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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Statistika dan Matematika (Statmat) Tematik : Jurnal Teknologi Informasi Komunikasi
I Wayan Wiprayoga Wisesa
Institut Teknologi Sumatera
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Other

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Pengembangan Mobile based Question Answering System dengan Basis Pengetahuan Ontologi Rajif Agung Yunmar; I Wayan Wiprayoga Wisesa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020742255

Abstract

Informasi terkait kegiatan penerimaan mahasiswa baru (PMB) sesungguhnya telah banyak tersedia pada halaman web maupun brosur. Namun demikian, dimungkinkan terdapat berbagai informasi yang tidak dapat ditemukan secara langsung dalam media tersebut. Penggunaan mesin pencari juga tidak menjamin pengguna untuk mendapatkan informasi atau jawaban yang relevan dengan kebutuhan. Melakukan kunjungan ke kampus seringkali terkendala oleh jarak, waktu, dan jam kerja. Dalam penelitian ini, dikembangkan sebuah question answering system (QAS) terkait penerimaan mahasiswa baru agar pengguna mendapakan informasi yang sesuai dengan kebutuhannya, selalu bernilai benar, dan dapat diakses kapan saja. QAS dibangun dengan arsitektur tree tier dengan aplikasi mobile sebagai antarmuka, memanfaatkan metode pengolahan bahasa alami dalam memproses pertanyaan pengguna, dan ontologi sebagai basis pengetahuannya. Penelitian ini menggunakan model pengembangan SDLC, dengan model analisis yang digunakan yaitu: analisis kebutuhan sistem, analisis rancangan sistem, implementasi sistem, dan pengujian sistem. Pengujian terhadap sistem dilakukan dengan beberapa cara, yaitu: usability testing, dan pengujian akurasi jawaban. Pengujian menunjukkan QAS yang dibangun dapat diimplementasikan dengan baik sesuai dengan kebutuhan dengan akurasi jawaban sebesar 82.14%. AbstractThe information regarding student admissions and related activities can be found and widely available on website or brochures. However, it is possible that the relevant information cannot be found directly from the media. The use of search engines also doesn’t guarantee users to get the relevant answer or information that satisfy their needs. Visiting the campus is often constrained by distance, time or working hours. In this study, a question answering system related to student admissions was developed so that users get the information that fits thier need, always give the correct answers, and can be accessed anytime. The QAS is built with a tree tier architecture with a mobile application as an interface. Natural language processing methods uses to process user questions, and ontology uses as the knowledge base. This study uses the SDLC development model, with the analysis model used namely: system requirements analysis, system design analysis, system implementation, and system testing. Testing the system is done by several ways, namely: usability testing, and test the accuracy of answers. The tests shows that the QAS can successfully implemented according to the requirement, with the accuracy of answer is 82.14%.
MODEL OPTIMASI BEBAN MENGAJAR DOSEN DENGAN MEMINIMUMKAN DEVIASI RATA-RATA BEBAN MENGAJAR Imam Ekowicaksono; I Wayan Wiprayoga Wisesa
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 2, No 2 (2020)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.003 KB) | DOI: 10.32493/sm.v2i2.6013

Abstract

Setiap awal semester, setiap program studi akan menentukan beban mengajar setiap dosen. Perhitungan beban mengajar setiap dosen dilakukan dengan mempertimbangkan mata kuliah dan kelompok keilmuan dosen tersebut. Selain itu, perhitungan beban mengajar dosen akan mempertimbangkan keseragaman rata-rata beban mengajar dosen. Penelitian ini bertujuan memodelkan secara matematis beban mengajar dosen dengan memperhatikan keseragaman beban mengajar setiap dosen. Model pemrograman linear digunakan untuk memodelkan beban mengajar dosen. Model beban mengajar ini diaplikasikan di Program Studi Teknik Informatika, Institut Teknologi Sumatera untuk semester genap. Hasil yang diperoleh, deviasi minimum total rata-rata beban mengajar dosen adalah 8.05 SKS yang dikalkulasikan menggunakan algoritma branch and bound dengan 7.258.538 iterasi.
An Optimization Model for Teaching Assignment based on Lecturer’s Capability using Linear Programming Imam Eko Wicaksono; I Wayan Wiprayoga Wisesa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.9705

