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All Journal Jurnal Ilmu Komputer Coding: Jurnal Komputer dan Aplikasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Journal of Information Systems and Informatics Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Manajemen Informatika Jayakarta ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat International Journal of Electronics and Communications Systems Jurnal Sistem Informasi dan Teknologi (SINTEK) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Indonesian Journal of Multidisciplinary on Social and Technology SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Journal of Management, Entrepreneurship and Tourism Jurnal Pengabdian Pada Masyarakat IPTEKS Sintek Kuwera
Triwijoyo, Bambang Krismono
Universitas Bumigora
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Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
Deep Learning Approach For Sign Language Recognition Bambang Krismono Triwijoyo; Lalu Yuda Rahmani Karnaen; Ahmat Adil
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25051

Abstract

Sign language is a method of communication that uses hand movements between fellow people with hearing loss. Problems occur when communication between normal people with hearing disorders, because not everyone understands sign language, so the model is needed for sign language recognition. This study aims to make the model of the introduction of hand sign language using a deep learning approach. The model used is Convolutional Neural Network (CNN). This model is tested using the ASL alphabet database consisting of 27 categories, where each category consists of 3000 images or a total of 87,000 images of 200 x 200 pixels of hand signals. First is the process of resizing the image input to 32 x 32 pixels. Furthermore, separating the dataset for training and validation respectively 75% and 25%. The test results indicate that the proposed model has good performance with a value of 99% accuracy. Experiment results show that preprocessing images using background correction can improve model performance.
Lightweight and Interpretable Coin Recognition and Counting UsingGeometric Detection and Fuzzy Score-Based Classification Ni Gusti Ayu Dasriani; Bambang Krismono Triwijoyo; I Gede Yoga Sudarma Yasa; Dadang Priyanto; Cong Dai Nguyen
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6067

Abstract

Deep learning-based coin recognition approaches typically require large, annotated datasets and substantial computational resources, yet offer limited interpretability. Such characteristics limit their applicability in lightweight, resource-constrained vision systems. Therefore, this study aims to develop and systematically evaluate a lightweight, interpretable coin recognition and counting method based on geometric detection and fuzzy-score-based classification. The main contribution of this work lies in integrating the Hough Circle Transform, contour-based circularity validation, and a weighted fuzzy score mechanism that aggregates diameter, circularity, and HSV color features without relying on data-driven model training. The proposed approach prioritizes computational efficiency and decision transparency, while maintaining robustness under varying lighting and object configurations. An experimental evaluation was performed on 40 test images containing 362 coins under both bright and dim lighting conditions, with aligned, scattered, and overlapping arrangements. The system achieved a detection rate of 87% and an object-level classification accuracy of 79%. Although image-level accuracy reached 50% under strict evaluation criteria, detailed error analysis indicates that performance degradation is primarily associated with segmentation limitations in overlapping configurations rather than instability in the fuzzy scoring mechanism. These findings demonstrate that a calibrated geometric and fuzzy-based approach can provide a transparent and computationally efficient alternative for small-scale vision applications without requiring large training datasets.
Estimasi Tinggi Gelombang Smart Buoy Berbasis IMU Menggunakan Madgwick dan Kalman Filter Naufal A. Furqan; Muhammad Zulfikri; Rifqi Hammad; Bambang Krismono Triwijoyo; Husain Husain
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12301

Abstract

Pengukuran tinggi gelombang laut merupakan informasi penting untuk mendukung keselamatan pelayaran, aktivitas perikanan, dan pemantauan kondisi oseanografi. Namun, perangkat pengukuran gelombang komersial umumnya memiliki biaya yang tinggi sehingga penggunaannya masih terbatas. Penelitian ini bertujuan mengembangkan smart buoy berbasis Inertial Measurement Unit (IMU) untuk mengestimasi tinggi gelombang laut secara real-time menggunakan kombinasi Madgwick Filter dan Kalman Filter. Sistem dikembangkan menggunakan mikrokontroler ESP32, sensor MPU6050, dan komunikasi LoRa untuk mengirimkan data ke Google Sheets yang kemudian divisualisasikan melalui Google Looker Studio. Data akselerometer dan giroskop diproses menggunakan Madgwick Filter untuk memperoleh estimasi orientasi, dilanjutkan dengan kompensasi gravitasi, integrasi numerik untuk memperoleh sinyal heave, serta penyaringan menggunakan Kalman Filter sebelum menghitung Significant Wave Height (Hs). Hasil pengujian menunjukkan bahwa sistem mampu melakukan akuisisi, pemrosesan, pengiriman, dan visualisasi data secara real-time. Hasil estimasi Hs memiliki kesesuaian yang tinggi terhadap data referensi dengan nilai RMSE sebesar 0,0135 m, MAE sebesar 0,0116 m, MAPE sebesar 4,19%, akurasi sebesar 95,81%, dan koefisien determinasi (R²) sebesar 0,9918. Hasil tersebut menunjukkan bahwa kombinasi Madgwick Filter dan Kalman Filter mampu menghasilkan estimasi tinggi gelombang yang akurat, sehingga smart buoy yang dikembangkan berpotensi menjadi alternatif sistem pemantauan gelombang laut yang sederhana, berbiaya relatif rendah, dan mudah diimplementasikan.
Klasifikasi Uang Kertas dan Logam Asia Tenggara Menggunakan CNN MobileNetV3 Berbasis Web Bagas Edra Athallah Rafif; Bambang Krismono Triwijoyo; I Nyoman Switrayana
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 3 (2026): Juli - Oktober
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i3.13030

