The Houw Liong
Departemen Teknik Informatika Institut Teknologi Harapan Bangsa Jalan Dipatiukur No. 83–84 Bandung

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PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Chaniago, Ria; Liong, The Houw; Wardani, Ken Ratri Retno
Jurnal Informatika Vol 12, No 2 (2014): NOVEMBER 2014
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.721 KB) | DOI: 10.9744/informatika.12.2.90-95

Abstract

Weather is one of the nature elements that can influence decision making in human's life. Based on that issue, the author wants to make an application that is able to predict weather with good accuracy. The application is a weather forecasting system, using computer technology that implements expert system. The methods used are Adaptive Neuro Fuzzy Inference System (ANFIS) and Case Based Reasoning (CBR), and a combination of both methods will applied to the system. The system also has learning methods like Backpropagation Error (BPE) and Recursive Least Error (RLSE), to increase its accuracy. Clustering and data cleaning also done inside the system, as it needed by forecasting process to achieve a good result. K-Means is the clustering algorithm, while Box and Whisker Plot is the algorithm for data cleaning. The result from this project is to create a weather forecasting system with high accuracy.
Sistem Wawancara Virtual untuk Penerimaan Mahasiswa Jurusan Teknik Informatika di ITHB dengan Metode Natural Language Processing Hartanto, Harry; Liong, The Houw; Martina, Inge
Jurnal Telematika Vol. 8 No. 1 (2013)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v8i1.69

Abstract

Institut Teknologi Harapan Bangsa khususnya jurusan Teknik Informatika memiliki sistem penerimaan mahasiswa baru dengan metode wawancara secara langsung oleh kepala departemen. Metode natural language processing, adalah metode yang memproses input teks menjadi kata-kata kunci jawaban user. Proses-proses yang terlibat adalah stemming, parsing, dan scoring. Stemming adalah proses untuk mencari kata dasar. Sistem sudah mempunyai kumpulan kata dasar yang dikenali dan disimpan di dalam lexicon. Hasil stemming akan diberi atribut yang berupa bobot dan tipe kata. Proses selanjutnya adalah parsing yaitu merangkai kata-kata dasar menjadi struktur kalimat. Parsing seperti ini disebut bottom-way parsing. Proses terakhir adalah scoring yaitu menghitung bobot dan menilai jawaban user. Hasil akhir dari sistem adalah mengkalkulasikan semua nilai dari setiap jawaban dan menampilkan total skor dari user tersebut. Institut Teknologi Harapan Bangsa majoring in Informatics Engineering in particular has a new admission system by doing direct interview by the head of department. Natural Language Processing is a method for processing the input text into the user answer keywords. Processes involved are stemming, parsing and scoring. Stemming is a process for finding base form of the word. System already has dictionary of known words that called lexicon. The result of stemming will be given attributes that consist of weight and type of the word. The next process is parsing that will build those base words into sentence structure. Such parsing is so called bottom-way parsing. The final proccess is scoring that will calculate the score of the user answer. The end result of the system is to calculate all the values of each answer and display  the total score of that user.
Perbandingan Penyelesaian Persamaan Diferensial Biasa Menggunakan Metode Backpropagation, Euler, Heun, dan Runge-Kutta Orde 4 Wijaya, Jayme Yeremia; Liong, The Houw; Wardani, Ken Ratri Retno
Jurnal Telematika Vol. 11 No. 1 (2016)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v11i1.135

Abstract

Persamaan diferensial banyak digunakan sebagai model matematika atau dalam bidang sains lainnya. Dalam persamaan tersebut dibutuhkan tingkat akurasi yang sangat tinggi sehingga diciptakan beberapa metode untuk menyelesaikan persamaan diferensial itu. Salah satu metode yang digunakan adalah Metode Numerik dan Metode Artificial Neural Network (ANN). Ada 4 metode yang terlibat dalam penelitian ini, yaitu Metode Euler, Heun, Runge-Kutta Orde 4 yang termasuk pada metode Numerik, dan Backpropagation Neural Network (BPNN) yang termasuk dalam Metode ANN. Penelitian ini untuk membuktikan bahwa dalam menyelesaikan persamaan diferensial penggunaan Metode BPNN lebih baik daripada Metode Numerik. Hal ini dibuktikan dengan hasil Euclidean Distance dari BPNN lebih baik dibandingkan metode yang lain. Hasil penyelesaian akan terlihat lebih jelas ketika persamaan diferensial tersebut mengandung unsur chaos. Jika dilihat dari grafik penyelesaiannya, BPNN memiliki grafik yang mirip dengan grafik dari solusi sejatinya. Berbeda dengan penyelesaian yang menggunakan Metode Numerik, hasil grafik garis yang diperoleh tidak memiliki kemiripan dengan solusi sejatinya. Differential equation are widely used as a model in the mathematics model or other science. In this equation takes a very high level of accuracy that was created several methods to solve the differential equations. One of the method used is Numerical Method and Artificial Neural Network (ANN). There are four methods involved in this study, Euler Method, Heun, and Runge-Kutta Order 4 are included in Numerical Methods, and Backpropagation Neural Network (BPNN) which included in ANN Method. This research is to prove that in solving differential equations using BPNN Method is better than Numerical Method. This is evidenced by the result of Euclidean Distance from BPNN is better than other methods. The result of the solving will be seen more clearly when the differential equation contains elements of chaos. If seen from the graph, BPNN have a graph similar to the graph of the Analitic Solution. Contrast to the solving using Numerical Methods, the line graph has no resemblance to the Analitic Solution.