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All Journal Jurnal Pendidikan Teknologi dan Kejuruan EMITTER International Journal of Engineering Technology The Indonesian Journal of Computer Science
Setiawardhana
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Journal : The Indonesian Journal of Computer Science

 1 
Klasifikasi Tinggi Badan Menggunakan Metode Mask R-CNN Permana Sanusi, Amadea; Fariza, Arna; Setiawardhana
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3348

Abstract

Tinggi badan adalah parameter penting saat memasuki sebuah wahana. Penggunaan alat keselamatan saat bermain wahana permainan tidak akan maksimal jika wisatawan tidak memiliki tinggi badan yang sesuai dengan kriteria untuk memasuki wahana tersebut. Dalam penerapannya, seleksi wisatawan yang diperbolehkan masuk ke dalam wahana permainan masih menggunakan pengukuran tinggi badan secara manual. Penelitian ini bertujuan untuk mengurangi resiko terjadinya kecelakaan pada kendaraan dengan mengklasifikasikan dan mengimplementasikan sistem otomasi menggunakan pendekatan deep learning. Penggunaan deep learning yang berkembang saat ini dapat digunakan untuk mengklasifikasikan pengunjung. Penelitian ini mengusulkan proses klasifikasi tinggi badan menggunakan metode Mask R-CNN yang dapat digunakan untuk melakukan klasifikasi lebih dari satu orang, sehingga mempercepat antrean wisatawan pada wahana permainan. Hasil pengujian menunjukkan bahwa model Mask R-CNN yang dibangun berhasil mengklasifikasikan objek dengan memberikan bounding box, masking, dan label yang sesuai dengan objek. Membangun model Mask R-CNN sangat dipengaruhi oleh variatif gambar pada dataset dan proses anotasi gambar di dalam dataset. Evaluasi model menunjukkan hasil perhitungan mAP yang didapatkan sebesar 71%. Penelitian ini telah memenuhi tujuan utama dalam penelitian karena model Mask R-CNN berhasil melakukan klasifikasi yang sesuai.
Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Wajah Menggunakan Penyematan FaceNet Catoer Ryando; Riyanto Sigit; Setiawardhana; Bima Sena Bayu Dewantara
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4323

Abstract

In recent years, face recognition has grown significantly in importance and popularity. Google created FaceNet, a deep learning system, in 2015, and it performs very well in creating extremely precise and personalised numerical representations of faces, or embeddings. In order to swiftly and effectively identify people, this study evaluates FaceNet's effectiveness in producing face embeddings and applies it to a variety of classification techniques, including support vector machine (SVM), decision tree, random forest, and k-nearest neighbours (KNN). A dataset with a wide range of positions, facial expressions, and lighting settings is used for the assessment. The findings of the experiment demonstrated that SVM with an radial basis function (RBF) kernel outperformed the other assessed classification techniques, achieving the maximum accuracy of 95%. These findings demonstrate the wide range of applications that face recognition technology may be used for, including identity management and security in different settings.
Online Terrain Classification Using Neural Network for Disaster Robot Application Sanusi, Muhammad Anwar; Dewantara, Bima Sena Bayu; Setiawardhana; Sigit, Riyanto
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3132

Abstract

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.
Platform Budidaya Perairan Ekosistem Tambak Berbasis Internet Of Things Arisdiawan, Rossi; Setiawardhana; Agus Indra Gunawan
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3627

Abstract

Indonesia has a potential pond area of 2,963,717 hectares. Ponds are a place for breeding ecosystems such as shrimp. The digitalization system in ponds is very necessary for management and development and has an impact on economic growth. Development in the field of aquaculture involves extensification and intensification. One of the intensification programs is to utilize Internet of Things (IoT) technology to identify various parameters from the Pond which are sent to the Webserver. This research is to produce a webserver-based platform to serve as a data center and monitor several IoT devices on the farm. This platform uses an internet network with HTTP and MySql protocols. Operations related to web servers and devices refer to standard quality settings from pond farmers.
Pre- Estimasi Daya Aktif pada Gedung Bertingkat dengan menggunakan Time Series Neural Network Armanto, Ony; Novie Ayub Windarko; Setiawardhana
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3766

Abstract

Penggunaan energi listrik untuk kehidupan sehari – hari semakin meningkat tanpa adanya pengawasan dan pembatasan yang mengakibatkan penggunaan energi semakin semena – mena , penggunaan energi berlebihan juga disebabkan perkembangan teknologi yang semakin memudahkan pekerjaan manusia. Namun kebutuhan energi listrik yang besar tidak disertai dengan kapasitas energi listrik yang memadai. Oleh sebab itu diperlukan sebuah metode estimasi beban listrik jangka menengah dengan menggunakan Time Series Neural Network. Penelitian ini diharapkan dapat mengurangi jumlah energi listrik yang tidak terpakai dan digunakan se efisien mungkin. Pada penelitian ini menghasilkan nilai MAPE sebesar 5.36% dan nilai RMSE sebesar 9.2
Co-Authors Agus Indra Gunawan Amadea Permana Sanusi Anwar Arisdiawan, Rossi Armanto, Ony Arna Fariza Bima Sena Bayu Dewantara Catoer Ryando Jovan Josafat Jaenputra Kadek Reda Setiawan Suda Muhtar, Muhdalifah Novie Ayub Windarko Rika Rokhana Riyanto Sigit, Riyanto Sanusi, Muhammad Anwar Sigit Riyanto Taufiq Hidayat