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All Journal Media Teknik Sipil TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Telematika Building of Informatics, Technology and Science ACADEMICS IN ACTION Journal of Community Empowerment JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems Jurnal Sains Indonesia IT For Society : Journal of Information Technology
Tjong Wan Sen
Universitas Presiden
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences Other

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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%.
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. 
Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM Siti Aisyah Naili Mutia; 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.v4i2.912

Abstract

Raw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the current raw material inventory. As a result, there are raw materials that are over order or lacking, which causes production to be constrained. The manual calculation method is not effective enough to meet the raw material inventory. In this research, the researcher proposes an algorithm which is contained in Data Mining, that is Enhanced KNN using GWO to predict raw material needs. Because GWO and Enhanced KNN algorithms give the results are easy to understand, have good accuracy compared to other machine learning methods, can cover the trapped problem from KNN traditional and capable of improving the accuracy using feature selection method. The method used in this study is to compare Enhanced KNN with and without GWO that gives a significant increase in the accuracy value by 16.5%, from 44.6% to 61.1%.
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution Muhamad Ghofur; 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.v4i2.914

Abstract

This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms.
Co-Authors Abdul Ghofir Andres, M Arry Akhmad Arman Bambang Riyanto Trilaksono ChungKiat, Ong Darien Daud Earlliawand Diky Djafar Sidik Eko Wahyudi Fahmi, Hasanul Faisal, Sutan Fauzi Ahmad Muda Forenziana, Jessica Hariyanto, Dian Tri Hartono Wijaya, Sony Hermanto Hermanto Lianzhai, Duan Linli, Peng Muhamad Ghofur Muhamad Muhaimin Rachma Oktari Riady, Sasmitoh Rahmad Rila Mandala Riyanto Adji Rosalina Rosalina Rusdianto Roestam Sahuri, Genta Sahuri Sinung Suakanto Siregar, Amril Mutoi Siregar, Amril Mutoi Siti Aisyah Naili Mutia Sutisna, Nanang Ali Tukino, Tukino Yahya Suryana Yana Cahyana Yulianti, Farah