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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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) 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 JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) TIN: TERAPAN INFORMATIKA NUSANTARA Teknika Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Eduvest - Journal of Universal Studies Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Deny Wiria Nugraha
Universitas Tadulako
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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.
Implementasi Smart Housing Services Akses Hunian Berkualitas Melalui Teknologi Digital Ayu Hernita; Nouval Trezandy Lapatta; Sri Khaerawati Nur; Deny Wiria Nugraha
Indonesia Berdampak: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 2 (2025): JULI-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/0r4aa406

Abstract

Transformasi digital dalam layanan publik menjadi salah satu prioritas dalam meningkatkan efisiensi dan aksesibilitas pelayanan kepada masyarakat, termasuk dalam sektor perumahan. Salah satu solusi inovatif adalah penerapan Smart Housing Services, yaitu sistem layanan digital yang memungkinkan masyarakat mengakses informasi, bantuan, dan fasilitas perumahan secara daring. Namun, pemahaman dan kesiapan instansi pemerintah daerah dalam mengimplementasikan layanan ini masih perlu ditingkatkan. Pengabdian kepada masyarakat ini bertujuan untuk memberikan sosialisasi dan pelatihan mengenai pemanfaatan Smart Housing Services kepada pegawai Dinas Komunikasi dan Informatika Kabupaten Parigi Moutong. Kegiatan ini dilaksanakan dalam bentuk pelatihan satu hari yang mencakup pengenalan konsep layanan perumahan digital, simulasi penggunaan sistem, serta pembuatan media sosialisasi visual berupa infografis dan poster. Peserta juga dibekali dengan modul dan template yang dapat digunakan kembali untuk kegiatan penyebaran informasi kepada masyarakat. Metode pelaksanaan meliputi tahapan persiapan, penyampaian materi, praktik langsung, dan evaluasi hasil. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep Smart Housing Services sebesar 85%, berdasarkan hasil pre-test dan post-test. Selain itu, peserta berhasil menghasilkan media edukatif sederhana yang dapat mendukung upaya literasi digital di bidang layanan perumahan. Kegiatan ini diharapkan menjadi kontribusi awal dalam mendukung perluasan akses hunian berkualitas berbasis teknologi, serta memperkuat peran Dinas Kominfo sebagai motor penggerak transformasi digital di daerah.
Analisis Penyakit Mental Menggunakan Algoritma XGBoost Landusa, Natalia Anastasya; Ardiansyah, Rizka; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa; Angreni, Dwi Shinta
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 4 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i4.5135

Abstract

Kesehatan mental merupakan bagian penting dalam kesejahteraan individu, dengan gangguan mental seperti skizofrenia, bipolar, dan depresi yang dapat memengaruhi kualitas hidup. Namun, diagnosa yang akurat untuk membedakan jenis gangguan ini seringkali menjadi tantangan karena gejala yang saling tumpang tindih. Penelitian ini bertujuan untuk mengklasifikasikan tiga jenis gangguan mental menggunakan algoritma XGBoost dan mengidentifikasi fitur penting yang berpengaruh dalam proses klasifikasi. Metode yang digunakan mencakup pengumpulan data dari dataset Kaggle yang berisi 3753 data pasien dengan 53 atribut dan 3 kelas gangguan mental. Proses pre-processing dilakukan untuk menormalkan data, yang kemudian digunakan untuk melatih model XGBoost. Hasil penelitian menunjukkan akurasi model sebesar 98,67% dengan nilai precision, recall, dan F1-score yang sangat tinggi, menunjukkan bahwa XGBoost efektif dalam mengklasifikasikan gangguan mental. Fitur utama yang berpengaruh dalam klasifikasi antara lain halusinasi, pikiran atau ucapan yang tidak teratur, dan delusi. Penelitian ini menyarankan penelitian lebih lanjut untuk pengembangan fitur dan validasi klinis model ini dalam konteks dunia medis.
Interaksi Augmented Reality Menggunakan Boxcollider Dalam Aplikasi Pembelajaran Bahasa Inggris Zulkifli, Zulkifli; Joefrie, Yuri Yudhaswana; Nugraha, Deny Wiria; Lapatta, Nouval Trezandy; Syahrullah, Syahrullah; Angreni, Dwi Shinta
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.v10i2.6248

