Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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Pendeteksi dini dan penjejak kendaraan yang datang dari jarak jauh dengan pendeteksi referensi titik hilang untuk lampu lalu lintas adaptif
No full textReal-time traffic monitoring is essential for the operation of an adaptive traffic lighting system and plays a significant role in decision-making, particularly signaling in roadworks. When only one lane is accessible due to temporary road blockage, early detection of oncoming vehicles is crucial to minimize bottlenecks near the traffic light that could result in congestion and accidents. This research aimed to enhance the detection and tracking of traffic at a distance from the traffic light. We utilized the vanishing point as a reference for detection and calculated the region of interest. We implemented the proposed method on twelve traffic surveillance videos and evaluated the system performance based on how quickly it could detect incoming traffic compared with the R-CNN method. The proposed method detected target vehicles in an average of 17.75 frames, while the R-CNN method required an average of 63.36 frames. Moreover, the proposed method’s precision depends on the number of pixel orientations used to estimate the vanishing point and the definition of the region of interest. Therefore, the proposed method for enhancing the safety and reliability of an adaptive traffic light system is reliable.Pemantauan lalu lintas secara kontinu sangat penting dalam pengoperasian sistem lampu lalu lintas adaptif dan memiliki peranan signifikan dalam pengambilan keputusan, khususnya pengaturan di area pekerjaan jalan. Karena penutupan salah satu jalur secara sementara, deteksi dini kendaraan yang akan datang sangat esensial untuk meminimalkan antrian kendaraan di dekat lampu lalu lintas yang berisiko kemacetan dan kecelakaan pada area pekerjaan jalan. Penelitian ini bertujuan untuk peningkatan deteksi dan pelacakan kendaraan pada jarak yang jauh dari lampu lalu lintas. Referensi titik hilang digunakan dalam pendeteksian kendaraan dan penghitungan region of interest (RoI). Pengujian sistem dilakukan terhadap dua belas video pemantauan lalu lintas dan pengevaluasian kinerja sistem berdasarkan seberapa cepat pendeteksian kendaraan datang dibandingkan dengan metode R-CNN. Metode yang diusulkan mampu mendeteksi kendaraan target dalam rata-rata 17,75 frame, sedangkan metode R-CNN membutuhkan rata-rata 63,36 frame. Selain itu, ketepatan metode yang diusulkan tergantung pada jumlah orientasi piksel yang digunakan untuk memperkirakan titik hilang dan definisi wilayah yang diminati. Oleh karena itu, metode yang diusulkan untuk meningkatkan keamanan dan keandalan sistem lampu lalu lintas adaptif dapat diandalkan. 
Assessment decisions of independent learning activities using SMART–FCM method
No full textSekolah Tinggi Ilmu Komputer (STIKOM) PGRI Banyuwangi implemented the Independent Learning - Independent Campus (MBKM) activity for two semesters. The results of student assessments for MBKM activities for one semester are influenced by the results of daily and weekly logbook monitoring, monitoring and evaluation assessments, and assessments of supervisors, examiners, and work partners. Assessments that are less objective cause many students to get good grades even though the implementation of MBKM activities is not well. The Simple Multi-Attribute Rating Technique (SMART) method is used to produce student eligibility group data and a more objective assessment. The results of the SMART calculations are combined with the Fuzzy C-Means (FCM) algorithm so that the results of grouping student data are more appropriate based on the similarities and characteristics of the members. Silhouette Coefficient is used to compare the grouping results. The results obtained that the use of SMART-FCM is better than the SMART results because it has a Silhouette Coefficient value close to 1 of 0.31187
Can PhET simulate basic electronics circuits for undergraduate students?