Abstract

In the campus, the arrangement of teaching assignment for the lecturers have been the porblem encounterd by the management on the beginning of each semester. This process including assigning a class with suitable lecturer while adjusting the appropriate load for the lecturer. Such problem is non-trivial and can be considered as a linear system model. In this article, we try to solve the problem of teaching assignment using optimization model. We tried to maximize the capability of lecturers on particular subject while also considering their loads. Using branch and bound algorithm, the optimal solution were found and the problem are well solved.
IoT-based Architecture for Automatic Detection of Fall Incident using Accelerometer Data I Wayan Wiprayoga Wisesa; Genggam Mahardika
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.9686

Abstract

Fall is an unintentional incident that could happened in our daily life. For the elderly, fatal fall incident might increase the risk of death. There is a need to quickly do the first aid after fall incident occur. IoT based architecture made it possible to monitor fall incident remotely. The monitoring device records the activity and object movement using tri-axial accelerometer sensor attached to user’s waist. The system implemented simple thresholding technique based on total acceleration recorded over time. Various scenarios were performed in order to test the system including normal daily activities and fall incident. Using sensitivity and specificity measurement to evaluate the system, the proposed system achieved the value of 98% and 96% respectively.
Pendeteksian Pelanggaran Pada Penyebrangan Jalan Menggunakan Single-Shot Detector Pada ESP32 Novaldi, Fahri; Amrulloh, Iqbal; Wisesa, I Wayan Wiprayoga; Manullang, Martin Clinton Tosima
TEMATIK Vol 9 No 2 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i2.997

Abstract

Tingginya jumlah kendaraan bermotor dan pertumbuhannya di kota-kota besar, serta tingginya angka pelanggaran membuat identifikasi pelanggaran terhadap pengendara kendaraan bermotor menjadi sulit, terutama dalam hal pengendara yang berhenti di marka persimpangan jalan (zebra cross). Pemanfaatan teknologi computer vision diharapkan dapat membantu mengidentifikasi pelanggaran dengan mengenali objek berupa kendaraan bermotor yang terdapat pada area visual yang tertangkap kamera. Sistem menggunakan metode pendeteksi single-shot detector dari model yang dilatih dan diimplementasikan pada perangkat keras ESP32. Sistem yang dikembangkan tidak hanya berupa perangkat keras tetapi juga perangkat lunak antarmuka yang dapat digunakan untuk mengkonfigurasi dan menentukan region yang diinginkan. Dua macam pengujian dilakukan, empat pengujian dalam skenario real time dan 20 pengujian secara offline menggunakan dataset Pedestrian Traffic Light. Seluruh keadaan pada skenario real-time dapat dideteksi dengan tepat. Sementara itu, eksperimen offline menggunakan dataset dari Dataset Pedestrian Traffic Light menghasilkan akurasi 96,78%.
Transformer-Based Deep Learning Model for Coffee Bean Classification Ekowicaksono, Imam; Wisesa, I Wayan Wiprayoga; Fitriani, Vita
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10301

Abstract

Coffee is one of the most popular beverage commodities consumed worldwide. The process of selecting high-quality coffee beans plays a vital role in ensuring that the resulting coffee has superior taste and aroma. Over the years, various deep learning models based on Convolutional Neural Networks (CNN) have been developed and utilized to classify coffee bean images with impressive accuracy and performance. However, recent advancements in deep learning have introduced novel transformer-based architectures that show great promise for image classification tasks. By incorporating a self-attention module, transformer models excel at generating global context features within images. This ability demonstrate improved and more consistent performance compared to CNN-based models. This study focuses on training and evaluating transformer-based deep learning models specifically for the classification of coffee bean images. Experimental results demonstrate that transformer models, such as the Vision Transformer (ViT) and Swin Transformer, outperform traditional CNN-based models. Swin Transformer model achieves excellent on the coffee bean image classification task, with 95.13% Accuracy and 90.21% F1-Score, while ViT achieves 94.47% Accuracy and 88.93% F1-Score. It indicates their strong capability in accurately identifying and classifying different types of coffee beans. This suggests that transformer-based approaches could be a better alternative for coffee bean image classification tasks in the future.
Co-Authors Amrulloh, Iqbal Ekowicaksono, Imam Fitriani, Vita Genggam Mahardika Imam Eko Wicaksono Imam Ekowicaksono Martin Clinton Tosima Manullang, Martin Clinton Tosima Novaldi, Fahri Rajif Agung Yunmar