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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Algoritma Jurnal Ilmiah Bisnis, Manajemen, dan Akuntansi INOVTEK Polbeng - Seri Informatika
Ucta Pradema Sanjaya
Universitas Ngudi Waluyo
Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Penyuluhan Inovatif: Talent Management dan AI Digital Brainstorming sebagai Upaya Pembentukan Kepribadian Unggul Peserta Didik MA Darul Ma’arif Pringapus Satria Avianda Nurcahyo; Ahmad Ali; Ucta Pradema Sanjaya; Priyanto Priyanto; Teguh Harso Widagdo; Kustiyono Kustiyono; Noor Laila Ramadhani; Pipit Sundari; Yeni Indraningtyas
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3154

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan peserta didik MA Darul Ma’arif Pringapus dalam mengelola potensi diri melalui penerapan Talent Management dan teknologi AI Digital Brainstorming sebagai sarana pembentukan kepribadian unggul. Latar belakang kegiatan ini berangkat dari tantangan dunia pendidikan modern yang menuntut generasi muda untuk memiliki kepribadian adaptif, kreatif, dan berdaya saing di era digital. Metode pelaksanaan meliputi penyuluhan interaktif, simulasi penggunaan AI brainstorming tools, serta pendampingan personal dalam mengidentifikasi bakat dan minat peserta didik. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta memahami konsep talent mapping, berpikir kritis, serta mengoptimalkan teknologi digital untuk pengembangan diri. Peserta didik juga menunjukkan antusiasme tinggi terhadap penerapan teknologi kecerdasan buatan sebagai sarana eksplorasi ide dan pemecahan masalah secara inovatif. Kegiatan ini memberikan dampak positif terhadap pembentukan karakter mandiri, percaya diri, serta tangguh menghadapi perubahan. Dengan demikian, penyuluhan inovatif berbasis Talent Management dan AI Digital Brainstorming menjadi langkah strategis dalam menyiapkan peserta didik berkepribadian unggul di lingkungan pendidikan madrasah.Kata kunci: penyuluhan inovatif, talent management, AI digital brainstorming, kepribadian unggul, peserta didik
Sistem Rekomendasi Ular Peliharaan Berbasis Web dengan Algoritma Content-Based Filtering Abiyyu Dhonan Pratomo; Ucta Pradema Sanjaya
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3006

Abstract

This study developed a web-based pet snake recommendation system using a Content-Based Filtering (CBF) algorithm with a risk-aware approach to help prospective owners choose snake species that suit their profiles, while reducing negative bias against snakes. This system maps user attributes and snake species through ANY-match, exact-match, and multi-level fallback mechanisms that integrate risk-based hard filters, including venom level, aggressiveness, and body size. This study adds scientific contributions in the application of risk-sensitive CBF, where recommendations are prioritized based on user safety. Weight sensitivity tests, comparisons with baseline rule-based systems, and validation with snake keeper experts confirm the system's reliability. User Acceptance Testing (UAT) results show that the system has an average user satisfaction rating of 4.84 out of 5, with 97.47% of respondents giving positive ratings. Although this system has been proven effective in providing relevant and safe recommendations, the limitations of this study lie in the limited number of species and respondents, as well as the scale of testing, which still needs to be expanded. This study is expected to serve as the basis for the development of risk-based recommendation systems for other exotic animals.
Sistem penunjang keputusan pemilihan karyawan garmen terbaik Menggunakan metode Weighted Product Cahya Annisyah Waruwu; Ucta Pradema Sanjaya
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3158

Abstract

The garment industry sector is highly dependent on human resources to play a role in maintaining quality at every stage of the production process and final products. Employee assessment methods in this sector tend to rely on subjective evaluations, which can lead to potential unfairness in decision-making. This study discusses the development of a decision support system that can be used by management to identify employees in a more objective manner. The method applied in this research is the Weighted Product method, which evaluates a number of alternatives by multiplying rating values derived from several criteria, namely punctuality, discipline, work quality, responsibility level, and teamwork. Employee data were obtained through interviews and observations in the production department. The calculation results indicate that the Weighted Product method is able to generate more consistent and objective employee rankings. The system is implemented as a web-based application so that it can be accessed by management. With the implementation of this system, the employee evaluation process becomes more efficient and transparent and can be used as a basis for awarding incentives and job promotions.
A Analisis Sentimen Publik terhadap Program Makan Bergizi Pemerintah (MBG) pada Media Sosial X Menggunakan Metode Support Vector Machine Windi Haria Ningsi; Ucta Pradema Sanjaya
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3413

