Friska Abadi
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OBJECT COUNTING PADA DATA VIDEO Rudy Herteno; M. Reza Faisal; Radityo A Nugroho; Friska Abadi; Rahmat Ramadhani
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 1 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i1.307

Abstract

One object counting implementation is counting the number of road users from video data sources obtained from CCTV streaming. Video processing on CCTV is usually done on the server side by sending video data. If the need is only to determine the density of traffic, then the method is considered too expensive to be implemented because of the cost of internet connection and bandwidth that must be spent. The solution is to use a small computing device that can process the video first, and the calculation results are sent to the server regularly. In this study, a comparison between the Tensorflow Object Counting learning algorithm and the MOG2 Background Subtractor image processing algorithm with the aim to determine the accuracy of the calculation. The result is known that better accuracy is given by the MOG2 Background Subtractor technique and also the process is carried out using only a small percentage of the amount of memory and processor compared to the Tensorflow Object Counting technique. MOG2 Background Substractor technique is expected to be used on devices that have small data sourcesKeywords : Object Counting, Tensorflow, MOG2 Background SubstractorSalah satu implementasi object counting adalah menghitung jumlah pengguna jalan dari sumber data video yang didapat dari streaming CCTV. Pemprosesan video pada CCTV biasanya dilakukan disisi server dengan mengirimkan data video. Jika keperluannya hanya untuk mengetahui kepadatan lalu lintas, maka cara tersebut dinilai terlalu mahal untuk diimplementasikan karena biaya koneksi internet dan bandwidth yang harus dikeluarkan. Pemecahannya adalah menggunakan perangkat komputasi kecil yang dapat memproses video tersebut terlebih dahulu, dan hasil perhitungannya dikirimkan ke server secara berkala. Pada penelitian ini dilakukan perbandingan antara algoritma pembelajaran Tensorflow Object Counting dan algoritma image processing MOG2 Background Substractor dengan tujuan untuk mengetahui akurasi penghitungan. Hasilnya diketahui akurasi yang lebih baik diberikan oleh teknik MOG2 Background Substractor dan juga proses yang dilakukan hanya menggunakan prosentase jumlah memori dan prosessor yang kecil dibandingkan teknik Tensorflow Object Counting. Sehingga teknik MOG2 Background Substractor ini diharapkan dapat digunakan pada perangkat yang memiliki sumber data kecil. Kata kunci : Object Counting, Tensorflow, MOG2 Background Substractor.
Metrics Based Feature Selection for Software Defect Prediction Radityo Adi Nugroho; Friska Abadi; M. Reza Faisal; Rudy Herteno; Rahmat Ramadhani
Jurnal Komputasi Vol 8, No 2 (2020)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2670

Abstract

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.
IMPLEMENTASI SSD_RESNET50_V1 UNTUK PENGHITUNG KENDARAAN Muhammad Nur Rizal; Radityo Adi Nugroho; Dodon Turianto nugrahadi; Muhammad Reza Faisal; Friska Abadi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 8, No 2 (2021)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v8i2.383

Abstract

Google has released the Tensorflow Object Detection API to facilitate deep learning application development using the Tensorflow Object Detection API. The TensorFlow Object Detection API is an open-source framework that can be used to develop, train, and deploy object detection models. In this study, the Tensorflow Object Detection API is implemented in a vehicle counter application with the SSD_Resnet50_v1 detection model. From the research that has been done, applications with the detection of the SSD_Resnet50_v1 model get an accuracy of 56.49% in calculating motor-type vehicles and 54.43% for car-type vehicles.Kata Kunci : SSD_Resnet50_v1, Vehicle Counting, Tensorflow Object Detection APIGoogle telah merilis Tensorflow Object Detection API untuk mempermudah pengembangan aplikasi Deep learning dengan menggunakan Tensorflow Object Detection API. TensorFlow Object Detection API adalah open source framework yang dapat digunakan untuk mengembangkan, melatih, dan menggunakan model deteksi objek. Pada penelitian ini Tensorflow Object Detection API diimplementasikan pada aplikasi penghitung kendaraan dengan model deteksi SSD_Resnet50_v1. Dari penelitian yang telah dilakukan, aplikasi dengan model deteksi SSD_Resnet50_v1 mendapatkan akurasi sebesar 56,49% dalam menghitung kendaraan berjenis motor dan 54,43% untuk kendaraan berjenis mobil.Kata Kunci : SSD_Resnet50_v1, penghitung kendaraan, Tensorflow Object Detection API
PEMILIHAN KETUA DAN WAKIL KETUA BADAN EKSEKUTIF MAHASISWA UNIVERSITAS LAMBUNG MANGKURAT (BEM ULM) BERBASIS ELEKTRONIK Muhammad Azmi Adhani; Radityo Adi Nugroho; Rudy Herteno; Muliadi Muliadi; Friska Abadi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 8, No 3 (2021)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v8i3.404

