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All Journal Jurnal Informatika Upgris Buletin Ilmiah Sarjana Teknik Elektro Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Inspiration: Jurnal Teknologi Informasi dan Komunikasi Signal and Image Processing Letters
Nuryono, Aninditya Anggari
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Navigasi Objek Virtual Bergerak Bebas untuk Augmented Reality menggunakan Kamera 3D Intel Realsense Nuryono, Aninditya Anggari; Ardiyanto, Igi; Wibirama, Sunu
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2025.731 KB)

Abstract

Augmented Reality adalah sebuah teknik untuk menggabungkan konten digital dengan dunia nyata secara real time. Kamera 3D Intel RealSense digunakan untuk menghasilkan konten digital pada Augmented Reality berbasis markerless. Kamera ini merekonstruksi lingkungan nyata secara tiga dimensi. Scene perception merupakan metode untuk merekonstruksi ulang lingkungan nyata secara tiga dimensi. Pemanfaatan kamera ini pada Augmented Reality berupa autonomous agent. Autonomous agent memiliki fungsi navigasi agar sampai ke titik tujuan dengan mencari jalur yang disebut pathfinding. Autonomous agent miliki tiga perilaku yaitu seek, arrive, dan action selection. Perilaku-perilaku ini digunakan autonomous agent agar sampai ke titik tujuan dengan menghindari halangan virtual dan nyata yang ada di dunia nyata. Metode scene perception digunakan untuk membuat sebuah mesh. Mesh ini merupakan grid virtual di dunia nyata yang digunakan sebagai area Augmented Reality. Hasil navigasi dari autonomous agent menggunakan metode scene perception pada Augmented Reality dapat bekerja dengan baik. Autonomous agent dapat menuju ke titik tujuan dengan menghindari halangan virtual dan nyata.
Studi Analisis Perbandingan Algoritme Pathfinding pada Simulasi Unity 3D Nuryono, Aninditya Anggari; Ardiyanto, Igi; Wibirama, Sunu
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2183.661 KB)

Abstract

Pathfinding digunakan suatu objek untuk mencari jalur dari satu tempat ke tempat lain berdasarkan keadaan peta dan objek lainnya. Dalam pathfinding dibutuhkan algoritme yang dapat dengan cepat memproses dan menghasilkan arah yang terpendek untuk mencapai suatu lokasi tujuan. Algoritme pathfinding yang diulas adalah algoritme A*dan A* smooth Algoritme A* memiliki fungsi heuristik. Algoritme A* smooth merupakan modifikasi dari algoritme A*. Algoritme A* smooth ini bekerja dengan melakukan modifikasi raycast A*. Algoritme A* memanfaatkan node dengan petak-petak kecil. Setiap algoritme ini diimplementasikan ke dalam game object Unity 3D. Setiap game object akan bergerak secara bersamaan untuk menuju titik tujuan dengan posisi awal dan tujuan yang berbeda-beda dengan menghindari banyak halangan. Hasil uji yang didapat adalah algoritme A* smooth lebih unggul dibandingkan dengan algoritme A* dan NavMesh. Waktu tempuh yang dibutuhkan game object dengan algoritme A* smooth lebih cepat 1,6 detik dan 9,6 detik dibandingkan dengan algoritme A* dan NavMesh.
Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks Putra, Rizal Kusuma; Alfarisy, Gusti Ahmad Fanshuri; Nugraha, Faizal Widya; Nuryono, Aninditya Anggari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11619

Abstract

Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images and categorizing the test image accordingly. Thus, SNN has an advantage over classification-based approaches as it can identify various combinations of disease spots on potato plants using a similarity-based approach. This study is divided into two main scenarios: testing with data categories which were previously seen during the training process (traditional testing) and testing with the addition of new data categories that were not seen during training. In the first scenario, SNN showed better accuracy with an accuracy rate of 98.4%, while in the second scenario, SNN achieved an accuracy of 97.1%. That result suggests that SNN can categorize data very well, even recognizing data which never seen during training. These results offer hope that SNN can recognize more disease spots/patterns on potato plants or even identify new diseases by adding these new diseases to the SNN support set without retraining.
Implementasi Metode Hybrid AHP dan MAUT dalam Sistem Pendukung Keputusan Pemilihan Indekos di Karang Joang Kota Balikpapan Fadhliana, Nisa Rizqiya; Paninggalih, Ramadhan; Perwita, Yustika; Nuryono, Aninditya Anggari
Jurnal Informatika Upgris Vol 10, No 2: Desember 2024
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v10i2.21096