Full text linkPhET is one of the most powerful and impressive simulator innovations, widely used in the STEM-based learning process. Based on literature reviews, students are allowed to independently practice their skills and understanding of the material concept using this tool. PheT involves students in process competencies comprehensively and also provides a highly interactive virtual environment for STEM materials, including basic electronics, a sub-category of physics. This tool can also be easily accessed online at https://phet.colorado.edu/ or offline with a note that the user should download and install the application on a PC. An interesting question regarding this education tool is, "can PhET support basic electronics learning in Higher Education (HE)?" Numerous preliminary studies have not answered this question, which is associated with the technical aspect of the tool, because they only focused on the pedagogical aspect. Therefore, this research aims to fill this gap by exploring the capability of PhET in simulating basic electronic circuits that were commonly studied by students in HE, including Kirchoff Current Law (Kirchoof I), Kirchoff Voltage Law (Kirchoff II), Voltage Divider, Series/Parallel Resistors, Wheatstone Bridge, and Star – Delta Resistors. These circuits are simulated in two PhET products, namely, online (1.2.7) and offline (3.20) versions, with numerous setups used to compare their performances to the theoretical calculations. Finally, the answers were obtained clearly from the experimental results in the simulation environment
Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN
Full text linkIn Indonesia, Tea is an important economic crop that is widely grown, and in many countries, accurate mapping of tea plantations is essential for the operation, management, and monitoring of the growth and development of the tea industry. We propose a classification of tea plantations using orthomosaics from aerial images based on the Convolutional Neural Network (CNN) which identifies the condition of the tea plantations with the parameters observed, namely the condition of the tea leaves, estimated yields achieved, and monitoring of treeless areas caused by tree death. In this study, we took a sample of 20 hectares. We classify images based on maps generated by drones in previous studies. Image segmentation is performed to maintain image objects, while an enhanced CNN model is used to extract deep image features. To get complete results, this study uses UAV (Unmanned Aerial Vehicle) imagery as the basis for the map, which is then combined or stacked into one image. The results of the images that are used as maps undergo image classification, where the information contained in the map is mapped and divided according to its type. The area of the tea plantations sampled is 20 ha, and the threshold for the image captured by the UAV is 5% of the total area captured, which is around 1 ha. If the image created by the UAV has an error of more than 5%, then the image does not meet the classification requirements. We determine this margin of error based on the performance of the drone camera capture when capturing Fig. 2, and the resolution used is 4096 x 2160 for each image captured by the drone. We conclude that the proposed method for mapping tea plantations using ultra-high resolution remote sensing imagery is effective and has great potential for mapping tea plantations in areas such as the development of drone aerial photography methods for tea plantations based on image classification for forecasting. tea plantations Image stitching can be used to improve the monitoring of tea plantations and predict harvest time using a classification process. The tea garden map has 5 types of information categorized by harvest time, medium leaf tea, milled tea, tea, and old tea. The success of image recognition shows the error matrix data by testing 123 random points spread over the map, of which 113 random points were identified with an average accuracy of 91.87%, this value is of course very good and exceeds the specified threshold of 75%. When using this method, an error occurs that the colors of similar pixels cannot be distinguished, resulting in an incorrect detection. In addition, the image stitching method using the orthomosaics method has succeeded in performing image stitching and can be well applied to classification using the CNN approach
Analysis of Lampung Provincial Social Service website using PIECES framework
