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All Journal SMARTek MEKTEK Foristek Jurnal MEKANIKAL JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi dan Manajemen Informatika Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi MEKTRIK CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research SemanTIK : Teknik Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika ScientiCO : Computer Science and Informatics Journal Sains, Aplikasi, Komputasi dan Teknologi Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Algoritma Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Deny Wiria Nugraha
Universitas Tadulako
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IMPLEMENTASI SVM DAN SMOTE PADA ANALISIS SENTIMEN MEDIA SOSIAL X TERHADAP PELANTIKAN AGUS HARIMURTI YUDHOYONO Fajriyah, Nurul; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Laila, Rahmah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6246

Abstract

Pelantikan Agus Harimurti Yudhoyono sebagai Menteri Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN) telah memicu berbagai reaksi publik yang terekam dalam media sosial X. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap pelantikan tersebut menggunakan algoritma Support Vector Machine (SVM) dan teknik Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan dalam penelitian ini diambil dari komentar masyarakat di media sosial X, yang kemudian diolah untuk membedakan antara sentimen positif, negatif, dan netral. Dalam proses analisis, data awal yang diperoleh cenderung tidak seimbang, dengan jumlah data sentimen negatif yang lebih banyak dibandingkan dengan sentimen positif dan netral. Oleh karena itu, teknik SMOTE diterapkan untuk mengatasi ketidakseimbangan kelas dan meningkatkan performa model. Algoritma SVM kemudian digunakan untuk melakukan klasifikasi sentimen. Hasil penelitian menunjukkan bahwa model SVM yang diimbangi dengan SMOTE memiliki tingkat akurasi yang tinggi dalam mengklasifikasikan sentimen publik dibandingkan dengan model tanpa SMOTE dengan akurasi sebesar 0.93, presisi sebesar 0.93 dan recall sebesar 0.93.
Analysis and Design of Food Price Data Processing Information System Priska, Salsa Dilah; Syahrullah, Syahrullah; Nugraha, Deny Wiria; Lapatta, Nouval Trezandy; Lamasitudju, Chairunnisa Ar
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3904

Abstract

Food prices have an important role in maintaining economic stability and public welfare, as price fluctuations can have a direct impact on purchasing power and inflation. The manual process of recording and reporting food price data at the Department of Agriculture and Food Security of Palu City leads to inefficiencies, data inaccuracies, and difficulties in tracking historical information. These limitations highlight the need for a structured system that can support accurate and efficient data management. This study applies a prototyping method to develop a web-based information system tailored to the needs of the institution. The development process involves continuous interaction between users and developers to ensure the system meets practical requirements. Data were collected through interviews, observations, and documentation. System functionality was tested using black box testing, while usability was assessed using the System Usability Scale (SUS) questionnaire. The results indicate that the system's features, including daily price input, automatic average calculations, report submission, and approval workflows, function correctly. Users are able to interact with the system efficiently, and the SUS results show that the system falls into the acceptable usability category, indicating that it is easy to use. In conclusion, the development of this web-based information system improves the efficiency and accuracy of food price data processing and reporting. It provides a reliable tool for managing information within the department and supports better operational performance.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
CLUSTERING DAERAH TERDAMPAK SAMPAH DI INDONESIA MENGGUNAKAN ALGORITMA DBSCAN. Santi, Dessy; Maharani, Wulan; Syahrullah, Syahrullah; Nugraha, Deny Wiria; Mukhlis, Baso; Kali, Agustinus
Foristek Vol. 15 No. 1 (2025): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v15i1.751

Abstract

The waste problem in Indonesia is a complex and evolving environmental issue, particularly in areas with high population density and economic activity. This study aims to cluster regions affected by waste issues using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN was chosen for its ability to identify spatial patterns and detect outliers without requiring a predefined number of clusters. The data used includes spatial and non-spatial information related to waste volume and regional characteristics across various provinces in Indonesia. The results show that DBSCAN effectively groups waste-affected areas into several clusters based on data density and spatial proximity. These clusters can serve as a foundation for determining policy priorities for regional and national waste management. This research is expected to contribute to the development of more targeted and data-driven waste management strategies.
Segmentasi Pelanggan Menggunakan Kerangka LRFMV dan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran Wawagalang, A. Nolly Sandra; Syahrullah, Syahrullah; Ardiyansyah, Rizka; Angreni, Dwi Shinta; Pratama, Septiano Anggun; Nugraha, Deny Wiria
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.31025

