Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.37-50
Yusup Miftahuddin, Sofia Umaroh, Adleo Malik Yamani
ABSTRAKPenyakit leaf blast disebabkan oleh jamur yang bernama Pyricularia Grisea yang dapat menginfeksi daun padi dan menyebabkan gejala penyakit seperti bercak yang berbentuk seperti belah ketupat yang berwarna coklat yang dapat mengakibatkan kematian pada tanaman. Tingkat penyebaran penyakit leaf blast sudah meluas hingga di Indonesia yakni pada sentra-sentra produksi padi. Penelitian dilakukan untuk mengidentifikasi Daun Padi dengan ekstraksi ciri GLCM dan klasifikasinya dengan menerapkan metode Random Forest. Jumlah data uji sebanyak 200 yang terdiri dari 100 data daun padi sehat dan 100 data daun padi berpenyakit leaf blast. Penelitian menguji keberhasilan identifikasi penyakit leaf blast dan tidak berpenyakit leaf blast. Pengujian dilakukan dengan berbagai skema yaitu 40 data uji, 80 data uji, 120 data uji, 160 data uji dan 200 data uji. Pengujian menghasilkan nilai akurasi optimal pada data uji 200 sebesar 65%, recall 65%, precision 64% dan F-measure 65% dengan rata – rata pengujian waktu klasifikasi Random Forest sebesar 0.3522s.Kata kunci: Leaf blast, Random Forest, Padi, GLCM ABSTRACTLeaf blast is a disease caused by a fungus called Pyricularia Grisea which can infect rice leaves and cause disease symptoms such as brown rhombus-shaped spots that can cause plant death. The level of spread of leaf blast disease has spread to Indonesia, namely in rice production centers. The research was conducted to identify Rice Leaf with GLCM feature extraction and classification by applying the Random Forest method. The number of test data was 200 consisting of 100 data of healthy rice leaves and 100 data of rice leaves with leaf blast disease. The study tested the success of identification of leaf blast disease and not leaf blast disease. The tests were carried out with various schemes, namely 40 test data, 80 test data, 120 test data, 160 test data and 200 test data. The test resulted in the optimal accuracy value on the 200 test data of 65%, recall 65%, precision 64% and F-measure 65% with an average testing time of Random Forest classification of 0.3522sKeywords: Leaf blast, Random Forest, Gray-level Cooncurrence Matrix, GLCM
叶叶囊肿是由一种叫做红斑菌的真菌引起的，这种真菌可以感染水稻，并导致一种疾病的症状，如斑点状的棕色结节状斑点，可能会导致植物死亡。叶状爆炸病的流行速度在印度尼西亚已经达到了分散水稻生产的水平。研究已经进行了研究，通过使用随机森林方法提取出水稻叶子的特征和分类来确定水稻。测试数据包括100片健康水稻叶片和100片病死棕榈叶爆炸。研究测试了叶叶病变和非病叶病变的成功诊断。测试采用了各种模式，包括40个测试数据、80个测试数据、120个测试数据、160个测试数据和200个测试数据。测试在测试数据中产生了65%的准确率，65%的回忆，64%的准确率和F-measure 65%的平均测试——随机森林分类时间平均为0.3522s。关键词:叶叶爆炸，兰多森林，稻米，GLCM ABSTRACTLeaf爆炸是一种疾病，由一种叫做红斑菌的真菌引起的，这种真菌可以被称为红斑菌的叶子感染，导致这种棕色斑点的死亡。叶子爆炸性疾病的扩散水平已向印尼的大米生产中心蔓延。该研究采用了一种随机森林方法，以GLCM的提取和分类为识别大米。测试数据的编号是100个健康大米的数据，100个叶子的大米数据。研究测试了确定叶爆病和非叶氏病的成功。测试结果显示有多种多样，namely 40个数据测试，80个数据测试，120个数据测试，160个数据测试和200个数据测试。在测试中最准确的准确值超过200个数据测试中，回顾65%，等级64%，F-measure 65%与平均测试0.3522种不同词汇的平均时间:Leaf blast, Random Forest classification
{"title":"Peningkatan Random Forest dengan menerapkan GLCM (Gray Level Co-Occurence Matrix) pada Klasifikasi Leaf Blast Tumbuhan Padi","authors":"Yusup Miftahuddin, Sofia Umaroh, Adleo Malik Yamani","doi":"10.26760/mindjournal.v7i1.37-50","DOIUrl":"https://doi.org/10.26760/mindjournal.v7i1.37-50","url":null,"abstract":"ABSTRAKPenyakit leaf blast disebabkan oleh jamur yang bernama Pyricularia Grisea yang dapat menginfeksi daun padi dan menyebabkan gejala penyakit seperti bercak yang berbentuk seperti belah ketupat yang berwarna coklat yang dapat mengakibatkan kematian pada tanaman. Tingkat penyebaran penyakit leaf blast sudah meluas hingga di Indonesia yakni pada sentra-sentra produksi padi. Penelitian dilakukan untuk mengidentifikasi Daun Padi dengan ekstraksi ciri GLCM dan klasifikasinya dengan menerapkan metode Random Forest. Jumlah data uji sebanyak 200 yang terdiri dari 100 data daun padi sehat dan 100 data daun padi berpenyakit leaf blast. Penelitian menguji keberhasilan identifikasi penyakit leaf blast dan tidak berpenyakit leaf blast. Pengujian dilakukan dengan berbagai skema yaitu 40 data uji, 80 data uji, 120 data uji, 160 data uji dan 200 data uji. Pengujian menghasilkan nilai akurasi optimal pada