No full textThe official website of the Social Office Lampung Provincial contains news content, routine activities, and information on public services. The website has been changed from an internal point of view but does not yet know how satisfaction is based on user perceptions. As a result, users no longer want to visit the Social Office website, which is one of the problems, so it is necessary to build a measurement model for the classification of website user problems using the Framework Pieces. Previously, the reliability and validity test of the questionnaire was taken from 338 samples of respondents, then entered the data analysis stage by assessing user characteristics based on the Likert scale and class intervals. Framework Pieces succeed to classify the problems of 338 samples in the range of good and poor. For the Performance domain with a score of 3,62 (Good), the Informations or Data domain with a score of 2,57 (Poor), Economics domain with a score of 3,35 (Moderate), Control or Security domain with a score of 3,61 (Good), Efficiency domain with a score of 3,36 (Moderate), finally for Service domain with a score of 2,60 (Poor).Situs resmi Dinas Sosial Provinsi Lampung memuat konten berita, kegiatan rutin, dan informasi pelayanan publik. Website telah diubah dari sudut pandang internal tetapi belum mengetahui bagaimana kepuasan berdasarkan persepsi pengguna. Akibatnya pengguna tidak mau lagi mengunjungi website Dinas Sosial yang menjadi salah satu permasalahannya, sehingga perlu dibangun model pengukuran klasifikasi permasalahan pengguna website dengan menggunakan Framework Pieces. Sebelumnya dilakukan uji reliabilitas dan validitas kuesioner yang diambil dari 338 sampel responden, kemudian masuk ke tahap analisis data dengan menilai karakteristik pengguna berdasarkan skala likert dan interval kelas. Framework Pieces berhasil mengklasifikasikan permasalahan dari 338 sampel dalam rentang baik dan buruk. Untuk domain Performance dengan skor 3,62 (Baik), domain Informasi atau Data dengan skor 2,57 (Buruk), domain Ekonomi dengan skor 3,35 (Sedang), domain Kontrol atau Keamanan dengan skor skor 3,61 (Baik), domain Efisiensi dengan skor 3,36 (Sedang), terakhir untuk domain Layanan dengan skor 2,60 (Buruk)
The combination of color-texture features and machine learning for detecting Dayak beads
Full text linkDayak is one of the tribes in East Kalimantan, Indonesia, which has a lot of cultural wealth. Beads craft is one of the Dayak traditional cultures made using various materials with distinctive motifs. The Dayak beads have many different motifs and color combinations. Hence not everyone can distinguish between the bead motif of Dayak and non-Dayak easily. This study aims to develop a bead detection method to differentiate between the bead types of Dayak and non-Dayak. The main processes required include preprocessing, feature extraction, and classification. The features were extracted based on color and texture. Experiments were carried out using several machine learning approaches. The highest results were achieved using the combination of color and texture features with the implementation of K-Nearest Neighbor (KNN) methods as indicated by the parameters precision, recall, and accuracy achieved of 92%, 92%, and 92.2% using Cross-Validation with a K-Fold value of 10
Prediksi Kejadian Banjir Di Kota Bandar Lampung Menggunakan Jaringan Syaraf Tiruan
No full textThe city of Bandar Lampung is currently experiencing seasonal flooding which occurs almost every year, resulting in significant losses. Floods recorded by BNPB in the last 10 years there were 16 incidents of flooding in the Bandar Lampung area. More than 14,000 people suffered, more than 500 people had to be evacuated, more than 900 houses were damaged, and 4 public facilities were damaged. To study the pattern of flood events in the past, the Artificial Neural Network Backpropagation learning method will be used which will utilize its non-linear variable learning abilities. The configuration settings for the Artificial Neural Network were carried out experimentally without any basis for assigning values, especially for the parameters of the number of hidden layers, number of neurons, and epochs used in training and variable testing. The results obtained from this study are the results of training and testing of datasets that have been carried out by ANN backpropagation are able to properly study patterns of flood events and also non-flood events in the dataset, this is evidenced by the results of high model configuration accuracy and also the results of predictive tables that able to describe actual conditions, setting the configuration model experimentally is able to produce an accuracy value of 90-100%, an average training correlation value of 0.96 and an average test correlation value of 0.89, and an average error value of 0.0089 out of 20 model configuration, and the flood prediction table are made based on the 1 best configuration with a training and testing accuracy rate of 100% with an error value of 0.00134, namely configuration model 20, the prediction table uses an average air temperature of 27˚C with 80% humidity. The prediction table is able to produce excellent flood potential results which are able to represent flood events as well as non-flood events based on the results of the dataset learning.Kota Bandar Lampung saat ini mengalami banjir musiman yang terjadi hampir setiap tahun sehingga menimbulkan kerugian yang cukup besar. Banjir yang tercatat BNPB dalam 10 tahun terakhir