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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) ComEngApp : Computer Engineering and Applications Journal Jurnal Rekayasa Mesin Jurnal Energi Dan Manufaktur AUSTENIT Jurnal Mechanical Proceeding of the Electrical Engineering Computer Science and Informatics Science and Technology Indonesia Prosiding Seminar Nasional Teknoka Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Science (IJES) Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Bulletin of Applied Mathematics and Mathematics Education Jurnal Pelita Sriwijaya Prosiding Seminar Nasional Unimus
Irsyadi Yani
Department Of Mechanical Engineering, Faculty Of Engineering, Sriwijaya University, Palembang, 30139, Indonesia
Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (956.44 KB) | DOI: 10.26554/sti.2020.5.4.111-116

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2617.77 KB) | DOI: 10.26554/sti.2022.7.1.29-35

Abstract

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

Abstract

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

Abstract

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.
A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests Yulia Resti; Chandra Irsan; Jeremy Firdaus Latif; Irsyadi Yani; Novi Rustiana Dewi
Science and Technology Indonesia Vol. 8 No. 2 (2023): April
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.2023.8.2.288-297

Abstract

Control of diseases and pests of maize plants is a significant challenge to ensure global food security, self-sufficiency, and sustainable agriculture. Classification or early detection of diseases and pests of corn plants is intended to assist the control process. Random forest is a classification model in tree-based statistical learning in making decisions. This approach is an ensemble method that generates many decision trees and makes classification decisions based on the majority of trees selecting the same class. However, tree-based methods are often unstable when small changes or disturbances exist in the learning data. Such instability can produce significant variances and affect model performance. This study classifies diseases and pests of the corn plant using a random forest method based on bootstrap-aggregating. It fits multiple models of a single random forest, then combines the predictions from all models and determines the final result using majority voting. The results showed that the bootstrap aggregating could improve the classification of diseases and pests of maize using a random forest if the number of trees is optimal.
BIOCOMPATIBLE P(TM co SA-CAA) HYDROGELS WITH pH RESPONSIVE AND ENHANCED MECHANICAL PERFORMANCE Gustini Gustini; Kaprawi Kaprawi; Hasan Basri; Irmawan Irmawan; Irsyadi Yani; Nurhabibah Paramita Eka Utami
AUSTENIT Vol. 15 No. 2 (2023): AUSTENIT: October 2023
Publisher : Politeknik Negeri Sriwijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/austenit.v15i2.7536

Abstract

In recent years, development of hydrogel that combines biocompatibility, pH responsive and mechanical performance has attracted the attention of researchers. A novel biocompatible hydrogel, composed of P(TM co SA) and P(TM co CAA) was synthesized by a simple admixture and heating process. The results show that with increasing levels of SA-CAA monomer concentration, an increase in tensile strength and elongation at breakpoint was observed and optimal at the ratios P(TM co SA CAA). Tensile strenght and young’s modulus registered an impressive increase of 43% and 40% respectively. These improvements are attributed to strong synergistic hydrogen bonding interactions between the TM and SA-CAA chains. During the experiment, maximum increase in weight was also achieved at pH 10 NaOH solution, it is show the pH-responsive hydrogels. The investigation of P(TM co SA-CAA) hydrogel mechanism showed that more homogenous dispersed through crosslinks dominated by β-sheets from Amide I structures. Furthermore, the SA-CAA molecules contributed to the biocompatibility, pH responsive and mechanical performance of P(TM co SA-CAA) hydrogels. Conclusively, its P(TM co SA-CAA) hydrogels clearly demonstrated the relevance of the provide a bioresponsive material for biomedical applications, such as tissue engineering, regenerative medicine and pH-sensitive drug delivery.
Dampak Permainan Kreatif pada Kemampuan Literasi dan Numerasi bagi Anak-anak Panti Asuhan Al-Fatih Palembang Ning Eliyati; Yulia Resti; Irsyadi Yani; Ismail Thamrin
Prosiding Seminar Nasional Unimus Vol 4 (2021): Inovasi Riset dan Pengabdian Masyarakat Post Pandemi Covid-19 Menuju Indonesia Tangguh
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rendahnya kemampuan literasi dan numerasi merupakan masalah mendasar yang memiliki dampaksangat luas bagi kemajuan bangsa, karena berkontribusi secara signifikan terhadap kemiskinan,pengangguran dan kesenjangan. Kemampuan literasi merupakan kemampuan menganalisis informasidalam bentuk teks, sedangkan kemampuan numerasi adalah kemampuan menganalisis informasidalam bentuk angka. Kedua kemampuan ini membantu meningkatkan pemahaman seseorang didalam mengambil kesimpulan dari informasi yang dibaca. Permainan kreatif merupakan salah satusarana yang dapat meningkatkan kemampuan literasi dan numerasi. Penelitian ini bertujuan untukmenganalisis dampak permainan kreatif pada kemampuan literasi dan numerasi anak-anak PantiAsuhan Al-Fatih Palembang. Permainan kreatif yang diajarkan adalah permainan teka-teki AngkaAlice Oglesby. Permainan ini disajikan dalam bentuk cerita yang harus diselesaikan menggunakanoperasi aritmatika. Metode penelitian mengunakan desain pretes-postes. Hasil penelitianmenunjukkan bahwa terdapat perbedaan signifikan terhadap kemampuan literasi dan numerasiantara sebelum dan setelah mengenal permainan kreatif. Dengan kata lain, permainan kreatifmemiliki dampak signifikan terhadap kemampuan literasi dan numerasi anak-anak Panti Asuhan AlFatihPalembang.Kata Kunci : literasi, numerasi, permainan kreatif, teka-teki Angka Alice Oglesby.
Pengaruh Eksperimen dan Permainan Edukatif untuk Penguatan Literasi Sains Anak-anak Usia Sekolah di Panti Asuhan Al-Fatih Palembang Irsyadi Yani; Dewi Puspitasari; Ismail Thamrin; Zulkarnain Zulkarnain; Yulia Resti
Prosiding Seminar Nasional Unimus Vol 4 (2021): Inovasi Riset dan Pengabdian Masyarakat Post Pandemi Covid-19 Menuju Indonesia Tangguh
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Literasi sains didefinisikan sebagai pengetahuan dan kecakapan ilmiah untuk mampumengidentifikasi pertanyaan, memperoleh pengetahuan baru, menjelaskan fenomena ilmiah, sertamengambil simpulan berdasarkan fakta, memahami karakteristik sains, membangun kesadaranbagaimana sains dan teknologi membentuk lingkungan alam, intelektual dan budaya, sertameningkatkan kemauan untuk terlibat dan peduli dalam isu-isu yang terkait sains. Kemampuanliterasi sains merupakan salah satu faktor penting bagi kemajuan sebuah negara dalam menjalanikehidupan di era globalisasi, dan harus diimbangi dengan menumbuhkembangkan kompetensi yangmeliputi kemampuan berpikir kritis/memecahkan masalah, kreativitas, komunikasi, dan kolaborasi.Sayangnya kemampuan literasi sains anak-anak Indonesia masih sangat rendah. Kemampuan literasisains dapat ditingkatkan dengan bermacam cara diantaranya melalui eksperimen dan permainanedukatif. Tujuan dari penelitian ini adalah menganalisis pengaruh eksperimen dan permainanedukatif terhadap penguatan literasi sains anak-anak usia sekolah di panti asuhan Al-FatihPalembang. Adapun kegiatan yang diajarkan di antaranya adalah air pelangi, kapilaritas air kubis,dan juga game labirin-matematika. Desain pre-tes dan post-tes diajukan sebagai metode penelitian.Hasil penelitian menunjukkan bahwa terdapat perbedaan signifikan terhadap kemampuan literasisains antara sebelum dan setelah mengenal eksperimen dan permainan edukatif. Eksperimen danpermainan edukatif memiliki pengaruh signifikan terhadap penguatan literasi sains anak-anak PantiAsuhan Al-Fatih Palembang. Kata Kunci : literasi sains, eksperimen, permainan kreatif labirin-matematika.
Ensemble of naive Bayes, decision tree, and random forest to predict air quality Resti, Yulia; Eliyati, Ning; Rahmayani, Mau’izatil; Alwine Zayanti, Des; Sri Kresnawati, Endang; Setyo Cahyono, Endro; Yani, Irsyadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3039-3051