Abstract

Perbedaan karakteristik visual uang dari berbagai negara di Asia Tenggara menyebabkan proses identifikasi uang menjadi cukup sulit dilakukan secara manual, khususnya bagi masyarakat umum maupun wisatawan. Penelitian ini bertujuan mengembangkan sistem klasifikasi uang kertas dan logam Asia Tenggara berbasis web menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNetV3 melalui pendekatan transfer learning. Dataset yang digunakan terdiri atas 1.610 citra yang terbagi ke dalam 21 kelas, meliputi uang kertas, uang logam dari sepuluh negara Asia Tenggara, serta satu kelas non-uang. Tahap penelitian meliputi preprocessing, augmentasi data, pelatihan model menggunakan Stratified K-Fold Cross Validation dengan variasi K=5, K=10, dan K=15, serta implementasi model ke dalam aplikasi web. Hasil penelitian menunjukkan bahwa konfigurasi K=10 memberikan performa terbaik dengan rata-rata accuracy sebesar 93,91%, precision sebesar 94,72%, recall sebesar 93,97%, dan F1-score sebesar 93,97%. Hasil tersebut menunjukkan bahwa MobileNetV3 mampu memberikan performa klasifikasi yang baik dan stabil sehingga layak diterapkan sebagai sistem identifikasi uang Asia Tenggara berbasis web.
The Use of Machine Learning in Social Media Sentiment Analysis: Communication Strategies in The Digital Age Noviansyah Noviansyah; Bambang Krismono Triwijoyo; Neny Sulistianingsih
Journal of Management Entrepreneurship and Tourism Vol. 3 No. 2 (2025): July, Journal of Management Entrepreneurship and Tourism
Publisher : Sumber Belajar Sejahtera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61277/jmet.v3i2.216

Abstract

The development of digital technology has fundamentally transformed the way society communicates and consumes information, particularly through social media. Amidst the rapid and massive flow of information, sentiment analysis has become an essential tool for understanding public opinion. This study explores the use of machine learning as an analytical approach to identify and classify users' sentiments toward specific issues on social media. Through case studies on Twitter, Facebook, TikTok, and Instagram, machine learning algorithms such as Naive Bayes and Support Vector Machine were used to map public sentiment trends positive, negative, or neutral toward specific communication campaigns. The results indicate that machine learning can provide a faster, more accurate, and more dynamic sentiment analysis compared to manual methods. These findings serve as a strategic foundation for communication practitioners in designing more targeted, responsive, and data-driven messages. Thus, integrating machine learning into digital communication strategies not only enhances the effectiveness of message delivery but also strengthens the relationship between institutions and the public in an increasingly complex information age. 
Peningkatan Performa Arsitektur CNN Pada Klasifikasi Kualitas Cengkeh Berbasis Augmentasi Data DCGAN Chandra, Lutfi Adika; Triwijoyo, Bambang Krismono; Hairani, Hairani
Coding: Jurnal Komputer dan Aplikasi Vol. 14 No. 2 (2026): Edisi September 2026
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/coding.v14i2.111201