Abstract

 The ULM General Election is held annually in each TPU in each faculty to channel student votes in the election of the Chair and Vice Chair of the ULM BEM. However, the implementation of conventional general elections which require no small amount of money, the process is quite time-consuming and accompanied by the COVID-19 outbreak which can be an obstacle to the smooth running of the general election because it tends to be seen from 2018 to 2019 there is a decrease in the number of participants. This study uses an electronic approach, namely the e-voting system to overcome problems in conventional general elections in order to get improvements in terms of cost savings, shorter time and minimize the spread of the COVID-19 outbreak. To get the value of the profit that can be achieved by investing in the development of the application that the researcher proposes, it is necessary to conduct a feasibility study (Feasibility Analysis) as a tool in getting conclusions about what will be done, and after getting the results of the general election conducted electronically, it will comparisons were made with the implementation of previous years. The implementation cost requires Rp. 10,578,000.00 which when compared to the average data for the previous three years requires Rp. 25,166,666.00, it can be seen that there is an implementation cost savings of Rp. 14,588,666.00, when the implementation cost savings are included. into the economic feasibility study, the ROI and BEP values since the first year the application was implemented showed positive values. Until the third year the ROI and BEP values enter the feasible criteria so that in terms of Economic Feasibility it can be seen that the application is economically feasible. Then, for the implementation time which takes 12 hours, the number of participants is 11830 people, which when compared to the average data for the previous three years was only 7333 people, it can be seen that there was an acceleration as seen from the increase in participants as many as 4497 people. Then, for the fastest encryption algorithm is AES-128-CBC with a total time of encryption and decryption on the amount of data as much as 1000 is 0.0116 seconds.Keywords: Conventional Election, Electronic Election, Feasibility Study, Comparison, Encryption AlgorithmPemilihan Umum ULM setiap tahunnya dilaksanakan dimasing-masing TPU disetiap fakultas untuk menyalurkan suara mahasiswa dalam perihal pemilihan  Ketua dan Wakil Ketua BEM ULM. Namum pelaksanaan pemilihan umum secara konvensional yang membutuhkan biaya yang tidak sedikit, proses yang cukup memakan waktu serta diiringi wabah COVID-19 yang dapat menjadi penghambat kelancaran dari pemilihan umum karena cenderung bisa dilihat dari tahun 2018 ke-tahun 2019 terjadi penurunan pada jumlah partisipan. Pada penelitian ini menggunakan pendekatan elektronik yaitu sistem e-voting untuk mengatasi masalah di pemilihan umum secara konvensional agar bisa mendapatan peningkatan dari segi penghematan biaya, waktu yang lebih singkat serta meminimalisir penyebaran wabah COVID-19. Untuk mendapatkan nilai dari profit yang mampu dicapai dengan berinvestasi kedalam pengembangan aplikasi yang peneliti usulkan, maka perlu dilakukan studi kelayakan (Feasibility Analysis) sebagai alat pembantu dalam mendapatkan kesimpulan terhadap apa yang akan dilakukan, dan setelah mendapatkan hasil pemilihan umum yang dilakukan secara elektronik maka akan dilakukan perbandingan terhadap pelaksanaan tahun-tahun sebelumnya. Pada biaya pelaksanaan membutuhkan Rp.10.578.000,00 yang jika dibandingkan dengan rata-rata data tiga tahun sebelumnya membutuhkan Rp.25.166.666,00 maka bisa dilihat bahwa terjadi penghematan biaya pelaksanaan sebesar Rp14.588.666,00, ketika penghematan biaya pelaksanaan dimasukan kedalam studi kelayakan ekonomi, nilai ROI dan BEP sejak tahun pertama aplikasi diimplementasi menunjukkan nilai positif. Sampai tahun ketiga nilai ROI dan BEP masuk ke kriteria layak sehingga dari segi Economic Feasibility bisa diketahui bahwa aplikasi layak secara ekonomis. Kemudian, untuk waktu pelaksanaan yang membutuhkan waktu 12 jam bisa mendapatkan jumlah partisipan sebanyak 11830 orang yang jika dibandingkan dengan rata-rata data tiga tahun sebelumnya hanya 7333 orang maka bisa dilihat bahwa terjadi percepatan yang dilihat dari peningkatan partisipan sebanyak 4497 orang. Lalu, untuk algoritma enkripsi yang tercepat adalah AES-128-CBC dengan total waktu dari enkripsi dan dekripsi pada jumlah data sebanyak 1000 adalah  0,0116 detik.Kata kunci: Pemilihan konvensional, Pemilihan Elektronik, Studi Kelayakan, Perbandingan, Algoritma Enkripsi