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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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Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan Diky Djafar Sidik; Tjong Wan Sen
IT for Society Vol 4, No 1 (2019)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1025.128 KB) | DOI: 10.33021/itfs.v4i1.1180

Abstract

Curah hujan sebagai bentuk informasi dari data meteorologis, penting dalam segala kegiatan manusia yang berhubungan dengan alam, oleh karena itu prediksi atas curah hujan dengan hasil yang akurat merupakan hal yang sangat penting. Salah satu metode yang digunakan untuk prediksi/klasifikasi curah hujan adalah data mining dengan berbagai algoritma dan parameter data yang berbeda. Pada penelitian ini digunakan penggabungan metode klasifikasi dengan Teknik Ensemble Stacking/Stacked Generalization yang menggunakan Naïve Bayes dan C4.5 sebagai base learner dan KNN sebagai meta learner untuk klasifikasi curah hujan. Dataset yang dipergunakan adalah data klimatologi harian yang diambil dari website resmi BMKG (Badan Meteorologi, Klimatologi, Dan Geofisika) untuk stasiun UPT Bandung, Bogor, Citeko dan Jatiwangi dari periode 01 Januari 2000 sampai dengan 31 Desember 2018. Dengan menggunakan tiga skenario pengujian dan validasi menggunakan 10 fold cross validation diperoleh bahwa metode stacking dapat meningkatkan akurasi dari base classifier.
Frekuensi Dominan Dalam Vokal Bahasa Indonesia Tjong Wan Sen
IT for Society Vol 1, No 2 (2016)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.69 KB) | DOI: 10.33021/itfs.v1i2.302

Abstract

Pengenalan ucapan otomatis sudah dikembangkan selama lebih dari empat puluh tahun dan masih perlu untuk terus dikembangkan agar dapat memiliki kemampuan yang menyerupai manusia. Teknologi ini sangat bermanfaat tetapi hingga saat ini masih belum bisa dipakai secara umum dengan mudah dalam aktivitas manusia sehari-hari. Ketersediaan pilihan bahasa pun masih menjadi kendala, diperlukan waktu pelatihan yang cukup panjang untuk masing-masing bahasa yang diinginkan. Salah satu bahasa yang banyak digunakan di dunia adalah Bahasa Indonesia. Meskipun demikian belum banyak sistem Pengenalan Ucapan Otomatis yang menggunakan Bahasa Indonesia. Beberapa sistem yang dikembangkan oleh perusahaan besar telah memiliki pilihan Bahasa Indonesia, tetapi basis data yang digunakan tidak dapat diakses oleh masyarakat umum. Oleh karena itu perlu dikembangkan basis data suara ucapan dalam Bahasa Indonesia. Dalam artikel ini dilaporkan pengembangan data suara ucapan vokal Bahasa Indonesia. Pengumpulan data dilakukan dengan cara merekam suara vokal dari berbagai sumber. Sumber ucapan dibedakan secara etnisitas, jenis kelamin dan usia. Dari masing-masing kelompok diidentifikasi kelompok frekuensi yang dominan. Himpunan frekuensi dominan antar kelompok tersebut  kemudian dibandingkan satu dengan lainnya untuk mengetahui persamaan dan perbedaannya. 
Implementation of Bayes Network in Giving First Aid Solution Darien Daud Earlliawand; Tjong Wan Sen
IT for Society Vol 1, No 1 (2016)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.86 KB) | DOI: 10.33021/itfs.v1i1.19

Abstract

In a critical situation, such as traffic accident, sometimes a person is required to do a first aid in order to save someone’s life, until the emergency unit arrives. If a person does not know how to do a first aid, he or she will need a guide, such as an application in a device that people always bring, a smartphone. Technology is one of the science fields that grow very fast. It is not only about the hardware but also the software, such as artificial intelligence. Artificial intelligence is one of the fields that are commonly used nowadays, either for gaming purpose, education and research purpose, or other purposes. This application, ES First Aid, is an Android application to help the user to decide what to do in emergency situation. It will ask a few questions related to the condition of the casualty then it will tell the user clearly about the actions needed to be taken.
Business Intelligence Using N-Beats And Rnn Methods End Influence On Decision Making In The Flexible Packaging Manufacturing Eko Wahyudi; Tjong Wan Sen
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.
Pengembangan Mesin 3D Printing Sebagai Alternatif Pembangunan Rumah Yang Lebih Efisien: Pengembangan Mesin 3D Printing Sebagai Alternatif Pembangunan Rumah Yang Lebih Efisien riyanto adji; Nanang Ali Sutisna; Wan Sen Tjong
Media Teknik Sipil Vol. 19 No. 2 (2021): Agustus 2021
Publisher : Department of Civil Engineering, Faculty of Engineering, University of Muhammadiyah Malang