Abstract

The Nutritious Meal Program (MBG) is one of the government’s initiatives to improve the nutritional quality of society. This study aims to analyze public sentiment toward the MBG program on social media platform X in order to provide a quantitative overview of public perception. The data used consisted of 1,938 public posts containing keywords related to the MBG program. The analysis stages included text preprocessing, namely data cleaning, tokenization, stopword removal, and stemming. Furthermore, feature representation was carried out using the TF-IDF method, while sentiment classification was performed using the Support Vector Machine (SVM) algorithm to categorize the data into positive, neutral, and negative sentiments. The results indicate that the classification model achieved an accuracy rate of 96.69 percent, demonstrating excellent model performance. Based on the classification results, sentiment distribution was dominated by negative sentiment, followed by neutral and positive sentiments, indicating that public responses toward the MBG program tend to be critical. These findings suggest that although the MBG program has received significant public attention, there are still various criticisms and feedback that can serve as evaluation material for the government in improving the effectiveness and implementation of the program.
Analisis Kebutuhan Pelaku UMKM Desa terhadap Platform Pemasaran Produk Lokal Berbasis Peta dan Logistik Kolaboratif Setya Indah Isnawati; Irsal Fauzi; Ucta Pradema Sanjaya; Prieza Dheva Tanti
Jibaku: Jurnal Ilmiah Bisnis, Manajemen dan Akuntansi Vol. 6 No. 2 (2026): Juli
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/jibaku.v6i2.5188

Abstract

Digital marketing for village products has become a strategic need for rural MSMEs to expand market access, increase product visibility, and strengthen the local economy. However, rural MSME actors still face several challenges, including limited digital promotion, the absence of integrated business location information, weak distribution coordination, and unstable internet connectivity. This study aims to analyze the needs of rural MSME actors for a map-based local product marketing platform integrated with collaborative logistics as a foundation for developing GeoDesaConnect. This research employed a descriptive qualitative approach and was conducted in Lerep Village, Sumogawe Village, and Bergas Kidul Village, Semarang Regency. Informants were selected through purposive sampling, consisting of MSME actors, village officials, BUMDes managers, tourism awareness groups, and MSME facilitators. Data were collected through in-depth interviews, observation, and documentation studies, then analyzed using thematic analysis. The findings indicate that rural MSME actors need a platform that can provide product catalogs, map business locations, support market location recommendations, facilitate distribution coordination, and remain accessible under limited internet connectivity. The study concludes that GeoDesaConnect should be developed as a contextual, adaptive, inclusive, and user-need-based platform to support marketing, spatial mapping, collaborative logistics, and sustainable local economic development in rural areas.   Abstrak Digitalisasi pemasaran produk desa menjadi kebutuhan strategis bagi pelaku UMKM desa untuk memperluas akses pasar, meningkatkan visibilitas produk, dan memperkuat ekonomi lokal. Namun, pelaku UMKM desa masih menghadapi kendala berupa keterbatasan promosi digital, belum terintegrasinya informasi lokasi usaha, lemahnya koordinasi distribusi, serta keterbatasan konektivitas internet. Penelitian ini bertujuan menganalisis kebutuhan pelaku UMKM desa terhadap platform pemasaran produk lokal berbasis peta dan logistik kolaboratif sebagai dasar pengembangan GeoDesaConnect. Penelitian menggunakan pendekatan kualitatif deskriptif dengan lokasi di Desa Lerep, Desa Sumogawe, dan Desa Bergas Kidul, Kabupaten Semarang. Informan dipilih secara purposive, meliputi pelaku UMKM, perangkat desa, BUMDes, pokdarwis, dan pendamping UMKM. Data dikumpulkan melalui wawancara mendalam, observasi, dan studi dokumentasi, kemudian dianalisis menggunakan analisis tematik. Hasil penelitian menunjukkan bahwa pelaku UMKM desa membutuhkan platform yang mampu menyajikan katalog produk, memetakan lokasi usaha, mendukung rekomendasi lokasi pasar, memfasilitasi koordinasi distribusi, serta tetap dapat digunakan pada kondisi jaringan terbatas. Kesimpulannya, GeoDesaConnect perlu dikembangkan sebagai platform yang kontekstual, adaptif, inklusif, dan berbasis kebutuhan riil pengguna, sehingga mampu mendukung pemasaran, pemetaan spasial, logistik kolaboratif, serta penguatan ekonomi lokal desa secara berkelanjutan.
From Data Imbalance to Precision: SMOTE-Driven Machine Learning for Early Detection of Kidney Disease Aldani Adi Bhirawa; Ucta Pradema Sanjaya
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7jgjmg64