Abstract

Teknologi Augmented Reality (AR) telah menjadi salah satu inovasi terdepan dalam meningkatkan pengalaman belajar interaktif. Penelitian ini mengkaji penggunaan AR dalam aplikasi pengenalan bahasa Inggris dengan memanfaatkan fitur BoxCollider untuk interaksi pengguna. Ap-likasi ini dirancang untuk membantu pengguna, terutama pelajar, dalam mengenali dan memahami kosakata bahasa Inggris melalui pengalaman visual dan interaktif. BoxCollider digunakan untuk mendeteksi interaksi antara pengguna dan objek virtual yang ditampilkan di layar, memung-kinkan respons langsung terhadap tindakan pengguna seperti menyentuh atau menggerakkan objek. Hasil penelitian menunjukkan bahwa penggunaan BoxCollider dalam AR meningkatkan keterlibatan pengguna dan memudahkan proses belajar. Pengguna dapat berinteraksi dengan berbagai objek yang mewakili kata-kata bahasa Inggris, sehingga mem-berikan konteks visual yang kuat dan mendukung pemahaman kosakata secara lebih efektif. Aplikasi ini diharapkan dapat menjadi alat bantu yang efektif dalam pengajaran bahasa Inggris, menawarkan metode bela-jar yang lebih menarik dan interaktif dibandingkan dengan metode kon-vensional
Co-Authors A.Y. Erwin Dodu A.Y. Erwin Dodu A.Y. Erwin Dodu Abdul Mahatir Najar Agustinus Kali Ahmad Ilham, Amil Aisyah Syahskiah Sunandar Amil Ahmad Ilham Aminuyati Amriana Amriana Amriana Amriana Amriana Amriana Andani Achmad Andi Hendra Andipa Batara Putra Angraeni, Dwi Shinta Ardiyansyah, Rizka Arief Pratomo Arief, Ardiaty Asminar Asminar Asri Arif Asriani Asriani, Asriani Asrul Sani Ayu Hernita Ayyub, Mohammad Azhar Baso Mukhlis Candriasih, Ni Kadek Chaira Saidah Yusrie 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 Dwiwijaya, Kadek Agus Erwin Dodu, Albrecht Yordanus Fajriyah, Nurul Fanny Astria, Fanny Fauziah Fauziah Hajra Rasmita Ngemba Hamid, Odai Amer Hasanuddin Hasanuddin I Made Reyvinno Dirga Narke Ihalauw, Sahron Angelina Imat Rahmat Hidayat Isminarti, Isminarti Jeprianto Rurungan, Jeprianto K. Julianto, K. Kalatiku, Protus P Krisna Rendi Awalludin Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Luh Putu Ratna Sundari Maharani, Wulan Mery Subito Moh. Eno Farhan Mohamad Ilyas Abas Mohamad Irfan, Mohamad Mohammad Hikmal Naki Muhammad Ali Chandra Muhammad Rifaldi Dwimanhendra Muhsin, Abid Nanang Roni Wibowo Natalia Warani Nouval Trezandy Lapatta Novilia Chandra Nur Azhary Iriawan Eka Putra 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 Rizaldi Agil Faturrahman Rizka Ardiansyah Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar Septiana, Stevi Septiano Anggun Pratama Setiawan, Dita Widayanti Sri Khaerawati Nur Syahrullah Syahrullah Syahrullah Syaiful Hendra Thia Wydia Astuti Usama Wawagalang, A. Nolly Sandra Widya Wisanty Wirdayanti Wisanti, Widya Wiwik Supriyatin Yuli Asmi Rahman Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli