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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Ekonomi Bisnis Techno.Com: Jurnal Teknologi Informasi Makara Journal of Science
Hendri Murfi
Departemen Matematika, FMIPA Universitas Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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Studi Komparasi Terhadap Kapabilitas Generalisasi dari Jaringan Saraf Tiruan Berbasis Incremental Projection Learning Murfi, Hendri; Kusumoputro, Benyamin
Jurnal Teknik Elektro Vol 1, No 2 (2001): SEPTEMBER 2001
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1338.699 KB) | DOI: 10.9744/jte.1.2.

Abstract

One of the essences of supervised learning in neural network is generalization capability. It is an ability to give an accurate result for data that are not learned in learning process. One of supervised learning method that theoretically guarantees the optimal generalization capability is incremental projection learning. This paper will describe an experimental evaluation of generalization capability of the incremental projection learning in neural networks%2C called projection generalizing neural networks%2C for solving function approximation problem. Then%2C Make comparison with other general used neural networks%2C i.e. back propagation networks and radial basis function networks. Base on our experiment%2C projection generalizing neural networks doesn%5C%27t always give better generalization capability than the two other neural networks. It gives better generalization capability when the number of learning data is small enough or the noise variance of learning data is large enough. Otherwise%2C it does not always give better generalization capability. Even though%2C In case the number of learning data is big enough and the noise variance of learning data is small enough%2C projection generalizing neural networks gives worse generalization capability than back propagation networks Abstract in Bahasa Indonesia : Salah satu hal yang penting dari suatu metode pembelajaran pada jaringan saraf tiruan adalah kapabilitas generalisasi. Yaitu kemampuan untuk memberikan hasil yang akurat terhadap data yang tidak diajarkan pada tahap pembelajaran. Salah satu metode pembelajaran yang memberikan jaminan secara teori diperolehnya kapabilitas generalisasi yang optimal adalah projection learning. Pada tulisan ini kami akan melakukan evaluasi eksperimental terhadap kapabilitas generalisasi dari jaringan saraf tiruan berbasis projection learning yang bersifat incremental%2C yang disebut projection generalizing neural networks%2C untuk memecahkan masalah aproksimasi fungsi. Kemudian melakukan studi komparasi dengan jaringan saraf tiruan yang sudah umum digunakan%2C yaitu back propagation networks dan radial basis functions networks. Berdasarkan hasil uji coba komputasi yang kami lakukan%2C projection generalizing neural networks tidak selalu memberikan kapabilitas generalisasi yang lebih baik. projection generalizing neural networks memberikan kapabilitas generalisasi yang lebih baik ketika jumlah data pembelajaran cukup kecil atau variansi noise dari data pembelajaran cukup besar. Selain dari dua kondisi tersebut%2C projection generalizing neural networks tidak selalu memberikan kapabilitas generalisasi yang lebih baik. Bahkan%2C untuk kondisi dimana jumlah data pembelajaran cukup besar dan variansi noise cukup kecil%2C projection generalizing neural networks memberikan kapabilitas generalisasi yang lebih buruk dari back propagation networks. supervised+learning%2C+incremental+projection+learning%2C+generalization+capability%2C+artificial+neural+networks%2C+function+approximation+problem
Sensing Trending Topics in Twitter for Greater Jakarta Area Angga Pratama Sitorus; Hendri Murfi; Siti Nurrohmah; Afif Akbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (816.414 KB) | DOI: 10.11591/ijece.v7i1.pp330-336

Abstract

Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted a study to implement Twitter as a media for the urban monitoring in Jakarta and its surrounding areas called Greater Jakarta. Firstly, we analyze the accuracy of the detected topics in term of their interpretability level. Next, we visualize the trend of the topics to identify popular topics easily. Our simulations show that the topic detection methods can extract topics in a certain level of accuracy and draw the trends such that the topic monitoring can be conducted easily.
LOSS SEVERITY DISTRIBUTION ESTIMATION OF OPERATIONAL RISK USING GAUSSIAN MIXTURE MODEL FOR LOSS DISTRIBUTION APPROACH Seli Siti Sholihat; Hendri Murfi
Jurnal Ilmiah Ekonomi Bisnis Vol 21, No 2 (2016)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.195 KB)