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

Abstract

Technological advances will always experience very fast changes, especially in 3D printing technology which is currently acceptable in various industrial worlds, the presence of 3D printing machines really helps humans in visualizing an idea and the results of their thoughts into a very profitable replica. The world of construction has not been fully touched by 3D printing technology, and only a few countries have made it, one of which is the People's Republic of China, which in 2014 succeeded in building a house using a 3D printing machine, with a house size of 40 m2 and 5 floors high. The advantages of building a house with a 3D printing machine include saving labor, saving time in house construction because the construction of a house is determined by the type of concrete and the machine as the main tool, besides that the construction waste generated can be minimized because there is no need to use scaffolding or other tools. the material that is installed is definitely installed neatly because it is arranged in layers. The use of this 3D printing machine will be very useful in Indonesia because it takes into account the high level of population, and the need for housing will be directly proportional to population growth.
ROBUST AUTOMATIC PHONEME RECOGNITION FEATURES USING COMPLEX WAVELET PACKET TRANSFORM COEFFICIENTS Sen, Tjong Wan
Jurnal Telematika Vol. 6 No. 1 (2010)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v6i1.41

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 Wan Sen, Tjong; Suakanto, Sinung; Siregar, Amril Mutoi
Jurnal Telematika Vol. 15 No. 1 (2020)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v15i1.349

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%. 
Optimization in Time and Score using IID Algorithm for K-Modes Clustering Yulianti, Farah; Sen, Tjong Wan
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2791

Abstract

Nowadays, there are numerous methods for analyzing data, one of which is cluster analysis. Because most practical data in today's analysis contains categorical attributes, categorical data clustering has recently received a lot of attention. To cluster categorical data, unsupervised machine learning techniques, which used frequency-based method, such as K-Mode’s clustering are used. The K-Modes algorithm takes advantage of the differences between the data points (total mis-matches or dissimilarities). The lower the dissimilarities, the more similar the data points, and thus the better the cluster. This paper aims to improve K-Mode’s clustering performance by incorporating the intercluster and intracluster dissimilari-ty measure, or IID measure, into the K-Modes algorithm rather than just using the standard simple-matching method to increase the algorithm's accuracy and execution time. This combined algorithm improves accuracy and execution time of the K-Modes algorithm. As a result, this algorithm can be used as an alternative to better cluster categorical data.
Machine Learning Algorithms for Prediction of Boiler Steam Production Lianzhai, Duan; Roestam, Rusdianto; Sen, Tjong Wan; Fahmi, Hasanul; ChungKiat, Ong; Hariyanto, Dian Tri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1339

Abstract

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.
SENTIMENT ANALYSIS OF STUDENT SATISFACTION TOWARDS DISTANCE LEARNING USING MACHINE LEARNING METHOD Andres, M; WanSen, Tjong; Roestam, Rusdianto
IT for Society Vol 9, No 1 (2024): Vol 9, No 1
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/itfs.v9i1.5073

Abstract

The Covid-19 pandemic forces the entire societyto change their way of life. One of them is the process of face-to-face learning changing into distant learning. Various responsesarise from students during the implementation of this newsystem, both positive and negative, indicating the level of studentsatisfaction. The sentiment analysis of students' commentsduring distance learning was conducted using machine learningalgorithms and tools Rapid miner. Literature study shows thatthe Naive Bayes, K-NN, and Decision Tree algorithms have veryhigh accuracy, so this research uses those methods to get high-accuracy results. The research shows the following results;Naive Bayes is 93.80% and class precision for pred. Positive93.80% and pred. negative 100.00%. The K-NN algorithm is92.49% and class precision for pred. positive is 92.37%, pred.negative 100%. The Decision Tree method is 90.81% with astandard deviation of (+-) 0.58 and class precision for pred.positive 90.81% and class pred. negative 0.00%.
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