Abstract

Kualitas cengkeh merupakan salah satu faktor yang memengaruhi nilai ekonomi dan mutu hasil panen sehingga proses klasifikasi kualitas menjadi tahapan penting dalam pengendalian mutu. Namun, proses klasifikasi yang masih dilakukan secara manual rentan menghasilkan penilaian yang subjektif dan kurang konsisten, sedangkan keterbatasan jumlah data dapat memengaruhi performa model klasifikasi berbasis deep learning. Penelitian ini bertujuan menganalisis pengaruh penambahan data sintetis menggunakan Deep Convolutional Generative Adversarial Network (DCGAN) terhadap performa model Convolutional Neural Network (CNN), MobileNetV2, ResNet50V2, dan VGG19 dalam klasifikasi kualitas cengkeh. Dataset terdiri atas tiga kelas, yaitu berjamur, berkuncup, dan tidak berkuncup, yang melalui tahap cropping dan resize. Data sintetis hasil DCGAN ditambahkan pada data latih, sedangkan data validasi dan data uji menggunakan data asli. Evaluasi dilakukan menggunakan metrik accuracy, loss, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa penambahan data sintetis meningkatkan accuracy pada seluruh model, yaitu CNN dari 85,56% menjadi 93,33%, MobileNetV2 dari 95,56% menjadi 96,67%, ResNet50V2 dari 91,11% menjadi 93,33%, dan VGG19 dari 95,56% menjadi 97,78%. Berdasarkan hasil tersebut, penambahan data sintetis menggunakan DCGAN memberikan peningkatan performa klasifikasi pada seluruh model yang diuji, dengan VGG19 memperoleh performa terbaik berdasarkan nilai accuracy, precision, recall, dan F1-score
PENERAPAN DEEP LEARNING DENGAN ATTENTION MECHANISM PADA PERAMALAN TRAFIK JARINGAN UNTUK MANAJEMEN QoS ADAPTIF Muhammad Farhan Zul Fahmi; Bambang Krismono Triwijoyo; Neny Sulistianingsih
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 2 (2026): September 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i2.636

Abstract

Static Quality of Service (QoS) management in government network infrastructures often fails to dynamically adapt bandwidth allocation to fluctuating and non-linear traffic conditions. This study develops an adaptive QoS management system based on a Hybrid Long Short-Term Memory (LSTM)-Attention model for network traffic forecasting at the Communication and Information Technology Office (Diskominfo) of Lombok Barat Regency, serving approximately 200 Regional Government Organization (OPD) users with 700 Mbps dedicated 1:1 ISP capacity. A dataset of 43,488 data points was collected from the WAN interface of a MikroTik Router via RouterOS API during January–May 2026 at 5-minute intervals. The preprocessing pipeline included Z-Score outlier detection with capping, Min-Max Scaling normalization, and Sliding Window transformation (timestep = 60). The model architecture consists of two stacked LSTM layers (128 and 64 units) and one custom Attention Layer, totaling 122,241 trainable parameters, with a sequential 70/15/15 train-validation-test split. Evaluation on the testing set (May 2026) yielded RMSE of 43.66 Mbps and MAE of 27.96 Mbps for download traffic (RX), and RMSE of 30.72 Mbps and MAE of 19.66 Mbps for upload traffic (TX). MAPE values of 19.64% (RX) and 17.95% (TX) fall in the "Good" category. The model outperformed the ARIMA baseline on three of four evaluation metrics, with RMSE improvement of 16.7% and MAE improvement of 28.8%, while MAPE was marginally higher (+1.00 point) than ARIMA, a nuance attributable to the differing sensitivity of scale-dependent versus percentage-based metrics; an ablation study further confirmed the Attention layer's contribution is asymmetric, consistently improving accuracy on upload (TX) traffic and substantially reducing training variance on both signals (62.5-83.1% lower standard deviation across seeds) at a marginal parameter cost (+3.5%). Integration with MikroTik Queue Tree via RouterOS API successfully executed 603 automated adaptive cycles, achieving 26.7% throughput increase, 37.0% delay reduction, 59.3% packet loss reduction, and 49.6% jitter reduction compared to conventional static QoS management.
Co-Authors Abi Mas'ud Agus Pribadi Aguswandi, Lalu Heri Ahmat Adil Amrullah, Rahayun Andi Sofyan Anas Anthony Anggrawan Ayu Dasriani, Ni Gusti Azhar, Raisul Bagas Edra Athallah Rafif Cahaya Ardi, L. Nanda Chandra, Lutfi Adika Christopher Michael Lauw Cong Dai Nguyen Dadang Priyanto Damar, Lalu Riyandi Deny Jollyta Diah Supatmiwati Dian Syafitri Chani Saputri Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Ervina Farijki Fahly, Moh. Rizky Galih Hendro Martono Hairani Hairani Hariri, Muhammad Husain Husain I Gede Yoga Sudarma Yasa I Nyoman Switrayana Islamiah, Nuratun Kartarina, Kartarina Khasnur Hidjah Lalu Riyandi Damar Lalu Yuda Rahmani Karnaen Lilik Widyawati Martono, Gallih Hendro Mayadi Mayadi Miftahul Madani Miswaty, Titik Ceriyani Muhammad Farhan Zul Fahmi Muhammad Haris Nasri MUHAMMAD TAUFIK Muhammad Zulfikri Naufal A. Furqan Neny Sulistianingsih Ni Wayan Yuli Ariyanti Noviansyah Noviansyah Rifqi Hammad Risca Anggraeni Dwiputri Santoso, Heroe Satrani, Azral Sirojul Hadi Tajuddin, Muhammad Tomi Tri Sujaka Zulfikri, Muhammad Zulfiqri, Muhammad