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All Journal Jurnal Informatika JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sarjana Teknik Informatika JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Teknologi dan Sistem Komputer JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika GERVASI: Jurnal Pengabdian kepada Masyarakat INSIST (International Series on Interdisciplinary Research) Jurnal Informatika Global Jurnal Teknologi Terpadu bit-Tech Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Idealis : Indonesia Journal Information System Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Mandiri IT Indonesian Community Journal Jurnal Teknologi Sistem Informasi Jurnal Ilmiah Teknik Informatika dan Komunikasi International Journal of Health, Engineering and Technology Jurnal INFOTEL SISFOTENIKA Jurnal Teknik Informatika dan Teknologi Informasi TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Rendra Gustriansyah
Universitas Indo Global Mandiri, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Penyuluhan aman dalam berbisnis pada usaha kue dan snack di Keluruahan Talang Jambe Palembang Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 4 (2025): Juli
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i4.31489

Abstract

AbstrakMaraknya penipuan digital melalui WhatsApp dan media sosial menjadi ancaman serius bagi pelaku usaha kue dan snack rumahan di Kelurahan Talang Jambe, Palembang. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan literasi digital pelaku usaha dalam mengidentifikasi dan mencegah modus penipuan daring. Metode yang digunakan meliputi presentasi materi dan diskusi interaktif, dengan pendekatan berbasis teori Digital Literacy dan Technology Acceptance Model (TAM). Kegiatan ini dilaksanakan pada Mei 2025 dengan melibatkan pelaku UMKM lokal. Hasil evaluasi menunjukkan peningkatan pemahaman peserta terhadap berbagai jenis penipuan, dari rata-rata 65% sebelum kegiatan menjadi 90,8% setelahnya. Diskusi juga mengungkap pengalaman peserta yang sebelumnya nyaris menjadi korban penipuan. Temuan ini menunjukkan bahwa pendekatan edukatif langsung efektif meningkatkan kesadaran dan kesiapsiagaan digital pelaku usaha. Diharapkan kegiatan ini dapat direplikasi untuk memperkuat keamanan digital UMKM secara lebih luas. Kata kunci: keamanan digital; usaha kue dan snack; penipuan online; pengabdian kepada masyarakat. AbstractThe rise of digital fraud through WhatsApp and social media poses a serious threat to home-based cake and snack businesses in Talang Jambe, Palembang. This Community Service (PkM) initiative aimed to enhance digital literacy among business owners by equipping them with practical knowledge to identify and prevent common online scams. The program employed presentations and interactive discussions, guided by the frameworks of Digital Literacy and the Technology Acceptance Model (TAM). Conducted in May 2025, the activity engaged local micro-entrepreneurs who actively use digital platforms for marketing. Evaluation results showed a significant increase in participants’ understanding of various types of fraud—from an average of 65% before the program to 90.8% afterward. Discussions revealed that several participants had previously been close to falling victim to such scams. These findings highlight the effectiveness of direct educational approaches in strengthening cybersecurity awareness. The program is expected to serve as a model for similar efforts aimed at improving the digital resilience of MSMEs. Keywords: digital security; cake and snack business; online fraud; community service.
Analisis Sentimen Terhadap Opini Publik Tentang Kebijakan Regulasi Kripto Di Indonesia Menggunakan Metode Regresi Logistik Nazka yasidi; Rendra Gustriansyah; Lastri Widya Astuti
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5733

Abstract

This study investigates public sentiment toward cryptocurrency regulation policies in Indonesia by employing a logistic regression approach on social media data. A total of 300 Indonesian-language tweets were collected from platform X between January 2022 and April 2025 through a web scraping method using targeted keywords related to cryptocurrency payment regulations. Data preprocessing included text cleaning, case folding, stemming with the Sastrawi library, stopword removal, and tokenization, followed by feature extraction using TF-IDF. Sentiment labels were manually assigned in collaboration with legal experts to ensure classification accuracy. The logistic regression model achieved strong predictive performance, with 91.67% accuracy on the test set and stable results across K-Fold Cross Validation, yielding an average accuracy of 92–93%. The sentiment analysis revealed that the majority of public opinion expressed positive sentiment (85%), while negative sentiment represented only 15%. Positive sentiment was primarily associated with terms such as “protect,” “regulate,” “benefit,” and “legality,” highlighting public support for regulatory measures that enhance investor protection and provide legal certainty. Conversely, negative sentiment featured terms including “forbidden,” “restrict,” and “obstruct,” which reflected concerns regarding regulatory barriers and religious considerations surrounding cryptocurrency usage. The findings demonstrate that Indonesian society generally perceives cryptocurrency regulation as a constructive initiative toward building a secure and trustworthy digital asset ecosystem. Furthermore, the empirical evidence contributes to the growing literature on public perception of financial technology regulations in developing countries. For policymakers, the results emphasize the importance of transparent communication and balanced regulatory frameworks to maintain public trust while addressing potential risks. Overall, this research provides valuable insights into how sentiment analysis can inform the design of more effective regulatory strategies in the evolving landscape of digital finance.
Analisis Sentimen Kepuasan Pengguna Lintas Rel Terpadu (LRT) menggunakan Metode Support Vector Machine Rangga Febri Kasih; Rendra Gustriansyah; Zaid Romegar Mair
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5832

