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All Journal Jurnal Ilmu Komputer dan Informasi JOIV : International Journal on Informatics Visualization Makara Journal of Technology Jurnal Sistem Informasi
Heru Suhartanto
Fakultas Ilmu Komputer, Universitas Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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ALGORITMA PARALLEL SUPERVISED PNN STRUCTURE DETERMINATION DAN IMPLEMENTASI BERBASIS MESSAGE PASSING INTERFACE Heru Suhartanto; Herry .
Jurnal Ilmu Komputer dan Informasi Vol 2, No 1 (2009): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.212 KB) | DOI: 10.21609/jiki.v2i1.121

Abstract

Probabilistic Neural Network (PNN) adalah salah satu tipe jaringan neural yang umum digunakan untuk memecahkan permasalahan klasifikasi pola. Di samping struktur jaringan dan metode pelatihan yang sederhana, PNN memiliki kelemahan utama yaitu dalam menentukan struktur jaringan yang terdiri dari penentuan nilai parameter smoothing dan jumlah neuron yang digunakan pada lapisan pola. Dengan adanya kelemahan ini, beberapa peneliti mengajukan algoritma Supervised PNN Structure Determination (SPNN) dengan tujuan untuk mempermudah penentuan struktur PNN. Akan tetapi dalam implementasi iteratif yang telah dilaporkan, SPNN masih memerlukan waktu komputasi yang cukup lama untuk menentukan struktur PNN yang baik. Makalah ini menjelaskan usaha perbaikan kinerja waktu proses implementasi SPNN dengan memperhatikan bagian-bagian proses yang independent serta memodifikasi algoritmanya untuk dapat diterapkan pemrosesan secara paralel. Hasil eksperimen menunjukkan percepatan yang cukup berarti.
A Study on Parallel Computation Tools on Networked PCs Suhartanto, Heru
Makara Journal of Technology Vol. 10, No. 2
Publisher : UI Scholars Hub

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

Abstract

A Study on Parallel Computation Tools on Networked PCs. Many models for natural phenomena, engineering applications and industries need powerfull computing resources to solve their problems. High Performance Computing resources were introduced by many researchers. This comes in the form of Supercomputers and with operating systems and tools for development such as parallel compiler and its library. However, these resources are expensive for the investation and maintenance, hence people need some alternatives. Many people then introduced parallel distributed computing by using available computing resource such as PCs. Each of these PCs is treated as a processors, hence the cluster of the PC behaves as Multiprocessors Computer. Many tools are developed for such purposes. This paper studies the peformance of the currently popular tools such as Parallel Virta\ual Machine (PVM), Message Passing Interface (MPI), Java Remote Method Invocation (RMI) and Java Common Object Request Broker Architecture (CORBA). Some experiments were conducted on a cluster of PCs, the results show significant speed up. Each of those tools are identified suitable for a certain implementation and programming purposes.
STUDI KASUS LINGUSQL: APLIKASI TRANSAKSI PERDAGANGAN SAHAM Rikky Wenang Purbojati; Ade Azurat; Api Perdana; Heru Suhartanto
Jurnal Sistem Informasi Vol. 5 No. 1 (2009): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.657 KB) | DOI: 10.21609/jsi.v5i1.258

