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All Journal Media Teknik Sipil TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of ICT Research and Applications Faktor Exacta Indonesian Journal of Artificial Intelligence and Data Mining JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS 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 SENMEA
Tjong Wan Sen
Universitas Presiden
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM Mutia, Siti Aisyah Naili; Sen, Tjong Wan
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 Ghofur, Muhamad; Wan Sen, Tjong
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.
Business Intelligence Using N-Beats And Rnn Methods End Influence On Decision Making In The Flexible Packaging Manufacturing Wahyudi, Eko; Sen, Tjong Wan
JISA(Jurnal Informatika dan Sains) Vol 6, No 1 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i1.1626

Abstract

Today's complex decision-making solutions for intelligent manufacturing depend on the ability to be able to model a manufacturing system realistically, valid and consistent data integrated easily and in a timely manner, able to solve problems efficiently with computational effort to obtain optimal production and product quality optimizations continuously. When an organization uses a data-driven approach, it means that it makes strategic decisions based on data collection, analysis, and interpretations or insights. The purpose of this research is to analyze the business intelligence approach in optimizing print machines by speed, material and time. in this research, using the N-Beats is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers and Recurrent Neural Networks (RNN). The novelty of this research is increasing machine speed using new insights by combining two deep learning methods. Observing and retrieving raw data from the printing machine process with sensors data for use and ensuring the justification of the addition of new methods. The result is expected to be able to provide new insights that can increase engine speed, the data based decision making provides businesses with the capabilities to generate real time insights and predictions to optimize their performance and provide confidence in decision making that are fast, precise and better.
The Prediction of Gold Price Movement by Comparing Naive Bayes, Support Vector Machine, and K-NN Suryana, Yahya; Sen, Tjong Wan
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 Riady, Sasmitoh Rahmad; Sen, Tjong Wan
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. 
Dynamic pooling using average-thresholding to improve image classification performance Pajri Aprilio; Tjong Wan Sen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27619

Abstract

Pooling layers are essential in convolutional neural networks (CNNs) for reducing data size while preserving key features. Traditional methods such as Max and Average pooling have limitations. Max pooling is sensitive to noise, while Average pooling treats all activations equally. Although T-Max-Avg pooling addresses these limitations through adaptive top-k selection, its rigid decision rule requires multiple threshold comparisons and limits efficiency, motivating a simpler decision mechanism. This study introduces average-thresholding pooling (ATP), a simplified adaptive method that replaces multiple threshold comparisons with a single decision based on the average of the top-k activations. This design improves computational efficiency and reduces sensitivity to outliers. Experiments on the STL-10 dataset using a LeNet-5 architecture show that the proposed method achieves accuracy comparable to T-Max-Avg pooling (~55.5%) while consistently improving both training efficiency and inference speed. These results indicate that ATP provides a lightweight and practical alternative for CNN-based image classification, offering an improved balance between classification performance and computational efficiency.
QnA-based Learning Platform Using Instant Messaging and Deep Learning Hadi Suprayitno; Wan Sen Tjong
Faktor Exacta Vol 18, No 1 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i1.28040

Abstract

The explosion of instant messaging apps has created a new paradigm for learning and knowledge sharing. In this paper, we introduce an innovative Q&A-based learning platform that leverages the power of instant messaging and deep learning to provide inexpensive personalized learning experiences for students. This helps those who may not have access to teachers or need more support outside of regular class hours. Our platform utilizes deep learning technology and/or natural language processing to analyze and respond to students' queries in almost real-time and nonstop 24/7. By integrating instant messaging with deep learning, this platform enables students to engage in interactive and conversational learning experiences. Personal or private messages enable learning systems that are tailored to individual needs and learning styles. We used the Rapid Application Development method with an object-oriented approach to create this platform. We demonstrate the proof of concept of this platform through a series of experiments and evaluations. Based on the results of the trial, the designed platform can attract the attention of many students when learning. The contributions of this paper are threefold. First, we propose a novel Q&A-based learning platform that integrates instant messaging and deep learning to provide personalized learning experiences. Second, we demonstrate the effectiveness of our platform through a series of experiments and evaluations. Finally, we provide a framework for future research and development in the area of intelligent Q&A platforms for personalized learning.
Conversational Information System Dashboard for Zero New Stunting Campaign in Kecamatan Cikarang Timur Tjong Wan Sen; Yosi Yonata
Jurnal Telematika Vol. 20 No. 2 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i2.734

Abstract

The critical issue of stunting eradication necessitates innovative solutions for effective monitoring, intervention, and prevention. In this paper, we introduce an innovative integrated information system dashboard that leverages the power of data analytics and real-time data processing to provide comprehensive insights into stunting eradication efforts. This assists healthcare providers and policymakers in accessing actionable information crucial for making informed decisions. Our dashboard utilizes digitalization technology to analyze and visualize stunting data in almost real-time, operating nonstop 24/7. This platform enables users to engage in interactive and data-driven decision-making processes. The personalized and user-friendly interface allows for a tailored experience that meets the specific needs and preferences of different stakeholders. We used the Rapid Application Development method with an object-oriented approach to develop this dashboard. We demonstrate the proof of concept of this dashboard through a series of experiments and evaluations. Based on the results of the trial, the designed platform can be very useful for Zero New Stunting campaigns in Kecamatan Cikarang Timur.
Co-Authors Abdul Ghofir Andres, M Arry Akhmad Arman Bambang Riyanto Trilaksono ChungKiat, Ong Darien Daud Earlliawand Diky Djafar Sidik EKO WAHYUDI Fahmi, Hasanul Forenziana, Jessica Ghofur, Muhamad Hadi Suprayitno Hariyanto, Dian Tri Hermanto Hermanto Lianzhai, Duan Linli, Peng Muhamad Muhaimin Mutia, Siti Aisyah Naili Pajri Aprilio Rachma Oktari Riady, Sasmitoh Rahmad Rila Mandala Riyanto Adji Rosalina Rosalina Rusdianto Roestam Sahuri, Genta Sahuri Sinung Suakanto Siregar, Amril Mutoi Suryana, Yahya Sutisna, Nanang Ali Yosi Yonata Yulianti, Farah