Abstract

This study aims to analyze public sentiment toward the Palembang LRT service by utilizing user reviews available on the Google Maps platform. Sentiment analysis was conducted to understand public perceptions of service quality, which can serve as a basis for decision-making in improving public transportation services. The method employed in this research is the Support Vector Machine (SVM) algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) for word weighting, which classifies reviews into two sentiment categories: positive and negative. A total of 500 reviews were randomly selected as the dataset and processed through a text preprocessing stage, including data cleaning, tokenization, and stopword removal to enhance data quality. The SVM model was then evaluated using an 80:20 split for training and testing, achieving an accuracy of 91%, which indicates excellent performance in identifying sentiment patterns in the Indonesian language. The findings of this study confirm that SVM-based approaches are effective and reliable for sentiment analysis in the context of public transportation. These results provide practical contributions for Palembang LRT management, as insights into public sentiment can be used as a strategic reference for decision-making, reputation management, and improving service quality based on user needs. Future research is recommended to expand the dataset, include neutral sentiment categories, and compare SVM performance with other machine learning algorithms to achieve more comprehensive and robust results.
Metode Pembelajaran Mesin untuk Memprediksi Status Gizi Balita Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.988

Abstract

Malnutrition is one of the leading health problems experienced by toddlers in various countries. Based on the 2022 Indonesian Nutritional Status Survey results, malnutrition in children under five in Indonesia is higher than the average malnutrition in Africa and globally. Therefore, a way is needed to predict the nutritional status of children under five early and quickly so that the Government (through District Health Office) can immediately provide the necessary treatment. This study aims to predict or classify the toddlers' nutritional status based on age, body mass index (BMI), weight, and body length using various machine learning (ML) methods, namely naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbor, random forest, and support vector machine. The predictive performance of each ML method was evaluated based on accuracy, sensitivity, specificity, the area under curve, and Cohen's Kappa coefficient. The test results show that the RF method is the most recommended for predicting toddlers' nutritional status. The study's contribution is to obtain information about toddlers' nutritional status easier.
Klasifikasi Penyakit TBC Menggunakan Metode UMAP dan K-NN Nazori Suhandi; Rendra Gustriansyah; Abel Destria
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2227

Abstract

Tuberkulosis (TBC) adalah penyakit infeksi yang disebabkan oleh bakteri Mycobacterium tuberculosis, yang dapat menyebar dengan cepat melalui udara. Deteksi dini yang akurat sangat penting dalam penanganan penyakit ini untuk mencegah penyebaran lebih lanjut serta meningkatkan efektivitas pengobatan. Diagnosis yang tidak tepat dapat menyebabkan keterlambatan dalam pengobatan, sehingga meningkatkan risiko komplikasi serius bagi pasien. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi TBC menggunakan metode Uniform Manifold Approximation and Projection (UMAP) dan K-Nearest Neighbors (K-NN) di Puskesmas Prabumulih Timur. Dataset yang digunakan terdiri dari 278 data pasien dengan berbagai atribut klinis terkait gejala TBC. Proses analisis diawali dengan tahap pra-pemrosesan data, termasuk penghapusan data duplikat, encoding data kategorikal, serta penanganan nilai yang hilang. Untuk meningkatkan akurasi klasifikasi, metode Elbow diterapkan guna menentukan nilai K optimal, dengan hasil terbaik pada K=3. Data kemudian dibagi menjadi 80% data pelatihan dan 20% data uji guna menghindari overfitting dan meningkatkan reliabilitas model. Pengujian dilakukan dengan membandingkan dua skenario, yaitu K-NN tanpa UMAP dan K-NN dengan UMAP. Hasil evaluasi menggunakan Confusion Matrix menunjukkan bahwa penerapan UMAP meningkatkan accuracy dari 93,48% menjadi 100%, dengan precision dan recall juga mencapai nilai maksimal. Penelitian ini berkontribusi dalam pengembangan sistem klasifikasi berbasis machine learning yang lebih akurat dan efisien untuk membantu tenaga medis dalam mendiagnosis TBC secara cepat, tepat, dan optimal dalam sistem layanan kesehatan.
Predicting Precious Metal Prices Using the Long-Short-Term Memory (LSTM) Method Marshanda Amalia Vega; Rendra Gustriansyah; Indah Permatasari
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2985