data uji 200 sebesar 65%, recall 65%, precision 64% dan F-measure 65% dengan rata – rata pengujian waktu klasifikasi Random Forest sebesar 0.3522s.Kata kunci: Leaf blast, Random Forest, Padi, GLCM ABSTRACTLeaf blast is a disease caused by a fungus called Pyricularia Grisea which can infect rice leaves and cause disease symptoms such as brown rhombus-shaped spots that can cause plant death. The level of spread of leaf blast disease has spread to Indonesia, namely in rice production centers. The research was conducted to identify Rice Leaf with GLCM feature extraction and classification by applying the Random Forest method. The number of test data was 200 consisting of 100 data of healthy rice leaves and 100 data of rice leaves with leaf blast disease. The study tested the success of identification of leaf blast disease and not leaf blast disease. The tests were carried out with various schemes, namely 40 test data, 80 test data, 120 test data, 160 test data and 200 test data. The test resulted in the optimal accuracy value on the 200 test data of 65%, recall 65%, precision 64% and F-measure 65% with an average testing time of Random Forest classification of 0.3522sKeywords: Leaf blast, Random Forest, Gray-level Cooncurrence Matrix, GLCM","PeriodicalId":43900,"journal":{"name":"Time & Mind-The Journal of Archaeology Consciousness and Culture","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86547561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.61-71
Azizah Aulia Rahman, Sisly Destri Agustin, Nurhayati Ibrahim, N. Kumalasari
ABSTRAKMinimnya visibilitas pejalan kaki dan pengendara pada malam hari karena kurangnya pencahayaan pada lampu jalan menyebabkan kecelakaan rentan terjadi pada rentang waktu tersebut. Sistem penglihatan komputer berbeda dengan manusia, semua objek dengan suhu di atas nol dapat memancarkan radiasi inframerah jika direkam menggunakan kamera termal. Dalam penelitian ini penulis mengidentifikasi citra termal dalam bentuk citra RGB dengan algoritma YOLOv4 dan Scaled YOLOv4 sebagai deteksi objek. Performa sistem diukur berdasarkan nilai presisi, recall, f1-score, dan mAP. Eksperimen dilakukan pada dataset citra termal dengan objek manusia. Skenario yang digunakan adalah mendeteksi objek dengan jarak 5m, 10m, 15m, dan 20m. Hasil deteksi didapatkan algoritma Scaled YOLOv4 CSP lebih unggul dengan nilai pengujian precision 94,3%, recall 83,8%, f1-Score 88,7%, dan mAP 86,9%. Hasil tersebut dipengaruhi oleh ukuran citra dan jumlah dataset dari citra training, citra validation, dan citra uji.Kata kunci: Citra Termal, YOLO, YOLOv4, Scaled-YOLOv4, Deteksi ObjekABSTRACTThe lack of visibility of pedestrians and drivers at night due to lack of lighting in street lights makes accidents prone to occur during this time. The computer vision system is different from the humans, any object with a temperature above zero can emit infrared radiation when using a thermal camera. In this study, the authors identify thermal images in RGB using YOLOv4 and Scaled YOLOv4 as object detection algorithms. System performance is measured based on the value of precision, recall, f1-score, and mAP. Experiments were carried out on a thermal image dataset with human objects. The scenario used was to detect objects at a distance of 5m, 10m, 15m, and 20m. The detection results show that Scaled YOLOv4 CSP algorithms is the best, based on the test value of 94.3% precision, 83.8% recall, 88.7% f1-Score, and 86.9% mAP. These results are influenced by the size of the image and the number of datasets from training images, validation images, and test images.Keywords: Thermal Image, YOLO, YOLOv4, Scaled-YOLOv4, Object Detection