Abstract

Banks must be able to manage all of banking risk; one of them is operational risk. Banks manage operational risk by calculates estimating operational risk which is known as the economic capital (EC). Loss Distribution Approach (LDA) is a popular method to estimate economic capital(EC).This paper propose Gaussian Mixture Model(GMM) for severity distribution estimation of  loss distribution approach(LDA). The result on this research is the value at EC of LDA method using GMM is smaller    2 % - 2, 8 % than the value at EC of LDA using existing distribution model. Keywords:  Loss Distribution Approach, Gaussian Mixture Model, Bayesian Information Criterion, Operational Risk.
KAJIAN KEMAMPUAN GENERALISASI SUPPORT VECTOR MACHINE DALAM PENGENALAN JENIS SPLICE SITES PADA BARISAN DNA Kerami, Djati; Murfi, Hendri
Makara Journal of Science Vol. 8, No. 3
Publisher : UI Scholars Hub

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

Abstract

Study on Generalization Capability of Support Vector Machine in Splice Site Type Recognition of DNA Sequence. Recently, support vector machine has become a popular model as machine learning. A particular advantage of SVM over other machine learning is that it can be analyzed theoretically and at same time can achieve a good performance when applied to real problems. This paper will describe analytically the using of SVM to solve pattern recognition problem with a preliminary case study in determining the type of splice site on the DNA sequence, particularity on the generalization capability. The result obtained show that SVM has a good generalization capability of around 95.4 %.
Analisis Performa Deep Embedded Clustering untuk Pendeteksian Topik Cahyadi, Danu Julian; Murfi, Hendri; Satria, Yudi; Abdullah, Sarini; Widyaningsih, Yekti
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.11841

Abstract

Pendeteksian topik adalah solusi untuk mengungkap struktur laten dalam sebuah dokumen. Kerangka umum pendeteksian topik berbasis clustering terdiri dari dua langkah: pembelajaran representasi dan pendeteksian topik melalui clustering. Dalam penelitian ini, Bidirectional Encoder Representations from Transformers (BERT) digunakan untuk pembelajaran representasi karena BERT mampu menangkap konteks setiap kata berdasarkan kata-kata di sekitarnya. Representasi teks yang diperoleh dari BERT digunakan untuk pendeteksian topik dengan clustering. Deep Embedded Clustering (DEC) dan Improved DEC (IDEC) adalah model clustering berbasis deep learning yang digunakan dalam penelitian ini untuk pendeteksian topik. DEC dan IDEC mampu mengubah data ke dalam ruang dimensi yang lebih rendah serta mengoptimalkan cluster secara simultan. Output dari teknik clustering berupa kata-kata kunci yang menggambarkan setiap topik cluster. Setelah mendapat kata kunci yang mewakili topik, evaluasi model dilakukan dengan melakukan perbandingan nilai topic coherence menggunakan Topic Coherence - Word2Vec (TC-W2V) sebagai analisis kuantitatif. Penelitian ini merupakan perluasan dari penerapan DEC dan IDEC pada pendeteksian topik dengan menambahkan analisis visualisasi dan kata kunci. Simulasi menunjukkan bahwa DEC dan IDEC mengungguli Uniform Manifold  Approximation and Projection (UMAP)-based k-means (UKM) dan Eigenspace-Based Fuzzy C-Means (EFCM) dari segi nilai TC-W2V, hasil visualisasi, dan kata kunci.   Kata kunci: analisis teks, deep clustering, pemrosesan teks
Co-Authors Abdullah, Sarini Afif Akbar Angga Pratama Sitorus Benyamin Kusumoputro Cahyadi, Danu Julian Djati Kerami Erwan Setiawan, Erwan Seli Siti Sholihat Siti Nurrohmah Yekti Widyaningsih Yudi Satria