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Pemanfaatan Sistem Distribusi dalam Berbagi Paket Pulsa untuk Short Message Service (SMS) Sasmitoh Rahmad Riady; Tjong Wan Sen
Jurnal Sains Indonesia Vol 1 No 2 (2020): Volume 1, Nomor 2, 2020 (Juli)
Publisher : PUSAT SAINS INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59897/jsi.v1i2.9

Abstract

Penelitian ini membahas mengenai Short Message Service (SMS) yang telah menjadi fitur default yang terdapat pada ponsel pintar atau ponsel tradisional dan salah satu fitur yang mulai dieksploitasi sepenuhnya oleh pengguna ponsel dalam beberapa tahun terakhir. Dalam konsep Short Message Service kali ini berbeda seperti biasanya dimana proses pengiriman pesan dari aplikasi seluler Client ke aplikasi seluler Transceiver dan kemudian diteruskanke ponsel target, dalam mengirim pesan ini dilakukan secara berurutan. untuk memanfaatkan paket SMS di dalam aplikasi Transceiver yang akan digunakan oleh aplikasi Client dalam mengirim sebuah pesan. Konsep SMS menggunakan konsep Device-to-Device untuk komunikasi antara ponsel ke ponsel, kemudian untuk komunikasi antara ponsel menggunakan TCP / IP Socket sebagai jalur komunikasi dalam mengirim SMS lalu memanfaatkan paket pulsa yang terdapat dalam aplikasi Transceiver dalam meneruskan pesan SMS dari ponsel Client tersebut ke ponsel Target. Ini adalah sebuah teknik Sistem Terdistribusi untuk berbagi sumber daya paket pulsa SMS dalam pengiriman pesan.
Pelatihan Pembuatan Media Pembelajaran untuk Guru-Guru SMA di Daerah Cikarang Rosalina Rosalina; Genta Sahuri; Tjong Wansen; Abdul Ghofir
ACADEMICS IN ACTION Journal of Community Empowerment Vol 2, No 1 (2020)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/aia.v2i1.995

Abstract

Dalam era teknologi informasi saat ini, guru merupakan tenaga pendidik profesional yang perannya sebagai pemateri sudah bisa digantikan oleh teknologi itu sendiri. Menyikapi kondisi tersebut, para guru dituntut untuk bisa berfungsi sebagai fasilitator yang mampu mengembangkan kreativitas dan dinamika dalam menggali potensi sumber dan media pembelajaran sehingga diharapkan perannya sebagai penyampai tetap diperlukan yang pada akhirnya bisa tetap menjaga kualitas belajar mengajarnya. Untuk itu, guru dituntut untuk bisa menjadi fasilitator yang membekali dirinya dengan wawasan dan keterampilan dalam pengembangan dan pembuatan media pembelajaran yang mutakhir dan menyesuaikan situasi dan perkembangan zaman. Dari diskusi dan interaksi yang dilakukan terlihat bahwa para guru di SMA Cikarang pada umumnya masih mengalami kesulitan dalam beradaptasi dengan metode pembelajaran yang melibatkan media teknologi informasi. Salah satu penyebabnya adalah karena masih kurangnya sarana dan prasarana yang dapat menunjang keaktifan dan semangat murid dalam belajar, serta para guru yang masih belum bisa dan terbiasa dalam membuat media pembelajaran yang sesuai dengan lingkungan dan zaman siswa. Oleh karena itu tim pelaksana memberi Pelatihan Pembuatan Media Pembelajaran kepada Guru-guru SMA di Kabupaten Bekasi dengan harapan dapat meningkatkan efektivitas dan kualitas proses pembelajaran di SMA pada akhirnya akan menunjang tercapainya tujuan pendidikan di sekolah sasaran.
Aplikasi Mobile untuk Usaha Jasa Pengantaran Barang di Dusun Cibeber, Kawasan Industri Jababeka, Bekasi Tjong Wan Sen
ACADEMICS IN ACTION Journal of Community Empowerment Vol 1, No 1 (2019)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.223 KB) | DOI: 10.33021/aia.v1i1.741

Abstract

This Community Services Activity is meant to provide alternative income sources for citizens in Dusun Cibeber which is located in the center of Jababeka Industrial Estate, Cikarang, Bekasi. In this activity advancement of computer and information technology, global positioning system, and telecommunication networks are used to produce a mobile application for delivery services that is easy to use, low cost, and could be operated by common person. The application together with its supporting components has already successfully developed, implemented, and simulated. Next phase would be to measure its performance widely in the real environment.
Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients Tjong Wan Sen; Bambang Riyanto Trilaksono; Arry Akhmad Arman; Rila Mandala
Journal of ICT Research and Applications Vol. 3 No. 2 (2009)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2009.3.2.4