{"title":"Perbandingan Algoritma YOLOv4 dan Scaled YOLOv4 untuk Deteksi Objek pada Citra Termal","authors":"Azizah Aulia Rahman, Sisly Destri Agustin, Nurhayati Ibrahim, N. Kumalasari","doi":"10.26760/mindjournal.v7i1.61-71","DOIUrl":"https://doi.org/10.26760/mindjournal.v7i1.61-71","url":null,"abstract":"ABSTRAKMinimnya visibilitas pejalan kaki dan pengendara pada malam hari karena kurangnya pencahayaan pada lampu jalan menyebabkan kecelakaan rentan terjadi pada rentang waktu tersebut. Sistem penglihatan komputer berbeda dengan manusia, semua objek dengan suhu di atas nol dapat memancarkan radiasi inframerah jika direkam menggunakan kamera termal. Dalam penelitian ini penulis mengidentifikasi citra termal dalam bentuk citra RGB dengan algoritma YOLOv4 dan Scaled YOLOv4 sebagai deteksi objek. Performa sistem diukur berdasarkan nilai presisi, recall, f1-score, dan mAP. Eksperimen dilakukan pada dataset citra termal dengan objek manusia. Skenario yang digunakan adalah mendeteksi objek dengan jarak 5m, 10m, 15m, dan 20m. Hasil deteksi didapatkan algoritma Scaled YOLOv4 CSP lebih unggul dengan nilai pengujian precision 94,3%, recall 83,8%, f1-Score 88,7%, dan mAP 86,9%. Hasil tersebut dipengaruhi oleh ukuran citra dan jumlah dataset dari citra training, citra validation, dan citra uji.Kata kunci: Citra Termal, YOLO, YOLOv4, Scaled-YOLOv4, Deteksi ObjekABSTRACTThe lack of visibility of pedestrians and drivers at night due to lack of lighting in street lights makes accidents prone to occur during this time. The computer vision system is different from the humans, any object with a temperature above zero can emit infrared radiation when using a thermal camera. In this study, the authors identify thermal images in RGB using YOLOv4 and Scaled YOLOv4 as object detection algorithms. System performance is measured based on the value of precision, recall, f1-score, and mAP. Experiments were carried out on a thermal image dataset with human objects. The scenario used was to detect objects at a distance of 5m, 10m, 15m, and 20m. The detection results show that Scaled YOLOv4 CSP algorithms is the best, based on the test value of 94.3% precision, 83.8% recall, 88.7% f1-Score, and 86.9% mAP. These results are influenced by the size of the image and the number of datasets from training images, validation images, and test images.Keywords: Thermal Image, YOLO, YOLOv4, Scaled-YOLOv4, Object Detection","PeriodicalId":43900,"journal":{"name":"Time & Mind-The Journal of Archaeology Consciousness and Culture","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79349140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.87-97
Deddy Prihadi, Bei Harira Irawan, M. S. H. Simarangkir
ABSTRAKAngka kenaikan pasien Covid-19 khususnya varian omicron mulai menunjukkan kenaikan signifikan mulai awal tahun 2022 ini. Gejala varian omicron yang hampir mirip dengan influenza dan faringitis menyebabkan masyarakat cukup sulit membedakan apakah terkena Covid-19 varian omicron atau hanya influenza atau faringitis. Penelitian ini menggunakan metode sistem pakar Certainty Factor untuk memberikan kepastian dari sebuah ketidakpastian akan gejala Covid-19 varian omicron ini. Dari hasil pengujian sampel dengan gejala demam, kelelahan, pegal-pegal, sakit kepala, sakit menelan dan pilek, diperoleh nilai persentase keyakinan penyakit Covid-19 yaitu 68,94%, penyakit influenza sebesar 56,32% dan penyakit faringitis sebesar 49,17%. Nilai persentase keyakinan terbesar dari masing-masing penyakit adalah 68,94% yaitu kemungkinan pasien menderita penyakit Covid-19 varian omicron.Kata kunci: omicron, influenza, faringitis, certainty factor, sistem pakarABSTRACTThe increase in the number of Covid-19 patients, especially the omicron variant, began to show a significant increase starting in early 2022. The symptoms of the omicron variant, which are almost similar to influenza and pharyngitis, make it quite difficult for people to distinguish whether they have the omicron variant of Covid-19 or just influenza or pharyngitis. This study uses the Certainty Factor expert system method to provide certainty from an uncertainty about the symptoms of this omicron variant of Covid-19. From the results obtained from several testing samples with symptoms of fever, fatigue, aches, headaches, swallowing and runny nose, the confidence value for Covid-19 disease is 68.94%, for influenza disease is 56.32 % and for pharyngitis is 49.17%. The largest confidence value for each disease is 68.94%, which is the possibility of the patient suffering from the omicron variant of Covid-19 disease.Keywords: omicron, influenza, pharyngitis, certainty factor, expert system