Abstract

Indekos merupakan sarana penunjang bagi Mahasiswa yang berasal dai luar kota. Dengan adanya beberapa perguruan tinggi di Kelurahan Karang Joang, Kota Balikpapan, Kalimantan Timur, seperti Institut Teknologi Kalimantan (ITK), Politeknik Negeri Balikpapan, dan Sekolah Tinggi Teknologi Minyak dan Gas Bumi (STT Migas), maka semakin banyak mahasiswa baru yang membutuhkan tempat tinggal sementara yang dekat dengan pusat pendidikan mereka. Namun, pemilihan indekos yang sesuai dengan preferensi dan kebutuhan calon penyewa menjadi hal krusial dikarenakan setiap orang memiliki tingkat kepentingan yang berbeda-beda. Oleh karena itu, untuk membantu pemilihan indekos yang tepat maka dikembangkan Sistem Pendukung Keputusan yang memberikan rekomendasi berdasarkan kriteria dan subkriteria tertentu. Sistem Pendukung Keputusan (SPK) yang dibangun dengan mengimplementasikan pendekatan hybrid AHP dan MAUT. Kedua pendekatan tersebut dapat memberikan rekomendasi indekos berdasarkan peringkat nilai utilitas tertinggi. Pengujian terhadap SPK dilakukan dengan input satu pengguna dan dua pengguna. Hasil pengujian menunjukkan bahwa untuk input satu pengguna, bobot kriteria tertinggi adalah kondisi kamar indekos dengan nilai 0.54173. Sementara itu, untuk input dua pengguna, bobot kriteria tertinggi adalah lokasi dengan nilai 0.45223.
Topic Modelling of Disaster Based on Indonesia Tweet Using Latent Dirichlet Allocation Nuryono, Aninditya Anggari; Iswanto, Iswanto; Ma'arif, Alfian; Putra, Rizal Kusuma; Nugroho H, Yabes Dwi; Hakim, Muhammad Iman Nur
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.132

Abstract

Twitter (now X) is a critical social media platform for disseminating information during crises. This study models disaster-related topics from Indonesian-language tweets using Latent Dirichlet Allocation (LDA). From a dataset of 8,718 tweets collected from official sources like BMKG and BNPB, we performed several preprocessing steps, including case folding, stop word removal, stemming, and normalization of slang and abbreviations. The optimal number of topics was determined using coherence scores, with the model achieving a peak coherence value of approximately 0.57. Keywords such as “banjir”, “kecelakaan”, “tanah longsor,” and others were used to collect data from Twitter accounts like "BMKG" (Meteorology, Climatology, and Geophysical Agency) and "BNPB" (National Disaster Management Agency). The results revealed that the most frequently discussed topics with high coherence values were “angin topan” “topan”, “virus corona”, “kecelakaan”, “tenggelam”, “badai”, “angin puting.” A word cloud was used to visualize these disaster-related topics.
Kalman Filter for Noise Reducer on Sensor Readings Ma'arif, Alfian; Iswanto, Iswanto; Nuryono, Aninditya Anggari; Alfian, Rio Ikhsan
Signal and Image Processing Letters Vol 1, No 2 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i2.2

Abstract

Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.
Lightweight Deep Learning Approach Using 1D-CNN and Attention for Sequential Credit Card Fraud Detection Nugroho H, Yabes Dwi; Rahmawati, Aulia; Araz, Rezty Amalia; Nuryono, Aninditya Anggari
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.115

Abstract

Fraudulent activity in credit card transactions continues to be a pressing concern in the financial industry, primarily because transaction data is highly complex and heavily skewed toward legitimate cases. To address this issue, the present study proposes a hybrid deep learning framework that merges the strengths of a one-dimensional convolutional neural network (1D-CNN) with the selective capabilities of an attention mechanism. The performance of this enhanced model was rigorously compared with a conventional 1D-CNN, employing widely recognized evaluation metrics such as accuracy, precision, recall, and the F1-score. The experimental outcomes demonstrate that introducing the attention layer substantially improves the network’s ability to recognize critical temporal dependencies in transaction sequences. As a result, the model achieved exceptional performance levels, with an accuracy of 98%, precision of 97%, recall of 98%, and an F1-score of 98%. These findings provide strong evidence of the superiority of the attention-based approach, highlighting its effectiveness in producing more reliable and resilient fraud detection systems. Beyond the algorithmic gains, the research contributes a practical foundation for real-time applications in financial security, enabling institutions to curtail potential losses, reinforce public confidence in digital payment services, and enhance the efficiency of day-to-day operations.
Co-Authors Alfian Ma’arif Alfian, Rio Ikhsan Araz, Rezty Amalia Aulia Rahmawati, Aulia Fadhliana, Nisa Rizqiya Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Igi Ardiyanto Iswanto Iswanto Muhammad Iman Nur Hakim Nugraha, Faizal Widya Nugroho H, Yabes Dwi Paninggalih, Ramadhan Perwita, Yustika Putra, Rizal Kusuma Sunu Wibirama