Abstract

Gold price fluctuations pose significant challenges for investors in determining accurate investment strategies. The volatility is strongly influenced by inflation, exchange rates, and global economic dynamics, making reliable forecasting increasingly important. Although various statistical and machine learning models have been applied, many are limited in capturing complex temporal dependencies, especially in the context of Indonesia’s ANTAM gold prices. This study addresses that gap by applying the Long Short-Term Memory (LSTM) method, a deep learning approach designed to model sequential patterns in time series data. The novelty of this research lies in the application of LSTM specifically for ANTAM gold price forecasting in Indonesia, which has received limited attention in previous studies. Unlike conventional approaches, LSTM is capable of preserving long-term dependencies, thereby improving predictive accuracy for volatile commodities. Using historical daily data from November 2023 to March 2025, the model was trained to recognize price dynamics and evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate high predictive accuracy, with a MAPE of 1.39% and RMSE of 0.0137. These findings confirm the suitability of LSTM for gold price prediction and underline its potential contribution to both theoretical advancements in time series forecasting and practical decision-making in investment management. Thus, this study not only strengthens evidence of LSTM’s effectiveness but also offers valuable insights for investors and policymakers in managing risks associated with commodity price volatility.
Hierarchical clustering for crime rate mapping in Indonesia Gustriansyah, Rendra; Alie, Juhaini; Suhandi, Nazori
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1135.275-283

Abstract

The Sustainable Development Goals (SDGs) are a blueprint for improving the human life quality. Goal 16 (G16) is related to security, and it is in line with the Universal Declaration of Human Rights and the Preamble to the 1945 Constitution. To support the implementation of the G16 achievement, the Indonesian National Police (Polri) has made serious efforts to provide a sense of safety for the community and to minimize crime rates. One of the efforts that could be made is to map areas based on the level of crimes so that the Polri can determine the appropriate strategy/priority of action for mitigation. Therefore, this study aimed to cluster provinces in Indonesia based on the four G16 indicators of the SDGs related to security, namely the number of homicide cases, the victim proportion, the proportion of people who feel safe walking alone in the area where they live, and the proportion of victims of violence that  reported to the police in the past year using five hierarchical clustering methods, namely: Single-Linkage, Average-Linkage, Complete-Linkage, Ward, and Division Analysis. Then, methods were validated and compared using six cluster validations to obtain the most compact method. The results showed that Ward's method outperformed the others and produced three clusters. Clusters 1, 2, and 3 contained 18, 5, and 11 provinces respectively.
Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees Dewi Sartika; Rendra Gustriansyah
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 1 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i1.843

Abstract

The employee performance assessment at the Palembang City Fire and Disaster Management Service (DPKPB) is applied to other than the employee performance assessment implementation team based on the Decree of the Head of the Palembang City DPKPB Number 146 of 2021 concerning the employee performance assessment implementation team and awards for exemplary employees. Subjective assessments are avoided to obtain assessment results that are by the achievements of each employee. The application of data mining can be an alternative to avoid subjectivity in performance assessment. In this research, a comparison of the Naive Bayes and Decision Tree algorithms was carried out to assess the performance of Palembang City DPMPB employees. The results of further research will be used as an alternative solution in conducting performance assessments that are more objective than previous assessments. Both algorithms were evaluated for model performance using the Confusion Matrix. Based on the results of the evaluation carried out, it was stated that the Decision Tree algorithm had better accuracy, namely 91.74% compared to Naïve Bayes which had an accuracy of 88.99% with a test size of 0.4
Attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari
Jurnal Mandiri IT Vol. 15 No. 1 (2026): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i1.552