{"title":"Identifikasi Diagnosa Kategori Covid Varian Omicron dengan Flu Biasa dan Faringitis menggunakan Metode Certainty Factor","authors":"Deddy Prihadi, Bei Harira Irawan, M. S. H. Simarangkir","doi":"10.26760/mindjournal.v7i1.87-97","DOIUrl":"https://doi.org/10.26760/mindjournal.v7i1.87-97","url":null,"abstract":"ABSTRAKAngka kenaikan pasien Covid-19 khususnya varian omicron mulai menunjukkan kenaikan signifikan mulai awal tahun 2022 ini. Gejala varian omicron yang hampir mirip dengan influenza dan faringitis menyebabkan masyarakat cukup sulit membedakan apakah terkena Covid-19 varian omicron atau hanya influenza atau faringitis. Penelitian ini menggunakan metode sistem pakar Certainty Factor untuk memberikan kepastian dari sebuah ketidakpastian akan gejala Covid-19 varian omicron ini. Dari hasil pengujian sampel dengan gejala demam, kelelahan, pegal-pegal, sakit kepala, sakit menelan dan pilek, diperoleh nilai persentase keyakinan penyakit Covid-19 yaitu 68,94%, penyakit influenza sebesar 56,32% dan penyakit faringitis sebesar 49,17%. Nilai persentase keyakinan terbesar dari masing-masing penyakit adalah 68,94% yaitu kemungkinan pasien menderita penyakit Covid-19 varian omicron.Kata kunci: omicron, influenza, faringitis, certainty factor, sistem pakarABSTRACTThe increase in the number of Covid-19 patients, especially the omicron variant, began to show a significant increase starting in early 2022. The symptoms of the omicron variant, which are almost similar to influenza and pharyngitis, make it quite difficult for people to distinguish whether they have the omicron variant of Covid-19 or just influenza or pharyngitis. This study uses the Certainty Factor expert system method to provide certainty from an uncertainty about the symptoms of this omicron variant of Covid-19. From the results obtained from several testing samples with symptoms of fever, fatigue, aches, headaches, swallowing and runny nose, the confidence value for Covid-19 disease is 68.94%, for influenza disease is 56.32 % and for pharyngitis is 49.17%. The largest confidence value for each disease is 68.94%, which is the possibility of the patient suffering from the omicron variant of Covid-19 disease.Keywords: omicron, influenza, pharyngitis, certainty factor, expert system","PeriodicalId":43900,"journal":{"name":"Time & Mind-The Journal of Archaeology Consciousness and Culture","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78162464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.1-10
Fauzi Frahma Taliningsih, Yunendah Nur FU’ADAH, Syamsul Rizal, Achmad Rizal, Muhammad Adnan Pramudito
ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network
{"title":"Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi","authors":"Fauzi Frahma Taliningsih, Yunendah Nur FU’ADAH, Syamsul Rizal, Achmad Rizal, Muhammad Adnan Pramudito","doi":"10.26760/mindjournal.v7i1.1-10","DOIUrl":"https://doi.org/10.26760/mindjournal.v7i1.1-10","url":null,"abstract":"ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network","PeriodicalId":43900,"journal":{"name":"Time & Mind-The Journal of Archaeology Consciousness and Culture","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89442237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.51-60
J. Kusuma, B. Hayadi, Wanayumini Wanayumini, Rika Rosnelly
ABSTRAKPenyebab kematian utama saat ini di dunia salah satunya dikarenakan oleh penyakit kanker. Menurut data Globocan 2018, dengan tingkat kematian rerata 17 per 100.000 jiwa dan insiden sebanyak 2,1 per 100.000 jiwa untuk kanker payudara yang menyerang wanita di Indonesia. Hal ini menjadikan Indonesia menempati peringkat ke-23 di Asia dan ke-8 di Asia Tenggara. Seiring perkembangan teknologi, sistem berbantuan komputer telah membantu orang di berbagai bidang misalnya di bidang medis. Penentuan jenis kanker payudara menggunakan mechine learning dapat membantu ahli patologi melakukan pemeriksaan secara lebih konsisten dan efisien. Pada penelitian ini, akan dilakukan komparasi metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk klasifikasi kanker payudara. Adapun hasil yang didapatkan menunjukan bahwa, dalam klasifikasi metode Multi Layer Perceptron (MLP) dengan