Abstract

In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means algorithm to identify customer behavior characteristics and determine high-value segments. The combination of these five dimensions has rarely been used in previous studies, thus providing a new contribution to data-based customer behavior analysis. This study adopts an exploratory descriptive quantitative approach. The data used consists of 2,098 transactions from 452 customers, sourced from a public GitHub dataset. The data analysis process includes preprocessing, determining LRFMV values, and segmentation using K-Means Clustering. The Silhouette Coefficient is used to evaluate cluster quality and determine the optimal number of clusters. The results show that the best configuration is obtained at k=5 with a Silhouette value of 0.842. The findings show five customer segments with different characteristics and Customer Lifetime Value (CLV) values. Clusters 0 and 2 are categorized as Loyal Customers (L↑R↓F↑M↑V↑) with the highest CLV. Clusters 3 and 1 are Inactive New Customers (L↓R↑F↓M↓V↓) with low contribution. Cluster 4 consists of Inactive Customers (L↓R↓F↓M↓V↓), indicating overall inactivity. These segmentation results are used to develop more targeted strategies, such as loyalty programs or reactivation campaigns, to optimize marketing strategies based on customer value.
Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment Wiria Nugraha, Deny; Ahmad Ilham, Amil; Achmad, Andani; Arief, Ardiaty
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8454

Abstract

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
Performance Comparison of Multilayer Perceptron (MLP) and Random Forest for Early Detection of Cardiovascular Disease Setiawan, Dita Widayanti; Lapatta, Nouval Trezandy; Amriana, Amriana; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa Ar.
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10826

Abstract

Cardiovascular disease is a disorder of the heart and blood vessels that can lead to heart attacks, strokes, and heart failure, so early detection is essential. This study compares Multilayer Perceptron (MLP) and Random Forest for risk classification in a Kaggle dataset containing 70,000 samples with balanced targets. Pre-processing included age conversion, outlier cleaning, standardization, and feature selection based on feature importance. Both models were optimized using RandomizedSearchCV and evaluated using accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, and k-fold cross-validation. The results show that the accuracy of MLP is 73.90% and Random Forest is 74.23% with an AUC of 0.80 for both. Random Forest is more stable across all folds and performs better on the negative class, while MLP is slightly more sensitive to the positive class. Independent t-test and Mann-Whitney U tests show p>0.05, indicating that the difference in performance is not significant. The most influential features were diastolic blood pressure, age, cholesterol, and systolic blood pressure. The non-clinical Streamlit prototype demonstrated the model's potential for education and initial decision support.
Implementation of Collaborative Filtering in the Salted Fish Recommendation Process Rizky, Moh Taufiq; Rinianty, Rinianty; Nugraha, Deny Wiria; Amriana, Amriana; Lapatta, Nouval Trezandy
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11576

Abstract

The development of e-commerce in the current era has been so rapid that buying and selling transactions are carried out online through various media, including websites and applications. With so many products available in the application, users often feel confused when choosing the product they want to buy, so it takes a long time to choose a product to avoid regret after purchasing it. In this study, a web-based recommendation system was created for the process of recommending salted fish with the aim of making it easier for customers to choose the type of salted fish. The Collaborative Filtering method was used, employing Pearson Correlation as a tool to calculate the similarity value between users, then using Weighted Sum to calculate the prediction value. Collaborative Filtering often experiences the cold start problem, where the system has difficulty providing recommendations to users who do not yet have a transaction history. Therefore, the author proposes a popularity-based strategy as a measure to overcome this problem. Based on testing, the author obtained results of MAE = 0.63 and RMSE = 0.81 based on train-test split results with a data distribution of 80:20, 80% of the dataset for training and 20% of the dataset for testing with an accuracy of 70-80%, indicating that this system works well. This system has been tested using the Blackbox method.
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lapatta, Nouval; Wiria Nugraha, Deny; Wirdayanti; Ar Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Implementasi Algoritma Levenshtein Distance dan SHA-256 Pada Sistem Pengelolaan Arsip Dengan Evaluasi TAM Muhsin, Abid; Wiria Nugraha, Deny
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3085

