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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Al-Jabar : Jurnal Pendidikan Matematika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENDIDIKAN TAMBUSAI Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Sistem Cerdas dan Rekayasa Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yohannes Yohannes
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Analisis Pengaruh Daya Mesin Dan Campuran Bahan Bakar Oli Bekas Dan Dexlite Terhadap Emisi Gas Buang CO2 Mesin Diesel Dongfeng Model R175 Karina Yolanda; Aryo Sasmita; Yohannes Yohannes
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 8 (2021): Edisi 2 Juli s/d Desember 2021
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

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Abstract

Used oil is a potential alternative fuel and can reduce the waste of used oil. Used oil utilization has been carried out at The Production Technology Laboratory, Department of Mechanical Engineering, Riau University on modified diesel engines. The problem is that the resulting emissions still exceed the quality standard at a power load of 2000 W. In the present work, CO2 emission produced by Dongfeng R175 diesel engine with the mixed used oil and dexlite as alternative fuels was investigated. A total of three fuel samples, such as D10 (10% dexlite), D20 (20% dexlite), D30 (30% dexlite) respectively are used. CO2 emissions were analyzed with varied power loads, starting at idle, 1000 W, and 2000 W. The result was analyzed using a Microsoft Excel application with graphical output of the emission levels. The results showed that the best mixture was D10 where the highest loading power of 2000 W produced CO2 emission of 2,5%. While the highest CO2 emission produced by the D30 mixture at a power load of 2000 W of 6,6%.Keywords : Diesel Engine, Used Oil, Dexlite, Exhaust Emissions, CO2.
Pengembangan Sistem Kontrol Penggerak Stang Las Pada Sliding Adaptive Two Axis Mesin Pengelasan Smaw Berbasis Mikrokontroller Arduino Uno Surya Dita Prasetya; Yohannes Yohannes
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 6 (2019): Edisi 1 Januari s/d Juni 2019
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

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Abstract

SMAW (Shield Metal Arc Welding) is a welding technique using electric current that forms a current arc and webbed electrodes. In the SMAW welding process there are parameters that determine the quality of the welding results. This welding handlebar drive control system is made to simplify work, especially in the field of welding and get constant parameters. The research method is used the working principle of CNC 2 axis machines. The CNC 2 axis machine is able to move in the direction of the X and Y axis in the work plane and have high precision and precision. This welding handlebar drive control system is controlled by Universal G-Code Sender software. Making microcontroller-based welding handlebar drive control systems makes changes to the metal welding process easier, because operators only input data as desired. From the test results of the X and Y axis movements get the same results between the program and the actual, X and Y axis joint movements, limit switch testing and sliding movement speed that get results in the form of graphs and Tabels.Keywords : SMAW, Control System, Microcontroller ATMega 328P, Limit Switch, Arduino UNO
PENERAPAN SISTEM SIRKULASI PERPUSTAKAAN DI SMA PEMBANGUNAN JAYA 2 SIDOARJO Yohannes Yohannes; Fransiska Kurniawati
Jurnal Sistem Cerdas dan Rekayasa (JSCR) Vol 2 No 1 (2020): Jurnal Sistem Cerdas dan Rekayasa (JSCR) 2020
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Widya Kartika (LPPM UWIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.757 KB) | DOI: 10.61293/jscr.v2i1.271

Abstract

SMA Pembangunan Jaya 2 Sidoarjo didirikan oleh Yayasan Pendidikan Jaya pada tahun 2014. Beragam aktivitas yang menggali & meningkatkan potensi siswa dipersiapkan untuk menghantarkan lulusan ke perguruan tinggi hingga siap menghadapi dunia kerja, salah satunya adalah ketersediaan perpustakaan. Perpustakaan sebagai faktor penting di dalam penunjang transformasi antara sumber ilmu dengan pencari ilmu. Penerapan sistemnya masih menggunakan proses transaksi tertulis di kertas dan buku laporan. Hal ini ada pengaruh pada human error yang harus merekap dan melaporkan tanpa sistem sirkulasi yang berbasis komputer. Perancangan sistem sirkulasi yang dilakukan harapannya dapat membantu sistem perpustakaan menjadi lebih cepat dan mudah dalam pelaporannya. Perancangan ini dilakukan dengan menggunakan metode air terjun. Adapun metode penelitian yang dilakukan adalah dengan melakukan studi lapangan, studi pustaka, analisa, perancangan, penerapan, serta evaluasi dan monitoring. Penerapan sistem sirkulasi perpustakaan ini dapat membantu kinerja administratif di perpustakaan
KLASIFIKASI MAMALIA MENGGUNAKAN EXTREME GRADIENT BOOSTING BERDASARKAN FITUR HISTOGRAM OF ORIENTED GRADIENT Yohannes; Johannes Petrus
JURNAL ILMIAH BETRIK Vol. 13 No. 03 DESEMBER (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i03 DESEMBER.44

Abstract

Mammals are one type of animal that has many characteristics and characteristics. The shape of the face in each type of mammal has a similar shape. The faces of mammals in the form of frontal images are a challenge in image classification. In this study, the Histogram of Oriented Gradient (HOG) is used as a feature of the facial shape of mammals. HOG is used as a strengthening feature in the classification process using the eXtreme Gradient Boosting (XGBoost) method. The test was carried out using a dataset of frontal facial imagery of mammals consisting of 15 species. The results of the tests show that the XGBoost method with the HOG feature is able to provide better classification results for mammals than without the HOG feature. This is indicated by an increase in the precision value of 0.61; recall of 0.62; and an f1-score of 0.60 on XGBoost with HOG feature which is almost double that of XGBoost without HOG feature.
Deteksi Teks Secara Otomatis Pada Natural Image Berbasis Superpixel Menggunakan Maximally Stable Extremal Regions dan Stroke Width Transform Yohannes Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 2 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i2.676

Abstract

Text detection in natural image is something to do before performing character recognition. The process of text detection plays an important role in the acquisition of information in an image. This research aims to detect text automatically in natural image based on superpixels with Maximally Stable Extremal Regions (MSER) and Stroke Width Transform (SWT). The superpixel method used is Simple Linear Iterative Clustering (SLIC). The SLIC method is used for segmenting text images into superpixel spaces. Image segmentation to superpixel aims to group pixels into homogeneous regions that capture redundant images. SLIC is a technique that effectively divides images into homogeneous regions (superpixels). Furthermore MSER is used as a feature to locate the text candidate region in a segmented image with superpixel. Then edge detection is done to validate the text area that has been found. Next, the SWT method is used to distinguish both text and non-text image regions. The dataset used is ICDAR 2003. Based on test result, MSER with superpixel is able to detect region of text in natural image. SWT is also able to recover the region which is the candidate of the text in natural image.
Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG Yohannes Yohannes; Yulya Puspita Sari; Indah Feristyani
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1584

Abstract

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.
Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang Yohannes Yohannes; Siska Devella; Ade Hendri Pandrean
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1978

Abstract

Songket is a historical heritage in the city of Palembang. Where Songket has many different types and motifs. Besides having historical value, Palembang's original Songket has high quality and complexity in the manufacturing process. As known Palembang Songket has a lot of motives, one of the ways to recognize Palembang Songket is through its motives, so that research was conducted for the classification of Palembang Songket motifs. The method used to extract features is the Speeded-Up Robust Feature (SURF), while the classification method is Random Forest. The process of forming the SURF feature is divided into two stages, the first stage is Interest Point Detection, which consists of Integral Images, Hessian Matrix Based Interest Points, Scale Space Representation and Interest Point Localization, the second stage of Interest Point Description consists of Orientation Assignment and Descriptor Based on Sum Haar Wavelet Responses. The resulting feature is used for the Random Forest classification. This study used 345 images of Palembang Songket motifs, among others, Bunga Cina, Cantik Manis and Pulir. The images taken are based on 5 colors from each Palembang Songket motif. For the separation of data there are 300 images used as data train and 45 images for testing data. From the tests that have been done the results of the overall overall accuracy are 68.89%, per class accuracy 79.26%, precision 69.27, and recall 68.89%.
Rancang Bangun Edugame "History of Shodanco Supriyadi": Sejarah Perlawanan Pasukan PETA Blitar Terhadap Jepang Philips Denny Azarya; Pandi Pandi; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.1979

Abstract

Games are not only for entertainment but games can be a means of learning. Historical subjects are often considered boring and uninteresting lessons because there are no innovations to attract students' curiosity. Therefore, a learning media was created and an introduction to the history of the resistance of the Blitar PETA forces through an adventure edugame. The methodology used is an iteration with three increments, each of which consists of the analysis, design, code, and test phases. The game design uses Unity 3D as a tool. Tests carried out include integration testing, system testing, and acceptance testing. From the results of these tests it was found that the edugame application that had been developed was able to assist students in introducing the history of the resistance figure PETA Blitar named Supriyadi.
Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine Yohannes Yohannes; Daniel Udjulawa; Febbiola Febbiola
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3399

Abstract

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.
Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih Yohannes Yohannes; Siska Devella; William Hadisaputra
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3707

Abstract

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.
Co-Authors Ade Hendri Pandrean Annisa, Adde Ariani, Nisha Aryo Sasmita Azarya, Philips Denny Bumi, Harinu Dadang Juandi Dandy, Dandy Daniel Udjulawa Devella, Siska Dody, Muhammad Famerdi, Farhan Agung Febbiola Febbiola Feristyani, Indah Fransiska Kurniawati Gerry Jeven Timoti Hafiz Irsyad Hartati, Ery Indah Feristyani Johannes Petrus Jordan, Stevianus Karina Yolanda Leonardo Leonardo M Ezar Al Rivan Marcella, Dewi Martin, Rizky Meiriyama, Meiriyama Molavi Arman Muhammad Farid Athar Mustaqim Mustaqim, Mustaqim Novan Wijaya Nur Arifin, Nur Pandi Pandi Pandi Pandi, Pandi Pandrean, Ade Hendri Philips Denny Azarya Prabowo, Adrianus Prasthio, Rial Putra, Lipi Amanda RR. Ella Evrita Hestiandari Sahpira, Mulia Sari, Yulya Puspita Setiawan, Jerry Suparman Suparman Surya Dita Prasetya Tamur, Maximus William Hadisaputra Yoannita Yoannita Yoannita Yoannita, Yoannita Yulya Puspita Sari