Abstract

Proses pengembangan perangkat lunak yang ideal selalu mensyaratkan pengujian yang menyeluruh untuk memperoleh hasil perangkat lunak yang memiliki tingkat kebenaran tertentu. Namun pada prakteknya pengujian secara menyeluruh sangat jarang dilakukan karena membutuhkan sumber daya waktu dan biaya yang banyak. LinguSQL adalah sebuah tool pengembangan eksperimen yang mengintegrasikan proses pengujian secara whitebox dan blackbox ke dalam aktifitas pembuatan kodenya. Paper ini memaparkan penerapan LinguSQL dalam pengembangan studi kasus sebuah aplikasi transaksi perdagangan saham. Penerapan LinguSQL pada studi kasus yang cukup kompleks diharapkan akan menampilkan keuntungan konsep pengujian secara menyeluruh serta, dalam konteks implementasi tool, menunjukkan bagian-bagian yang masih perlu dikembangkan lebih lanjut. The ideal process software development always requires thorough testing to obtain the software that has a certain degree of truth. However, in practice very rarely thorough testing done because it requires so much resources of time and cost. LinguSQL is an experimental tool that integrates the development process is whitebox and blackbox testing in manufacturing activity code. This paper describes the implementastion of LinguSQL in the development of a stock trading application case study. Implementation of LinguSQL on a complex case study will show the expected benefit of testing the concept a thorough and in the context of the implementation tool, showing the parts that still need to be developed further.
SURVEI 2009: MUTU SITUS E-LEARNING SEKOLAH INDONESIA MASIH SANGAT MINIM Heru Suhartanto
Jurnal Sistem Informasi Vol. 6 No. 1 (2010): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.005 KB) | DOI: 10.21609/jsi.v6i1.280

Abstract

Di beberapa negara yang infrastruktur internet telah memadai, banyak sekolah yang telah memanfaatkannya sebagai salah satu faktor pendukung kesuksesan proses pembelajaran. Pemerintah Indonesia pun telah memulai teknologi ini guna memperluas akses sumber daya pembelajaran sehingga tak terbatas oleh waktu dan ruang. Makalah ini melaporkan hasil survei yang menggambarkan seberapa jauh sistem e-learning telah dipakai di sekolah Indonesia. Data hasil survei menunjukkan bahwa kualitas pemanfaatan situs e-learning masih kurang dan perlu mendapat perhatian dan dukungan dari semua pihak terkait. In some countries that have adequate internet infrastructure, many schools are already using it as one of the factors supporting the success of the learning process. The Indonesian government also has initiated this technology to expand the access to learning resources that is unlimited by time and space. This paper reports the results of the survey that describe the extent to which e-learning system has been used in Indonesian schools. Survey data indicate that the quality of utilization of e-learning is still lacking and need attention and support from all parties concerned.
Assessing Data Imbalance in Financial Distress Prediction: A Comparative Approach of Machine Learning and Economic Models Rahayu, Dyah Sulistyowati; Suhartanto, Heru; Husodo, Zaäfri Ananto
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3397

Abstract

This study aims to compare the effectiveness of machine learning models and economic models in predicting corporate bankruptcy, with a focus on addressing the issue of data imbalance. In this context, the number of companies experiencing financial difficulties is significantly smaller than that of healthy companies, which can lead to bias in predictions. The method used is an experiment with various data handling techniques and involves several classification models, namely Decision Tree, Neural Network (NN), K-Nearest Neighbors (KNN), Case-Based Reasoning (CBR), Support Vector Machine (SVM), and Merton Structural Model, which are tested on several data scenarios with resampling techniques, including Random Oversampling (ROS), Random Undersampling (RUS), and a combination of both. The evaluation results show that the Decision Tree, excluding ROA variables, and the Neural Network provide the best performance, with the Decision Tree achieving 86% accuracy and an AUC of 77.75, and the Neural Network achieving 86.76% accuracy and an AUC of 90.5. Other models, such as KNN and SVM, exhibit lower performance, achieving around 80% accuracy and a lower AUC. Based on these results, Decision Tree without ROA and Neural Networks are the best choices for predicting corporate bankruptcy. This study also demonstrates that financial models, such as the Merton Structural Model, are not significantly affected by data imbalance. The ultimate goal of this study is to provide recommendations for more reliable prediction models that enable financial institutions, investors, and companies to make more informed strategic decisions, as well as reduce financial risks through the early detection of companies at risk of failure.
SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design
Co-Authors Ade Azurat Ade Azurat Aniati Murni Api Perdana Api Perdana Api Perdana Dyah Sulistyowati Rahayu Herry . Husodo, Zaafri Ananto Jasni Mohammad Zain Kusmardi Kusmardi Perdana, Api Rikky Wenang Purbojati Rikky Wenang Purbojati Rikky Wenang Purbojati Toto Haryanto Wenang Purbojati, Rikky Yusoff, Marina