Abstract

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.
Real-Time Detection of Face Masked & Face Shield Using YOLO Algorithm with Pre-Trained Model and Darknet Muhamad Muhaimin; Wan Sen Tjong
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.14235

Abstract

There are new regulations requiring the use of masks or face shields to prevent the transmission of Covid-19. Using deep learning, a model can be made to detect faces that use masks and face shields by training the model using the previous pre-trained model and using a custom dataset. The purpose of this study is to create a deep learning model that can detect faces with and without masks and as well as face shields for the prevention of covid-19 transmission using YOLO (You Only Look Once) with pre-trained models and custom datasets in real-time. In this study, using pre-trained models from YOLOv3, YOLOv3-Tiny, YOLOv4, YOLOv4-Tiny, and YOLOv4-Tiny-3l with Darknet Framework and compare between average pooling and max pooling in the convolutional neural network YOLO to detect face masks and face shields as a real-time. From experiment the highest mAP was obtained from YOLOv4 using average pooling with a value is 97.64% although the difference is not too much with YOLOv4 using max pooling with value 97.57% and the lowest was YOLOv3-Tiny using max pooling, which was 94.09%, and for the highest FPS was obtained by YOLOv4-Tiny with Fps values is 171 and mAP 96.75%. And for real-time detection of face masks and face shields, the best model used in testing using webcam 1080p is from YOLOv4-Tiny, because the FPS is quite good and the mAP is quite high.
Optimization of the Naïve Bayes Classifier (NBC) Algorithm Using the Sparrow Search (SSA) Algorithm to Predict the Distribution of Goods Receipts Rachma Oktari; Tjong Wan Sen
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.15339

Abstract

Distribution must be able to meet all needs based on sales orders from consumers, be responsible for the delivery order process running optimally, and ensure the good receipt process is in accordance with consumer sales order requests. PT. Diamond Cold Storage currently uses Enterprise Resource Planning (ERP) to record all reports from production to sales. But in reality there are still some obstacles in the distribution section. In the good receipt process, several items were found that did not match the sales order, such as: the item did not match the order request or the item did not match the order request. The process of mismatching the good receipt with the sales order will be met with the completion of the good receipt process or the bad thing is that there is a cancellation, so this causes a loss for the company. This study uses data mining techniques with the Naïve Bayes Classifier algorithm to predict the distribution of goods receipts based on distribution data, and uses the Sparrow Search Algorithm (SSA) algorithm to optimize the Nave Bayes Classifier by selecting features to improve accuracy. In this study, the results obtained that the SSA algorithm can improve the performance of NBC from 95.05% to 97.95%.
ROBUST AUTOMATIC PHONEME RECOGNITION FEATURES USING COMPLEX WAVELET PACKET TRANSFORM COEFFICIENTS Tjong Wan Sen
Jurnal Telematika Vol 6, No 1 (2010)
Publisher : Institut Teknologi Harapan Bangsa

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

Abstract

Untuk meningkatkan kinerja sistem pengenalan fonem otomatis pada saat dioperasikan pada lingkungan berderau, kami mengembangkan teknik baru yang dapat melakukan estimasi terhadap suatu fitur fonem bersih dari bentuk berderaunya. Fitur-fitur kokoh tersebut diperoleh dari koefisien transformasi paket wavelet kompleks (ComplexWavelet Packet Transform/CWPT). Karena koefisien CWPT merepresentasikan semua pita frekuensi yang berbeda dari suatu sinyal masukan, mendekomposisi sinyal masukan tersebut ke dalam pohon CWPT yang lengkap akan mencakup semua frekuensi yang terlibat dalam proses pengenalan. Setiap komponen frekuensi dalam sinyal masukan akan ditempatkan pada tepat satu pita frekuensi yang spesifik. Untuk suatu campuran sinyal domain waktu dengan frekuensi yang berbedabeda, misalnya sinyal fonem dengan derau, semua koefisien fonem dalam pita frekuensi yang sama, yaitu semua koefisienyang melewati jalur filter bank wavelet yang sama, akan berubah sesuai dengan magnituda komponen frekuensi derau. Oleh karena itu, jika ada sebuah pita frekuensi yang tidak mengandung derau sama sekali, seluruh koefisien fonem pada pita frekuensi tersebut tidak akan mengalami perubahan. Informasi dari semua koefisien yang dikandung oleh pita frekuensi tersebut kemudian dapat dimanfaatkan untuk melakukan estimasi terhadap kemungkinan fonem bersihnya. Karena jumlah fonem dalam suatu bahasa adalah terbatas dan relatif kecil dan sudah diketahui dengan baik sebelumnya, teknik yang dikembangkan ini fisibel secara komputasi. Hasil-hasil simulasi menunjukkan bahwa teknik baru yang dikembangkan ini merupakan pengekstrak fitur yang efisien dan tidak hanya dapat meningkatkan kekokohan sistem pengenal fonem otomatis jika dioperasikan pada berbagai macam lingkungan yang berderau tetapi juga tetap memelihara kinerja baiknya pada lingkungan yang bersih.To improve the performance of Automatic Phoneme Recognition in noisy environment, we developed a new technique that could estimate clean phoneme feature from its noisy one. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would covered all frequencies that involved in recognitionprocess. Each frequency would be placed into exactly one of its frequency bands. For time overlapping signals with different frequency contents, e. g. phoneme with noises, all coefficientsbelongs to the same frequency band, which is coming through the same wavelet filter banks path, would be changed according to noise frequencies magnitude. Thus, if there is one frequency band which contain no noises at all, all coefficients belongs to that frequency band would not change. Information from all coefficients belongs to that frequency band could be used then to estimate the clean phonemes. Since the numbers of phonemes are limited and already well known, this technique is computationally feasible. Simulation results showed that this new technique is an efficient features extractor that improves the robustness of the systems in various adverse noisy conditions but still reserve the good performance in clean environments.
License Plate Localization for Low Computation Resources Systems Using Raw Image Input and Artificial Neural Network Tjong Wan Sen; Sinung Suakanto; Amril Mutoi Siregar
Jurnal Telematika Vol 15, No 1 (2020)
Publisher : Institut Teknologi Harapan Bangsa

