Garuda - Garba Rujukan Digital

Pinrolinvic D. K. Manembu

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Author-ID : 7135511

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Library & Information Science

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Sistem Traffic Light Otomatis Berdasarkan Panjang Antrian Kendaraan dengan Pengolahan Citra: Automatic Traffic Light System Based on Vehicle Queue Length with Image Processing Pierre Tawalujan; Sherwin R. U. A. Sompie; Pinrolinvic D. K. Manembu
Jurnal Teknik Informatika Vol. 19 No. 02 (2024): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.v19i02.52144

Abstract — This research is motivated by problems with traffic light as traffic signs which in several cases and certain places have not been able to overcome the problem of traffic jams which continue to occur accompanied by the rate of growth and population density in Indonesia. The traffic light system that is generally used today is the Fixed Time Traffic Light Controller, which in reality is less effective for this problem. For this reason, this research developed an Automatic Traffic Light System Based on Vehicle Queue Length with Image Processing with the aim of helping reduce congestion that occurs at intersections, by processing information through a working system on a computer. By using Image Processing, Python, Yolo, Matlab and going through testing stages, it can be concluded that the system can run and detect vehicles on every road lane. From overall car detection on each road section in five tests , it was found that the systems accuracy rate for detecting cars was 81%, and motorbike detection was 54%. Key words— Automatic Traffic Light; Fuzzy Mamdani; Image Processing; YOLO. Abstrak — Penelitian ini dilatarblakangi oleh permasalahan pada traffic light sebagai rambu lalu lintas yang pada beberapa kasus dan tempat tertentu belum bisa mengatasi permasalahan macet yang terus terjadi diiringi oleh laju pertumbuhan dan kepadatan penduduk di Indonesia. Sistem lampu lalu lintas yang umumnya digunakan sekarang yaitu Fixed Time Traffic Light Controller, yang pada kenyataannya kurang efektif untuk permasalahan tersebut. untuk itu penelitian ini mengembangkan Sistem Traffic Light Otomatis Berdasarkan Panjang Antrian Kendaraan dengan Pengolahan Citra dengan tujuan untuk membantu mengurangi kemacetan yang terjadi dipersimpangan, dengan mengolah informasi melalui sistem kerja pada komputer. Dengan menggunakan Pengolahan Citra, Python, YOLO, Matlab dan melalui tahapan pengujian, dapat disimpulkan bahwa sistem dapat berjalan dan mendeteksi kendaraan pada setiap jalur jalan. Dari pendeteksian mobil secara keseluruhan pada setiap ruas jalan dalam lima kali pengujian didapatkan bahwa tingkat akurasi sistem mendeteksi mobil sebesar 81%, dan pendeteksian sepeda motor sebesar 54%. Kata kunci —Fuzzy Mamdani; Lampu Lalu Lintas Otomatis; Pengolahan Citra; YOLO.

Sistem Pengambilan Keputusan Pemilihan Produk Facial Wash Untuk Kulit Wajah Menggunakan Metode SAW: Decision Support System Selection Facial Wash Using The Saw Method Fryanda Rauan; Pinrolinvic D. K. Manembu; Salvius P. Lengkong
Jurnal Teknik Informatika Vol. 19 No. 02 (2024): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.v19i02.53880

Abstract — A healthy facial skin appearance is a priority for many individuals. Meanwhile, in a world filled with various facial cleansing products, consumers often feel confused about choosing a product that suits their skin type. This research aims to provide an innovative solution in the form of an Android-based decision making system that utilizes the Simple Additive Weighting (SAW) method to provide guidance to users in choosing optimal facial cleansing products for facial skin needs. By building this Android-based application, it is hoped that the system built can become a practical solution in the current digital era to support consumer decisions in choosing facial cleansing products that suit their individual skin preferences and needs. The results of this research show that the decision making system using the SAW method developed can simplify the process of selecting facial cleansing products. Thus, the proposed system can play a role in improving the quality of facial skin care and help consumers achieve a healthier skin appearance. Keywords — Decision Support Systems(DSS) ; Facial Wash Products ; Simple Additive Weighting(SAW ) Method Abstrak — Penampilan kulit wajah yang sehat adalah suatu prioritas bagi banyak individu. Pada saat yang bersamaan, dalam dunia yang dipenuhi oleh berbagai produk facial wash yang beragam, yang membuat konsumen sering merasa bingung dalam memilih produk yang sesuai dengan jenis kulit mereka. Penelitian ini bertujuan memberi solusi inovatif dalam bentuk sistem pengambilan keputusan yang berbasis android yang memanfaatkan metode Simple Additive Weighting (SAW) untuk memberikan panduan kepada pengguna dalam memilih produk facial wash yang optimal untuk kebutuhan kulit wajah mereka. Dengan di bangun aplikasi yang berbasis android ini, sistem yang dibangun diharapkan dapat menjadi salah satu solusi yang praktis di era digital saat ini untuk mendukung keputusan konsumen dalam memilih produk facial wash yang sesuai dengan preferensi dan keputuhan kulit masing-masing. Hasil penelitian ini menunjukkan bahwa sistem pengambilan keputusan dengan metode SAW yang di bangun ini dapat mempermudah proses pemilihan produk facial wash. Denga cara ini, sistem yang diusulkan dapat berperan dalam meningkatkan kualiatas perawatan kulit wajah dan membantu konsumen meraih penampilan kulit yang lebih sehat. Kata kunci — Produk Facial Wash; Simple Additive Weighting (SAW); Sistem Pengambilan Keputusan

North Sulawesi Single Local Fruit Detection Using Efficient Attention Module Based on Deep Learning Architecture Vecky C. Poekoel; Putro, Dwisnanto; Jane Litouw; Rivaldo Karel; Pinrolinvic D. K. Manembu; Abdul Haris Junus Ontowirjo; Feisy D. Kambey; Reynold F. Robot
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.54754

A Local fruit detection system is an agricultural vision field that can be implemented to increase the profit of a commodity. Besides that, North Sulawesi has a variety of local fruits which are widely used by people in their area and have a high selling value. The sorting system is an essential process of agricultural robots to sequentially separate fruit one by one. This automation process requires an accurate vision system to detect and separate fruit precisely and precisely. In addition, the implementation of a practical application demands a method to be able to work in real-time on low-cost devices. This work aims to design a local single fruit detection system for Sulawesi North by applying deep learning architecture to produce high performance. The architecture is designed to consist of an effective backbone for rapidly separating the distinctive features, an efficient attention module to improve feature extraction performance, and a classifier module employed to estimate the probabilities of each local fruit category. As a result, the designed model produces an accuracy value of 99,27% and 99,57% on the Fruits-360 and the local datasets, respectively. It outperforms other light architectures. In addition, deep learning models are designed to produce higher efficiency values than other competitors and can operate quickly at 100,488 Frames per Second.

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