ada 16 kejadian banjir di kawasan Bandar Lampung. Lebih dari 14.000 orang menderita, lebih dari 500 orang harus mengungsi, lebih dari 900 rumah rusak, dan 4 fasilitas umum rusak. Untuk mempelajari pola kejadian banjir di masa lalu akan digunakan metode pembelajaran Artificial Neural Network Backpropagation yang akan memanfaatkan kemampuan belajar variabel nonliniernya. Pengaturan konfigurasi Jaringan Syaraf Tiruan dilakukan secara eksperimental tanpa dasar pemberian nilai, terutama untuk parameter jumlah hidden layer, jumlah neuron, dan epoch yang digunakan dalam pelatihan dan pengujian variabel. Hasil yang diperoleh dari penelitian ini adalah hasil pelatihan dan pengujian dataset yang telah dilakukan backpropagation JST mampu mempelajari dengan baik pola kejadian banjir dan juga kejadian non-banjir pada dataset, hal ini dibuktikan dengan hasil yang tinggi. akurasi konfigurasi model dan juga hasil tabel prediksi yang mampu menggambarkan kondisi sebenarnya, setting model konfigurasi secara eksperimental mampu menghasilkan nilai akurasi 90-100%, nilai korelasi pelatihan rata-rata 0,96 dan nilai korelasi uji rata-rata 0,89 , dan nilai error rata-rata 0,0089 dari 20 model konfigurasi, dan dibuat tabel prediksi banjir berdasarkan 1 konfigurasi terbaik dengan tingkat akurasi training dan testing 100% dengan nilai error 0,00134 yaitu model konfigurasi 20, tabel prediksi menggunakan suhu udara rata-rata 27˚C dengan kelembaban 80%. Tabel prediksi tersebut mampu menghasilkan hasil potensi banjir yang sangat baik yang mampu merepresentasikan kejadian banjir maupun kejadian non banjir berdasarkan hasil pembelajaran dataset
Sistem Ujian Tilawatil Qur`an
No full textA group Decision Support System (GDSS) is used when a decision system has many stakeholders providing recommendations in a system. One of them is the Tilawatil of the Qur'an for students in the religious field test. The assessment consists of several raters. The purpose of this study is to apply the SMART (Simple Multi-Attribute Rating Technique) and Borda methods in calculating the results of the Tilawatil Qur'an test based on a decision support system. The SMART method is used in assessing the results of the Tilawatil Qur'an test and the Borda method is used in optimizing the overall results of the assessors on the SMART method. The results of the study were based on the SMART value accuracy test manually with the system yielding 95%. After that, the results of the optimization test of the Borda method were calculated by calculating the average value of the SMART method with optimal ranking results.Sistem Pendukung Keputusan kelompok (SPKK) digunakan ketika sistem keputusan memiliki banyak pemangku kepentingan yang memberikan rekomendasi dalam suatu sistem. Salah satunya adalah Tilawatil Al-Qur'an untuk siswa dalam ujian bidang agama. Penilaian terdiri dari beberapa penilai. Tujuan dari penelitian ini adalah untuk menerapkan metode SMART (Simple Multi-Attribute Rating Technique) dan Borda dalam menghitung hasil tes Tilawatil Qur'an berbasis sistem pendukung keputusan. Metode SMART digunakan dalam menilai hasil tes Tilawatil Qur'an dan metode Borda digunakan dalam mengoptimalkan hasil keseluruhan asesor pada metode SMART. Hasil penelitian didasarkan pada uji akurasi nilai SMART secara manual dengan sistem menghasilkan 95%. Setelah itu, hasil uji optimasi metode Borda dihitung dengan menghitung nilai rata-rata metode SMART dengan hasil rangking yang optimal
The effect of power spectral density on the electroencephalography of autistic children based on the welch periodogram method
Full text linkAutism spectrum disorder (ASD) is a serious mental disorder affecting social behavior. Some children also face intellectual delay. In people with ASD, the signals detected have abnormalities compared to normal people. This can be a reference in diagnosing the disorder with electroencephalography (EEG). This study will analyze the effect of Power spectral density (PSD) on the EEG of autistic children and also compare it with the PSD value on the EEG of normal children using the Welch Periodogram method approach. In the preprocessing stage, the Independent Component Analysis (ICA) method will be applied to remove artifacts, and a Finite Impulse Response (FIR) filter to reduce noise in the EEG signal. The study results indicate differences in the PSD values obtained in the autistic and normal EEG signals. The PSD value obtained in the autistic EEG signal is higher than the normal EEG signal in all frequency sub-bands. From the study results, the highest PSD value obtained by the autistic EEG signal is in the delta sub-band, which is 54.06 dB/Hz, while the normal EEG signal is only 33.14 dB/Hz at the same frequency sub-band. And in the Alpha and Beta sub-bands, the normal EEG signal increases the PSD value, while in the autistic EEG signal, the PSD value decreases in the Alpha and Beta sub-bands. In addition, FIR and ICA methods can also reduce noise and artifacts contained in autistic and normal EEG signals