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

Abstract

License Plate localization using Computer Vision needs a lot of computation resources. Thus, it is hard to deploy it on small systems. This paper presents an efficient license plate localization method using raw image input and artificial neural network. This is achieved by eliminating feature extraction stage and try to use as minimum as possible neural network architecture. Raw image input in dataset is cropped and labelled manually from random car images and video frames. The minimum architecture of the model has only three layers and 32,770 neurons. This is feasible to be deployed in today most single chip systems. The results, from various experiments, yield more than 90% of localization accuracy. Nomor plat kendaraan bermotor yang diperoleh dengan menggunakan Computer Vision membutuhkan banyak daya komputasi. Hal ini menyebabkan implementasinya ke dalam sistem minimum yang sederhana menjadi tidak mudah. Dalam penelitian ini, dikembangkan sebuah metoda untuk mendapatkan plat nomor kendaraan bermotor yang effisien menggunakan masukan langsung tanpa ektraksi ciri dan jaringan saraf tiruan. Penghematan daya komputasi dicapai dengan cara menghilangkan tahap ekstraksi ciri dan penggunaan arsitektur jaringan saraf tiruan yang seminimum mungkin. Citra masukan diperoleh dengan cara memotong dan memberi label gambar mobil dan frame video yang diperoleh secara acak. Arsitektur minimum yang dihasilkan berupa model yang hanya terdiri dari tiga lapisan dan 32,770 neuron. Model ini cukup fisibel untuk diterapkan pada kebanyakan system on a chip yang ada pada saat ini. Tingkat akurasi model dalam menemukan lokasi nomor kendaraan dari berbagai eksperimen berhasil mencapai lebih dari 90%. 
The Prediction of Gold Price Movement by Comparing Naive Bayes, Support Vector Machine, and K-NN Yahya Suryana; Tjong Wan Sen
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.922

Abstract

Gold is a yellow precious metal that can be forged so it is easy to form with various forms of jewelry such as pendants, earrings, rings, bracelets and others, gold has a high value. Gold itself is an exchange rate used in ancient times before the existence of money as it is today. Gold also can be used as an investment that is profitable for the investor and it has less risks. Investment is a form of fund management to give benefit by putting fund in allocation that is predicted will give additional benetifs. Prediction of gold price movements or predictions of gold price in gold stock investment, this research uses 3 (three) algorithms that will be implemented in analysis and increase accuracy, in the discussion or research that was made using the Naïve Bayes algorithm, Support Vector Machine and K-Nearest Neighbor, the dataset is obtained from the website, namely www.finance.yahoo.com the data was then tested using Rapid miner tools so that the average value of the Support Vector Machine algorithm with an accuracy rate of 57.59%, precision 58 ,73% and recall 51,78%. The next is the Naïve Bayes algorithm so that it is known to have an accuracy rate of 55.59%, precision 54.55% and recall 51.70%. Based on the comparison of the three algorithms, it is known that the one with the best accuracy, precision, and recall is the K-NN algorithm with 61.90% accuracy, 60.98% precision, and 60.35% recall. Furthermore, the results of testing the K-Nearst Neighbor algorithm have good results compared to the 3 (three) other algorithm tests and the Naïve Bayes algorithm testing has a low level of accuracy, namely 55.59%, precision 54.55% and recall 51.70%. The research uses 3 algorithms, namely naive bayes, K-nearst neighbor and Support Vector Machine, because the three algorithms are well-established algorithms to be applied to research, especially in time series gold price research and are very good, especially for classification
Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique Sasmitoh Rahmad Riady; Tjong Wan Sen
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.904

Abstract

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.