fungsi aktivasi Logistic dan fungsi optimisasi Adam memberikan nilai accuracy, precision dan recall terbaik dibandingkan Support Vector Machine yaitu sebesar 97.7%.Kata kunci: Multi Layer Perceptron (MLP), Aktivasi Logistic, Optimisasi Adam, Support Vector Machine (SVM), Kanker PayudaraABSTRACTThe leading cause of death today in the world is due to cancer. According to Globocan 2018 data, with an average mortality rate of 17 per 100,000 people and an incidence of 2.1 per 100,000 people for breast cancer that affects women in Indonesia. This makes Indonesia ranked 23rd in Asia and 8th in Southeast Asia. As technology has evolved, computer-aided systems have helped people in various fields such as in the medical field. Determination of the type of breast cancer using mechine learning can help pathologists perform examinations more consistently and efficiently. In this study, a comparison of the Multi Layer Perceptron (MLP) and Support Vector Machine (SVM) methods will be carried out for breast cancer classification. The results obtained showed that, in the classification of multi layer perceptron (MLP) methods with logistic activation function and Adam optimization function provides the best accuracy, precision and recall value compared to Support Vector Machine which is 97.7%.Keywords: Multi Layer Perceptron (MLP), Logistic Activation, Adam Optimization, Support Vector Machine (SVM), Breast Cancer
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Pub Date : 2022-06-29DOI: 10.26760/mindjournal.v7i1.72-86
Lisa Kristiana, Muhammad Rafly
ABSTRAKPenerapan teknologi dalam sistem pemantauan dapat mengatasi kelemahan pada pemantauan secara manual. Untuk meningkatkan kualitas pemantauan, diperlukan suatu sistem yang dapat memantau mobil remote control yang berada di lintasan. Wireless Sensor Network (WSN) merupakan teknologi yang dapat meningkatkan efisiensi pemantauan secara otomatis pada area tertentu. WSN pada lintasan menerapkan sistem komunikasi Single-Hop. Sistem Single-Hop menggunakan sensor node (obstacle infrared) untuk mendeteksi mobil di area lintasan, lalu dikirim langsung oleh sink node ke server dengan menggunakan ESP8266-01. Arsitektur WSN menggunakan Topologi Point to Point terhubung langsung antara sensor node dengan Access Point. Metode pengiriman Single-Hop menggunakan Transmission Control Protocol/Internet Protocol (TCP/IP). Hasil pengujian TCP/IP berdasarkan jarak baca sensor menggunakan parameter Quality of Service (QoS) meliputi parameter Delay, Jitter, Throughput, Packet loss, menghasilkan delay, jitter dan throughput yang sangat baik dan stabil, dan paket loss 0%. Kemudian TCP/IP menguji tingkat akurasi pengiriman data ke server sebanyak 400 kali menghasilkan tingkat akurasi 100%.Kata kunci: lintasan mobil remote control, single-hop wireless sensor network ABSTRACTApplication of technology in monitoring system can overcome weaknesses in manual monitoring. To improve quality of monitoring, need system that can monitor remote control cars on track. Wireless Sensor Network (WSN) is technology that can increase monitoring efficiency automatically in certain areas. WSN on track implements Single-Hop communication system. Single-Hop system uses sensor node (obstacle infrared) to detect cars in the track area, then sent directly by sink node to server using ESP8266-01. WSN architecture uses Point to Point topology connected directly between sensor nodes and Access Point. Single-Hop delivery method uses Transmission Control Protocol/Internet Protocol (TCP/IP). TCP/IP test results based on sensor reading distance using Quality of Service (QoS) parameters including Delay, Jitter, Throughput, Packet loss parameters, resulting in excellent and stable delay, jitter and throughput, and 0% packet loss. Then TCP/IP tests the accuracy of sending data to server 400 times resulting in an accuracy rate of 100%.Keywords: remote control car track, single-hop wireless sensor network
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Pub Date : 2022-04-03DOI: 10.1080/1751696X.2022.2122347
D. Coles
This major British Museum exhibition is the first to feature Stonehenge at its centre and covers many of the new discoveries made in recent years. I visited it early in March and was greeted on entering with a digital display of some of the major stone circles of the British Isles. Thereafter, the exhibition was laid out as a series of themed areas. The first section, 'Working With Nature', touches on the Mesolithic period that preceded the arrival of the early farmers and the Neolithic and Bronze Age. It includes two headdresses manufactured from deer skulls, one taken from the many that were found at Star Carr and the other from a female burial of what seems to have been a shaman, from Germany. The similarity between these is of no surprise since Britain and the continent were still linked by a land bridge at the time of the Star Carr settlement and the nomadic Mesolithic peoples would have moved freely between Britain and mainland Europe. A fine selection of jadeite axes found in Britain and a massive display of other European polished stone axes showcase the objects often regarded as definitive of the Neolithic. There is also an image of the burial of a pair of oxen together with a cart and the information that oxen were used in the construction of monuments like Stonehenge. In this section the establishment of Stonehenge as the circular monument that exists today is discussed, very briefly. We move on to the section entitled 'Sermons in Stones'. Much of this is devoted to artistic representations on stone and other materials. Thus we have a fine collection of carved stone balls from North East Scotland, the Folkton Drums from Yorkshire, the glorious Knowth macehead together with stones from Northern Britain marked with cup and ring. There are shared motifs among these objects – the eyebrows, the spirals, the concentric rings. There are also a couple of large anthropomorphic stones from Southern Europe whose design includes motifs clearly intended as sun imagery. The section includes reference to the Orcadian civilisation, in particular Skara Brae and the Ness of Brodgar, and to the great tombs of Ireland. Next up is 'Seahenge'. Dendro-dating has precisely determined the date for its construction to 2049 BCE so it was in use at the same time as Stonehenge. This wooden circle from Holme-next-the-Sea has been recreated in the Museum, oriented so that, as the curators say, they ‘have managed to align the monument so that you can see the sun rising and setting through the doorway of the monument’. The inclusion of Seahenge is to give an idea of the smaller circles that were being used by family groups, perhaps, for ceremonies. TIME AND MIND 2022, VOL. 15, NO. 2, 269–273 https://doi.org/10.1080/1751696X.2022.2122347
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Pub Date : 2022-04-03DOI: 10.1080/1751696X.2022.2079422
A. Skinner, Sam Challis
ABSTRACT In the Maloti-Drakensberg mountains of southern Africa, beliefs about snakes and their representations in rock art images are emblematic of hybrid histories of regional societies. The snake symbol initially represented an attempt at ‘reaching out’ as forager societies incorporated a prominent figure in the mythologies of incoming societies into their own – a figure which became a symbolic reference to cross-cultural symbiosis and admixture. Reflecting the long history of such contact, the ritual uses and ontological positions of snakes in contemporary knowledge systems of the Maloti-Drakensberg are coherent with those of earlier societies. This offers fertile ground for novel forms of interpretation. Using contextual historic and modern ethnographic material, this paper presents a relational account of regional idioms. It dwells on the language of taming and domestication that permeate these ethnographies, and the concern they show for the mitigation of ‘wild’, sometimes ‘monstrous’, consequences of spiritual power in the social world. Symbolic resolutions of these consequences are discernible in rock art images, particularly those of snakes, demonstrating the ritual brokerage of relations between human and non-human communities, with both forms of agency depicted in various states of ‘domestication’, bridging forager and farmer understandings of human–animal relations.
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Pub Date : 2022-04-03DOI: 10.1080/1751696X.2022.2133817
M. R. Lami
As autumn unfolds with all its colourful display, Time & Mind’s second issue for 2022 is ready to take you on an equally colourful journey through rock art, storytelling, cooking and sacred groves. Aptly defined as a global phenomenon that transcends time and space (Goldhahn 2019), rock art and its potential in conveying people’s worldviews is the common thread running through the first three papers in this volume. We start off in the Maloti Drakensberg mountains of southern Africa, where Andrew Skinner and Sam Challis discuss the vast repertoire of snake carvings in rock art, traditionally interpreted as a symbolic reference to cross-cultural encounters as forager societies started incorporating external elements into their communities. Based on historical and ethnographic sources, the authors investigate the role of snakes and similar monstrous figures in contemporary mythologies through the lens of language and regional idioms. The resulting picture reflects a symbolism coherent with that observed in earlier societies’ carved imagery, shedding light on the mutual agency and complex relationship between human and non-human actors. Max Carocci brings us to the Northern Plains region stretching from South Dakota, Nebraska and Kansas to Minnesota and Iowa, where rock art imagery is abundantly documented over a long time span (from 1000 BCE to historical times). Within such a vast and diverse iconographic repertoire, Carocci’s account focuses on a specific theme, male genitalia, which appears to have been of special relevance to Plains Indian culture. In a similar vein to Skinner and Challis’ study of snake representations, Carocci integrates archaeological, historical and ethnographic materials to reassess the meaning of phallic imagery beyond the traditional interpretations revolving around shamanism and fertility. The many modes of representations and differing contexts illustrated by the author reveal a multiplicity of worldviews sedimented in one iconic symbol that expresses the multifaceted concept of masculinity in both the spiritual and secular domains of Plains Indian culture. The contribution by Marja Ahola and Katri Lassilla shifts from the formal attributes of rock art and their many symbolic meanings to the performative practices that infused life into those carved scenes. It is widely agreed that rock depictions provided a visual script to storytellers, who through the expert use of light and darkness, shadow play and sounds could animate static images and deliver a much more vivid narration. According to Ahola and Lassilla, material traces of such performative arts may be sought also outside the realm of rock TIME AND MIND 2022, VOL. 15, NO. 2, 97–99 https://doi.org/10.1080/1751696X.2022.2133817
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Pub Date : 2022-04-03DOI: 10.1080/1751696X.2022.2114846
Max Carocci
ABSTRACT Depictions of male primary sexual characteristics are extremely varied and relatively frequent in Plains Indian rock art. These representations have been usually associated with shamanic practice, mythical themes, and fertility. However, existing explanations ought to be evaluated against cultural models and various strands of evidence derived from ethnographic parallels and semiotics so as to build a more nuanced picture of this common symbol. New interpretations advanced here may help us to gain an informed understanding of the complexities of these representations, and the workings of symbols and icons in Northern Plains Indian rock art.
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