Abstract

Chronic Kidney Disease (CKD) has become a significant global health issue, with its prevalence rising sharply, particularly in developing countries like Indonesia. According to the Kementrian Kesehatan (KEMENKES), the Synthetic Minority Over-sampling Technique (SMOTE) has been widely adopted to address this. SMOTE generates synthetic samples for the minority class, enhancing the model’s ability to identify high-risk patients. Studies demonstrate SMOTE’s effectiveness, particularly when combined with ensemble learning algorithms like Random Forest and Gradient Boosting. The data collection focused on relevant medical parameters critical for the study, encompassing laboratory test results, diagnostic reports, and clinical observations related to kidney function. This dataset in kidney disease is used to predict whether someone has chronic kidney disease or not with a total sample of 400 data obtained from the Ungaran Regional Hospital and several clinics that can detect kidney disease. Recent research highlights that SMOTE significantly improves model accuracy, with Random Forest achieving 99.30% accuracy. These findings emphasise the importance of data balancing in enhancing diagnostic precision, offering promising avenues for early CKD detection and improved patient outcomes.
Comparative Analysis of Machine Learning and IndoBERT Models for Sentiment Analysis of YouTube Comments on the Free Nutritious Meals Program Irvan Theo Shandy; Ucta Pradema Sanjaya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12754

Abstract

The Free Nutritious Meals Program has become one of the most widely discussed public policies in Indonesia and has generated various public responses on social media, particularly YouTube. Public comments on YouTube can be utilized as a valuable data source to understand public sentiment toward the implementation of the program. Therefore, this study aims to analyze and compare the performance of several classification algorithms in sentiment analysis of YouTube comments related to the Free Nutritious Meals Program. The dataset used in this study was obtained through a crawling process on one of Raymond Chin’s YouTube videos discussing the MBG program. A total of 903 comments were collected, and after the preprocessing stage, 401 comments were selected for further analysis. The preprocessing steps included cleaning, normalization, tokenization, stopword removal, and stemming. Furthermore, the text data were transformed using the TF-IDF weighting method. This study compared several classification algorithms, namely Random Forest, Gradient Boosting, Support Vector Machine (SVM), XGBoost, Multinomial Naïve Bayes, IndoBERT, and LightGBM. Model evaluation was conducted using confusion matrix analysis and performance metrics consisting of accuracy, precision, recall, and F1-score. The experimental results show that the Random Forest algorithm achieved the best performance with an accuracy of 0.9672, precision of 0.9683, recall of 0.9672, and F1-score of 0.9620. However, the confusion matrix analysis indicates that the model tends to be biased toward the positive sentiment class due to the imbalance in sentiment distribution within the dataset. In addition, the relatively small dataset and the use of comments from a single YouTube source may affect the generalization of the model results. Based on these findings, Random Forest can be considered the most effective algorithm for sentiment classification in this study. The results of this research are expected to provide insights into public perceptions regarding the MBG program and serve as evaluation material for policymakers in improving the implementation of public nutrition programs in Indonesia.
Efficient Attention-Guided MobileNet V2 with Explainable AI for Multi-Class Skin Disease Classification on HAM10000 Devi Larasati; Ucta Pradema Sanjaya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13010

Abstract

The increasing global incidence of skin cancer, particularly melanoma, coupled with a scarcity of dermatologists, necessitates the development of accurate and accessible AI-driven diagnostic tools. However, deep learning models often struggle with severe class imbalance in public dermoscopic datasets, leading to poor performance on minority lesion types. This research aims to enhance the diagnostic precision of a lightweight MobileNetV2 architecture for multi-class skin lesion classification by integrating the Convolutional Block Attention Module (CBAM) and employing Focal Loss. The methodology involves evaluating four model variants (Baseline, Baseline+CBAM, Baseline+Focal Loss, and Baseline+CBAM+Focal Loss) on the HAM10000 dataset, with performance measured by accuracy, precision, recall, and F1-score. The optimal model (M4) successfully achieved convergence without overfitting, demonstrating exceptional F1-scores for six of seven classes, including near-perfect classification for melanoma (0.96) and dermatofibroma (0.97). The primary limitation was the actinic keratosis class (F1-score 0.60) due to high morphological similarity with other lesions. In conclusion, the synergistic combination of CBAM and Focal Loss effectively mitigates class imbalance and enhances feature representation in a computationally efficient model, providing a robust and interpretable solution for skin cancer screening.
Co-Authors Abiyyu Dhonan Pratomo Ahmad Ali Aldani Adi Bhirawa Cahya Annisyah Waruwu Devi Larasati Irsal Fauzi Irvan Theo Shandy Kustiyono Noor Laila Ramadhani Pipit Sundari Prieza Dheva Tanti Priyanto Priyanto Satria Avianda Nurcahyo Setya Indah Isnawati Teguh Harso Widagdo Windi Haria Ningsi Yeni Indraningtyas