Abstract

Kemajuan teknologi informasi telah mendorong digitalisasi pengelolaan arsip pada instansi pemerintahan untuk meningkatkan efisiensi, transparansi, dan akuntabilitas pelayanan publik. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pengelolaan arsip berbasis web dengan menerapkan metode Agile sebagai pendekatan pengembangan, algoritma Levenshtein Distance untuk meningkatkan akurasi pencarian arsip, serta algoritma SHA-256 guna menjaga integritas dan keamanan data. Evaluasi penerimaan pengguna dilakukan menggunakan Technology Acceptance Model (TAM), yang mencakup variabel Perceived Usefulness (PU), Perceived Ease of Use (PEOU), dan Behavioral Intention to Use (BI). Hasil pengujian menunjukkan algoritma Levenshtein Distance mencapai tingkat keberhasilan pencarian 95,8% meskipun terjadi kesalahan pengetikan kata kunci, sedangkan algoritma SHA-256 menghasilkan Avalanche Effect rata-rata 48–52%, menandakan kemampuan tinggi dalam mendeteksi perubahan file. Evaluasi TAM memperoleh skor rata-rata 4,216 dalam kategori “setuju”, yang mengindikasikan bahwa sistem bermanfaat, mudah digunakan, dan mendorong minat pengguna. Dengan demikian, sistem yang dikembangkan terbukti efektif, aman, dan mampu mendukung efisiensi administrasi serta peningkatan kualitas layanan publik.
Co-Authors A.Y. Erwin Dodu A.Y. Erwin Dodu A.Y. Erwin Dodu Abdul Mahatir Najar Agustinus Kali Ahmad Ilham, Amil Albrecht Yordanus Erwin Dodu Amil Ahmad Ilham Aminuyati Amriana Amriana Amriana Amriana Andani Achmad Andi Hendra Andipa Batara Putra Angraeni, Dwi Shinta Ardiyansyah, Rizka Arief Pratomo Arief, Ardiaty Ar Lamasitudju, Chairunnisa Asminar Asminar Asri Arif Asriani Asriani, Asriani Asrul Sani Ayu Hernita Ayyub, Mohammad Azhar Baso Mukhlis Candriasih, Ni Kadek Chairunnisa Ar. Lamasitudju Chandra, Ferri Rama Dessy Santi Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dodu, A. Y. Erwin Dodu, A.Y Erwin Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwimanhendra, Muhammad Rifaldi Dwiwijaya, Kadek Agus Erwin Dodu, Albrecht Yordanus Fajriyah, Nurul Fanny Astria, Fanny Hajra Rasmita Ngemba Hamid, Odai Amer Hasanuddin Hasanuddin Ihalauw, Sahron Angelina Imat Rahmat Hidayat Isminarti, Isminarti Jeprianto Rurungan, Jeprianto K. Julianto, K. Kalatiku, Protus P Krisna Rendi Awalludin Luh Putu Ratna Sundari Maharani, Wulan Mery Subito Mohamad Ilyas Abas Mohamad Irfan, Mohamad Muhsin, Abid Narke, I Made Reyvinno Dirga Nouval Trezandy Lapatta Novilia Chandra Paloloang, Muhammad Fairus B. Priska, Salsa Dilah Protus Pieter Kalatiku Putra, Subkhan Dinda Rahma Tanti Rahmah Laila Raivandy, I Made Randhy Rieska Setiawaty Rinianty, Rinianty Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar, Ryfial Septiana, Stevi Septiano Anggun Pratama Setiawan, Dita Widayanti Sri Khaerawati Nur Stevi Septiana Syahrullah Syahrullah Syaiful Hendra Thia Wydia Astuti Wawagalang, A. Nolly Sandra Wirdayanti Wisanti, Widya Yuli Asmi Rahman Yuri Yudhaswana Joefrie Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli