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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) ComEngApp : Computer Engineering and Applications Journal Jurnal Rekayasa Mesin Jurnal Energi Dan Manufaktur AUSTENIT Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Mechanical Proceeding of the Electrical Engineering Computer Science and Informatics Science and Technology Indonesia MEDIA BINA ILMIAH Prosiding Seminar Nasional Teknoka Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Science (IJES) Prosiding Seminar Nasional Unimus

Irsyadi Yani

Department Of Mechanical Engineering, Faculty Of Engineering, Sriwijaya University, Palembang, 30139, Indonesia

Author-ID : 2892444

Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences

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IDENTIFIKASI PLAT MOBIL DENGAN MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN KOHONEN PADA SISTEM PARKIR CERDAS Irsyadi Yani; Fadhian Fadhillah Siregar; Donny Sahala Tua Sitorus
Prosiding Seminar Nasional Teknoka Vol 4 (2019): Prosiding Seminar Nasional Teknoka
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Masyarakat modern bepergian menggunakan kendaraan pribadi. Semakin banyaknya penggunaan kendaraan harus diimbangi dengan ketersediaan lahan parkir. Masalah ini dapat diatasi dengan membuat suatu system yang dapat mengetahui lokasi lahan parkir yang kosong untuk dapat di isi kendaraan. Identifikasi plat nomor menggunakan jaringan syaraf tiruan kohonen ini mempunyai beberapa tahapan, yaitu mobil yang memasuki lahan parkir akan melewati portal yang telah dipasangi webcam untuk mengambil citra dari mobil, lalu citra akan di resize dan melalui proses seperti grayscaling, vertical edge detection, dilasi, horizontal rank filter, lalu diperoleh lokasi plat nomor yang akan di identifikasi oleh program yang telah dibuat. Dilanjutkan dengan proses pemerataan latar belakang untuk memfokuskan program agar mendeteksi karakter dari plat nomor saja, tahap yang di lakukan pada proses ini antara lain adalah proses grayscaling, contrasting, binary, proses filter, dan membersihkan border dari plat. Selanjutnya proses mengekstraksi karakter satu per satu. Hasil di labelisasi itulah akan menjadi data inputan atau database untuk proses training, training menggunakan 3 sampel berbeda untuk setiap karakter nya. semakin banyak sampel semakin tinggi tingkat akurasi dari pembacaan program. Hasil dari uji coba program ini menghasilkan 65% keberhasilan mengenali pola huruf, 60% pola angka, dan 20% keberhasilan mengenali plat nomor dengan benar dari total percobaan 10 plat nomor.

Rancang Bangun Alat Penghancur Sampah Botol Plastik Kapasitas ±33 Kg/Jam Firmansyah Burlian; Irsyadi Yani; Ivfransyah; Jhosua Arie S
Prosiding Seminar Nasional Teknoka Vol 4 (2019): Prosiding Seminar Nasional Teknoka
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Semakin bertambah jumlah sampah botol plastik setiap tahunnya meningkatkan kerusakan lingkungan, sehingga harus dilakukan pengolahan sampah botol plastik dengan cara didaur ulang. Salah satu proses pendaur ulangan adalah dengan cara dihancurkan sebelum dicairkan. Mesin penghancur sampah botol plastik yang dirancang berukuran (620 mm x 420 mm x 800 mm). Daya utama penggerak mesin menggunakan motor listrik 1 hp (746 watt) dengan putaran poros motor 1400 rpm. Kapasitas mesin yang dirancang sebesar ± 33 kg/jam sehingga membutuhkan putaran poros sebesar 126 rpm. Tipe pisau yang digunakan berdiameter 120 mm dengan bentuk cakram yang memiliki 4 pisau potong setiap ujungnya. Mesin menggunakan dua poros yang berputar dengan 7 buah pisau penghancur yang diletakkan setiap porosnya. Penelitian bertujuan untuk mengetahui waktu dan ukuran hasil potongan sampah botol plastik dengan tiga bentuk susunan yang akan dicoba menggunakan botol plastik seberat 1 kg. Bentuk pertama menghasilkan potongan berukuran 4,5-10 cm dengan waktu 108,22 detik. Bentuk kedua menghasilkan potongan berukuran 4-8,5 cm dengan waktu 112,19 detik. Bentuk ketiga memerlukan waktu 110,15 detik dengan ukuran 4,3-9 cm.

Analysis on swarm robot coordination using fuzzy logic Ade Silvia Handayani; Siti Nurmaini; Irsyadi Yani; Nyayu Latifah Husni
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp48-57

In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots was analyzed. Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination. In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots.

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests Yulia Resti; Chandra Irsan; Muflika Amini; Irsyadi Yani; Rossi Passarella; Des Alwine Zayantii
Science and Technology Indonesia Vol. 7 No. 3 (2022): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach Yulia Resti; Firmansyah Burlian; Irsyadi Yani; Des Alwine Zayanti; Indah Meiliana Sari
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes Yulia Resti; Firmansyah Burlian; Irsyadi Yani
Science and Technology Indonesia Vol. 5 No. 4 (2020): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

Prediction of Plastic-Type for Sorting System using Fisher Discriminant Analysis Irsyadi Yani; Yulia Resti; Firmansyah Burlian; Ansyori Yani
Science and Technology Indonesia Vol. 6 No. 4 (2021): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2021.6.4.313-318

Recycling is a more environmentally friendly method of managing and reducing plastic waste that can significantly reduce land degradation, pollution, and greenhouse gas emissions. According to its composition, an essential first step in the recycling process is sorting out plastic waste. However, inadequate sorting of plastic types can result in cross-contamination and increasing industrial operating costs. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. The purpose of this paper is to demonstrate how to use Fisher Discriminant Analysis (FDA) to predict the plastic type from a digital image of the RGB model and then evaluate the performance using cross-validation. This work has four main steps: collecting plastic digital image data, forming statistical tests, predicting plastic types, and evaluating prediction performance. FDA is quite effective for predicting the type of plastic. Performance measures the accuracy of 87.11 %, the recall-micro of 91.67 %, the recall-micro of 80.97 %, the specificity-micro of 90.33 %, and the specificity-macro of 90.38 %, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor Yulia Resti; Chandra Irsan; Mega Tiara Putri; Irsyadi Yani; Ansyori Ansyori; Bambang Suprihatin
Science and Technology Indonesia Vol. 7 No. 1 (2022): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

PREDICTION OF PLASTIC-TYPE FOR SORTING SYSTEM USING DECISION TREE MODEL Astuti Astuti; Anthony Costa; Akbar Teguh Prakoso; Irsyadi Yani; Yulia Resti
Indonesian Journal of Engineering and Science Vol. 4 No. 1 (2023): Table of Content
Publisher : Asosiasi Peneliti Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51630/ijes.v4i1.86

Plastic is the most widely used inorganic material globally, but its hundred-year disintegration time can harm the environment. Polyethylene Terephthalate (PET/PETE), High-Density Polyethylene (HDPE), and Polypropylene are all commonly used plastics that have the potential to become waste (PP). An essential first step in the recycling process is sorting out plastic waste. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. This paper proposes the Decision Tree model to predict the three plastic-type sorting systems based on discretizing predictor variables into two and three categories. The resampling method of k-fold cross-validation with ten folds for less biased. Discretization of the predictor variables into three categories informs that the proposed decision tree model has higher performance compared to the two categories with an accuracy of 81.93 %, a recall-micro of 72.89 %, a recall-macro of 72.30 %, a specificity-micro of 86.45%, and the specificity-macro of 86.51%, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

ASSESSMENT MATERIAL SELECTION FOR CHAIN - SUBMERGED SCRAPPER CONVEYOR Gunawan Gunawan; Amir Arifin; Irsyadi Yani; M. A. Ade Saputra; Barlin Oemar; Zulkarnain Ali Leman; Dendy Adanta; Akbar Teguh Prakoso
Indonesian Journal of Engineering and Science Vol. 4 No. 1 (2023): Table of Content
Publisher : Asosiasi Peneliti Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51630/ijes.v4i1.92

Chain–submerged scrapper conveyor bottom ash handling in the petrochemical industry has failed several times and was repaired with AISI 420, which can only operate for three months. AISI 420 is recommended in applications requiring moderate corrosion resistance, high hardness, excellent wear resistance, and good edge retention in cutting surfaces. The initial cracks and fractures occur in the pin-link joint hole, which causes chain failure. Some evaluation has been performed for both as-received and failed links. It can be concluded that chain link failure occurs due to fatigue failure with low-stress levels. Microstructure observation, XRD, and hardness properties showed no significant difference in both as-received and failed links. Since the operating conditions of the chain are in a corrosive environment, experiencing dynamic loading and working temperatures between 23 ºC and 60 ºC, the selection of HSL materials such as AISI 4140 should be considered.

Co-Authors Adanta, Dendy Ade Silvia Ade Silvia Ade Silvia Handayani Agung Mataram Ahmad Irham Jambak Akbar Teguh Prakoso Amir Arifin Amir Arifin Amrifan Saladin Mohruni Ansyori Ansyori Ansyori Ansyori Ansyori Yani Anthony Costa Astuti Astuti Bambang Suprihatin Barlin Barlin Barlin Oemar Chandra Irsan Chandra Irsan Chandra Irsan Des Alwine Zayanti Des Alwine Zayantii Dewi Puspitasari Dewi, Tresna Donny Sahala Tua Sitorus Ekawati Prihatini Eric Rahman Fadhian Fadhillah Siregar Falah Yuridho Firdaus MS Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Gunawan Gunawan Gunawan Gunawan gustini gustini Hasan Basri helena astari Husni, Nyayu Latifah Husni, Nyayu Latifah Indah Meiliana Sari Irmawan Irmawan Ismail Thamrin Ivfransyah Jeremy Firdaus Latif Jhosua Arie S Jhosua Arie Swandi Kaprawi Kaprawi Lugantha Perkasa M. A. Ade Saputra M.A. Ade Saputra Mega Tiara Putri Muflika Amini Muhammad Dahlan Ning Eliyati Novi Rustiana Dewi Nukman Nukman Nukman Nurhabibah Paramita Eka Utami Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Rossi Passarella Siti Nurmaini Tresna Dewi Yulia Resti Zulkarnain Ali Leman Zulkarnain Zulkarnain

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