Abstract

Skin cancer is one of the most prevalent cancers worldwide, making early and accurate diagnosis essential for improving patient outcomes and reducing mortality. However, automated skin lesion classification remains challenging due to high inter-class similarity, class imbalance, and variations in lesion appearance. This study proposes an attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion. The proposed framework integrates channel and spatial attention with hierarchical feature fusion to enhance discriminative feature learning and improve classification robustness. Experiments were conducted on the PAD-UFES-20 dataset using image preprocessing and data augmentation. The proposed model achieved approximately 98% training accuracy, 95% validation accuracy, and a validation loss below 0.5, outperforming DRMv2Net, DenseNet201, ResNet101, and MobileNetV2 while demonstrating faster convergence and stronger generalization. These findings demonstrate the potential of hybrid attention and hierarchical feature fusion to improve the reliability and robustness of artificial intelligence-based diagnostic systems in dermatology, supporting more effective clinical decision-making and early skin cancer screening.
Customer Segmentation For Digital Marketing Based on Shopping Patterns Juhaini Alie; Rendra Gustriansyah
Jurnal Aplikasi Bisnis dan Manajemen Vol. 10 No. 1 (2024): JABM, Vol. 10 No. 1, January 2024
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.10.1.209

Abstract

Customer segmentation is the customer grouping based on similar shopping behavior or patterns. Inappropriate customer segmentation can have negative impacts, such as lost marketing opportunities, resource inefficiencies, loss of potential customers, and decreased performance, and business profits, especially in customer satisfaction. Therefore, this study aims to develop a customer segmentation model for digital marketing. This model is based on customer shopping patterns using the Recency-Frequency-Monetary (RFM) model and the Partitioning Around Medoids (PAM) method. The research data is historical customer purchase data consisting of 18,535 transactions and 541,909 transaction details from 4,339 customers for 3,665 product items over two years. The research variables focus on the model used: recency, frequency, and monetary. The five customer segments generating from this study are main, potential, general, minimum, and prospective customer. The internal validation results show that the minimum C-Index value is 0.1429 (close to zero), and the maximum Calinski-Harabasz Index value is 512.9553. It shows that the quality of customer segmentation results is good. In other words, the model can identify correlations between customer segments and shopping patterns and preferences. In this way, marketers can optimize services, adjust strategies, and offer the right products for each customer segment. Further research can be directed at product segmentation. Keywords: partitioning around medoids, digital marketing, shopping pattern, recency-frequency-monetary, customer segmentation
Co-Authors Abel Destria Ade Fathurahman Adhe Suryani Aditya Putra, Aditya Agung Prakesakwa Ahmad Sanmorino Ahmad Sanmorino Aldi Saputra Amalia, Marshanda Aminuyati Andri Agus Agus S Antony, Fery Ariati, Nining Arief Ramadhan Aries Fadilla, Muhammad Bagas Ario Pratama, Surya Aurahaqqi Apriano Putri Cahyani, Septa Dana Indra Sensuse Darius Antoni Desfourtheen, Rinda Destria, Abel Dewi Sartika DEWI SARTIKA Dewi Sartika Dewi Sartika Dhamayanti Dhamayanti Dhamayanti, Dhamayanti Ditho Hersilava Dwi Asa Verano Ermatita - Fachry Abda El Rahman Febrianty Fery Antony Fery Antony Filian Falanda Hartini Hartini Hartini Hartini Hastha Sunardi Haversyalapa, Ditho Indah Permatasari Isabella, Isabella Juhaini Alie Khadafi, M. Khrisna Brilian Kris, Via Lastri Widya Astuti Lastri Widya Astuti M Rifqi Tri Putra M. Ricky Hikmawan Maharani, Aiga Rizki Marshanda Amalia Vega Megat Norulazmi Megat Mohamed Noor Miftahul Falah MILLANO, FIDO Mitha Sofiana Muhammad Farhan Muhammad Haviz Irfani Nabilah Mufti Nadia Damayanti Naretha Kawadha Pasemah Gumay Nazka yasidi Nazori Suhandi Pratama, M Duta Purbasari, Nadira Putri Purnamasari, Evi Rachmansyah Rachmansyah Rangga Febri Kasih Reza Iman Pribadi Ricky Saputra Rudi Heriansyah Ryan Ramanda Putra Sari, Nursella Sartika, Dewi Shinta Puspasari Shinta Puspasari Shinta Puspasari Suryati Suryati Suryati Suryati Terttiaavini, Terttiaavini Verano, Dwi Asa Wilza Nadia Wiyanto, Ari Yesinta Florensia Zulkifli Zulkifli