Abstract

Air quality prediction is an important research issue because air quality can affect many areas of life. This study aims to predict air quality using the ensemble method and compare the results with the prediction results using a single method. The proposed ensemble method is built from three singlesupervised methods: naïve Bayes, decision trees, and random forests. The results show that the ensemble method performs better than the single methods. The ensemble method achieves the highest performance with scores of 99.89% accuracy, 79.6% precision, 79.81% recall, and 79.7% F1-score. The performance comparison between single and ensemble models is expected to provide information on the percentage increase in predictive model performance metrics from the single to ensemble methods.
Co-Authors Ade Silvia Ade Silvia Ade Silvia Handayani Agung Mataram Ahmad Irham Jambak Akbar Teguh Prakoso Ali Amran Ali Syahbana Alwine Zayanti, Des Amir Arifin Amir Arifin Amrifan Saladin Mohruni Aneka Firdaus Ansyori Ansyori Ansyori Ansyori Ansyori Yani Anthony Costa Astuti - Astuti Astuti Bambang Suprihatin Barlin Barlin Barlin Barlin Barlin Oemar Chandra Irsan Chandra Irsan Chandra Irsan Dendy Adanta Des Alwine Zayanti Des Alwine Zayanti, Des Alwine Des Alwine Zayantii Dewi Puspita Sari Dewi Puspitasari Dewi Puspitasari Dewi, Novi R. Dewi, Tresna Donny Sahala Tua Sitorus Eka Utami, Nurhabibah Paramitha Ekawati Prihatini Ellyanie Ellyanie Eric Rahman Fadhian Fadhillah Siregar Falah Yuridho Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan gustini gustini Hasan Basri Hasan Basri helena astari Hoiri, Sajiril 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 Kresnawati, Endang S. Lugantha Perkasa M. A. Ade Saputra M.A. Ade Saputra MARWANI Marwani Marwani, Marwani Mega Tiara Putri MS, Firdaus Muflika Amini Muhammad Dahlan Muhammad Yanis Ning Eliyati Nova Yuliasari Novi Rustiana Dewi Nukman Nukman Nukman Nurhabibah Paramita Eka Utami Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Rahmayani, Mau’izatil Resnawati Rossi Passarella Saputra, M. A. Ade Saputra, M.A. Ade Setyo Cahyono, Endro Siti Nurmaini Sri Kresnawati, Endang Sulong, Abu Bakar Teguh Prakoso, Akbar Tresna Dewi Yuli Andriani Yulia Resti Zayanti, Des A. Zulkarnain Ali Leman Zulkarnain Zulkarnain Zulkarnain Zulkarnain