanda Eka Saputra, Wahyu Nanda Eka Wahyu Riansah Wawan Priyanto, Wawan Yaarozatulo Harefa, Herman Yogi Saputra, Yogi Zhafira, Alya